Pharmaceutical Markets and Insurance Worldwide (Advances in Health Economics and Health Services Research)

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PHARMACEUTICAL MARKETS AND INSURANCE WORLDWIDE

ADVANCES IN HEALTH ECONOMICS AND HEALTH SERVICES RESEARCH Series Editors: Michael Grossman & Bjo¨rn Lindgren Recent Volumes: Volume 15: Volume 16:

Volume 17: Volume 18:

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Health Policy Research in the States – Edited by Joel. C. Cantor Substance Use: Individual Behavior, Social Interaction, Markets and Politics – Edited by Michael Grossman and Bjorn Lindgren The Economics of Obesity – Edited by John H. Cawley and Kristian Bolin Evaluating Hospital Policy and Performance – Edited by Jos L. T. Blank and Vivian G. Valdmanis Beyond Health Insurance: Public Policy to Improve Health – Edited by Lorens Helmchen, Robert Kaestner and Anthony Lo Sasso Neuroeconomics – Edited by Daniel Houser and Kevin McCabe Innovations in Health System Finance in Developing and Transitional Economies – Edited by Dov Chernichovsky and Kara Hanson

ADVANCES IN HEALTH ECONOMICS AND HEALTH SERVICES RESEARCH VOLUME 22

PHARMACEUTICAL MARKETS AND INSURANCE WORLDWIDE EDITED BY

AVI DOR George Washington University, Washington, DC, USA, and NBER, New York, NY, USA

United Kingdom – North America – Japan India – Malaysia – China

Emerald Group Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2010 Copyright r 2010 Emerald Group Publishing Limited Reprints and permission service Contact: [email protected] No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. No responsibility is accepted for the accuracy of information contained in the text, illustrations or advertisements. The opinions expressed in these chapters are not necessarily those of the Editor or the publisher. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-84950-716-5 ISSN: 0731-2199 (Series)

Awarded in recognition of Emerald’s production department’s adherence to quality systems and processes when preparing scholarly journals for print

CONTENTS LIST OF CONTRIBUTORS

ix

OVERVIEW

xiii

PART I: INTERNATIONAL REVIEWS PHARMACEUTICAL POLICIES IN EUROPEAN COUNTRIES Pedro Pita Barros INTERNATIONAL EXPERIENCE WITH COMPARATIVE EFFECTIVENESS RESEARCH: CASE STUDIES FROM ENGLAND/WALES AND GERMANY John F. P. Bridges, Joshua P. Cohen, Peter G. Grist and Axel C. Mu¨hlbacher

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29

PART II: NATIONAL CASE STUDIES PHARMACEUTICAL POLICY IN THE NETHERLANDS: FROM PRICE REGULATION TOWARDS MANAGED COMPETITION Lieke H. H. M. Boonen, Ste´phanie A. van der Geest, Frederik T. Schut and Marco Varkevisser IMPACT OF PHARMACEUTICAL REGULATION AND POLICIES ON HEALTH SYSTEM PERFORMANCE GOALS IN ISRAEL Philip Sax and Amir Shmueli v

53

77

vi

CONTENTS

GLOBAL BUDGETS AND PROVIDER INCENTIVES: HOSPITALS’ DRUG EXPENDITURES IN TAIWAN Shin-Yi Chou, Mary E. Deily, Hsien-Ming Lien and Jing Hua Zhang MEDICARE PART D TURNS FOUR: TRENDS IN PLAN DESIGN, ENROLLMENT, AND THE IMPACT OF THE PROGRAM ON BENEFICIARIES Jack Hoadley and Kosali Simon

103

123

PART III: U.S. STUDIES – FROM MARKETS TO POLICY INNOVATIONS DOES PRESCRIPTION DRUG ADHERENCE REDUCE HOSPITALIZATIONS AND COSTS? THE CASE OF DIABETES William E. Encinosa, Didem Bernard and Avi Dor COST SHARING, BENEFIT DESIGN, AND ADHERENCE: THE CASE OF MULTIPLE SCLEROSIS Avi Dor, Maureen J. Lage, Marcy L. Tarrants and Jane Castelli-Haley GENERIC UTILIZATION AND COST-SHARING FOR PRESCRIPTION DRUGS Teresa Bernard Gibson, Catherine G. McLaughlin and Dean G. Smith

151

175

195

DRUG PRICES, OUT-OF-POCKET PAYMENTS, AND INSURER COSTS: HOW DO PAYERS VARY? Jie Chen and John A. Rizzo

221

ADVERSE SELECTION AND THE EFFECT OF HEALTH INSURANCE ON UTILIZATION OF PRESCRIBED MEDICINE AMONG PATIENTS WITH CHRONIC CONDITIONS Yuriy Pylypchuk

233

vii

Contents

THE EFFECT OF DRUG VINTAGE ON SURVIVAL: MICRO EVIDENCE FROM PUERTO RICO’S MEDICAID PROGRAM Frank R. Lichtenberg

273

PART IV: SPECIAL TOPICS RETAIL PHARMACY MARKET STRUCTURE AND INSURER-INDEPENDENT PHARMACY BARGAINING IN THE MEDICARE PART D ERA Yang Xie, John M. Brooks, Julie M. Urmie and William R. Doucette PATENTS, INNOVATION, AND THE WELFARE EFFECTS OF MEDICARE PART D Adam Gailey, Darius Lakdawalla and Neeraj Sood

295

317

LIST OF CONTRIBUTORS Pedro Pita Barros

Faculdade de Econonomia, Universidade Nova de Lisboa (UNL), Lisbon, Portugal

Didem Bernard

Center for Finance, Access, and Cost Trends, Agency for Healthcare Research and Quality, Rockville, MD, USA

Lieke H. H. M. Boonen

Institute of Health Policy and Management (iBMG), Erasmus University Rotterdam, Rotterdam, The Netherlands

John F. P. Bridges

Johns Hopkins University, Baltimore, MD, USA

John M. Brooks

College of Pharmacy, The University of Iowa, Iowa City, IA, USA

Jane Castelli-Haley

Teva Neuroscience Inc, Kansas City, MO, USA

Jie Chen

College of Staten Island, City University of New York, New York, NY, USA

Shin-Yi Chou

Department of Economics, Lehigh University, Bethlehem, PA, USA

Joshua P. Cohen

Tufts Centre for the Study of Drug Development, Boston, MA, USA

Mary E. Deily


Avi Dor

George Washington University, Washington, DC, USA, and NBER, New York, NY, USA

William R. Doucette

College of Pharmacy, The University of Iowa, Iowa City, IA, USA ix

x

LIST OF CONTRIBUTORS

William E. Encinosa

Center for Delivery, Organization, and Markets, Rockville, MD, USA, and Georgetown University, Washington, DC, USA

Adam Gailey

RAND Corporation, Santa Monica, CA, USA

Ste´phanie A. van der Geest


Teresa Bernard Gibson

Thomson-Reuters, Ann Arbor, MI, USA

Peter G. Grist

John Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Jack Hoadley

Health Policy Institute, Georgetown University, Washington, DC, USA

Maureen J. Lage

Health Metrics Outcomes Research, Groton, CT, USA

Darius Lakdawalla

University of Southern California, Los Angeles, CA, USA, and RAND Corporation, Santa Monica, CA, USA

Frank R. Lichtenberg

Graduate School of Business, Columbia University, New York, NY, USA

Hsien-Ming Lien

Department of Public Finance, National Cheng-Chi University, Taipei, Taiwan

Catherine G. McLaughlin

School of Public Health, University of Michigan, Ann Arbor, MI, USA

Axel C. Mu¨hlbacher

Hochschule Neubrandenburg, Neubrandenburg, Germany

Yuriy Pylypchuk

Social and Scientific Systems, Agency for Healthcare Research and Quality, Rockville, MD, USA

xi

List of Contributors

John A. Rizzo

Department of Economics and Department of Preventive Medicine, Stony Brook University, Stony Brook, NY, USA

Philip Sax

Centre for Drug Studies, PHARMA Drug Bulletin, Jerusalem, Israel

Frederik T. Schut


Amir Shmueli

Hebrew University, Jerusalem, Israel

Kosali Simon

Department of Policy Analysis and Management, Cornell University, Ithaca, NY, USA

Dean G. Smith

School of Public Health, University of Michigan, Ann Arbor, MI, USA

Neeraj Sood

University of Southern California, Los Angeles, CA, USA, and RAND Corporation, Santa Monica, CA, USA

Marcy L. Tarrants

Teva Neuroscience Inc, Kansas City, MO, USA

Julie M. Urmie


Marco Varkevisser


Yang Xie


Jing Hua Zhang


OVERVIEW In this volume, we focus on the interaction between insurance systems and pharmaceuticals in developed countries, with special emphasis on the impact of benefit design on the market for prescription drugs. The wide spectrum of health care systems range from the purely government-run and governmentfinanced model, notably the UK’s National Health Service, to models that wrest on competition between private insurers but with universal coverage, as in the Netherlands. The United States, often thought of as the most market-oriented country, actually occupies the middle ground. With large social insurance programs targeting the elderly, low income children or indigent adults, and other special populations, U.S. public spending accounts for nearly half of total health expenditures (OECD Health Data, 2009), leaving private insurers to compete over the rest. It was not long ago that government-sponsored health care systems in all such countries treated prescription drugs and provider-centered care as two distinct parts of the system, subject to different payment rules, and often administered separately. In high income European countries, government-sponsored health insurance schemes typically covered prescription drugs in full, with few consumer- or supplier-side cost-sharing features found in conventional medical insurance. In the United States, private plans often provide prescription drug benefits under standard insurance, but Medicare, the government plan covering mostly the elderly, excluded prescription drugs from its benefit package almost completely before 2006. In recent decades, the growing role of pharmaceuticals has also led to changes in the composition of health care expenditures. Between 1998 and 2003, pharmaceutical expenditures in OECD countries increased at an annual rate of 6.1%, faster than the annual rate of 4.8% for total health care expenditures (OECD Health Data, 2005); as of 2007, the share of pharmaceutical spending in total health expenditures was approximately 15% (OECD, 2009). Realizing the integral role of prescription drugs in routine medical care, especially in the treatment of chronic conditions, governments and large health plans began to reconsider how prescription drug coverage ought to be integrated into traditional health insurance. In many developed countries, government-sponsored national health care xiii

xiv

OVERVIEW

systems have introduced cost-sharing, consumer choice, negotiated prices, and other market mechanisms into their prescription drug coverage. In the United States, prescription drug benefits were recently added to the Medicare program; for the first time approaching near-universal coverage for prescription drugs more typical of European and other OECD countries, albeit only for Medicare’s eligible population. Further expansion of drug benefits will likely occur as health care reform gains momentum. As a result of enacting these reforms, differences between health care systems appear to be narrowing. How, or even whether, to regulate prices of prescription drugs remains a matter of considerable debate. In an effort to contain costs, some OECD countries continue to impose administrative price controls and most retain some variant of reference pricing for purposes of reimbursement (see the chapters Barros; Boonen et al.; Sax & Shmueli, in this volume). In the United States, policymakers have so far opted out of such price regulation to avoid exercising government monopsony power, due to concerns over curtailing innovation (see the chapter Gailey, Lakdawalla, & Sood, in this volume), but also as a way of gaining industry support for other future reforms (Frank & Newhouse, 2008; Steinbrook, 2009). U.S. health care reform is expected to overhaul both private and public coverage and move the system closer to universal coverage. At the time of this writing, the legislative debate is still ongoing within the chambers of Congress. Together, the contributions in this volume shed light on similarities and differences between health care systems in these countries; in addition, they provide analyses of how various incentives found in insurance contracts impact pharmaceutical outcomes. The book is divided as follows. Chapters 1 and 2 present an international overview, followed by national case studies, the Netherlands, Israel, and Taiwan, which are discussed in chapters 3–5. Chapter 6 makes the transition from country studies to the U.S. policy setting, focusing on the implementation of Medicare part D and its impact on insurer competition. Chapters 7–12 include studies that analyze specific issues within the U.S. health care system. These U.S.-focused chapters emphasize several important factors: patient compliance, cost-sharing, benefit design, type of insurance coverage, and pharmaceutical innovation, as well as their implications for costs, prices, and patient behavior. Finally, chapters 13 and 14 close with two special topics. These studies employ theoretical microeconomic foundations, such as bargaining theory and social welfare analysis to problems related to the interactions between pharmaceutical markets and public insurance programs, applied to Medicare Part D.

xv

Overview

INTERNATIONAL REVIEWS Pedro Pita Barros reviews pharmaceutical policies adopted by health care systems in European (OECD) countries. He notes that cost-sharing for pharmaceuticals is higher than cost-sharing for other services. However, although pharmaceutical cost-sharing is pervasive across the European Union, concerns over equity have led most countries to adopt sliding fee schedules and even outright exemptions from copayments for vulnerable populations such as the elderly and low income households. The most common form of price regulation in these countries is reference pricing, either ‘‘external’’ (pegging pharmaceutical payments to lowest prices in a group of countries) or ‘‘internal’’ (pegging pharmaceutical prices to the lowest price within a therapeutic class), as well as outright administrative price controls. In his theoretical results, Barros shows that reference pricing lowers cost to consumers the most, followed by administrative price lists, while the pure coinsurance system yields the higher total cost. To foster innovation, Barros proposes adoption of innovative payment schemes based on supply-side risk sharing whereby payments to drug manufacturers are tied to treatment results and patient outcomes. Such schemes are akin to pay-for performance methods used to reimburse physicians in certain managed care settings in the United States. Bridges et al. contrast two alternative approaches to health technology assessments exemplified by the National Institute of Health and Clinical Excellence (NICE) of England and Wales, and the German Institut fu¨r Qualita¨t und Wirtschaftlichkeit im Gesundheitswesen (IQWiG). They regard both approaches as important policy advances as they supplement clinical evaluation criteria with cost-effectiveness analysis to guide resource allocation decisions and payment rules on behalf of their respective national systems. NICE uses cost-effectiveness techniques such as cost per quality adjusted life years (QALYs) to make specific recommendations on the adoption of a single drug or medical intervention within a therapeutic class. More recently, IQWiG adopted an efficiency frontier approach, whereby several therapeutic options are provided based on the trade-off between cost and clinical effectiveness. While the authors note the ability to make exemptions on a case-by-case basis under NICE, they express their preference for the more flexible German approach. This approach allows drug manufacturers to adjust their pricing to meet requirements under one of the options, and patients have the ability to purchase higher priced drugs by paying the difference between the manufacturer and the ceiling price on an out-of-pocket basis. With comparative effectiveness research emerging as

xvi

OVERVIEW

a policy tool in the United States, the authors suggest that German approach may be the more suitable model. However, they caution that this may not necessarily result in lower costs.

NATIONAL CASE STUDIES Boonen et al. analyze pharmaceutical regulatory policies in the Netherlands. The introduction of a new law in 2006 took a major step toward managed competition by requiring all Dutch citizens to purchase standardized individual health insurance coverage from competing private health insurers. The new act also allows for selective contracting for pharmaceutical purchases, a departure from strict reliance on reference pricing in prior years. Spurred by strong price competition, health insurers introduced preferred drug formularies to force suppliers of generics to substantially lower their prices. As a consequence, cost savings on generic drugs are estimated at approximately 70%. Based on this, the authors argue that policies aimed at enhancing competition among private plans were more effective at lowering pharmaceutical expenditure than direct price controls. Philip Sax and Amir Shmueli discuss the case of Israel. That country’s health care system represents an interesting laboratory for policy experimentation, as it shares certain key characteristics with many European countries such as competing sick funds and commitment to the principles of equity and solidarity, while not encumbered by intra-national regulatory requirements common in the European Union. Although reference prices for pharmaceuticals are in place, they tend to track prices in relatively high income countries and thus place limited regulatory pressure on sick funds. The authors suggested that consumers benefited from these policies as intense competition between the funds, coupled with administrative controls, led to substitution from brand names to generics. Another advantage of these policies was to encourage Israel’s growing generic drug industry and deepen its comparative advantage. Shin-Yi Chou et al. explore the impact of global budgets in Taiwan on pharmaceutical expenditures. In Taiwan, hospitals are reimbursed by the national health insurance bureau. Fixed global budgets are utilized for the non-pharmaceutical portion of their services, while dispensed prescription drugs are reimbursed at full cost. The authors hypothesize that these policies create incentives for physicians at hospitals to dispense more prescriptions, potentially creating a moral hazard problem. Using panel data and a difference-in-difference estimation approach, they show that indeed drug

xvii

Overview

expenditures rose after the implementation of the global budget system. This occurred mainly in hospital departments that had higher prescription drug use to begin with. The efficacy of increased pharmaceutical expenditures in inpatient care remains an open question. The final country study is devoted to the United States, specifically to Medicare Part D that offers subsidized prescription drug coverage to Medicare beneficiaries through private prescription drug plans (PDPs). Jack Hoadley and Kosali Simon provide an overview and assessment of this program, from its inception in 2006 to the present. Using a variety of primary and secondary data sources, the authors examine trends in enrollment, premiums, and market structure for these competing plans. They report an unanticipated yet rapid increase in participation, with about 90% of beneficiaries currently enrolled, coupled with relatively moderate increases in (enrollment weighted) premium costs. They also describe a dynamic entry and exit process in this market that may explain its relatively competitive market structure, with about 1,500 insurers of various sizes competing nationally and regionally. Interestingly, they show that enrollment remained highly concentrated in lower cost plans and in plan that offer well-recognized brand names. The authors explain that reliance on market forces appears to have been successful as a cost-containment strategy; however, they express concerns over low participation among low income and vulnerable groups that persist even though subsidies are available.

U.S. STUDIES – FROM MARKETS TO POLICY INNOVATIONS Turning from Medicare to privately insured adults, Encinosa, Bernard, and Dor examine effects of medication adherence on hospitalizations, emergency room visits, and hospital costs in the case of diabetes. Using observational data to create a quasi-experiment, the authors compare outcomes for low compliance and high compliance groups. To estimate cost offsets, the authors use a variety of econometric techniques to calculate the savings generated from an incremental change of 50% to 100% compliance, relative to a corresponding increase in prescription costs. While savings associated with ER were small, the authors find cost offsets of $1.14 in reduced hospital spending for every dollar spent on diabetic prescription drugs. By focusing on the direct effect of medication adherence, rather than

xviii

OVERVIEW

simply utilization, the authors supplement previous cost-offset literature. Furthermore, the authors point out the relevance of their findings for emergent value-based insurance designs. They caution that while reduced copayments and coinsurance may lower overall costs through their impact on adherence, additional efforts to reduce financial and non-financial barriers are needed to ensure the viability of such programs. Multiple sclerosis provides the backdrop for the examination into costsharing effects on adherence among privately insured patients undergoing disease-modifying therapies (DMTs), by Dor, Lage, Tarrants, and CastelliHaley. Utilizing the MarketScan insurance claims database, Dor et al. build upon previous literature by analyzing patient adherence in two payment regimes: coinsurance and copayments. They discover that type of payment regime and cost-sharing amounts were related to adherence. The authors found that patient adherence to DMTs decreased as coinsurance levels increased; however, average copayments amounts were not significantly associated with DMT adherence. Their results imply that the uncertainty patients face regarding out-of-pocket payments acts as a barrier to patient compliance with prescribed therapies. These findings mirror earlier research by Dor and Encinosa (2004) who found substantially smaller adherence effects in the copayment regime compared with coinsurance. The authors note that these results contribute to our understanding of optimal benefit design, but also acknowledge the need for developing an understanding of how behavioral factors influence adherence. The third cost-sharing chapter by Gibson et al., also set within the context of diabetes, examines own-price and cross-price effects of copayments on brand-name utilization and generic substitution. Their chapter combines two empirical studies. The first study employs an innovative quasiexperimental design that compares two employer-based plans: one that implemented brand copay increases, and another with unchanged costsharing acting as the control. Using a difference-in-difference framework, they show that higher copayments resulted in lower utilization of brandname prescription drugs. The second study in this chapter uses the MarketScan database to examine the effect of cost-sharing amounts for brand drugs relative to generics. The results confirm findings from the first study, namely that increased cost-sharing reduces brand-name utilization. However, the evidence on generic substitution (positive cross-price elasticities) was inconsistent. As the authors suggest, one possible implication of this result is that higher copayments on brand names lead to discontinuation of therapies rather than therapeutic switching. They attribute this to perceived (putative) loss from generics. However, they

Overview

xix

acknowledge the need to gain further confidence in the latter findings from additional research using different samples and methods. Jie Chen and John Rizzo delve into drug price variations by different payers and how these prices are shared by payers and their patients. Their analysis, using data from the Medical Expenditure Panel Survey from 1996 to 2006, was repeated across several chronic conditions and therapeutic classes of drugs. While the authors found relatively modest price differences paid by payers, they found relatively large variations in cost-sharing among payers. Among their findings, the authors determined that Medicaid beneficiaries had the lowest out-of-pocket payments, while Medicaid paid significantly more for prescription drugs than other third-party payers. Chen and Rizzo’s timely research on price and cost-sharing variations will have implications for health care reform as policymakers debate the consequences of negotiated pharmaceutical prices. Yuriy Pylypchuk examines the effect of health insurance type on personlevel expenditures on prescribed medication used in the treatment of hypertension, diabetes, and asthma. Although he does not find persistent evidence of adverse selection into private health plans, he does find that the effect of health insurance varies across patients with different conditions. The strongest and most significant effect is evident among patients with hypertension while the weakest and least significant is found among patients with asthma. The author concludes that as long as patients are aware of their diagnosis, those with asymptomatic conditions are more likely to exhibit moral hazard compared with patients with obvious symptoms. A hybrid case of a U.S. and non-U.S. health care system provides the backdrop for Frank Lichtenberg’s analysis of vintage drug effects in Puerto Rico. As a U.S. territory with Commonwealth status, Puerto Rico participates in U.S. public programs, albeit with a greater degree of autonomy. For instance, in Puerto Rico’s Medicaid system, physicians bear the costs of prescription medications, whereas in the U.S. programs, pharmacies are reimbursed separately. Lichtenberg shows that the vintage effects in Puerto Rico are similar to those previously found in the United States, namely, the age of prescription drugs, sorted by the decade of their introduction, is associated with declining mortality rates. As Puerto Rico’s Medicaid program was slower to adopt new drugs, a possible lesson is the need to design policies that will provide adequate incentives to increase the speed of adoption. At the same time, further research is needed to examine the possibility of reversed causality, namely that higher mortality in certain disease categories induced more drug development.

xx

OVERVIEW

SPECIAL TOPICS Xie et al. use a game-theoretic bargaining framework to describe the bargaining process between independent pharmacies and PDPs that participate in Medicare Part D. Accordingly, PDPs are deterred from participating in this process when pharmacy bid prices are set too high, whereas pharmacies are deterred when reimbursements offered by the plans are set too low relative to acquisition costs. The authors hypothesize that PDPs settle on customized contract offers to independent pharmacies based on the structure of the local pharmacy market. Their empirical investigation focuses on contracts for a representative brand-name drug and an equivalent generic drug. They show that substantial price variation is maintained after the implementation of the Medicare program for both brands and generics. Their estimates further suggest that independent pharmacies in areas with higher pharmacy ownership concentration, meaning less competition, have higher bargaining power with PDPs. The authors then go on to speculate that current anti-trust laws that prohibit independent pharmacies from engaging in collective bargaining may lead to closures and loss of services in rural areas where operating costs are high. Gailey et al. weigh the social welfare gains from Medicare Part D against the welfare loss from paying monopoly prices for patent protected drugs under this program. In the standard economic model, patent protection involves a trade-off between inducing innovation and awarding inefficient monopoly power to drug manufacturers. In their theoretical analysis, Gailey et al. show that in a static model, subsidies to consumers by the insurance program result in indirect transfer payments to drug manufacturers. Combining this with paying monopoly prices to manufacturers contributes to deadweight loss. However, in the dynamic counterpart of this model, deadweight loss can be reduced by negotiating price discounts or by lowering coinsurance, thereby inducing efficient utilization by consumers. Bringing in the expected profit stream of the monopolist into their model, the authors show that lowering coinsurance has another long-term benefit: increasing future profits induces more pharmaceutical R&D, independently from the effects of patent protections. In their empirical analysis, the authors provide policy simulations indicating an overall annual gain of $5 billion from the reduction in static deadweight loss, and another gain of $3 billion dollars from added innovation, mainly due to reduced costsharing for patients entering the program.

xxi

Overview

CONCLUSION An important conceptual theme that runs through several of these chapters deserves special mention. Barros highlights the classical moral hazard problem, whereby low cost-sharing can lead to inefficient overuse of medical care, including prescription drugs. On the contrary, Encinosa et al., Dor et al., and Gailey et al. raise the problem of spillover effects, whereby excessive costsharing may lead to underuse of medications and other preventative therapies, causing inefficiencies elsewhere in the system. The contributions in this volume indicate that health care systems worldwide are grappling with this problem, as they search for optimal cost-sharing mechanisms suitable for their special circumstances. Throughout the developed world, societies and governments are increasingly prone to mixing markets with government involvement to attain stable economic growth, an approach that has been labeled as the ‘‘Third Way.’’ Assembled together, the contributions in this volume suggest that health care systems are also converging around a third way of their own. Regardless of variations in the mix of social and private insurance found in the countries surveyed, government-sponsored programs are finding ways of balancing incentives borrowed from private markets with direct intervention meant to ensure the broadest possible access. This is particularly evident in the case of prescription drug coverage and financing.

REFERENCES Dor, A., & Encinosa, W. (2004). Does cost-sharing affect compliance? The case of prescription drugs. National Bureau of Economic Research Working Paper no. 10738, September, 2004. NBER, Cambridge, MA. Frank, R. G., & Newhouse, J. P. (2008). Should drug prices be negotiated under Part D of Medicare? And if so, How? Health Affairs, 27(2), 3–43. OECD. (2009), Health at a glance 2009: OECD indicators. OECD Publishing. Available at http://dx.doi.org/10.1787/health_glance-2009-en. Accessed on December 2009. OECD Health Data. (2005). Drug spending in OECD countries up by nearly a third since 1998, according to new OECD data, August 8, 2005. Available at http://www.oecd.org/ dataoecd/35/13/34966969.pdf. Accessed on November 2009. OECD Health Data. (2009). Frequently requested data, November 9, 2009. Available at http:// www.irdes.fr/EcoSante/DownLoad/OECDHealthData_FrequentlyRequestedData.xls. Accessed on November 2009. Steinbrook, R. (2009). Lobbying, campaign contributions, and health care reform. New England Journal of Medicine, November 18, 2009. Available at http://nejm.org. Accessed on December 2009.

Avi Dor Editor

PART I INTERNATIONAL REVIEWS

PHARMACEUTICAL POLICIES IN EUROPEAN COUNTRIES Pedro Pita Barros ABSTRACT Purpose – Pharmaceutical expenditures have an important role in Europe. The attempts to control expenditure have used a wide range of policy measures. We reviewed the main measures adopted by the European Union countries, especially in countries where governments are the largest third-party payers. Methodology – To complement a literature review on the topic, data was gathered from national reviews of health systems and direct inquiries to several government bodies. Findings – Almost all countries regulate prices of pharmaceutical products. Popular policy measures include international referencing to set prices (using as benchmark countries that have set lower prices), internal reference pricing systems to promote price competition in domestic markets, and positive lists for reimbursement to promote consumption of generics (including in some cases substitution by pharmacists of drugs prescribed by physicians). Despite the wide range of policy measures, it is not possible to identify a ‘‘silver bullet’’ to control pharmaceutical expenditures. We also identified two main policy challenges: policy coordination among countries within the European Union to maintain incentives for R&D at the global level, and the development of new relationships with the pharmaceutical Pharmaceutical Markets and Insurance Worldwide Advances in Health Economics and Health Services Research, Volume 22, 3–27 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1108/S0731-2199(2010)0000022004

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PEDRO PITA BARROS

industry; namely, the so-called risk-sharing agreements between the pharmaceutical industry and governments/regulators (or large third-party payers).

1. INTRODUCTION In Europe, pharmaceutical products have an important role in the delivery of health care. This is likely to rise even further due to new and more expensive discoveries and the increase in chronically ill patients, whose health condition depends upon permanent medication. Pharmaceutical consumption and sales, besides gaining importance in quantitative terms, are also the areas of health care most visible to citizens. In countries where governments are the largest third-party payers and private companies sell pharmaceutical products, a wide range of policy measures is used to control expenditures. European governments strongly intervene in health care. Regardless of the particular manner, each country chooses to finance and deliver health care to its citizens. The pharmaceutical sector also fits this description. The great majority of countries have some form of intervention, be it price controls, profit limits, reimbursement levels to pharmaceutical consumption, etc. We review here some of the main policies that have been used in the past two decades in Europe. The reported data focus particularly on the countries known as EU15, the 15 countries that comprised the European Union before recent enlargements, and which coincide to a large extent with the Euro-zone countries. Nevertheless, most of discussion applies to other European countries as well. First, a brief overview of how much is spent on pharmaceuticals is provided. We then discuss the main policy measures that were adopted and the underlying rationale. The most common concern is cost containment, as pharmaceutical spending has grown considerably. Finally, we discuss two main challenges facing Europe with respect to the pharmaceutical market: policy coordination at the European Union level and new relationships between large third-party payers and the pharmaceutical industry.

2. BRIEF OVERVIEW OF EUROPEAN PHARMACEUTICAL SPENDING Over the past 25 years, pharmaceutical expenditure as a share of total health expenditure has increased significantly. Table 1 shows the evolution for the

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Pharmaceutical Policies in European Countries

Table 1. Pharmaceutical Expenditure as a Percentage of Total Health Expenditure.

Belgium Czech Republic Denmark Finland France Germany Greece Hungary Iceland Italy Luxembourg The Netherlands Norway Portugal Slovak Republic Spain Sweden Switzerland Turkey United Kingdom

1980

1985

1990

1995

2000

17.04

18.00

17.83

24.78

7.02 10.99 10.38 17.21

9.76 10.82 9.06 18.94 22.36 16.67 14.46 10.34 6.83 10.02 13.81

26.78 10.85 11.68 11.97 9.10 23.95 24.40 16.11 16.35 9.17 9.29 9.16 14.06 29.50

11.78

15.57

15.47 18.01

4.34

5.09

5.95

4.86

5.43

5.77 10.89

9.15 10.70

14.62

13.95

11.36

12.72 11.79 17.58 14.15

2005 16.15 30.48 11.14 12.64 12.71 9.84 29.91 24.88 14.92 14.77 7.17 8.92 9.20 13.98 21.27 13.77 11.77 10.01 24.15 12.83

Note: From EU15 countries, Austria and Ireland not reported due to lack of data. Source: OECD Health Data June 2009.

EU15 countries.1 The same pattern is broadly present in other European countries. Greece is a clear outlier in that pharmaceutical expenditure has grown at a much faster pace than other health care spending. Germany is at the opposite end with a slightly lower share of total health expenditure attributable to pharmaceutical products and with a slower growth of pharmaceutical expenditure. Most countries now have 10–14% of their total health expenditures going to pharmaceutical products alone. In general, health care expenditures have been growing faster than the GDP in most European countries (the exception within the EU15 group being Luxembourg). It comes as no surprise that the ratio of pharmaceutical expenditures over GDP has increased in all the EU15 countries. As of 2005, the pharmaceutical expenditures in most EU15 countries is more than 1% of their GDPs (Table 2).2 A comparison of pharmaceutical consumption in per capita values (in US dollars and adjusted for purchasing power parities) is also revealing

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PEDRO PITA BARROS

Table 2.

Pharmaceutical Expenditure as a Percentage of GDP. 1980


1985

1990

1995

2000

0.84

1.73

0.54 0.92 0.86 1.14

0.77 1.12 0.91 1.63 1.63 1.37 1.05 0.58 0.57 0.79 1.07 1.08

1.75 0.90 0.84 1.21 0.94 1.89 1.69 1.53 1.32 0.54 0.74 0.77 1.24 1.62

0.74 0.92

1.12 1.20

1.21 1.39

0.32

0.37

0.48

0.43

0.46

0.48 0.89

0.73 1.02

0.84

0.82

0.78

1.05 1.20 0.87 1.00

2005 1.66 2.17 1.06 1.07 1.41 1.05 2.82 2.07 1.41 1.32 0.55 0.88 0.84 1.42 1.50 1.14 1.08 1.12 1.38 1.06


(see Table 3). Greece again stands out as a major outlier. Its pharmaceutical expenditure per capita is well above all other countries. As mentioned later in the text, Greece is also one of the countries where interventions aimed at pharmaceutical cost containment have been more limited in range and depth. Substantial variation in per capita expenditure across countries is the consequence of different pricing policies and demand-control measures. Income-related differences are important but do not account for all of the dispersion observed. Germany and the Netherlands have per capita expenditures at roughly the level of poorer countries (within the EU15 group), such as Spain and Portugal. Many policy measures have been adopted over the years in European countries. Even though these measures have not prevented growing pharmaceutical expenditures (both in relative and absolute terms), distinct policies may have generated cross-country differences. The international comparison of pharmaceutical spending has several issues to consider: not all drugs are sold in every country and consumer

7


Table 3.

Pharmaceutical Expenditure Per Capita (PPP) (in US$). 1980


1985

1990

1995

2000

100

223

96 159 184 147

145 227 206 239 148 318 222 198 123 187 143 90

263 258 216 304 243 347 208 441 336 235 217 278 212 178

89 87

184 156

258 245

32

49

84

46

69

92 221

160 275

101

134

153

290 379 76 259

2005 533 443 351 327 420 330 704 351 493 375 288 308 396 293 242 312 348 402 149 346


prices usually include taxes as well as retailers’ and wholesalers’ margins. These can vary considerably across countries. Despite these differences, we observe an upward trend in pharmaceutical expenditures in per capita terms, percentage of GDP, and proportion of total health expenditure.

3. COST-CONTAINMENT MEASURES Governments (the main third-party payers in many European countries) and insurance institutions have adopted many strategies to cope and to (probably) control the increase in pharmaceutical spending. These strategies have focused on both the demand and the supply side of the market. Some measures seek to counteract market failures, while others intend only to control expenditure. Additional measures are aimed at the pharmaceutical industry’s market power. Demand-side interventions are aimed at patients and physicians, and attempt to influence the level and composition of demand at several points

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Table 4. Supply Side

Austria Belgium Denmark Finland France Germany Greece Ireland Italy The Netherlands Portugal Spain Sweden United Kingdom

Supply-Side Measures in European Countries.

Payback Profit Reference Positive Economic Price Controls Pricing Lists Evaluation Controls

O

O O

O

Oa O O

O

Oa O Oa

O

O

O O O O O Oc

O O

O O

O O

O O

O O O O

O O O O

O Oc

O

O O O O O

Guidelines Limits of for Promotion Activities/Costs Prescription O O Od Ob

O O

O O

O

O O O O O O

a

Only when generics competition/off-patent drugs exist. Intervention is by taxation of promotional expenditures. c Negative lists. d Nonbinding recommendation to medical doctors. b

where demand for consumption of pharmaceutical products is generated. On the supply side, price controls, profit controls, and different layers of administrative authorizations for reimbursement of pharmaceutical expenditures by patients have been experimented with. The rise in pharmaceutical sector regulations within selected OECD countries, in terms of both the number of countries adopting a particular measure and the range of measures used in each country, is documented in Sood, de Vries, Gutierrez, Lakdawalla, and Goldman (2009). They used panel data on 19 OECD countries for the period 1992–2004. Two popular measures were the use of economic evaluation, as additional hurdle to market entry, on top of the usual safety, quality, and efficacy requirements, and the generics substitution. Table 4 summarizes the main policy measures adopted in selected European countries, on the supply side. Supply-side measures involve many different interventions. The practice of paybacks, which are agreements by companies to return some amount to the governments whenever pharmaceutical expenditure exceeds a predefined limit, is used in Belgium, France, Italy, and Portugal. Each country uses a different sort of agreement. Agreements may vary by the type of expenditure included, the threshold, and the mechanisms by which the payback occurs.


9

Industry profit controls can be either a maximum limit set for profits within a time period or a limit on profit growth rates. Profit controls are used in a meaningful way only in the United Kingdom, where the Pharmaceutical Price Regulation Scheme is employed, and negotiated between the Department of Health and the Association of the British Pharmaceutical Industry. It uses a rate-of-return regulation. In some countries, for example, Denmark and Portugal, margins of retailers (pharmacies) and wholesalers are administratively set. Reference pricing, both internal and external, is quite common. In internal reference pricing, a third-party payer defines the maximum value it is ready to reimburse for a group of equal (or similar) pharmaceutical products. If the actual price exceeds the reference price, then the patient meets the price difference. The reference value is determined within the country. The breadth of each group of pharmaceutical products with the same reference price varies across countries. External reference pricing occurs when a third-party payer (or regulator) sets a maximum market price based on prices of the same pharmaceutical product in other countries. The most common measures, however, are price controls and positive lists (pharmaceutical products eligible for reimbursement by the third-party payer), which often involve the use of economic evaluation techniques to help determine the price and eligibility for the list. Price controls usually take the form of a price ceiling that firms are free to price below. Nevertheless, some countries centrally set the price. Measures aimed at improving prescription practices do exist to a reasonable extent, but vary in scope and breadth. More often than not, when measures exist, they are nonmandatory. In better prescription practices, doctors substitute generics for branded products and take into account prescription practice information on cost-effectiveness or cost–benefit of drugs. A main feature of pharmaceutical markets in Europe is the extensive insurance coverage provided by third-party payers. Third-party payers can be, according to the country we focus on, insurance companies, sickness funds, or national health services that are directly managed by governments. As is well known, the existence of insurance coverage leads to overconsumption because at the moment of consumption, the patient does not have to pay the full price of the product. In particular, if full insurance is provided, the patient faces no cost and the system easily runs into overspending in pharmaceutical products. It then comes as little surprise that cost sharing (patients paying a fraction of the price at the moment of consumption) is one of the most commonly used measures to control consumer demand. In most countries, significant co-sharing exists.

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Pharmaceutical cost sharing is generally higher than cost sharing of patients for other types of health care (OECD, 2008). Some countries opted for co-payments, while others selected co-insurance. We use co-payment to describe the payment of a fixed amount charged by the health care provider and co-insurance to describe the payment of a fixed proportion by the patient, with the third-party payer taking up the remaining. As shown in Table 2, European countries actively used demand-side policies. There is a general consensus on using government regulation to set margins in the retail activity. In other words, pharmacists do not have a role in pricing pharmaceutical products. They are paid by fixed margins of the retail price in a majority of the countries. In only a few cases do we see payment by prescription that is independent of the price of the product. Thus, pharmacists have an interest in selling the highest priced pharmaceuticals. Aimed directly at patients, European countries often use cost-sharing schemes. Under cost sharing, very different regimes exist. In some of the countries, such as Italy, the United Kingdom, Ireland, and Austria, patients pay a flat rate per prescription, while in others, such as Finland, Germany, Greece, and Portugal, they pay a percentage of the retail price. This percentage may vary according to the type of drugs (life-saving, which carries a smaller, or no-sharing payment by patients) or to the demographics (low-income or old age patients paying less, etc.). Sweden and Denmark use different co-sharing mechanisms. Denmark bases the co-payment made by the patient on previous consumption of reimbursed pharmaceutical products (within a year of first purchase). In Sweden, the patient pays the full price, up to a certain level. From that level on, a co-insurance rate is applied (the patient still pays a fraction of the cost of reimbursable pharmaceutical expenditures). Only in France and the Netherlands the patient is not asked to pay for pharmaceuticals. Moreover, as already pointed out in Kanavos (2006), in several European countries significant exemptions to such cost sharing existed. From the comparison of demand- and supply-side measures, the major difference between these two countries lies in the payment to pharmacists. A fixed mark-up rate is used in France, while a fee per prescription is used in the Netherlands. Enforcement of guidelines and the prescribing of generics also seem to be different. In France, physicians are not forced by law to prescribe generics. Agreements with health insurance funds provide an (indirect) incentive to do so. In the Netherlands, physicians appear to take into account, in their prescription decisions, the existence of generics and adhere to third-party payers’ guidelines. Both countries use international price referencing. France takes into account reference prices in

11


Table 5.

Demand-Side Measures.

Global CostBudgets Sharing by Patients Austria Belgium Denmark Finland France Germany Greece Ireland Italy The Netherlands Portugal Spain Sweden United Kingdom

Oe O O

Ob Oc O Oc Oc Oc Ob Ob Oc Oc Of Ob

Generics Generics Policies Substitution O O O O O O O O O O O

O O Od Od Od Od O Od O O g

Payments to Pharmacists

Price Indexa

fixed mark-up fixed mark-up fixed mark-up fixed mark-up fixed mark-up fixed mark-up fixed mark-up fixed mark-up fixed mark-up fee per prescription fixed mark-up fixed mark-up fixed mark-up fixed mark-up

107 111 119 77 95 93 106 121 91 128 73 118 109 94

a

Source: Konijn (2007). Flat rate per prescription. c Percentage of retail price. d Doctors may oppose it. e Budget-holding GPs. f Deductible co-payment. g To be introduced by 2010. b

Germany, Spain, Italy, and the United Kingdom, while the Netherlands considers prices in Belgium, Germany, France, and the United Kingdom. The last column of Table 5 reports the price index for pharmaceutical products computed by Konijn (2007). No particular measure seems to be associated with lower prices, thus indicating that no particular measure stands out as the ‘‘silver bullet’’ to keep pharmaceutical prices low.3 A common concern in Europe with patient cost sharing is equity, as several studies have consistently shown out-of-pocket payments to be regressive.4 This concern has led to the introduction of different co-payment levels for vulnerable groups (elderly and poor) in several countries.

4. COMPETITION AND THE ROLE OF GENERICS The market power of pharmaceutical companies is a concern shared by many European countries. It is a direct consequence of using a patent

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system to generate incentives for research and development of new pharmaceutical products. Patents generate monopoly power that may well extend beyond the life of the patent. Generics have been widely seen as a way to introduce competition into the marketplace. Measures aimed at promoting generics have addressed both the way markets operate and pharmacists’ ability to substitute branded drugs with generic ones. In the first case, some countries impose rules that if a generic version of a branded drug exists, then the physician must prescribe the active ingredient. This has helped generic products to gain market share. The second approach allows pharmacists to substitute prescribed drugs with generics, which has been adopted in some countries. Its success depends to a great extent on the way pharmacists are paid. In countries where pharmacists receive a percentage of the consumer price, a higher price is always better from the viewpoint of pharmacies and the high-price, branded drug will prevail over the cheaper generic version. The particular health insurance arrangements and the way pharmaceutical prices are determined in each country introduce important differences in the relationship seen between reimbursement levels and market prices. In countries where pharmaceutical companies have market power and set the price freely for patented products, the lower the level of cost sharing, the higher the price (as the cost sharing increases price sensitivity of patients). However, if the price of the new product is set by negotiation between the pharmaceutical company and the government, then a higher cost-sharing level is associated with a higher price. The existence of health insurance, whatever its origin, creates a divergence between the demand curve expressing the marginal price at which consumers are willing to acquire the product (net of the insurance effect) and the price per unit sold actually received by the pharmaceutical company. In Fig. 1, the downward-sloped line represents the market demand function for a particular pharmaceutical product. The market demand function can be interpreted as the monetary benefit attributed to the consumption of the marginal unit of the product. For a market price P0, a quantity Q0 is consumed and revenues to the pharmaceutical company equal the payments made by patients. Suppose that a third-party payer now covers 50% of the cost of the pharmaceutical product at the moment of consumption under a co-insurance scheme. Then, the relevant price for the patient decision is P1 ¼ P0/2, and a quantity Q1 will be consumed, higher than Q0. Patients pay P1 Q1 and the pharmaceutical company still receives P0 Q1, as only the price paid by patients is changed by the co-insurance system, not the price received by the

13

Pharmaceutical Policies in European Countries P (price)

P0

a

c

b

Q0

Q1

d

P1=P0 /2

0

Fig. 1.

Q (quantity)

The Moral Hazard Effect of Health Insurance.

pharmaceutical company. The third-party payer covers up the difference. Full insurance, meaning that no payment is due by the patient at the moment of consumption, leads consumption up to the point where the marginal value of the last unit of pharmaceutical product consumer is zero (point d in Fig. 1). This implies a net social loss as real resources are used to provide the consumption of pharmaceutical products but no benefit results from the last unit consumed. Moreover, if P0 was also the marginal cost of production, then the triangle [abd] would correspond to waste associated with excessive consumption (in the sense that it costs more to produce than the value it generates). The social waste is larger the more insurance the patient has.5 In the negotiation process, the owner of the new, patented drug faces the threat of not having the products listed for reimbursement. The value of this threat is greater when the prospective demand for the product is also greater. A low co-payment leads to more demand and thus creates a higher potential loss for the pharmaceutical company. The higher threat gives further bargaining power to the insurer (e.g., the government), who will press for lower prices (Jelovac, 2008). The diversity of health insurance systems from country to country and the different use of internal and external referencing systems, together with income effects in the demand for pharmaceutical products, lead to major difficulties in predicting, from a theoretical standpoint, regularities in prices, a view not contradicted by the data available on comparable price indices across countries (Table 5).

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5. PRICE REGULATION IN PHARMACEUTICAL MARKETS 5.1. General Aspects The first distinction to make when addressing price regulation in pharmaceutical markets is between those products that need a prescription to be sold and those that do not (over-the-counter pharmaceuticals). In the first case, the physician is the crucial decision maker, while in the second the consumer may decide alone. Still, within the prescription market, a further distinction proves useful: whether the pharmaceutical product is on-patent (that is, benefits from patent protection) or is off-patent (patent has expired, and competing similar products may enter the same market). In the latter case, competition from generics can exist, and price regulation mechanisms can use the competition from similar products. Of course, with pharmaceutical products under patent, some substitution with other products may be feasible. In such cases, even under patent there is no perfect monopoly to be enjoyed by pharmaceutical companies. Sometimes the dividing line between these markets is unclear. For example, dermatology often uses prescription products for treatment that are also sold without a prescription and for cosmetic purposes only. Price regulations aimed at these different markets will also reflect the particular conditions of each one. With regard to pharmaceutical products that do not require a prescription, it is often the case that no price regulation exists. For pharmaceutical products subject to prescription, we find several types of government intervention in the way prices are determined. The strongest form of intervention is to administratively set the prices. A milder type of intervention is the imposition of price caps. We also find indirect price controls such as rate-of-return regulation, and for segments where competition between branded drugs and generics exist, reference pricing systems are in place in several countries. Regulation of prices of patented pharmaceutical products emerges as a natural consequence of the market power a patent system provides to companies. From an economic perspective, price regulation in markets where there is competition from generics is at first sight paradoxical. Once competition from generics is allowed to develop, the justification for price regulation seems to disappear. However, several countries continue regulating such markets. The direct application of standard economic theory fails to recognize the role of two features of this market. First, even if


15

generics show bio-equivalence to the original branded product, idiosyncratic characteristics of patients (and past experience together with risk aversion) may introduce some differentiation in the eyes of patients. Second, costsharing arrangements, aimed at providing insurance protection to patients, decrease price sensitivity. These two features taken together may generate relatively high prices. Take the extreme example of complete health insurance coverage. In this scenario, price competition among firms produces no additional demand, and pharmaceutical companies will set the price at the highest possible level. Another element that may induce high prices is the existence of tacit collusion between manufacturers of branded drugs and producers of generics. In this case, one needs to discuss whether no price regulation is actually called for and competition policy should be the right approach to deal with the issue, or price regulation or government-created market mechanisms, like the reference pricing system, may be better for social welfare than no price regulation. A major issue in European countries is the establishment of a ‘‘fair’’ regulated price. Several countries opt for international referencing rules. The price of the pharmaceutical product is determined by the price in a set of reference countries. This sets in motion a cascade of effects, as the price fixed in one country will determine prices in other countries. An international spillover effect results from the price fixing system in one country. This leads firms to carefully time the introduction of their products into the market. Moreover, the sequence of markets in which to enter becomes an important variable in firms’ strategies. The expected effect of price competition resulting from generics entry appears to be highly dependent on the initial conditions of the market. In particular, the existence of price regulation seems to make it harder to detect beneficial effects from generics entry than when prices are freely set by companies.

5.2. Domestic Reference Pricing 5.2.1. Theory From the set of measures adopted in European countries, we can identify a balance between purely administrative measures (such as direct price controls) and market-based interventions (such as internal reference pricing). The use of reference pricing is an attempt to introduce competition among pharmaceutical companies. It was first introduced in Germany in 1989, and

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since then has been adopted by several countries.6 Before the introduction of reference pricing it was common to see reimbursement systems based on positive (or ‘‘white’’) lists (pharmaceutical products accepted for reimbursement) and on co-insurance (the third-party payer assumed a fraction of the full price, which could go up to 100% for some drugs in some countries). This earlier system provided strong insurance protection against pharmaceutical costs to citizens, but it was vulnerable to high prices set by pharmaceutical companies. The move toward reference pricing seeks to provide insurance protection while inducing more competition between pharmaceutical companies. It also has advantages in terms of market competition relative to positive lists, in which excluded drugs are not subject to reimbursement. The differences in economic properties of these different systems can be illustrated with a simple model.7 Take the demand for a class of pharmaceutical products, which is assumed to be exogenously determined and independent of price. That is, aggregate demand is not price sensitive, even though firm-level demands are sensitive to prices. For simplicity, assume that only two pharmaceutical products are present in the market. Doctors act as perfect agents for patients. Pharmaceutical products sold by the two firms are substitutes but not perfect substitutes. We see them as being located at the endpoints of a line segment describing patients’ preferences. Some patients prefer one drug and are located closer to that one, while others prefer the alternative drug. The line segment has length 1, without loss of generality, so that pharmaceutical company A is located at 0 and pharmaceutical company B is located at 1. Companies have freedom in price choices. Patients’ preferences are given by VðA; xÞ ¼ v pA tx þ I A VðB; xÞ ¼ v pB tð1 xÞ þ I B where v is the valuation of intervention; pi, i ¼ A, B, the price of pharmaceutical product of firm i; x the preference location of the patient; t the cost of not consuming the preferred type of product; and Ii is the insurance payment. A patient located at point 0 of the preferences spectrum has a zero preference cost of consuming the pharmaceutical product of firm A and a cost t of buying the pharmaceutical product of firm B. A patient located at x ¼ 1/2 is indifferent between the two products (for the same price) and has a preference cost of t/2 of buying each product. The insurance payment Ii, i ¼ A, B, allows us to consider in an integrated framework the different systems. Take first, the system: where a co-insurance

17


payment is made such that patients pay only a fraction a of the drug price, and all products available are covered. In this case I i ¼ ð1 aÞpi , i ¼ A; B. The case of using positive lists results in one drug being covered by the insurance, say product A, while the other one is excluded. It implies I A ¼ ð1 aÞpA and I B ¼ 0. Finally, take the reference pricing system. Reimbursement is at most the price of the selected drug, say drug A. Then I A ¼ ð1 aÞpA , I B ¼ ð1 aÞpA . Price choices made by pharmaceutical companies aim at profit maximization. Demand for each product is determined by the location of the indifferent patient for the prevailing prices. The indifferent patient is defined by the location x on the preferences line such that VðA; xn Þ ¼ VðB; xn Þ. Under a uniform distribution of N patients over the preferences line, the demand for pharmaceutical product A is given by x (all patients with location in preferences below x) and the demand for pharmaceutical product B is given by (1x) (all patients with preferences above x). Solving the maximization problem of each pharmaceutical company and finding the equilibrium under each system, we have: Price

Pure co-insurance Positive list (A included) Reference pricing

Total cost

A

B

t/a t/a t

t/a t t

Nt/a N(1þ1/a)/2 Nt

In terms of pharmaceutical costs, this simple model yields a clear prediction: reference pricing leads to the lower cost, followed by the positive list, and finally, the pure co-insurance system with the higher total cost. The intuition for this result is simple. The pure co-insurance system reduces to a great extent price sensitivity of patients, allowing companies to increase prices considerably. The system of positive lists creates an asymmetry between the products included and those excluded from the list. The latter need to be much more price-aggressive as they are at an important disadvantage (patients pay full price if they choose it while paying only a fraction a of the price if they choose the alternative product included in the positive list). The reverse side is that products in the positive list will be less price-aggressive as their price can be raised without losing much demand. Nevertheless, overall, competition brought on by exclusion from reimbursement of some products does make them more aggressive in the market.

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Finally, we have reference pricing. In the proposed representation of this system, the product that is not used as reference to set the reimbursement price faces a price sensitive demand. A price increase (or decrease) is felt one-to-one by the patient as long as the price is above the reference price. The company that sets the reference price has to consider in its price-setting decision that a higher price also makes its competitor’s product more attractive. The effect occurs through the referencing system: the difference between the two prices becomes smaller, which means patients pay less if they choose the other firm’s pharmaceutical, Thus, overall, both firms face an incentive to set lower prices than in the other regime. Of course, if the reference price is set in a completely exogenous way, both firms face exactly the same incentives (as described in the preceding text for the firm that does not set the reference price). The preceding discussion was made under the assumption of perfect agency of doctors. If doctors prescribing pharmaceuticals are imperfect agents for the patients, the argument is actually reinforced in favor of reference pricing. We may see imperfect agency as the doctors place relatively more value on health results than on the payments patients have to make. In our simple model, this effect can be captured by a higher value of parameter t: physicians have a higher cost than patients from deviation of the most preferred type of drug (in terms of price-equivalent value). It is relatively easy to see that a higher value for parameter t leads to a larger saving in pharmaceutical expenditure as a result of moving from traditional co-insurance to reference pricing. A final caveat is in order. Although we have formally discussed the main intuition behind reference pricing using a constant aggregate demand, such assumption is not essential. The search for market-based mechanisms that help to contain pharmaceutical expenditure growth has motivated the adoption of reference pricing across Europe. On the materialization of the expected benefits, in terms of increased competition, there is insufficient evidence to take conclusions. 5.2.2. Practice Different countries use different reference groups to determine a reference price system. Reference groups are commonly defined by the same active ingredient (generic reference pricing, including only off-patent drugs and generics), by chemically related ingredients with pharmacological equivalence, or by comparable therapeutic effects. The two latter types are termed therapeutic reference pricing by Brekke, Konigbauer, and Straume (2007). The broader type of reference pricing seems to have adverse effects on innovation incentives.8


19

The success of the reference pricing system has been limited at best. The effects of a system of reference pricing seem to be a short-lived, and produce a small reduction (lower growth) in pharmaceutical expenditure. LopezCasasnovas and Puig-Junoy (2005) point out three main reasons for this: First, reference pricing usually covers only a small fraction of the market (as it leaves out patented drugs when it concentrates on markets defined by the same active ingredient); second, companies react by shifting their attention to other products and increase prices in noncovered products; and third, reference pricing addresses the level of prices but does not influence quantities consumed (which increase to the extent of demand elasticity as prices become lower for the relevant drugs), nor the structure of consumption. The second argument in the preceding text requires some further qualification, as it is not clear why pharmaceutical companies were not already charging optimal prices in other products before the introduction of reference pricing, or why such optimal pricing has changed with the introduction of reference pricing in other products. Moreover, in some of the countries where reference pricing has been adopted, there are also direct price controls that limit (or even totally preclude) price increases in other pharmaceutical products.

5.3. International Referencing The use of international referencing by several European countries has been driven mainly by the wish to establish a commitment to set low prices domestically. The existing international referencing establishes a set of complex interrelations across countries. Table 6 gives an overview of international referencing in Europe. Countries indicated in column use international price referencing, taking as reference the countries reported in line. For example, France sets prices taking into account the prices in four countries (Germany, Italy, Spain, and the United Kingdom). Prices in France are used as reference by Ireland, The Netherlands, Portugal, and Spain. Garcia-Marinõso, Jelovac, and Olivella (2008) address the benefits of this policy vis-a`-vis price negotiation with the pharmaceutical company, and reach the conclusion that long-run impact is uncertain. This is because the pharmaceutical company recognized that international referencing makes the profit in one market dependent on negotiations in another country, and the order of entry in different markets and prices established will reflect it. There is currently no clear theoretical consensus on this issue.9

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International Price Referencing.

Table 6.

AT BE DK FI FR DE EL IE IT NL PT ES SE UK Austria (AT) Belgium (BE) Denmark (DK) Finland (FI) France (FR) Germany (DE) Greece (EL) Ireland (IE) Italy (IT) The Netherlands (NL) Portugal (PT) Spain (ES) Sweden (SE) United Kingdom (UK) European Union (EU) Norway Iceland

Source: Compiled by the author of this study.

The use of international benchmarking in pharmaceutical prices is done unilaterally by each country. This opens the door to the pharmaceutical industry to ‘‘play’’ the system. In particular, it provides incentives to launch earlier in higher-price markets and to delay launch of new products in lowerprice markets. The existing direct price controls on new pharmaceutical products seem to have a significant and negative impact on the introduction of such products in national markets (Danzon, Wang, & Wang, 2005; Heuer, Mejer, & Newhaus, 2007). Launch delay is usually defined as the difference, in months, between the launch in a particular market, and its first global launch. Approval of new pharmaceutical products is granted, in the European Union, by the European Medicines Evaluation Agency (EMEA), which allows for a centralized authorization process to introduce a new drug in the market. However, inclusion in positive lists for reimbursement has to be done on a country-by-country basis. The EMEA also allows for a mutual recognition procedure. Under this procedure, companies start the authorization procedure in one country and ask for mutual recognition in other countries. Danzon et al. (2005) report launch delays associated with new chemical entities (NCEs) in several OECD countries. There is substantial variation in launch delays in Europe, despite the existence of a centralized authorization


21

procedure through EMEA at the European Union level. The overall finding is that lower prices lead to both a lower number of products introduced in the market and longer delay. This may suggest a negative effect from international referencing as a way to determine prices. The delays are, according to Danzon et al. (2005), due to price regulation and pharmaceutical companies’ strategies.

6. EFFECTS ON R&D By setting heavy price regulation on pharmaceutical products, European countries tend to be seen by the United States as free riders in innovation developed in the United States and paid for, to a large extent, by US consumers. The US Department of Commerce (2004) review describes how European countries use price controls extensively to maintain pharmaceutical expenditures within manageable levels. It is also pointed out that governments engage in price negotiations with pharmaceutical companies and are able to obtain prices that are lower than they would be in free market. The existence of patents results from the need to provide an appropriation mechanism to firms for bringing innovation to the market. Of course, one can question whether or not the patent system is the only way to achieve innovation. Some authors have proposed a prize mechanism to reward innovation. Still, since the current system is based on the award of patents and the resulting market protection they confer, price regulation faces a dilemma between bringing drugs at a lower price to consumers and encouraging more innovation. This dilemma will carry different weight from country to country. Those with small domestic markets and no domestic innovation face a different efficiency trade-off than those with larger markets where several companies are actively pursuing innovation. The country with little drug innovation and small size essentially does not face the downside of tough price regulation, as no domestic innovation will be discouraged, and as a small international player, the country’s policies will have little effect on the overall R&D effort made by pharmaceutical companies. Despite important breakthroughs, most innovation in pharmaceutical products has been incremental, as shown in Table 7. The majority of ‘‘innovations’’ are not major improvements over existing pharmaceutical products, which has opened the way for the general application of appraisal instruments before the drug is considered for

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Table 7.

NCE Launched Between 1975 and 2002.

Chemical Interest

Chemical Structure

Therapeutic improvement No therapeutic improvement

New

Already known

143 201

295 821

Source: OECD (2008, p. 55).

reimbursement from third-party payers. The most common of these instruments is the requirement of economic evaluation studies, showing that, on top of the therapeutic value of the pharmaceutical product, there is an economic advantage over previous ones. The use of economic evaluation as a tool to control pharmaceutical expenditures has made its way into the policy arena. Several countries actively use it to approve new pharmaceutical products into the health insurance coverage (either run by governments, insurance companies, and/or sickness funds). The impact that uncoordinated policies of many small countries have on the incentives for R&D of major pharmaceutical companies is an open issue.

7. CHALLENGES FOR THE FUTURE 7.1. Political and Policy Coordination Governments of small European countries often see the impact of national policies on multinational companies’ strategies to be too small to make a difference to the latter. This is probably true at the individual level of each country. However, when all (or most) of them take the same approach, the sum of it may have a noticeable impact on firms’ strategies. The concern that tight price controls around Europe might have an adverse impact on pharmaceutical R&D in Europe has led the European Commission to launch R&D support programs.10 So far, no common policy on ex-factory prices or on characteristics of reimbursement systems has been agreed upon in Europe (in particular, within the European Union). Given the historical traditions, the different wealth levels and varying balances given to equity and efficiency considerations, it is unlikely that coordination within the European Union toward common demand-side and supply-side costcontainment measures will be possible in the short- and medium-term. The fact that each European country has only a marginal impact on global R&D incentives creates a (potential) coordination problem.


23

Price regulation may become strict as a consequence. The development of the pharmaceutical R&D done in Europe is a challenge identified by the European Commission. Whether or not coordination of policies aimed at domestic pharmaceutical markets is needed for increasing pharmaceutical R&D is unclear.

7.2. New Policies: A Role for Risk-Sharing Agreements? The continued growth of pharmaceutical expenditure and the inability of existing policy measures to keep it under control lead Governments and third-party payers to look for new approaches. An idea that has recently received some attention is the so-called risk-sharing agreement between the third-party payer and the pharmaceutical companies (Cook, Vernon, & Manning, 2008). The following quote is illustrative: ‘‘Risk-sharing arrangements, under which the price may be retroactively adjusted as information about utilization and outcomes under normal use become available have the potential to reduce the need to make a trade-off between the objectives of ensuring prompt access and getting good value for money, when faced with incomplete information about the relative efficacy and cost-effectiveness of a new product’’ (OECD, 2008, p. 17). Risk-sharing agreements essentially shift the after-approval risk of the drug to the pharmaceutical company, either partially or completely. These agreements have been justified on the grounds that prices of pharmaceuticals should be based on their value and measured by their effectiveness in real life. Phase III clinical trials may not yield an unbiased predictor of the effectiveness of new pharmaceutical products. The willingness to enter a risk-sharing agreement, in which prices can easily be revised downward if the drug reveals itself to be less successful than anticipated, can be a signaling device by pharmaceutical companies. Companies with good products are more likely to engage in risk sharing when quality is not easily (or completely) observable. Besides the signaling role, the risk-sharing agreements may change treatment decisions by physicians (Barros, 2008). For example, if payment is due only when treatment with the new drug is successful then doctors lose nothing by taking all patients to the new treatment. However, since pharmaceutical companies now have to sustain the losses derived from the unsuccessful patients, prices will face an upward pressure. The final result may end up being more patients treated at a higher price, with an overall loss to society.

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The proposal of risk-sharing agreements is increasingly seen by pharmaceutical companies as a way to obtain inclusion of new products in reimbursement lists. Companies seem to regard them as a tool for preferential market access. Still, risk-sharing agreements require a clear definition of success criteria and a relatively easy and inexpensive way of checking the success metrics. Otherwise, litigation will likely follow soon. With the current state of knowledge, there are only a handful of risk-sharing agreements across Europe (mainly present in the United Kingdom and Italy). Whether or not risk-sharing agreements will become widespread is yet to be seen, as their role is still unfolding (both empirically and in their rationale). Understanding and defining in a more precise way the role of risk-sharing agreements remains a challenge for European countries in the coming years.

8. FINAL REMARKS European countries share come common characteristics in their pharmaceutical markets. They all provide insurance coverage for pharmaceutical consumption, through reimbursement, either by the National Health Service or by the social insurance schemes, depending on the country’s health system. The existence of cost sharing by patients is also common, either copayment or co-insurance, and it is generally set at a level higher than cost sharing in other types of health care. Growing expenditures in pharmaceutical products are a common concern. In most countries, growth of pharmaceutical expenditures has outpaced GDP growth and also the growth of other types of health care expenditure. These shared experiences have led to the adoption of several measures aimed at controlling the growth of health care expenditures. A measure present in almost all countries is direct price regulation of pharmaceutical products. Even in the few countries that do not have direct price controls, indirect price control is sought through profit limits or other similar mechanisms. From the range available of policy measures directed to the demand and the supply of pharmaceutical products, each country selects the preferred combination. Policy measures that show popularity are international referencing to set prices (using as benchmark countries that have set lower prices), internal reference pricing systems (aimed at promoting price competition in domestic markets), creation of positive lists for reimbursement, and promotion of generics consumption (including in some cases substitution by pharmacists of drugs prescribed by physicians).


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The need for price controls is typically justified by the low elasticity of demand created by insurance coverage (even though cost sharing is relatively high). It also reflects the bargaining power of large third-party payers (especially in the case of countries with a National Health Service). The R&D impact of national policy aimed at controlling pharmaceutical expenditures is of secondary concern for most European countries (notable exceptions are the United Kingdom and Switzerland, which have a more significant domestic production of pharmaceutical innovations). Since each country per se has a marginal impact of global multinationals R&D efforts, adoption of policy measures that may have an adverse impact on R&D investments tend to ignore such impact on their assessment of interest. Despite the wide range of policy measures, it is not possible to identify a ‘‘silver bullet’’ to control pharmaceutical expenditures. Nonetheless, regulations and controls in European pharmaceutical markets introduce important changes in how market competition works. Concerns about equity and access to pharmaceutical products have generated insurance coverage for this type of health care consumption in every country. However, given the well-known effects of moral hazard associated with health insurance, important cost sharing is asked from patients. Cost sharing is often set as a percentage of the price of the drug, which while providing financial protection to patients, also substantially decreases demand price elasticities. In addition to direct price controls, which set a cap on pricing by pharmaceutical companies, positive lists and reference pricing systems also influence market competition. Positive lists of pharmaceuticals accepted for reimbursement by third-party payers generate a strong market asymmetry between the products included in the list and those excluded. The latter have to carry a much lower price as no reimbursement (or a much smaller one) is received by patients. Included drugs do not need to enter fierce competition. In some countries, the decision by the third-party payer to include a drug in the positive list and the level of reimbursement provided is more important to firms’ profits than authorization to market the product. The reference pricing system offers an important advantage over the positive list. Since patients have to pay, at the margin, the full cost of any price increase, they will tend to be more price sensitive while still enjoying insurance protection against a major part of the financial cost. The (domestic) reference pricing does provide more incentives for market competition than positive lists, which in turn still provides more market competition than high and equal across the board reimbursement rates.11

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Overall, it is still unclear what does work and what does not to control pharmaceutical expenditure growth in Europe. European pharmaceutical markets face two important challenges: policy coordination across countries, namely within the European Union, and the development of new relationships with the pharmaceutical industry, namely the so-called risk-sharing agreements.

NOTES 1. The EU15 includes Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, The Netherlands, Portugal, Spain, Sweden, and the United Kingdom. 2. See Clemente, Marcuello, and Mantanes (2008). 3. Of course, a detailed assessment of the contribution of each policy measure needs to take into account that countries with higher prices to start with are more likely to adopt policy measures aimed at controlling prices. In addition, volume effects may also be important, and are not captured by the price index. Tests of mean price differences according to the presence of a particular measure did not reject the null hypothesis of no differences. The same holds true in a simple multivariate analysis (though the small cross-section size advises caution with the results). 4. See Wagstaff and van Doorslaer (1992) for an overview of several countries. 5. There are also benefits to insurance coverage. Our point here is just to illustrate how insurance protection stimulates consumption. In addition, insurance protection may also contribute to a lower level of actions that reduce the probability of an individual needing health care. This is usually termed ex ante moral hazard, distinct from the ex post moral hazard described in the main text. 6. An account of the effects produced in Germany by this policy change can be found in Pavcnik (2002). 7. Adapted from Barros and Martinez-Giralt (2002). 8. See Danzon (2001), Brekke et al. (2007), and Lopez-Casasnovas and PuigJunoy (2005). 9. See related empirical evidence in Kyle (2007). 10. OECD (2008) reports several stylized facts about the pharmaceutical industry, see also Mrazek and Mossialos (2004). 11. See Barros and Martinez-Giralt (2002) for a formal argument.

REFERENCES Barros, P. P. (2008). The simple economics of risk-sharing agreements between the NHS and the pharmaceutical industry. Manuscript, Universidade Nova de Lisboa. Barros, P. P., & Martinez-Giralt, X. (2002). Public and private provision of health care. Journal of Economics & Management Strategy, 11(1), 109–133.


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Brekke, K., Konigbauer, I., & Straume, O. R. (2007). Reference pricing of pharmaceuticals. Journal of Health Economics, 26(3), 579–601. Clemente, J., Marcuello, C., & Montanes, A. (2008). Pharmaceutical expenditure: Total health care expenditure and GDP. Health Economics, 17, 1187–1206. Cook, J., Vernon, J., & Manning, R. (2008). Pharmaceutical risk-sharing agreements. PharmacoEconomics, 26(7), 551–556. Danzon, P. (2001). Reference pricing: Theory and empirical evidence. In: G. Lo´pez-Casasnovas, B. Jonsson, (Eds), The economics of reference pricing and pharmaceutical policy. Coleccioń de Economı´ a de la salud y Gestioń Sanitaria. Barcelona, Spain: Springer. Danzon, P. M., Wang, Y., & Wang, L. (2005). The impact of price regulation on the launch delay of new drugs: Evolution from twenty-five major markets in the 1990s. Health Economics, 14(3), 269–292. Garcia-Marinõso, B., Jelovac, I., & Olivella, P. (2008). External referencing and pharmaceutical price negotiation. Working Paper du GATE, Vol. 15. Heuer, A., Mejer, M., & Newhaus, J. (2007). The national regulation of pharmaceutical markets and the timing of new drug launches in Europe. Working Paper 437. Kiel Institute for the World Economy. Jelovac, I. (2008). On the relationship between the negotiated price of pharmaceuticals and the patients’ copayment. Working Paper 0208. Center for Economics Development, University of Science and Technology, Hong-Kong. Kanavos, P. (2006). Policy approaches to pharmaceutical pricing and reimbursing in European countries. Mimeo. Konijn, P. (2007). Pharmaceutical products – Comparative price levels in 33 European countries in 2005. Statistics in Focus, Economy and Finance, 45, 1–3. Kyle, M. (2007). Pharmaceutical price controls and entry strategies. The Review of Economics and Statistics, 89(1), 88–99. Lopez-Casasnovas, G., & Puig-Junoy, J. (2005). Reference pricing as a pharmaceutical reimbursement mechanism. In: J. Puig-Junoy (Ed.), The public financing of pharmaceuticals – An economic approach (pp. 103–123). Northampton, MA: Edward Elgar. Mrazek, M., & Mossialos, E. (2004). Regulating pharmaceutical prices in the European Union. In: E. Mossialos, M. Mrazek & T. Whalley (Eds), Regulating pharmaceuticals in Europe: Striving for efficiency, equity and quality (pp. 114–129). Berkshire, England: Open University Press. OECD. (2008). Pharmaceutical pricing policies in a global market. Paris: OECD. Pavcnik, N. (2002). Do pharmaceutical prices respond to potential patient out-of-pocket expenses? Rand Journal of Economics, 33(3), 469–487. Sood, N., de Vries, H., Gutierrez, I., Lakdawalla, D., & Goldman, D. P. (2009). The effect of regulation on pharmaceutical revenues: Experience in nineteen countries. Health Affairs, 28(1), w125–w137. US Department of Commerce. (2004). Pharmaceutical prices controls in OECD countries – Implications for US consumers, pricing, research and development, and innovation. Washington, DC, December. Available at http://www.trade.gov/td/health/DrugPricing Study.pdf Wagstaff, A., & van Doorslaer, E. (1992). Equity in the finance of health care: Some international comparisons. Journal of Health Economics, 11, 361–387.

INTERNATIONAL EXPERIENCE WITH COMPARATIVE EFFECTIVENESS RESEARCH: CASE STUDIES FROM ENGLAND/WALES AND GERMANY John F. P. Bridges, Joshua P. Cohen, Peter G. Grist and Axel C. Mu¨hlbacher ABSTRACT Purpose – Although the US has lagged behind international developments in health technology assessment (HTA), renewed interest in HTA in the US has been fueled by the appropriation of $1.1 billion comparative effectiveness research (CER) in 2009 and the debate over health care reform. Approach – To inform CER practices in the US, we present case studies of HTA from England/Wales and Germany: contrasting methods; relevance to the US; and impact on innovation. Findings – The National Institute of Health and Clinical Excellence (NICE) was established in 1999 to inform trusts within the National Health Service of England and Wales. It uses cost-effectiveness analysis Pharmaceutical Markets and Insurance Worldwide Advances in Health Economics and Health Services Research, Volume 22, 29–50 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1108/S0731-2199(2010)0000022005

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to guide the allocation resource across preventative and curative interventions. In Germany, the Institut fu¨r Qualita¨t und Wirtschaftlichkeit im Gesundheitswesen (IQWiG) was established in 2004 to inform reimbursement and pricing policies for the statutory sickness funds set by the Gemeinsamer Bundesausschuss (G-BA). IQWiG evaluates competing technologies within specific therapeutic areas, placing more weight on clinical evidence and the relative efficiency of competing therapies. Practical implications – Although having deep political and cultural antecedents, differences between NICE and IQWiG can be explained by perspective: the former guiding resource allocation across an entire system (macro-evaluation), the latter focusing on efficiency within the bounds of a particular therapeutic area (micro-evaluation). Given the decentralized nature of the US health care system, and the relative powers of different medical specialties, the IQWiG model presents a more suitable case study to guided CER efforts in the US.

INTRODUCTION Health care systems worldwide are struggling under the weight of rising costs, but this burden is especially relevant in the US where health care costs currently account for 17% of gross domestic product (Anderson & Frogner, 2008). Costs have risen sharply, in part, due to the rapid development and dissemination of new medical technologies. Coupled with other institutional and demographic factors, the US has not only seen the unit cost of health care rise, but also a dramatic increases in the demand for health care (Newhouse, 2004). Initially, managed care offered some control over growing health care costs, through a combination of selective purchasing and price negotiations, but cost containment was short live. The US is once again facing a ‘‘health care crisis’’ as indicated by double-digit increases in annual premiums and the rising number of uninsured and underinsured. In recent years, more attention has been placed on the variability in the consumption of health care services across the US and the concerns over the appropriateness of care (Wennberg, Fisher, & Skinner, 2002; Congressional Budget Office Testimony, 2008). This variation has been attributed to evidence gaps – the absence of knowledge-informing decision makers on the relative benefits of treatments – and incentives to use health care interventions that pose value for money (Orszag & Ellis, 2008). Emerging from this concern over physician prescribing patterns and the purchase of health

International Experience with Comparative Effectiveness Research

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care services was a new paradigm of research referred to as comparative effectiveness research (CER). Even before CER was defined, many argued it to be the key to both addressing variation and solving the cost-driven health care crisis, whereas others used it as a political boogeyman, with references to socialized medicine and ‘‘death panels.’’ The momentum of the CER movement strengthened last year with the appropriation of $1.1 billion to ‘‘accelerate the development and dissemination of research assessing the comparative effectiveness of health care treatments and strategies’’ within the economic stimulus package; the American Recovery and Reinvestment Act (US Congress, 2009). It remains unclear as to the precise form CER will take in the US, and what impact it will have on utilization and prices of health care services, including prescription drugs (Cohen & Bridges, 2009). In fact, the language of the Bill offers little instruction as to how the research should be conducted or how its findings should be used. We can glean insight on what to expect from experiences abroad. As it turns out, the notion of CER and health technology assessment (HTA) are remarkably similar. The same arguments that were used to promote CER in the US were successfully used to promote the adoption of HTA in Australia, Canada and across Europe (Chalkidou et al., 2009), where governments have traditionally been more involved in the regulation and provision of health care (Pugatch & Davidson, 2007). Both approaches involve the evaluation of the relative clinical effectiveness, safety and cost of two or more medical services, drugs, devices, therapies, procedures, and used to treat the same condition, such as comparing the relative merits of outcomes of one intervention with one or more others (American College of Physicians, 2008; Institute of Medicine, 2007). CER aims to lower health care spending without adverse effects on health, by generating more information about the relative effectiveness of medical treatments and enhancing the incentives for providers to supply, and consumers to demand, effective care (Congressional Budget Office Testimony, 2007; Orszag & Ellis, 2008). Although this comparative approach is generally accepted, it is less clear how effectiveness is measured and if costs will be included in the analysis.

HEALTH TECHNOLOGY ASSESSMENT HTA, an interdisciplinary field of research, evaluates the ‘‘medical, social, ethical, and economic implications of development, diffusion and use of health technology’’ (Hailey & Menon, 1999). Although HTA first emerged

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in the US (Chalkidou et al., 2009), it failed to take root at the policy level, leaving it primarily an academic endeavor (Bridges, Onukwugha, & Mullins, 2010). HTA was first applied systematically in the respective Medicare systems of Australia and Canada (Weedon, 1999; Menon & Topfer, 2000). Since then, it has become a global phenomenon focused on the practically-oriented assessments of relevant available knowledge on the direct and intended consequences of technology, as well as the indirect and unintended consequences. (Draborg, Gyrd-Hansen, & Poulsen, 2005)

It has become a common (mis)perception that HTA is synonymous with the use of cost-effectiveness analysis and the evaluation of the Quality Adjusted Life Year (QALY) – a societal measure of health outcomes focused on dimensions such as physical mobility, self-care, activities of daily living, and social and psychological functioning (Rawlins & Culyer, 2004). The main appeal of the QALY is its simplicity (Bridges et al., 2010; Grosse, 2008); a number bounded by zero and one that quantifies health adjusting for the quality of those life years, calibrated on a scale from 0 (death) to 1 (perfect health). The cost-per-QALY expresses the incremental cost of a treatment over current standard therapy set against the additional health benefits conferred by the treatment. Once this is calculated, it can be compared with other treatments in a league table, ranking all alternatives by their cost-per-QALY (Niemietz & Pugatch, 2007).

HTA IN THE US Seen as bucking the international trend, the US has not relied on HTA to control health expenditures, at least not systematically (Bridges et al., 2010). Although the US is generally considered having lagged behind other developing countries in HTA, a de facto fourth hurdle has been instituted (Cohen, 2006). Among watershed events in the past five years, we have seen the enactment of federal regulations that allow cost and clinical evidence to be considered in outpatient drug reimbursement decisions affecting Medicare beneficiaries. Also, the Centers for Medicare and Medicaid Services (CMS) has stated that Food and Drug Administration (FDA) authorization is insufficient to support reimbursement decisions for certain physician-administered drugs, such as the erythropoesis-stimulating agents covered under Medicare Part B. Additionally, the federal government is operating a CER program, under the auspices of the Agency for Healthcare Research and Quality (AHRQ). AHRQ undertakes evidence-based research and makes recommendations to improve the quality of health care


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decision-making, through the publication of scientific information on clinical guidelines, performance measures, and other quality-improvement tools (www.AHRQ.gov). However, there is not yet a systematic tie-in between AHRQ data and Medicare or Medicaid coverage decisions. Further, the AHRQ model does not explicitly consider cost. In contrast, the private sector has included some CER in its guidelines for pharmaceuticals. The Academy of Managed Care Pharmacy (AMCP) trade group has issued detailed guidelines specifying precise parameters to be included by drug sponsors in the formulary dossiers they submit to payers for the purposes of reimbursement. President Obama and many lawmakers have identified health care reform as a key immediate-term priority. One pillar of reform is establishment of an effective way to improve the system’s efficiency. The federal stimulus bill has made available $1.1 billion for comparative effectiveness research, which will create a federal comparative effectiveness program to conduct HTA/CER (Cohen & Bridges, 2009). If the US pursues a federal CER agenda, then European countries with well-established HTA institutions, offer models to learn from.

HTA IN EUROPE Unlike the US, European countries generally have national health systems, in which acquisition and access to new technologies are subject to extensive government regulation. Governments have set themselves the dual task of guaranteeing universal access to health care, whereas keeping costs under control. For this purpose, many have found it prudent to establish HTA programs (Pugatch & Davidson, 2007). With the founding in 1999 of NICE, England and Wales became a leader in HTA and cost-effectiveness analysis. NICE is highly respected internationally and has been looked to as a model for the development of HTA bodies in many other countries, even the US. Germany, however, has adopted a substantively different approach to HTA, through the Institut fu¨r Qualita¨t und Wirtschaftlichkeit im Gesundheitswesen (IQWiG)1 established in 2004. Although the IQWiG approach is still in its infancy, it offers an alternative model for HTA. The following provides an overview of these HTA bodies.

CASE STUDY 1: ENGLAND AND WALES Among all developments surrounding the fourth hurdle, none has had such far-reaching implications as the founding of NICE in England and Wales in

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1999. NICE is considered to be the most advanced in the delivery of HTA, having the most systematic approach to determine the value, or relative costs and benefits of various treatments (Department of Health, 2001; Sorenson, Drummond, Kanavos, & McGuire, 2008). It has become the world’s most prominent appraiser of new medical technologies, including drugs and biologics. England and Wales have a highly centralized health care system where the National Health Service (NHS) is the primary funder and main provider of health care. NICE functions in an advisory capacity to the NHS, as a Special Health Authority. It is intended that NICE have an arms-length relationship with government, however, it is funded solely by the Department of Health. The Department of Health commissions NICE to develop guidance on individual health technologies, the management of specific conditions and the safety and efficacy of diagnostic and therapeutic procedures (Rawlins & Culyer, 2004). Broadly, NICE’s purview includes provision of guidance on the ‘‘promotion of good health and the prevention and treatment of ill health’’ (NICE, 2009). In particular, NICE produces guidance with respect to: Health technologies – use of new and existing medicines, diagnostics, treatments and procedures. Clinical practice – appropriate treatment and care of patients with specific diseases and conditions (NICE, 2009). NICE’s stated goals are to rationalize clinical care and eliminate inequities by geographic area (Department of Health, 2001). NICE’s raison d’eˆtre is premised on the oft-cited ‘‘tragedy of the commons’’ were resources are assumed to be scarce but competing demands on them are infinite. A patient acts rationally in seeking an expensive treatment that produces a benefit, even if marginal, because the cost falls almost entirely on others. The same can be said for doctors who seek the best care for their patients. The costs, in terms of fewer resources for other patients, fall almost entirely on other doctors’ patients. Ultimately, however, from a societal perspective, to ensure efficient resource allocation, it is required that an intervention’s health benefits are greater than their opportunity cost, or forgone value of the next-best alternative. NICE tackles this resource allocation problem by evaluating the costs, benefits, and risks of new medical technologies, and recommending resource allocation accordingly. NICE does not commission primary research. Instead, it bases its recommendations on systematic analyses and economic modeling of available evidence. This evidence, however, may not be very


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good and is rarely complete (Rawlins & Culyer, 2004). The costeffectiveness technique used by NICE to assess different treatments – cost-per-QALY – facilitates system-wide comparisons across a wide range of treatments. Alternatively, if such data is unavailable, NICE employs the cost-per-life-year gained ratio (Rawlins & Culyer, 2004). Beyond costeffectiveness, equity considerations, such as burden of disease and rule of rescue, may inform reimbursement and use decisions, with treatments targeting more severely impairing conditions generally being prioritized. A threshold interval approach, instead of a single, fixed value, is used to determine an intervention’s cost-effectiveness. NICE’s (2004) Guide to the Methods of Technology Appraisal states that most interventions costing less than d20,000 per QALY are approved for reimbursement. When the ratio lies between d20,000 and d30,000 additional factors are brought to bear, such as uncertainty of the cost-effectiveness data and the ‘‘innovative nature’’ of the technology. Interventions costing more than d30,000 per QALY usually result in a ‘‘no’’ recommendation. NICE has published approximately 150 guidance documents covering over 200 different drugs and medical devices. Approximately 50% of assessed technologies are recommended for reimbursement and use in all licensed indications. Approximately 40% are conditionally recommended (e.g., only for specific indications, patient subgroups, or as step therapy). And 10% are not recommended at all for reasons related to lack of clinical or cost-effectiveness (Cohen, Cairns, Paquette, & Fadden, 2006; Raftery, 2006). Technologies supported by more randomized trial evidence are more likely to be recommended for reimbursement and use. In addition, the earlier technologies are appraised the less likely they are to be rejected (Appleby, Devlin, & Parker 2007). Moreover, the higher the burden of disease the more likely a technology gets recommended. At the same time, higher cost-effectiveness ratios increase the probability of rejection. And, the more uncertainty exists concerning a technology’s cost-effectiveness ratio the less likely a technology is recommended for reimbursement and routine use (Appleby, Devlin, & Parker, 2007). There is no evidence to suggest that overall drug spending or its growth rate has been reduced in England and Wales. In fact, NICE recommendations have turned out to be largely positive, which has increased the NHS drug budget (Raftery, 2006). This increase is due in part to the statutory obligation of the NHS to provide funding for NICE-recommended drugs. However, if NICE recommends against reimbursement and use of a pharmaceutical, the practical effect is that it ‘‘will be unavailable to patients’’ (Syrett, 2003). This is because in most instances, the 150 primary

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and 170 acute care NHS trusts, acting as local health care purchasers, will ‘‘point to the guidance issued by NICE as justification for not spending limited resources on that treatment’’ (Syrett, 2003). Moreover, delays between licensing and reimbursement have led to a phenomenon called ‘‘NICE blight.’’ NHS trusts hold back decisions to fund technologiespending publication of guidance recommendations by NICE. The breast cancer biologic Trastuzumab is a case in point. NICE took 18 months to appraise Trastuzumab before recommending its reimbursement and use (Cohen et al., 2006). During that period most trusts did not fund its use. In the face of products being refused reimbursement, some companies have struck risk-sharing deals with the Department of Health, in which companies pay a refund to the NHS for patients who do not respond to their drugs (Kennedy, 2009). Value-based pricing is also being implemented, whereby each drug will be reviewed and priced in accordance with the clinical benefits it produces relative to an appropriate comparator (Kennedy, 2009). Finally, on a case-by-case basis, NICE is altering its cost-effectiveness criteria to improve access to certain biopharmaceuticals for terminally ill patients (Kennedy, 2009). The new appraisal criteria effectively raise the costeffectiveness threshold for certain products, specifically those that meet the following conditions: A cost-effectiveness ratio greater than d 30,000 per QALY; licensed for a patient population with life expectancy less than 24 months; targeting a disease in which no more than 7,000 new patients are diagnosed per year; with sufficient evidence to indicate that the treatment offers a ‘‘substantial’’ average extension to life compared to existing alternatives (Towse, 2009).

CASE STUDY 2: GERMANY The German health care system is comparatively decentralized with over 90% of the population covered through mandatory social health insurance offered by many different private, not-for-profit health (sickness) funds, financed through wage-related contributions and taxes. The remaining 10% opt out of the social health insurance system by purchasing private health coverage. This group consists mainly of high-income individuals or self-employed persons (Chalkidou & Anderson, 2009; Kanavos, 2002). The health care elements to be reimbursed by sickness funds are defined by the Gemeinsamer Bundesausschuss (G-BA)2 (Bekkering & Kleijnen, 2008). The G-BA evaluates all health technologies and products to find which offer the best benefit in comparison to engaged resources.


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In 2004, with introduction of the Healthcare Modernization Act (Gesundheits-Modernisierungsgesetz, GMG) the German government established the Institute of Quality and Economic Efficiency in the Healthcare Sector (Institut fu¨r Qualita¨t und Wirtschaftlichkeit im Gesundheitswesen, IQWiG), to evaluate the use, quality, and effectiveness of health care services and pharmaceuticals (IQWiG, 2009). Further tasks of IQWiG were laid down in the Social Code Book (SGB) V, y 139a. as part of 2007 health reforms. These tasks included the evaluation of cost-effectiveness expanding IQWiG’s role to include the provision of advice on pricing and reimbursement of new medical technologies to the G-BA (Drummond & Rutten, 2008). The G-BA commissions IQWiG to carry out benefit assessments and economic evaluations, to provide an overall view of the scientific evidence regarding the benefits of certain interventions. The Ministry of Health can also request assessments (y139b SGB V). Lawsuits against IQWiG’s assessments are not permitted (y 35b SGB V). Based on these recommendations, the G-BA formulates its directives as to whether it will reimburse the medicine or technology in question (Bekkering & Kleijnen, 2008). Possible consequences of a negative assessment include reimbursement limited to subpopulations with proven benefits, with strict obligations to document single patients and reference pricing. Other negative consequences can happen with the issue of therapy guidelines that rank treatements, limit them to subgroups or have clear start and stop rules, all of which limit provider discretion about what is best for the individual patient. Conditional reimbursement where therapies are approved for limited times with the obligation to prove therapeutically relevant benefits and exclusion from reimbursement altogether also result from negative assessments. A recent negative IQWiG appraisal for short-acting insulin analogues in Diabetes Mellitus Type 1, resulted in G-BA excluding these products from funding in this indication, based on price comparison because it found no incremental benefit compared with regular short-acting insulin, but the new treatment had a higher price per defined daily does. Most biopharmaceuticals are automatically reimbursed once approved by a regulatory authority such as the European medicines magency or the German drug regulatory agency, with a reference price system that places limits on the level of reimbursement for off-patent and patented drugs considered therapeutically interchangeable. Unless a drug can demonstrate clear-cut ‘‘innovative features’’ or therapeutic superiority over existing therapies, reference pricing is applied. Nevertheless, insurance funds must cover any service that is medically necessary, which means that costeffectiveness analysis can only be used to exclude a treatment from coverage

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if at least one equivalent alternative exists (Nasser & Sawicki, 2009; Johnson, 2009). IQWiG has developed a unique analytical framework for economic evaluations of drugs and other interventions. It compares the efficiency of newer treatments in a single therapeutic area, usually for the same indication, with that of existing treatments. If the analysis suggests a newer treatment provides additional benefits or less harm in comparison to the existing treatments, then IQWiG determines the ceiling price at which the superior health technology should be reimbursed. Here, the ceiling price represents the maximum that statutory health insurance funds should pay for the benefits produced (IQWiG, 2009). However, this does not restrict access to the treatments priced above the ceiling price, as the patient is then charged the difference between the ceiling and the manufacturer’s price. IQWiG employs an ‘‘efficiency frontier’’ to evaluate the efficiency of a new drug or intervention, and subsequently assesses the reasonableness of the price of introduced technologies. The efficiency frontier indicates how much value is being obtained at what cost, and where existing interventions are placed relative to each other (IQWiG, 2009). Fig. 1 illustrates an example of an efficiency frontier mapping the cost and value of existing (which define the frontier) and novel therapeutics (that need to be assessed). Coordinates are determined by cost and benefit (i.e., value) units. Here, benefits relate to improvements in health status or quality of life, a reduction in disease duration, an increase in life expectancy or a decrease in adverse effects. The cost axis shows costs accrued to achieve the previously determined benefit. In this way, the graphic presentation indicates which existing therapies are efficient. As seen in Fig. 2, the existing interventions (marked as dots) define a frontier that separates better and worse interventions (compared directly to those that already exist in the market. In the evaluation of the new drug (in the figure marked as X), which might normally give a higher clinical benefit at a higher cost, we can extend the frontier to judge its relative efficiency. As there are no direct comparators in the neighborhood of X, we must impute the frontier, and X may be seen as either efficient or inefficient depending on the assumptions made. This efficiency frontier approach that will be applied by IQWiG does not apply a threshold across all interventions, instead it considers the efficiency of resource use within a single therapeutic area relative to existing interventions. The efficiency frontier plots the value of health effects provided by available interventions, against their costs, de facto producing a hedonic pricing formula. Further, the frontier connects interventions that provide

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New Drug X

Alternatives to X: Yes

Alternatives to X: No

Assess benefits: A (B, C,…) vs. X

Assess benefits: X vs. placebo

X > A (B, C,…)

Assess benefits and costs

X has benefits over placebo

Recommend a price for X X = A (B, C,…)

Reference pricing

X < A (B, C,…)

Exclusion of X

Manufacturer’s price X has no benefits Exclusion of X

Fig. 1. IQWiG Appraisal Framework. It Illustrates the IQWiG Appraisal Framework for the Assessment of a New Drug X. If There Exists No Alternative to X, Then IQWiG will Assess the Benefits of X Against Placebo. If X has Benefits, Then the Manufacturer can Set Price, but if There is No Benefits, Then X is Excluded from Reimbursement by Sickness Funds. If Drug X has Alternatives, Say A (or B, C, y), Then IQWiG will Compare the Benefits X to These Competitors. If X has Benefits Over and Above These Alternatives, Then IQWiG will Conduct an Analysis of the Costs and Benefits of X and Its Competitors Using an Efficiency Frontier. If X has Similar Benefits to Its Alternatives, Then Reference Pricing will be Used (i.e., All Alternatives will be Set at the Minimum Price of the Comparator Set. If X is Inferior to Its Comparator, It will be Excluded from Reimbursement.

the greatest benefits at a given cost. Points below the frontier indicate inefficient interventions, whereas points above redefine the frontier by presenting options that were not feasible given previous technologies (IQWiG, 2009). With the help of this graph, policymakers can determine an ‘‘appropriate’’ (i.e., ceiling) price for a new drug. For drugs that are more beneficial, yet more costly than existing alternatives, one can calculate where the price per unit or treatment cycle would have to lie so that the cost– benefit relationship is within an acceptable efficiency range. Subsequently, manufacturers can lower the price to meet the efficiency requirement. If they choose not to, patients must pay the difference between the manufacturer and ceiling price.

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JOHN F. P. BRIDGES ET AL. Patient relevant endpoint

X More efficient interventions

B

A Less efficient interventions

Costs (€)

Fig. 2. The Efficiency Frontier. It Illustrates the Efficiency Frontier for a Drug X, That More Effective Compared to Its Alternatives (A and B), as Measured by Some Chosen Patient Relevant Outcome. An Efficiency Frontier, Partitioning More Efficient and Less Efficient Interventions, is Drawn from the Origin Through Points A and B (as Drawn in the Outcome-Cost Space), and is Extrapolated to Consider the Value of X. Here X Appears to Provide Value for Money, as Compared to the Extrapolated Efficiency Frontier. De facto, the Efficiency Frontier Become a Pricing Formula, with Firms Able to Set the Highest Price Possible That Keeps Them on the Favorable Side of the Efficiency Frontier.

Pilot studies illustrating the efficiency frontier methods were commissioned through the IQWiG and results were presented (June 30, 2009), but the study reports were not published until now. Lack of real-world applications of the IQWiG approach limits our ability to properly consider its merits. While IQWiG has produced reports on a variety of topics, these have been based almost exclusively on systematic review data, and not the efficiency frontier method. There is a debate about the influence of each efficiency frontier on the decision made by the G-BA and unexplained so far is how different patient-relevant endpoints (referring to outcomes like mortality, morbidity, and quality of life) should be weighted.

DISCUSSION The growing use of clinical- and cost-effectiveness analyses by payers in other countries highlights opportunities and challenges for the US.


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Ostensibly, technology assessment aims to improve quality of health care. At the same time, policymakers explicitly acknowledge that HTA bodies are also concerned with finding equitable ways of allocating scarce resources. Furthermore, there is consensus among virtually all stakeholders in the US that it may be useful to make decisions about reimbursement and use of newly approved drugs on a rational, data-driven basis, which, in turn, could pave the way for less variation across providers. If we look at England and Wales, there is agreement that the technical quality of NICE assessments is world-class. Despite being controversial, its adoption of the cost-per-QALY method has some support among health economists. Also, NICE has a clearly articulated role and set of responsibilities, and is recognized as having a relatively high degree of transparency as well as participation by a wide range of stakeholders (Sorenson et al., 2008). Finally, pricing specialists suggest cutting case-by-case deals with NICE is a better cost containment option than administratively set, across-the-board price controls. In the case of Germany, IQWiG represents a constructive way of carrying out HTA and price setting. Although superficially similar to NICE, IQWiG does not use QALYs, but resorts to different, what it deems are less discriminatory evaluation methods. Its mandate is to address efficacy relative to costs for a given indication, and not to set funding priorities across the health care system. The IQWiG approach appears particularly advantageous when comparing new treatments with costs that approximate those of existing alternatives. However, HTA is not a panacea. Where it has been implemented, tensions with regard to efficiency, equity, and cost persist, as do questions about the methods themselves and the extent to which recommendations are actually implemented. In England and Wales, NICE has been criticized on numerous grounds, particularly that it imposes a bureaucratic ‘‘one-size-fitsall’’ population-based view of medicine. Although intuitively appealing to policymakers, the threshold range is not empirically grounded (Bridges et al., 2010; Birch & Gafni, 2007). The cost-per-QALY method delineates an arbitrary cut-off between technologies the NHS can afford for all, as opposed to those it will fund for (virtually) no one. NICE aims to have guidance that applies equitably across NHS trusts in England and Wales. This contrasts sharply with the individualized US approach to formulary management. Many US policymakers see at least some virtue in variation. To illustrate, if one US insurer does not reimburse a certain drug, a person can theoretically go elsewhere for coverage. In one study concerning 71 drugs NICE had evaluated, all 9 drugs NICE recommended against were on formularies of at least one major US plan (Cohen et al., 2006).

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In addition, one-size-fits-all decisions may undermine competition in the health care system, as different payers (i.e., NHS trusts) have a different capacity to pay, yet they are forced to act monolithically. Detractors also point to the cost-per-QALY method as being tilted against medications targeting life-threatening conditions, such as cancer; in particular, when they are labeled for use in advanced or metastatic disease. Biotechnology drugs that often target cancer appear especially vulnerable when cost-effectiveness enters the equation, as they are often being compared with relatively old and usually inexpensive medications. Further, if the cost comparator is an existing generic, it is far more difficult for a new treatment to come in at an acceptable cost-per-QALY. It has been observed the UK lags behind other European nations in terms of uptake of new cancer drugs, which may be connected to negative decisions by NICE (Wilking & Jonsson, 2007). Moreover, analysts indicate new arrangements at NICE to raise the cost-per-QALY threshold for certain, mainly anticancer products are unlikely to improve their availability, as ‘‘few of the rejected drugs would qualify under the new criteria’’ (Raftery, 2009). As a result, there are important limits to NICE’s influence. First, only in England and Wales have the kinds of technology appraisals that NICE conducts been fully integrated with all parts of the medical decision-making establishment. The decisions are legally binding, national in scale, and implemented within the confines of a single payer system. Secondly, other than Canada and Australia, no other technology assessment authority employs the cost-per-QALY method as extensively as NICE. IQWiG has discussed the cost-per-QALY approach, on the basis that it is ‘‘discriminatory,’’ because it implies patients are less worthy of treatment if they have diminished life expectancy (IQWiG, 2009). German policymakers further point out that the QALY is not a cardinal measure, conducive to interpersonal comparisons (Caro, 2009). By contrast, IQWiG has developed an alternative approach based on the efficiency-frontier model. This, in turn, narrows the focus in each evaluation to a single therapeutic area, comparing the efficiency of newer treatments with existing treatments; thus comparing like with like rather than judging whether a particular condition deserves treatment relative to others. Besides not setting a standard cost-utility threshold range across all therapeutic areas, IQWiG’s approach has the potential to be more patient-centric with its focus on patient-relevant endpoints (Kinter, Schmeding, Rudolph, dos Reis, & Bridges, 2009). In addition, the value patients derive from treatments is better informed, since effectiveness is measured ex post by those affected by their particular disease, rather than ex ante by a group of unafflicted members of society


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(Nord, 2001; IQWiG, 2009). Thus, each therapeutic group represents an individual market. Subsequently, welfare losses are minimized as two or more interventions are made available based on their relative within-group effectiveness, rather than being held to a comparatively rigid cost-utility threshold. Notwithstanding the need to work out the kinks of a novel approach to HTA, the IQWiG model is more flexible, and, correspondingly, more compatible with the pluralistic US health care system. The fact that both the negative list of nonreimbursable drugs remains small and cancer drug uptake in Germany tops the list of European Union countries (Wilking & Jonsson, 2007) suggests IQWiG has not had a deleterious effect on biopharmaceutical innovation in the short term. However, research is needed to determine the effect of new price regulations (i.e., ceilings) on launches of new products in Germany. Until recently, Germany was a place where many drug companies decided to make their first European launches (Danzon & Furukawa, 2003). That situation may have changed since IQWiG began its work. Indeed, from the little we know about IQWiG’s initial trials and tribulations, experts suggest it has proven difficult both to collect the data required to construct the efficiency frontier for each therapeutic area, and incorporate uncertainty (Drummond & Rutten, 2008). Furthermore, IQWiG has less stakeholder participation and transparency than NICE regarding HTA development and in the standards applied in the use of data and implementation of guidance. Perhaps most importantly, by imposing the restriction to only consider the efficiency of resource allocation within individual therapeutic areas IQWiG has maneuvered itself into a difficult position. This approach allows for inefficiencies by concentrating on one given therapeutic area at a time and not checking the consistency of decisions across areas. Moreover, the IQWiG approach does not provide unambiguous guidance for new health technologies that provide greater benefit at a higher cost, which is the bread-and-butter zone not only for decision-making, of, say, the NICE model, but also the de facto approaches used by payers in the US.

INNOVATION Germany and the UK are the largest biopharmaceutical markets in Europe. Their HTA approaches, and, by inference, ways of conducting comparative effectiveness research (CER), have far-reaching implications for future drug development. The NICE and IQWiG models reflect very different

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perspectives on therapy access and innovation, with IQWiG appearing to leave more room for incremental innovation. Comparatively small, yet important improvements within a therapeutic area could still be funded if the treatment’s relative efficiency is higher than those currently available. Also, the German approach does not restrict access to new treatments deemed relatively less efficient than the existing alternative, as it will pay for the cost of treatment up to a ceiling price based on the existing treatment, with the patient then charged the difference between the manufacturer and ceiling price. It is important to note that innovations do not always represent costeffective alternatives to current practices. Particularly in the area of medical technologies, the evidence available at the early stages of development is often insufficient to judge the long-term costs and benefits (Chalkidou, Lord, Fischer, & Littlejohns, 2008). This creates a challenge for decision makers who are under pressure from manufacturers, patients, clinicians, and taxpayers. Decision makers must provide access to promising new technologies while ensuring that they are paying for value and achieving a measurable improvement over the existing technology. CER offers a systematic, transparent approach to achieve this balance. Representatives of the biopharmaceutical industry have expressed concern that the adoption of cost-effectiveness analysis (of any kind) could impede innovation by creating another hurdle in the marketplace. By contrast, policymakers have suggested that the use of cost-effectiveness analysis may stimulate manufacturers to bring more cost-effective products to market in the first place (Neumann, 2005). By the same token, the absence of such analysis does not necessarily translate into an innovationfriendly environment. Instead, it may simply mean that payers will find other, less visible ways to ration care. And, as we see with NICE, costeffectiveness analysis need not be used rigidly. The threshold might be higher in some contexts (e.g., treatment of life-threatening conditions) and lower for others. To illustrate, in 2001 NICE recommended coverage of the drug Imatinib to treat chronic myeloid leukemia, citing equity considerations, despite a relatively high cost-effectiveness ratio (Cohen et al., 2006). However, NICE and innovation have an uneasy relationship. NICE will play a key role in establishing the new system of value-based pricing of medicines, whereby each drug will be reviewed and priced in accordance with the clinical benefits it produces relative to an appropriate comparator. But, whether such a system is consistent with innovation remains to be seen. This is because value-based pricing shares much in common with the QALY and incremental cost-effectiveness ratio approach to drug evaluation.


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And these approaches aim at efficiency, not innovation. The few instances in which NICE has declared a specific interest in innovation, its interpretation of what counts as innovative has been especially narrow. For example, the so-called innovation pass will make selected innovative medicines available to patients for three years, during which data would be collected to inform the standard NICE process later on. However, the devil as usual is in the details. In this case, only a handful of medicines would qualify for the innovation pass, mainly drugs targeting very rare diseases. Another initiative, the ‘‘end-of-life medicines guidance,’’ relaxes the standard NICE cost-effectiveness criteria, specifically for treatments used to extend life for terminal patients. This guidance only applies to treatments that affect small numbers of patients who are not expected to live more than 24 months, and that offer demonstrable survival benefits (at least an extra 3 months of life) compared with current NHS practice. Needless to say, thus far, only a small number of drugs (all biologics) have met the stringent criteria. In the US, the impact of CER on innovation will be shaped in large part by the policy choices facing federal agencies and others as they seek to implement CER. Should one of the choices be to remove cost as a CER consideration, this would make the NICE experience less relevant. Should another choice be to emphasize patient-centric methods of value determination, this would make the IQWiG experience more relevant, as IQWiG is in the process of identifying new methods of value elicitation that focus on real patients with diverse characteristics, rather than the mythical average patient. Although we do not wish to make specific recommendations on which aspects of the NICE and IQWiG approaches the US could adopt, we do think it makes sense to allow access to newly approved products while the real-world evidence base for their use is established, which essentially follows the IQWiG model. This ‘‘coverage with evidence development’’ model has already been adopted (successfully) by the Centers for Medicare and Medicaid Services in certain instances to avoid paying for ineffective technologies. As a consequence, policymakers have been able to expand patient access to potentially innovative treatments, whereas gathering the necessary data to inform future coverage decisions.

CONCLUSION Much of the costly health care that Americans receive is not grounded in empirical evidence of effectiveness. Such evidence gaps contribute to uneven

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health care quality and to variations in spending across communities and regions of the US. Many view CER as a tool to address this variation by gathering data on and promoting what works best in health care. Despite being government supported, institutions that carry out HTAs international are typically quasi-independent, with a purported high level of autonomy and independence (Sorenson et al., 2008). Proponents maintain that HTA agencies provide a nonarbitrary means of limiting the growth in health care spending, promoting better and more equitable care, while removing the influence of politics, emotion, and vested interests by introducing a more scientific, professional, and objective approach to the decision-making process. Opponents of HTA, however, argue that it is an inaccurate, possibly indiscriminate tool based on averages. Its utilitarian roots are the reason for its inability to account well for individual differences. Even its most ardent supporters acknowledge that while technology assessments are based on the best available evidence, this evidence is often inadequate (Rawlins & Culyer, 2004). Internationally, NICE demonstrates the feasibility and potential of costeffectiveness analysis as a tool to improve decision-making, but NICE also shows the limitations of this approach. Specifically, the cost-per-QALY method is subject to arbitrariness and fails to adequately capture individual preferences. It is probable that a ‘‘strict adherence to a QALY approach is likely to prove unacceptable in the United States, but a more flexible use of QALYs could be beneficial’’ (Neumann & Greenberg, 2009). The IQWiG model constitutes a positive step away from imposition of an arbitrary threshold range. However, the German efficiency frontier approach is still in its infancy and it is unclear whether it will offer a significant improvement over NICE. By narrowing the focus for comparison to treatments within the same therapeutic area, IQWiG does not adequately ensure consistency of decisions across therapeutic areas. Finally, international experience suggests that health care spending may actually increase under comparative cost-effectiveness programs, and evidence from the UK and Germany points in that direction (Chalkidou et al., 2009). In the US, questions remain about the mechanism for generating CER data, including funding, governance arrangements and the framework within which information could then be used to support population-level, as well as individual-level clinical decisions. As it currently stands, cost-effectiveness has been dropped from most of the bills pending in Congress regarding appropriation of CER funds. Legislation also precludes conclusions from CER from being used to determine coverage or reimbursement decisions. In the end, a CER program without bite could lead to more unrestricted


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health care spending, which, in turn, could exacerbate the cost problem. At the same time, policymakers must keep in mind of the dangers of comparatively high health care spending: The US has traditionally invested heavily in biopharmaceutical research and development, resulting in a relatively high level of innovation. Comparative-effectiveness research could force a trade-off between innovation and spending. Policymakers therefore need to be attuned to the balancing act of ensuring that the US does not lose its competitive edge in terms of biopharmaceutical innovation, while rationalizing its prescribing and reimbursement decisions. Finally, it is important not to lose sight of the role that politics plays in the CER debate. To pretend that science and politics are entirely separate spheres is folly. Institutions undertaking HTA, such as NICE and IQWiG, play an important role in improving the information available on the range of treatments available within a therapeutic area. However, evidence is mixed on whether these institutions have established significant improvements in the decision-making approach for the reimbursement of new medical technologies than the political process they have replaced. More rigorous evidence has made policy decisions better grounded empirically. But, paradoxically, the analytical enterprise that was supposed to mitigate the role of politics in decision-making faces political limits. Evidence and assessments are not the only factors influencing decision-making: ‘‘Among other factors is the need for cost containment, a culture in favor of evidence, lobbying activities of stakeholders in favor or against specific health technologies’’ (Fricke & Dauben, 2009). Societal value judgments, the institutional context within which drug reimbursement decisions are made, and the power play among stakeholders – in short, politics – help shape how policymakers develop and apply the same types of evidence-based approaches differently.

NOTES 1. Translated as the Institute of Quality and Economic Efficiency in the Healthcare Sector. 2. Translated as Federal Joint Committee (FJC).

REFERENCES American College of Physicians. (2008). Information on cost effectiveness: An essential product of a national cost effectiveness program. Annuals of Internal Medicine, 148, 956–961.

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Anderson, G. F., & Frogner, B. K. (2008). Health spending in OECD countries: Obtaining value per dollar. Health Affairs, 27(6), 1718–1727. Appleby, J., Devlin, N., & Parker, D. (2007). NICE’s cost-effectiveness threshold: How high should it be? BMJ, 335, 358–359. Bekkering, G. E., & Kleijnen, J. (2008). Procedures and methods of benefit assessments for medicines in Germany. European Journal of Health Economics, 9(Suppl 1), S5–S29. Birch, S., & Gafni, A. (2007). Being naughty about NICE? Questioning the methods used to maximize the health gains from NHS resources. Health Economics, Policy and Law, 2, 217–221. Bridges, J., Onukwugha, E., & Mullins, C. D. (2010). Health care rationing by proxy: Costeffectiveness analysis and the misuse of the $50,000 threshold in the US. Pharmacoeconomics, Forthcoming. Caro, J. J. (2009). Method of economic evaluation for the German statutory healthcare system. Pharmacoeconomics, 27(3), 263–264. Chalkidou, K., & Anderson, G. (2009). Comparative effectiveness research: International experiences and implications for the United States (http://www.academyhealth.org/ files/publications/CER_International_Experience_09%20(3).pdf ). Washington DC: Academy of Health. Chalkidou, K., Lord, J., Fischer, A., & Littlejohns, P. (2008). Evidence-based decision making: When should we wait for more information? Health Affairs, 27(6), 1642–1653. Chalkidou, K., Tunis, S., Lopert, R., Rochaix, L., Sawicki, P., Nasser, M., & Terri, B. (2009). Comparative effectiveness research and evidence-based health policy: Experience from four countries. Milbank Quarterly, 87(2), 339–367. Cohen, J. (2006). Translatlantic convergence. Pharmacoeconomics, 24, 87–94. Cohen, J., & Bridges, J. (2009). Assessing comparative effectiveness research in the US. Applied Health Economics and Health Policy, 7(4), 219–224. Cohen, J., Cairns, C., Paquette, C., & Fadden, L. (2006). Comparing patient access to pharmaceuticals in UK and US. Applied Health Economics and Health Policy, 5(3), 177–187. Congressional Budget Office Testimony. (2007). Research in the comparative effectiveness of medical treatments: Options for an expanded Federal role before the Subcommittee on Health Committee on Ways and Means United State House of Representatives. 110th Congress, Tuesday June 12. Congressional Budget Office Testimony. (2008). The overuse, underuse, and misuse of health care before the Committee on Finance United States Senate. July 17. Danzon, P. M., & Furukawa, M. F. (2003). Prices and availability of pharmaceuticals: Evidence from nine countries. Health Affairs, 101377/hlthaff.W3.521, Web Exclusive. W521–W536. Department of Health (2001). New statutory obligations for NHS to fund treatments recommended by NICE’’, Press Release 2001/0599. Available at http://www.dh.gov.uk/ en/publications and statistics/pressreleases/DH_4011543. Retrieved on September 18, 2009. Draborg, E., Gyrd-Hansen, D., Poulsen, P., et al. (2005). International comparison of the definition and the practical application of the health technology assessment. International Journal of Technology Assessment in Health Care, 21(1), 89–95. Drummond, M., & Rutten, F. (2008). The IQWiG methodology paper version 1.0 (http://www.vfa.de/de/politik/aktuellpo/stellungnahmen-iqwig-methodenentwurf.html/ stellungnahme-iqwig-methodenentwurf-drummond-rutten.pdf ). Berlin, Germany: German Association of Research-based Pharmaceutical Companies (VFA).


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Fricke, F. U., & Dauben, H. P. (2009). Health technology assessment: A perspective from Germany. Value in Health, 12(Suppl. 2), S20–S27. Grosse, S. (2008). Assessing cost effectiveness healthcare: History of the $50,000 per QALY threshold. Expert Review of Pharmacoeconomics and Outcomes Research, 8(2), 1–14. Hailey, D., & Menon, D. (1999). A short history of INAHTA. International Journal of Technology Assessment in Health Care, 15, 236–242. IQWiG. (2009). Allgemeine Methodik fu¨r die Bewertung von Verha¨ltnissen zwischen Nutzen und Kosten.’’ In Version 1.0 Institut fu¨r Qualita¨t und Wirtschaftlichkeit im Gesundheitswesen: Ko¨ln. Available at http://www.iqwig.de/download/Methodik_fuer_ die_Bewertung_von_Verhaeltnissen_zwischen_Kosten_und_Nutzen.pdf Johnson, F. R. (2009). Editorial: Moving the QALY forward or just stuck in traffic. Value in Health, 12(Suppl. I), S38–S39. Institute of Medicine. (2007). Roundtable on evidence based medicine, learning what works best: The nation’s need for evidence of comparative effectiveness in health care. Washington DC: Institute of Medicine. Kanavos, P. (2002). Pharmaceutical coverage and resource allocation: Lessons for the US from pharmaceutical policies in the UK, France and Germany. Academy of Health Services Research and Health Policy, 19, 3, Meeting. Kennedy I. (2009). Appraising the value of innovation and other benefits. NICE. Available at http://www.nice.org.uk Kinter, E., Schmeding, A., Rudolph, I., dos Reis, S., & Bridges, J. (2009). Identifying patientrelevant endpoints among individuals with Schizophrenia: An application of patient centered health technology assessment. International Journal of Technology Assessment in Health Care, 25(1), 35–41. Menon, D., & Topfer, L. A. (2000). Health technology assessment in Canada: A decade in review. International Journal of Technology Assessment in Health Care, 16(3), 896–902. Nasser, M., & Sawicki, P. (2009). Institute for quality and efficiency in health care: Germany. In: Commonwealth fund issue brief (Vol. 57). New York: The Commonwealth Fund. National Institute for Health and Clinical Excellence. (2004). Guide to the methods of technology appraisal. London, UK: NICE. Neumann, P. J., & Greenberg, D. (2009). Is the United States ready for QALYs? Health Affairs, 28(5), 1366–1371. Neumann, P. (2005). Using cost-effectiveness to improve health care. New York: Oxford University Press. Newhouse, J. (2004). Consumer-directed health plans and the RAND health insurance experiment. Health Affairs, 23, 107–113. NICE, National Institute for Health and Clinical Excellence (2009). Homepage. Available at http://www.nice.org.uk. Retrieved on September 18. Niemietz, K., & Pugatch, M. P. (2007). Health technology assessment in the UK and Germany. London, UK: Stockholm Network. Nord, E. (2001). The desirability of a condition versus the well being and worth of a person. Health Economics, 10, 579–581. Orszag, P. R., & Ellis, P. (2008). Addressing rising health care costs – A view from the congressional budget office. New England Journal of Medicine, 357, 1885–1887. Pugatch, M. P., & Davidson, H. (2007). A healthy market? Health technology assessment in context. London, UK: Stockholm Network.

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Raftery, J. (2006). Review of NICE’s recommendations, 1999–2005. BMJ, 332, 1266–1268. Raftery, J. (2009). Should NICE’s threshold range for cost-per-QALY be raised? No. BMJ, 338, b185. Rawlins, M. D., & Culyer, A. J. (2004). National institute of clinical excellence and its value judgement. BMJ, 329, 224–227. Sorenson, C., Drummond, M., Kanavos, P., & McGuire, A. (2008). National Institute for Health and Clinical Excellence (NICE): How does it work and what are the implications for the US? National Pharmaceutical Council, VA, USA. Syrett, K. (2003). A technocratic fix to the legitimacy problem: The Blair government and health care rationing in the United Kingdom. Journal of Health Politics, Policy and Law, 28, 715–746. Towse, A. (2009). Should NICE’s threshold range for cost-per-QALY be raised? Yes. BMJ, 338, b181. United States Congress (2009). American Recovery and Reinvestment Act. Washington DC: United States Congress. Weedon, D. (1999). Health technology assessment in Australia. Medical Journal of Australia, 171(10), 551–552. Wennberg, J., Fisher, E., & Skinner, J. (2002). Geography and the Debate Over Medicare Reform, Health Affairs, 10.1377/hlthaff.w2.96: Web Exclusive W96-114 http:// content.healthaffairs.org/cgi/reprint/hlthaff.w2.96v1 Wilking, N., & Jonsson, B. (2007). Market uptake of new oncology drugs. Annals of Oncology, 18(Suppl. 3), ii31–ii48.

PART II NATIONAL CASE STUDIES

PHARMACEUTICAL POLICY IN THE NETHERLANDS: FROM PRICE REGULATION TOWARDS MANAGED COMPETITION Lieke H. H. M. Boonen, Ste´phanie A. van der Geest, Frederik T. Schut and Marco Varkevisser ABSTRACT Purpose – To analyse the development of pharmaceutical policy in the Dutch market for outpatient prescription drugs since the early 1990s. Methodology – A literature review and document analysis is performed to examine the effects of pharmaceutical policy on the performance of the Dutch market for outpatient prescription drugs since the early 1990s. Findings – Government efforts to control prices of pharmaceuticals were effective in constraining prices of in-patent drugs, but had an opposite effect on the prices of generic drugs. The gradual transition towards managed competition – that particularly gained momentum after the introduction of the new universal health insurance scheme in 2006 – appears to be more effective in constraining prices of generic drugs than earlier government efforts to control these prices.

Pharmaceutical Markets and Insurance Worldwide Advances in Health Economics and Health Services Research, Volume 22, 53–76 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1108/S0731-2199(2010)0000022006

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Originality – Comparative analysis of the impact of price regulation and managed competition on generic drug prices in the Netherlands. Implications – Implications of the changing role of health insurers are discussed for the future market for prescription drugs and role of pharmacies in the Netherlands.

1. INTRODUCTION This chapter analyses the development of pharmaceutical policy in the Dutch market for outpatient prescription drugs since the early 1990s. During this period, health care policy in the Netherlands is marked by two, partially overlapping, regulatory strategies (Helderman, Schut, van der Grinten, & van de Ven, 2005). First, to control the growth of total public health care expenditure, the government increasingly intervened by constraining supply and prices of health services (Schut & van de Ven, 2005). Second, parallel to the increasing price and supply regulation, the government started to pave the way for a transition towards managed competition, by gradually providing health insurers with more incentives and tools to act as prudent buyers of health services on behalf of their enrolees. A major step in this gradual transition process from supply regulation towards managed competition was taken in 2006 by the introduction of the new Health Insurance Act. This act established a universal mandatory insurance scheme, by which all Dutch citizens are obliged to buy standardized individual health insurance coverage from competing private health insurers (Van de Ven & Schut, 2008). In comparison to the preceding social health insurance scheme, the Health Insurance Act gives insurers much more flexibility to design their products and provides them with more incentives and options to selectively contract or vertically integrate with health care providers.1 In addition, supply-side constraints are gradually being relaxed and prices of an increasing range of health services can be freely negotiated between health insurers and health care providers, instead of being set by the government. As a result, health insurers are gradually picking up their new role as prudent buyers of care, spurred by strong price competition in the health insurance market since 2006 (Van de Ven & Schut, 2009). In this chapter, we analyze the effects and implications of this transition for the market of outpatient prescription drugs. We will show that – in line with general health policy – pharmaceutical policy since the 1990s is marked

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by tightening price controls, a gradual reinforcement of the purchasing role of health insurers, followed by a relaxation of government price interventions. The chapter is structured as follows. In Section 2, we discuss the structure of the market of outpatient drugs in the Netherlands and the successive government cost control policies that were introduced since the 1990s. Section 3 discusses the implications of the transition towards managed competition. Section 4 concludes.

2. PRICE REGULATION AND DISCOUNT COMPETITION 2.1. Regulation of the Dutch Market for Outpatient Drugs As in most industrialized nations, pharmaceuticals need to undergo an authorization procedure before they are allowed on the Dutch market. For this approval, product quality, safety and efficacy are essential requirements. In the Netherlands, the Medicines Evaluation Board (CBG) carries out the assessment and may grant a marketing authorization for the Dutch market.2 The CBG is also responsible for determining the status of the drug, that is, whether the drug is to be available with or without a prescription from a licensed physician. Non-prescription drugs, also known as over-thecounter drugs or self-medication drugs, are generally not covered by social health insurance, whereas most outpatient prescription drugs are included in the basic benefit package and thus eligible for reimbursement by the health insurer. 2.1.1. Reimbursement from Social Health Insurance The extent to which the costs of outpatient prescription drugs are reimbursed from social health insurance is determined by the Drug Reimbursement System (GVS), which was introduced in 1991. The Minister of Health is formally responsible for the access of outpatient prescription drugs to the GVS but has delegated an advisory role to the Health Care Insurance Board (CVZ). When advising on the reimbursement of drugs, the CVZ considers the therapeutic value of the product together with its cost-effectiveness and the cost consequences for the total expenditure on drugs (the budget impact). The therapeutic value is a combination of efficacy, side effects and other secondary characteristics of the drug. If the therapeutic value of a product is low, it will not be considered eligible for

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reimbursement. Pharmaceuticals eligible for reimbursement within social health insurance are placed on a national positive formulary that consists of three separate annexes: 1A, 1B and 2. Therapeutically equivalent drugs are grouped into clusters and listed in annex 1A. Within the GVS, each of these clusters has a reimbursement limit that is set at the average of the prices of the drugs in the cluster. This system of therapeutic reference pricing is also used in, for example, Germany and New Zealand.3 If the price of a drug is higher than the reimbursement limit (or reference price) the patient pays the difference out-of-pocket.4 Since the implementation of the GVS system in 1991, these limits were only recalculated once (in 1999). Products that cannot be clustered are added to annex 1B and fully reimbursed. Some drugs on the national formulary are also classified into annex 2. These drugs are reimbursed only if some specific criteria are fulfilled. These criteria include, for example, that the pharmaceutical must be prescribed by a specialist physician or approved by the health insurer (prior authorization). 2.1.2. Maximum Price Regulation In the Netherlands, the pharmaceutical industry was, until 1996, completely free in setting drug prices. Drug prices in the Netherlands were substantially higher than the average prices in other European countries, in most of which prices of pharmaceuticals were strictly regulated. To bring the price level down to that of neighbouring countries, in 1996, the Pharmaceuticals Prices Act (WGP) was introduced. According to this act, drugs included in the national positive formulary may not be sold in the Netherlands at a price above a specified maximum level. This maximum price level is determined biannually by calculating the average price of comparable pharmaceuticals with the same active ingredient, strength and dosage form in four neighbouring reference countries (Belgium, France, United Kingdom and Germany). Whereas prices of in-patent drugs are typically equal to the maximum prices set by the WGP, the prices of generic drugs are often more constrained by the reimbursement limit set by the GVS, because suppliers want to safeguard their market share by avoiding out-of-pocket payments for patients.

2.2. Retail Prices and Discount Competition Prescription drugs pass from the manufacturer to the patient through a distribution chain of wholesalers and pharmacists. Wholesalers may offer a full range of products or provide selected product ranges to pharmacies.


57

As in other European countries, the Dutch wholesale market is dominated by a few key players. Due to vertical integration, the distribution chain has become increasingly consolidated. Only licensed pharmacists are legally allowed to deliver outpatient prescription drugs directly to patients. The professional monopoly of pharmacists in the supply of outpatient prescription drugs is determined by the Pharmaceuticals Act. In 2007, the total number of pharmacies in the Netherlands was 1,893 (SFK, 2008). On average, a Dutch pharmacy has 8,100 clients, which is relatively high compared to other European Union countries such as Germany (4,000), France (2,500) and Belgium (2,000). While dispensing drugs to patients, the pharmacist has to check the prescription, inform the patient and ensure the patient does not receive different pharmaceuticals that may interact. If doctors’ prescriptions include the name of an active ingredient rather than a brand name, which is often the case, pharmacists may deliver any generic or brand product that suffices the prescription. 2.2.1. Pharmacy Fee Regulation Pharmacy fees are regulated by the Dutch Healthcare Authority (NZa) based on the Healthcare Market Regulation Act (WMG). Pharmacies are allowed to charge a maximum service fee per prescription dispensed. Until July 2008, the fee was uniform for all prescriptions, but since then, it is differentiated by type and time of delivery. For example, a higher maximum service fee applies for delivering drugs during the weekend or at night as well as for special preparations. The NZa determines the maximum surcharges for these extra services. Since 2009, health insurers are free to negotiate the service fee per standard prescription dispensed with pharmacies up to a maximum set by the government. Currently, this maximum negotiable service fee is 7.91 euro per standard prescription. 2.2.2. Discount Competition In addition, pharmacies are allowed to charge health insurers for the delivered drug, the price as set by the supplier and included in an official price list (known as ‘Taxe’). This price list includes the official prices of all generic and brand-name drugs that are registered in the Netherlands and is published every month. The WGP determines the list prices’ maximum. Manufacturers and wholesalers are allowed to offer pharmacies discounts on the official list prices. As pharmacies are allowed to charge health insurers the full list price, these discounts are an important additional source of pharmacy income.

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Suppliers of off-patent drugs compete for market share by offering discounts to pharmacists. Until recently, pharmacies had no incentive to pass on these discounts to health insurers and their enrolees, because health insurers had to reimburse the full cost of all drugs covered by social health insurance up to the reimbursement limit (specified by the GVS). Initially, the Dutch government approved this practice to compensate pharmacists for a relatively low service fee per prescription. However, since the 1990s, an increasing number of patents on big-selling drugs expired, which reinforced discount competition between brand name and generic suppliers for the favour of pharmacists. The escalating discount competition among drug suppliers yielded increasingly large financial gains to pharmacies. Moreover, the level of discounts was further raised by strategic pricing behaviour of drug suppliers. To maximize the potential room for discounts to pharmacies, drug suppliers set their list prices close to the reimbursement limit. Reimbursement limits set for the drug clusters listed on annex 1A thus served as a focal price, particularly for the initially relatively cheap generic drugs. As a result, the average price difference between generic and brandname drugs reduced from 20% during the 1990s to only 4% in 2002 (SFK, 2003). As the discounts offered on generic products were generally higher than on the original off-patent brand-name drugs, pharmacists increasingly delivered generic products to patients. Accordingly, the actual market share of generics in the Netherlands has grown rapidly and is among the highest in Western Europe (Fig. 1). The vigorous discount competition among drug suppliers boosted the income of pharmacies. In 2007, the average amount of discounts received per pharmacy was estimated at 417,000 euro, minus the clawback of 132,000 euro imposed by the government (NZa, 2008).

2.3. Price Reduction and Clawback Policies Although the strong discount competition was very beneficial for pharmacies, it did not yield any cost savings to society. Suppliers had no incentive to reduce the official list prices. On the contrary, suppliers of generics had strong incentives to raise prices up to the reimbursement limit to be able to offer high discounts. In addition, pharmacists had no incentive to pass any discounts on to health insurers as a result of which consumers did not benefit at all from the discount competition among suppliers. Since the 1990s, the government therefore introduced several policy measures to

59

Pharmaceutical Policy in the Netherlands 70 60 50 40 30 20 10

% Share (Value)

Fig. 1.

Denmark

Germany

UK

Netherlands

Sweden

Portugal

Austria

Finland

France

Belgium

Ireland

Spain

Italy

0

% Share (Volume)

Generic Market Share (%) in Western Europe in 2004. Source: Based on Perry (2006).

induce suppliers to lower their official list prices and to reap part of the discounts from pharmacists. 2.3.1. Lowest Price Rule With the introduction of the GVS in 1991, the government also introduced the ‘lowest price rule’. By this rule, pharmacists were prohibited to charge a higher price to insurers than the lowest list price for therapeutically equivalent drugs on the official price list (under the condition that suppliers could supply the entire market). The aim of the lowest price rule was to induce drug suppliers to lower the list prices of therapeutically equivalent drugs. In practice, however, the lowest price rule was not effective and may even have had an opposite effect on the level of drug prices. This is because suppliers had no incentives to lower their list price, because any price reduction would lead to a similar reduction of the list prices of all therapeutically equivalent drugs. Lowering prices would not result in any competitive advantage. Due to its lack of success, the lowest price rule was abolished in January 2009.

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2.3.2. Lowering Reimbursement Limits Lowering the legally determined reimbursement limits is another strategy to induce lower list prices. To avoid out-of-pocket payments for consumers, suppliers have an incentive to set their list price below or equal to the reimbursement limit. Despite a lot of (political) discussion about updating and lowering the reimbursement limits to induce suppliers to lower their list prices, reimbursement limits were only adjusted once, in 1999.5 As a result, the current reimbursement limits do not correspond with actual prices anymore. In 2004, the maximum price (set by the WGP) for 45% of all prescription drugs was lower than the GVS limit. Hence, in due course, the impact of the GVS has been substantially reduced. 2.3.3. Clawback As the government was unable to stimulate suppliers to decrease list prices, it introduced a clawback system in 1998 to reap part of the discounts obtained by pharmacists. The clawback system obliged pharmacists to give health insurers a discount of 6.82% on the official list prices of all outpatient prescription drugs with a maximum of 6.80 euro per prescription drug. In practice, however, the clawback did not yield the expected savings because suppliers increased prices to compensate pharmacists for the clawback. Particularly, the list prices of generic drugs were raised, because these were often still below the reimbursement limit or legally set maximum prices. In 2003, the government therefore tried to implement a differentiated clawback system. This system would oblige pharmacies to offer a discount of 40% on generic drugs, with a maximum of 20 euro, and a discount of 8% on in-patent drugs, with a maximum of 8 euro. However, after two months, the differentiated clawback system had to be repealed because it was successfully challenged in court by the national association of pharmacists (KNMP) and a large wholesaler. According to the court ruling, the government had not sufficiently taken into account the impact of the differentiated clawback on the income of individual pharmacists. 2.3.4. Covenants about Voluntary Price Reductions In 2004, under the threat of a legally improved differentiated clawback system or other regulatory price interventions, the KNMP, the association of generic drug suppliers (Bogin), and the association of health insurers (ZN) concluded a covenant with the government about voluntary price reductions. Suppliers and pharmacies agreed to a price reduction (including the clawback) that would result in a 40% reduction of expenditure on generic drugs (projected to be 622 million euro).6 The agreed upon price


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reduction was equal to the savings the government intended to realize by the introduction of a differentiated clawback. An important difference, however, is that suppliers and pharmacists in terms of turnover offered an average discount on the list prices of 40%. With the clawback system, they were obliged to give 40% discount on all list prices of all generic drugs. The government agreed to refrain from structural interventions in the pricing by generic suppliers as long as the covenant was in place. Despite the substantial price reductions, pharmacies still managed to obtain an average discount of 16.5% of total purchasing value, aggregating to 575 million euro or 311,000 euro per pharmacy in 2004 (NZa, 2008). Minus the legally imposed clawback of about 98,000 euro per pharmacy (or in total 180 million euro), this resulted in a net benefit of 213,000 euro per pharmacy (in addition to an average income of 418,000 euro stemming from the regulated service fee per prescription). In three subsequent years (2005–2007), similar covenants were concluded, now also joined by the association of research-oriented drug manufacturers (Nefarma). The covenants of 2006 and 2007 included an agreement about an additional price reduction, amounting to 8.5% of 2005 expenditure.7 Nevertheless, the total amount of discounts obtained by pharmacists kept growing and was estimated at 732 million euro in 2007, of which 232 million euro had to be passed on to the health insurers because of the clawback (NZa, 2008). Hence, in spite of the covenants since 2004, total discounts increased by 30%. This increase was partly due to an increase in the number of drug prescriptions (by 20%) and partly caused by an increase in the average level of discounts, from 16.5 to 20% of total purchasing value (NZa, 2008). As shown in Fig. 2, the average discount on generic drugs was more than 60% (accounting for 65% of the total discounts), while the average discount on brand-name products was about 8% (accounting for 24% of total discounts). By the end of 2007, a new covenant was concluded for the years 2008 and 2009. This ‘Transition Agreement’ should pave the way for a transition from the system of regulated prices towards a ‘more normal market situation’ in line with the general policy goal of introducing managed competition. In the long run, both service fees and drug prices would have to be freely negotiable between pharmacies and health insurers. In the short run, however, the main thrust of the agreement again was an additional average price reduction, amounting to 10% of 2007 expenditure.8 In practice, the transition towards managed competition in the market for outpatient prescription drugs went much faster than expected and gained momentum when four of the five largest health insurers started a competitive bidding process for specific categories of generic drugs.

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LIEKE H. H. M. BOONEN ET AL. 70 60 50 40 30 20 10 0 Brand

Generic

Parallell import

Total

Fig. 2. Discounts for Pharmacies as Percentage of Total Purchasing Value in Each Drug Category in 2007. Source: Based on Karssen, Huizendveld, and Jurling (2008).

As a response, in 2008, the association of generic suppliers decided to terminate its cooperation to the Transition Agreement.

3. MANAGED COMPETITION IN THE DUTCH PHARMACEUTICAL MARKET 3.1. The Changing Role of Health Insurers During the 1990s, the government started to provide health insurers with incentives and tools to become prudent buyers of health care services. In 1992, the former regional monopolies of social health insurers (known as sickness funds) were abolished and these insurers were allowed to selectively contract with freestanding professionals, like GPs, dentists and pharmacists. Before 1992, social health insurers had to conclude standardized contracts with any willing provider practicing within their region.9 In the subsequent decade, two additional policy measures were taken to increase the countervailing power of health insurers vis-a`-vis pharmacies. In 1999, health insurers were allowed to set up their own pharmacies, as an exception

63


to the legally enforced strict purchaser–provider split. This enabled health insurers to obtain discounts from suppliers and wholesalers by themselves. A third important policy measure was taken in 2004 by legally permitting social health insurers to introduce formularies for prescription drugs and to exclude other therapeutically equivalent drugs from coverage.10 Although the GVS system introduced a classification of prescription drugs into therapeutically equivalent clusters, until this reform, it was mandatory for insurers to reimburse all prescription drugs up to the legally specified GVS reimbursement limit. Hence, health insurers were not able to effectively negotiate lower prices for generics with either pharmacies or suppliers (see also Section 2.3). Parallel to these measures to increase insurers’ countervailing power, insurers were also provided with increasing financial incentives to control medical expenses. Since 1992, social health insurers were put increasingly at risk for the medical expenses of their enrolees, by gradually replacing the system of retrospective cost reimbursement by a system of risk-adjusted capitation payments. As summarized in Table 1, the financial risk for health insurers on enrolees’ outpatient medical expenses – including outpatient prescription drugs – rapidly increased from 3% in 1993 to 92% in 2003. As a consequence, health insurers got a strong financial interest in controlling outpatient drug expenditure. Despite these policy measures, health insurers were not eager to act as a prudent buyer of outpatient prescription drugs. This situation changed,

Table 1.

1992 1993 1995 1996 1997 1998 1999 2001 2002 2003 2007 2008 a

Financial Risk Dutch Health Insurers.

Outpatient Expenses (%)a

Inpatient Expenses (%)

Total Expenses (%)

0 3 3 20 42 48 63 65 65 92 94 97

0 3 3 9 15 15 16 20 24 23 25 34

0 3 3 13 27 28 35 38 41 52 53 59

Including pharmaceuticals.

64


however, due to the introduction of the new Health Insurance Act in 2006. The integration of former social and private health insurance into a new universal mandatory health insurance scheme induced many people to reconsider their choice of health insurer. The increasing switching propensity inflicted strong price competition among health insurers. This generated strong incentives for health insurers to control cost. In addition, the new scheme made it possible for insurers to offer different policies simultaneously, for example, lower priced policies with preferred providers and more expensive policies with completely free provider choice. Although before 2006, social health insurers were already allowed to exclude pharmacies from their provider network or to exclude therapeutically equivalent drugs from coverage, such restrictions would have to apply for all enrolees because insurers were not allowed to differentiate contracts. Insurers thus had to offer a homogenous product to customers with heterogeneous tastes, which substantially reduced the attractiveness of selective contracting. Finally, as part of the ongoing transition towards managed competition, since 2008, the regulation of pharmacies’ service fees is gradually being liberalized. The deregulation of service fees is aimed at facilitating insurer–pharmacy negotiations over the provision of outpatient drugs and to increase the attractiveness of patient channelling to preferred pharmacies with which favourable contractual agreements (about prices, services, quality and innovations) are negotiated. As a consequence of these developments, health insurers in the Netherlands have gradually started to develop their new role of prudent buyers of health care in the pharmaceutical market. In the remainder of this section, we will discuss the following four elements of the changing role of health insurers: (i) insurers’ attempts to influence GPs’ prescription behaviour; (ii) the introduction of competitive bidding for big-selling generic drugs; (iii) the establishment of insurer-owned pharmacies; and (iv) the use of channelling incentives to encourage the use of preferred pharmacies.

3.2. Influencing GPs’ Prescription Behaviour In the Netherlands, GPs are funded by a mixture of capitation and fee-forservice. Both types of payments are regulated by the NZa. In addition, GPs and insurers may negotiate additional payments for innovation and extra primary care services. For instance, insurers may invest in changing GPs’ prescription behaviour, by encouraging substitution of generic drugs for therapeutically equivalent brand-name drugs. At the national level, GPs


65

have committed themselves to prescribe active ingredients rather than a brand name as much as possible when prescribing outpatient drugs. Overall, this has substantially increased generic substitution. However, for some therapeutic classes, brand-name market share remained relatively high even though therapeutically equivalent generic drugs have become available (DGV, 2008).11 Health insurers are therefore experimenting with bonuses for GPs who change their prescription behaviour. Most commonly, insurers reward GPs for participating in local/regional groups where pharmacists and GPs practising in the same catchment area regularly discuss how to improve the prescribing and dispensing of drugs. Participating in these pharmacotherapy audit groups (FTO-groups) is voluntary, but nearly all Dutch GPs and pharmacists participate. Currently, there are more than 820 FTO-groups with on average nine GPs and two pharmacists. Although empirical evidence about the effectiveness of audit groups is limited, professional collaboration between GPs and pharmacists in these groups seems to be an effective way to control early prescribing of new drugs in general practice, which is likely to enhance quality as well as to save cost (Florentinus et al., 2007). Support to FTO-groups is provided by the Dutch Institute for Rational Use of Medicine (DGV), an independent organization that aims for proper, efficient, safe and economic use of medicine. In addition to facilitating FTO sessions, DGV also provides prescription feedback to both GPs and pharmacists. In 1989, the Dutch College of General Practitioners (NHG) started to develop national pharmacotherapeutic guidelines for specific complaints and diseases. By monitoring GPs adherence to these guidelines, DGV provides both FTOgroups and health insurers with information that may be used to improve compliance and quality of pharmaceutical care. Some health insurers pay GPs a bonus when, within the setting of local/ regional FTO-groups, particular aggregate targets are being met, such as prescribing generic versions of omeprazole and simvastatin for at least 80% of the relevant patient population (Varkevisser & van der Geest, 2007). One major Dutch health insurer (Menzis) even offered GPs a bonus payment per individual patient when prescribing these generics instead of therapeutically equivalent brand-name drugs. This bonus payment was unsuccessfully challenged in court by four major pharmaceutical companies in 2005. The court ruled that the bonus payment did not infringe on good prescription behaviour because it rewards GPs for compliance with the professional guidelines of the NHG. Despite the initiatives employed by health insurers to improve adherence to guidelines, regional variation in GP prescription behaviour is still substantial (DGV, 2008).

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3.3. Preferred Drug Formularies for Generics On July 1, 2005, five out of the then six largest Dutch health insurers (Agis, Menzis, CZ, Unive´ and VGZ) jointly started a competitive bidding process for three blockbuster generic drugs for lowering cholesterol and preventing cardiovascular disease: omeprazole, simvastatin and pravastatin.12 Their collective purchasing strategy was straightforward: for each of these generics, a supplier was preferred when he offered the lowest price at the reference date (July 1 and December 1 of each calendar year) or when his price was within a bandwidth of 5% above the lowest price offered by another supplier in that particular market. Due to the bandwidth, there was no restriction on the number of suppliers that could obtain a preferred provider status. As these health insurers did not reimburse generics from non-preferred suppliers, their strategy entailed closed drug formularies for omeprazole, simvastatin and pravastatin. Anticipating that the collective bidding process might violate the Dutch Competition Act, the participating health insurers asked the Dutch Competition Authority (NMa) for an informal opinion.13 The NMa (2005) concluded that the proposed purchasing strategy would not result in a significant reduction of competition in the market for health insurance because (i) not all insurers participated in the bidding process and (ii) the process applied to a limited number of generic drugs who jointly only account for less than 1% of the insurers’ total health care expenditure. Next, the NMa concluded that the proposed collective purchasing strategy would not erode competition in the relevant markets for generics either, because the preferred supplier status was only granted for a period of six months after which the competitive bidding process would be repeated. The NMa, however, argued that if market conditions or the scope of the group purchasing strategy (in terms of number of insurers, number of drugs or duration) would change in the future, a new review would be required to ensure it would still be compatible with antitrust legislation. Initially, the price effects of the collective purchasing strategy fell short of expectations. Prices did not change, and all, except for one, generic suppliers of omeprazole, simvastatin and pravastatin became preferred providers because their prices were less than 5% higher than the cheapest generic drug in the market. Only the original brand-name drugs were excluded from the preferred drug formulary. Suppliers of generic drugs and pharmacies both even benefited from the insurers’ strategy: the aggregate market share of generics increased while list prices remained invariably high,

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implying also a higher amount of discounts and bonuses for pharmacies. In 2005 and 2006, insurers’ collective purchasing strategy of omeprazole, simvastatin and pravastatin reduced total expenditure on these pharmaceuticals only by about 1% (Vektis, 2006). At the reference date of July 1, 2007, generic suppliers again did not lower their list prices for omeprazole, simvastatin and pravastatin. By the end of 2007, however, things changed dramatically. One generic drug supplier posted a list price for simvastatin that was about 15% lower than what was until then the lowest price in the market. As all other prices were outside the bandwidth of 5%, as of January 2008, this supplier suddenly became the only preferred supplier for this generic drug. Subsequently, all other suppliers of simvastatin reduced their prices even further. As shown in Fig. 3, the emanating fierce price competition resulted in a dramatic drop (of more than 90%) in the average list price of simvastatin. In June 2008, four out of the then five biggest health insurers in the Netherlands also started to experiment with preferred drug formularies for generics on an individual basis. One insurer (UVIT) used competitive bidding for 33 different types of generics, whereas the other insurers started a bidding process for 10 (CZ and Menzis) and 6 (Agis) types of generics. The individual bidding strategies had a huge effect on the prices of generics. For the 10 biggest-selling generics, list prices were reduced by 76 to 93% (Table 2).

16.00

14.60

14.60

14.60

14.60

14.16

Price in (VAT excluded)

14.00 12.00 10.00 8.00 6.00 4.00 2.00

1.26

0.99

Jul. '08

Dec. '08

0.00 Jan. '06

Fig. 3.

Jul. '06

Dec. '06

Jul. '07

Dec. '07

Average List Price for Simvastatin (30 pieces – 40 mg). Source: Dutch Foundation for Pharmaceutical Statistics (SFK).

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Table 2. Generic Drug Name (mg)

Alendronate (70) Omeprazole (20) Citalopram (20) Omeprazole (40) Simvastatin (20) Amlodipine (5) Simvastatin (40) Paroxetine (20) Tamsulosin (0.4) Pravastatin (40)

Price Effects for the 10 Biggest-Selling Generic Drugs. List Prices (h, Earlier and up to June 30, 2008)

List Prices (h, on and after July 1, 2008)

Percentage Change (First and Second Half of 2008)

4.99 0.36 0.34 0.65 0.17 0.19 0.27 0.37 0.34 0.54

0.36 0.05 0.04 0.09 0.03 0.03 0.04 0.07 0.07 0.13

93 88 88 86 85 85 84 82 80 76

Source: Dutch Foundation for Pharmaceutical Statistics (SFK).

A weakness of the design of the competitive bidding process, however, was that it offered the opportunity for free rider behaviour to other health insurers. Insurers who did not invest in preferred drug formularies could not be excluded from the financial benefits because the lower official list prices also apply to them. Therefore, engaging in competitive bidding did not provide health insurers with a competitive advantage. Both the government and the insurers are therefore discussing ways to address this (potential) problem. For example, in March 2009, one major health insurer (UVIT) announced that it would use suppliers’ ‘closed envelope’ offers rather than the official list prices for deciding whether or not to include them in the preferred drug formulary. In this case, the insurer completely takes over the pharmacist’s role as a drug purchaser. The massive price reductions had important consequences for pharmacies because the margins for offering discounts were diminished (Douven & Meijer, 2008). As summarized in Table 3, the aggregate loss of revenue for Dutch pharmacies (equivalent to the aggregate cutback of discounts) caused by insurers’ use of preferred drug formularies for selected generics was estimated at 348 million euro (69%) per year.14 Nevertheless, the remaining discounts on generic drugs not included in the competitive bidding process were still quite substantial. The NZa (2008) estimated that the average revenue from discounts per pharmacy would drop from 417,000 in 2007 to 266,000 euro in 2009 (or from 285,000 euro to 174,000 euro after deduction of the clawback).

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Table 3.

Effect of Increased Generic Price Competition on Pharmacies’ Annual Revenue (2008).

Generic Drug Name

Dh Million

D%

Insurers’ Purchasing Strategy

Omeprazole Simvastatin Alendronate Paroxetine Amlodipine Pravastatin Enalapril Citalopram Other generics (n ¼ 28)a

67.9 43.7 27.3 20.7 18.3 18.2 15.7 10.9 124.8

78 88 89 76 81 75 80 77 55

Collectiveb Collectiveb Individual Individual Individual Collectiveb Individual Individual Individual

Total

347.5

69

Source: Dutch Foundation for Pharmaceutical Statistics (SFK). a These generics reduced pharmacies’ annual revenue by less than 10 million euro each. b The number of participating health insurers in 2008 is six.

3.4. Experiments with Consumer Channelling in the Pharmacy Market Selective contracting is one of the strategies health insurers can use to increase their bargaining power vis-a`-vis providers (e.g. Sorensen, 2003; Wu, 2009). So far, Dutch health insurers have been quite reluctant to selectively contract with health care providers (Van de Ven & Schut, 2009). Health insurers hesitate to offer preferred provider plans because they fear a loss of reputation if they would decide to restrict consumer choice to a (limited) network of preferred providers. Nevertheless, in the retail pharmacy market, some initiatives have been employed with preferred provider contracts and consumer channelling. In 2003, three health insurers (CZ, VGZ and De Friesland) started smallscale experiments with preferred pharmacy contracts. These health insurers did not actually exclude other pharmacies from their provider network but tried to stimulate their enrolees with positive financial (gift certificates) and qualitative (free body check-up) incentives to visit preferred pharmacies. These pharmacies were labelled ‘preferred’ because they offered discounts on the price of prescription drugs to insurers (Boonen, Schut, & Koolman, 2008). Two of the three health insurers concluded preferred provider contracts with a new entrant. This entrant was an incumbent drug store that decided to provide pharmaceutical care in its existing locations to facilitate convenience (one-stop) shopping for its customers. The third (regional) health insurer completed a public tender procedure for a new pharmacy

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located in a new residential area in which no other pharmacies were located and in which the health insurer had a large market share. To provide their enrolees with an incentive to visit this preferred pharmacy, discounts were given on several products including over-the-counter drugs and healthrelated products sold at the pharmacy. Empirical studies show that these channelling incentives had an impact on pharmacy choice (Boonen & Schut, 2006; Boonen et al., 2008).15 Financial incentives had a stronger impact on pharmacy choice than qualitative incentives. Despite the positive response to channelling incentives, preferred provider contracts with the drugstore were terminated in 2008. The drugstore was simply not able to attract enough patients to sustain a financially stable pharmacy suggesting that many enrolees returned to their previous pharmacy once insurers stopped rewarding them for visiting the preferred pharmacy. Hence, the effect of the incentives on enrolees’ pharmacy choice had been only temporary. Health insurers are, however, still experimenting with preferred provider contracts in the retail pharmacy market. For example, in 2006, two health insurers negotiated favourable contracts with internet pharmacies. Their enrolees earn a discount of 4 euro (CZ) or 2.50 euro (Unive´) per prescription if they order their drugs online.

3.5. Insurer-Owned Pharmacies Before the enactment of the Health Insurance Act in 2006, health insurers in the Netherlands were legally not allowed to vertically integrate with health care providers.16 In 1999, however, an exception was made for pharmacies. To increase the countervailing power of health insurers, they were allowed to set up their own pharmacies. It took several years, however, before the first health insurer started to use this new opportunity. In 2002, a regional health insurer (DSW) decided to set up two pharmacies to counteract the strategy of the largest wholesaler of taking over individual pharmacies.17 The insurer’s enrolees remained free to choose any pharmacy, while both pharmacies were open to enrolees from other insurers as well. As expected, incumbent pharmacies fiercely opposed the entry of their insurer-owned new competitors. Attempts to boycott the new entrants, for instance, by refusing to include them in the regional system for providing evening, night and weekend services, were successfully challenged in court by the insurer. After the same insurer in 2006 set up another own pharmacy, this was challenged in court by an incumbent pharmacy. Again, the insurer prevailed in court.


71

The court concluded there were no reasons to forbid the entry of another insurer-owned pharmacy in the region. Under the new Health Insurance Act, health insurers are no longer prohibited to vertically integrate with health care providers. Since then, several health insurers have started to (financially) participate in primary care centres where several types of providers (GPs, physiotherapists and pharmacists) closely cooperate. To date, these investments did not yet result in direct insurers’ interference with the provision of pharmaceutical care (Varkevisser & van der Geest, 2008).

4. CONCLUSIONS AND IMPLICATIONS The Dutch market for outpatient prescription drugs is in transition from a system of price controls to a system of managed competition. Since the 1990s, the government increasingly intervened to control prices of pharmaceuticals. Prices of in-patent drugs were effectively constrained by the introduction of maximum prices based on the prevailing prices in four neighbouring countries. The regulation of prices of generic drugs was considerably less effective. The uniform reimbursement limits per therapeutic category that were introduced in 1991 effectively served not only as maximum prices but increasingly also as minimum prices. Prices of generic drugs typically far exceeded the cost of production. An important reason for this was the powerful position of pharmacies in the distribution chain of pharmaceuticals. Because of their professional monopoly of dispensing prescription drugs and their legally granted power to substitute drugs provided that it suffices the doctor’s prescription, pharmacies were able to induce vigorous competition among suppliers of generics. As pharmacies were allowed to charge official list prices to health insurers, and consumers were fully reimbursed as long as prices were below the reimbursement limit, pharmacies were primarily interested in obtaining discounts. To maximize the room for offering discounts, suppliers of generics were forced to set prices close to the reimbursement limits. The resulting discounts boosted the income of pharmacies without benefiting health insurers and their enrolees. Most regulatory strategies were, therefore, targeted at reducing excessive list prices and at reclaiming part of the excessive discounts (see Table 4 for an overview). Under the threat of direct price interventions, suppliers of generics conceded to reduce list prices. Although these price concessions were presented by the government as substantial cost savings, these savings were

72


Table 4.

Overview of Pharmaceutical Policy in the Netherlands, 1990–2009.

Government Regulation 1991: Introduction of Drug reimbursement system (GVS) and introduction of ‘lowest price rule’ 1996: Introduction of maximum price regulation (WGP) 1998: Introduction of uniform ‘clawback’ to reduce pharmacies’ discounts and bonuses 1999: Recalculation of GVS reimbursement limits 2003: Unsuccessful attempt to introduce differentiated ‘clawback’ 2004–2008: National voluntary agreements (covenants) between the government and the associations of insurers, pharmacies and the pharmaceutical industry about price reductions for generic drugs

Market-Oriented Reforms 1992: Selective contracting by social health insurers allowed in the retail pharmacy market 1995–2003: Insurers’ financial risk on outpatient medical expenses was raised from 3% to more than 90% 1999: Insurers are allowed to integrate with pharmacies (set up their own pharmacies) 2003: Insurers are allowed to use drug formularies (can exclude therapeutic equivalent drugs from coverage) 2006: Enactment of the Health Insurance Act that provides insurers with more incentives to become prudent buyers of health care 2008: Deregulation of pharmacy fees for the delivery of outpatient prescription drugs

partly offset by an increasing volume of drugs. More important, however, both the enforced price concessions and the imposed clawback did not alter the incentives for suppliers to maximize list prices to offer high discounts to pharmacies. As a consequence, the total amount of discounts kept increasing, indicating that regulatory strategies were not very effective in constraining spending on drugs. Parallel to the increasing price regulation, the government started to pave the way for a health care system based on managed competition (see Table 4 for an overview). Gradually, health insurers were provided with more incentives and tools to act as prudent buyers of health services. This transition towards managed competition particularly gained momentum after the introduction of the new Health Insurance Act in 2006. With the introduction of preferred drug formularies in 2008, several health insurers were able to force suppliers of generics to lower their prices substantially. The experiences with preferred drug formularies indicate that health insurers may be more effective in constraining the prices of generic drugs than the government. Although the transition towards managed competition is likely to proceed, this does not imply, however, that the role of the government in regulating pharmaceutical prices is expected to come to an end. As health


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insurers cannot effectively negotiate lower prices for single-source (in-patent) drugs, the government is likely to maintain the present maximum price regulation (WGP). What may change, however, is the way these drugs are reimbursed. In a recent memorandum about the long-term goals of pharmaceutical policy, the Ministry of Health (VWS, 2008) proclaims to aim at providing more room and incentives for cost-effective therapeutic substitution. To this end, the detailed entitlements of the Health Insurance Act (the national positive formulary) have to be gradually replaced by entitlements based on evidence-based pharmacotherapeutic guidelines per therapeutic class (e.g. cholesterol-lowering drugs). In 2009, the government established a Council for Quality of Care to encourage and facilitate the development of these guidelines. The changing role of health insurers also has important implications for pharmacies. For them, the golden years are likely to be over, because the scope for obtaining discounts will be increasingly restricted by the downward pressure on generic drug prices exerted by health insurers. Therefore, to survive, pharmacies have to prove their added value in encouraging rational pharmaceutical drug prescription.

NOTES 1. The new universal health insurance scheme has the following important features in common with the previous social health insurance scheme (covering twothirds of the population): (1) insurers have to accept any applicant for mandatory basic coverage at a community rated premium; (2) basic coverage is partly financed by community-rated premiums, which are set by insurers, and partly by incomerelated contributions, which are set by the government; (3) a Risk Equalization Fund (REF) compensates insurers for enrolees with predictably high medical expenses. For lower income groups, community-rated premiums are subsidized by a tax-financed income-related subsidy from the government. 2. To obtain a European marketing authorization from the European Commission, a drug manufacturer must follow the Centralised Procedure (CP). It can then obtain an authorization to market the product across the European Union. The CP is coordinated by the European Medicines Agency (EMEA). The Medicines Evaluation Board is represented on the European assessment committees. 3. Other countries (e.g. United States, Sweden, Italy, Spain and Denmark) have adopted a generic reference pricing system in which generically equivalent products with the same active ingredient and formulation are classified into clusters. Therapeutic pricing is more controversial, because it treats drugs with different active ingredients as equivalent, despite possible differences in efficacy or side effects for some individual patients. Patients for whom the reference-priced product does not work properly face either co-payments or health risks if they switch.

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4. People can also buy supplementary insurance that may cover (part of) the price difference. 5. In 1994, manufacturers conceded to reduce list prices by 5% for a two-year period. By this concession, they successfully prevented a 15% reduction of the reimbursement limits as initially announced by the government. Since 2001, an adjustment of the GVS limits has been discussed several times but never took place as a result of the consecutive agreements with the pharmaceutical industry on voluntary price reductions. 6. The covenant also stipulated that the prices of new generic drugs would be set on average at least 40% below the price of the original brand-name product before the patent expiration date. 7. As the volume of drugs in 2006 and 2007 grew relatively fast, the actual price reduction required to generate the agreed upon savings was quite small (Vektis, 2007). 8. The agreement also stated that the prices of new generic drugs would be set on average at least 50% below the price of the original brand-name product before the patent expiration date. 9. Such regulation is quite similar to ‘any-willing-provider’ laws in the United States (Vita, 2001). 10. That is, under the restriction that for each active ingredient listed on annex 1A, at least one pharmaceutical product is fully reimbursed. 11. From empirical research by Windmeijer, de Laat, Douven, and Mot (2006), it follows that in the Netherlands, GPs’ drug price sensitivity is small and adversely affected by pharmaceutical promotion. 12. In 2006, two other health insurers (De Friesland and Delta Lloyd/OHRA) joined this initiative. 13. The NMa may issue an informal opinion to market parties when they ask for clarity on a certain legal problem. An informal opinion does not have the status of a formal decision. 14. This is exclusive of a reduction of the clawback of about 40 million euro (NZa, 2008). 15. Boonen, Schut, Donkers, and Koolman (2009) used stated preference data to examine consumers’ preferences for channelling incentives in the Dutch pharmacy market. With a discrete choice experiment (DCE) consumer sensitivity to financial incentives, qualitative incentives and pharmacy characteristics were estimated. The study found that consumers are sensitive to both financial and qualitative channelling incentives, but consumers’ willingness to switch pharmacies is substantially restricted by a status quo bias. Consumers appear to be reluctant to give up their current pharmacy even when better alternatives are available. Hence, to induce consumers to switch to preferred pharmacies, insurers must use sufficiently strong incentives to compensate for this status quo bias. 16. The strict purchaser–provider split dates back to the enactment of social health insurance legislation in 1941. Sickness funds that had their own health care facilities, however, were allowed to keep these facilities (but were not allowed to expand them). For that reason, before 1999, one regional health insurer (AZIVO) already had an own pharmacy.


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17. In the Netherlands, almost all wholesalers integrated into the retail pharmacy market (NZa, 2008). The largest wholesaler (Mediq) also has the largest pharmacy chain with more than 230 pharmacies and a combined market share of 14%. Most of these pharmacies operate under the same Mediq formula that has a combined turnover of more than 600 million euro in 2007.

REFERENCES Boonen, L. H. H. M., & Schut, F. T. (2006). Sturing in de zorgmarkt [Channelling in the health care market]. Economisch Statistische Berichten, 91(4486), 226–228. Boonen, L. H. H. M., Schut, F. T., Donkers, B., & Koolman, X. (2009). Which preferred providers are really preferred? Effectiveness of channelling incentives in pharmacy choice. International Journal of Health Care Finance and Economics, 9(4), 347–366. Boonen, L. H. H. M., Schut, F. T., & Koolman, X. (2008). Consumer channelling by health insurers: Natural experiments with preferred providers in the Dutch pharmacy market. Health Economics, 17(3), 299–316. DGV. (2008). Benchmark voorschrijven 2008 [Benchmark prescription behaviour 2008]. Dutch Institute for the Proper Use of Medicine, Utrecht. Douven, R., & Meijer, A. (2008). Prijsvorming van generieke geneesmiddelen: Forse prijsdalingen in het nieuwe zorgstelsel [Pricing of generics: Substantial price reductions in the new health care system]. CPB Document No. 175, Netherlands Bureau for Economic Policy Analysis, The Hague. Florentinus, S. R., van Hulten, R., Kloth, M. E. M., Heerdink, E. R., Griens, A. M. G. F., Leufkens, H. G. M., & Groenewegen, P. P. (2007). The effect of pharmacotherapy audit meetings on early new drug prescribing by general practitioners. Annals of Pharmacotherapy, 41(2), 319–324. Helderman, J.-K., Schut, F. T., van der Grinten, T. E. D., & van de Ven, W. P. M. M. (2005). Market-oriented health care reforms and policy learning in the Netherlands. Journal of Health Politics, Policy and Law, 30(1–2), 189–209. Karssen, B., Huizendveld, H. H., & Jurling, B. C. (2008). Inkoopvoordelen apotheekhoudenden in Nederland in 2007 [Purchasing gains for pharmacists in the Netherlands in 2007]. ConQuestor, Utrecht. NMa. (2005). NMa permits preference policy for health insurers. Netherlands Competition Authority, press release 05-21, The Hague. NZa. (2008). Eindrapport: Onderzoek inkoopvoordelen en praktijkkosten farmacie [Final report: Investigation of pharmacies’ purchasing gains and practice costs]. Utrecht. Perry, G. (2006). The European generic pharmaceutical market in review: 2006 and beyond. Journal of Generic Medicines, 4(1), 4–14. Schut, F. T., & van de Ven, W. P. M. M. (2005). Rationing and competition in the Dutch health care system. Health Economics, 14, S59–S74. SFK. (2003). Data en feiten 2003 [Data and facts 2003]. Stichting Farmaceutische Kengetallen, Den Haag. SFK. (2008). Data en feiten 2008 [Data and facts 2008]. Stichting Farmaceutische Kengetallen, Den Haag.

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Sorensen, A. T. (2003). Insurer-hospital bargaining: Negotiated discounts in post-deregulated Connecticut. Journal of Industrial Economics, 51(4), 469–490. Van de Ven, W. P. M. M., & Schut, F. T. (2008). Universal mandatory health insurance in the Netherlands: A model for the United States? Health Affairs, 27(3), 771–781. Van de Ven, W. P. M. M., & Schut, F. T. (2009). Managed competition in the Netherlands: Still work-in-progress. Health Economics, 18(3), 253–255. Varkevisser, M., & van der Geest, S. A. (2007). Farmaceutische zorg in de basisverzekering: Vrijheden en verplichtingen voor zorgverzekeraars [Pharmaceutical care in social health insurance: Freedom and obligations for health insurers]. Erasmus Competition and Regulation institute (ECRi), Erasmus University Rotterdam. Varkevisser, M., & van der Geest, S. A. (2008). Farmaceutische zorg in Nederland en het inkoopbeleid van zorgverzekeraars [Pharmaceutical care in the Netherlands and health insurers’ purchasing policies]. Erasmus Competition and Regulation institute (ECRi), Erasmus University Rotterdam. Vektis. (2006). Evaluatie preferentiebeleid 2005 [Evaluation preferred drug formularies 2005], Zeist. Vektis. (2007). Geneesmiddelen: Wie wordt er beter van? [Medicines: Who benefits?]. Zorgthermometer, zomer 2007, Zeist. Vita, M. G. (2001). Regulatory restrictions on selective contracting: An empirical analysis of ‘‘any-willing-provider’’ regulations. Journal of Health Economics, 20(6), 955–966. VWS. (2008). Nadere invulling langetermijnvisie geneesmiddelenvoorziening [Additional specification of the long term perspective on the provision of medicines]. TK 29477 (nr. 62), Den Haag. Windmeijer, F., de Laat, E., Douven, R., & Mot, E. (2006). Pharmaceutical promotion and GP prescription behaviour. Health Economics, 15(1), 5–18. Wu, V. Y. (2009). Managed care’s price bargaining with hospitals. Journal of Health Economics, 28(2), 350–360.

IMPACT OF PHARMACEUTICAL REGULATION AND POLICIES ON HEALTH SYSTEM PERFORMANCE GOALS IN ISRAEL Philip Sax and Amir Shmueli ABSTRACT Purpose – There is a particular need for health policy evaluations in terms of achievement of goals, which may help inform policy-making not only locally but for the wider international policy community. In this chapter, we review the impact of pharmaceutical regulation and policies in Israel on a range of health system performance goals that, in the pharmaceutical context, are mainly related to ensuring the availability, accessibility and affordability of medicines. Approach – We assess pharmaceutical policies and their impact, within the Israeli National Health Insurance (NHI) system enacted in 1995, on the degree to which the following main policy goals are being achieved: containment of drug expenditures; sustainability and equity of financing for pharmaceuticals; efficiency of expenditure in the pharmaceutical sector; and availability and accessibility of pharmaceuticals. Findings – The findings point to a number of accomplishments as well as outstanding challenges. The main accomplishment is successful cost Pharmaceutical Markets and Insurance Worldwide Advances in Health Economics and Health Services Research, Volume 22, 77–101 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1108/S0731-2199(2010)0000022007

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containment of (public) expenditure on medicines. Government price regulation operates as a mechanism responsible for sickness funds’ (SFs) savings, over which the state has no information or monitoring. Although the package of publicly financed drugs is comprehensive, delays in reimbursement decisions and high level of cost sharing mean that medicines have become increasingly unaffordable for many patients, especially for low-income persons with chronic diseases. Implications – Regulation of the pharmaceutical sector should focus on two aspects: decreasing the information gap between the SFs and the regulator and reforming the cost-sharing policy to increase affordability and equity.

INTRODUCTION There is a particular need for health policy evaluations in terms of achievement of goals, which may help to inform policy-making not only locally but for the wider international policy community. In this chapter, we assess the impact of pharmaceutical regulation and policies in Israel on a range of health system performance goals that are mainly related to ensuring the availability, accessibility and affordability of medicines. A case study of the Israeli pharmaceutical sector may be of particular interest due to some of the sector’s characteristics. The relative absence of the encumbrances or constraints to policy-making seen in other countries with similar health systems but with supranational ties (e.g. EU) (Guillen & Cabiedes, 2003) allows for flexibility and even innovative approaches. Secondly, particularly noteworthy when considering pharmaceutical policymaking options, is the historically and relatively weak influence of the research-based multinational pharmaceutical industry, at least till the late 1990s. This has implications even until today on the development of regulatory policy (Lofgren & de Boer, 2004), which necessarily takes into account the features of the country’s generic-based pharmaceutical industrial activity. Thirdly, in spite of frequent government and health ministerial changes, the requirement of time and consistency in decision-making involving many stakeholders has often been met as there was usually basic agreement over reforms, for example, consensus on need to open up the pharmaceutical sector to competition and to control growth in expenditure. Drug policy-making is complex, involving multidimensional objectives (public health, expenditures and industrial). On the background of the

Impact of Drug Policies on Policy Goals

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Israeli National Health Insurance (NHI) scheme enacted in 1995, based on the principles of solidarity, universality and equity, we assess Israeli policies and their impact on the degree to which the following main policy goals are being achieved: containment of drug expenditures; sustainability and equity of financing for pharmaceuticals; efficiency of expenditure in the pharmaceutical sector; availability of pharmaceuticals; and accessibility of pharmaceuticals. We examine the contribution to achieving these goals made by the increased government regulatory role in the era of NHI and in the context of a decentralised sickness fund (SF) social insurance system. Where appropriate, we also make recommendations as to how these policy goals could be (better) achieved.

A BRIEF DESCRIPTION OF THE ISRAELI HEALTH CARE SYSTEM The NHI scheme that was introduced in January 1995 consists of a managed competition model, where four private non-profit SFs (denoted by CHS, MHS, MSF and LSF) compete on the quality of care covered by a uniform package of benefits defined by the law. The SFs are Managed Care Organizations, integrating basically the insurance and the provision functions. They differ in the way the care is managed, with which providers they contract and the terms of the contracts. The package of benefits is comprehensive and includes primary, secondary and inpatient care as well as diagnostic and pharmaceutical care. This package does not cover psychiatric care and geriatric care, which are under the direct responsibility of the Ministry of Health (MoH), nor dental care which is financed privately. Care provided to victims of road and work accidents is also excluded from the SFs’ package of benefits and is financed by other sources. The central health budget of the package of benefits is determined annually by the government, partially being indexed to changes in input prices, demography and technological advances. It is financed by an earmarked health tax, transfers from the general revenues and co-payments. The scheme is compulsory (all are insured) and universal (no rejection). Citizens are free to switch SFs yearly; however, the switching rate is low (1–2% annually). There is no direct premium paid by the members to the SFs. The main source of income of the SFs in the basic insurance is the riskequalised payments from the central health fund (the budget of the package of services). Risk equalisation consists of two separate components: a

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prospective age-adjustment specifying fixed rates for each of 11 age groups, and a retrospective risk sharing arrangement, by which the SFs receive an annual fixed payment per person who is sick with one of five ‘severe conditions’ – renal failure on dialysis, thalassaemia major, Gaucher, AIDS and haemophilia. The 2007 average yearly risk-adjusted income was 3,300 NIS (about 1,100 USDPPP, using the 2007 rate of exchange of 1 USDPPP ¼ 3 NIS). While no overt evidence of implicit risk selection exists (explicit rejection is against the law), the risk adjustment system is clearly incomplete and leaves incentives to dump high cost individuals, distorts the quantity and quality of expensive services to deter enrolees from using them and raises financial difficulties to SFs with higher-than-average rates of high-risk populations. A controversial issue has been the technological update of the package of benefits. This process naturally affects largely the pharmaceutical sector, because most of the technologies discussed (and approved or not for inclusion) are medicines. On one hand, it has been suggested that a guaranteed 2–4% annual increase should be adopted. The other view claimed that the budget of the package was determined as a given sum in 1995, which would be set anew each year within the framework of the allocation of national resources to social priorities. In reality, this latter view has been adopted. Each year since 1998, a Public Committee selects the technologies – mainly for new drugs and additional uses for drug already in the package – to be included in the package under a limited increase in the budget. During the first decade of its operation, the technological update consisted of a mean annual increase of 1%; in recent years, it has stabilised at about 1.5%. This continuous gap resulted in accumulated erosion of the ‘real’ budget. In 2005, the budget available to meet the health needs of the population in Israel in 2005 was only about two-thirds, in real terms, of what it was in 1995 (Shmueli, Achduth, & Sabag-Endeld, 2008). Another problematic issue in Israel has been consumers’ cost sharing – most of it consists of co-payments for prescribed medicines. Since 1998, with some variations across SFs, co-payment for reimbursable prescribed medicines has been 35–40% of the cost (but above a minimal NIS 12–13 (4 USDPPP) co-payment, see below). Persons on disability allowance and patients with cancer, dialysis and several other ‘severe diseases’ are exempted. Elderly on welfare allowances are exempted from co-payment on medical services. The familial quarterly maximal amount of co-payments for medical services is 100–150 NIS (30–50 USDPPP), with elderly over 65 years paying half that ceiling. The maximal individual quarterly co-payment


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for medicines is 715–780 NIS (about 240–260 USDPPP), with elderly on welfare paying half that ceiling. While the lists of enrolees on disability or welfare allowances are received by the SFs from the National Insurance Institute, it is the families’ responsibility to claim co-payments paid beyond the ceilings. Because of limited information, many families do not claim overpayments. The co-payments are paid by individuals at the point of service, so that they inevitably fall on those individuals who are sick. This contradicts the principle of solidarity on which the NHIL was based in 1995, namely, that in general, payments into the system are related to income and not to health state. Moreover, the co-payments are essentially regressive and hence contradict the progressivity of the public financing of the health system. Furthermore, according to findings from population surveys undertaken by the Israel Medical Association (IMA), some 14% avoid using the services (specialists’ consultations, diagnostic tests and medications) because of the co-payments, most of them of low-income, sick individuals, so that further damage is inflicted on the principles of universal access and need-dependent use of health care underlying the social insurance.

THE DEVELOPMENT OF THE ISRAELI PHARMACEUTICAL SECTOR Changes in the pharmaceutical environment in the past two decades have been many, in the regulatory, institutional and market (competitive) environments. Many of these have been the result of government or Treasury initiatives, finding expression in the annual State Budget Arrangements Law. Most of the changes relate to two main areas: pricing-related measures designed to increase competition on price, and measures to improve the organisation and delivery of services, mainly by breaking up monopolies, and designed to increase efficiency; the latter may also have served to have made long-term cost containment less elusive. Table 1 lists the main measures undertaken in these two areas including several that took place in the decade before the introduction of NHI in 1995. The NHI law and its eventual implementation fundamentally changed the financing of Israel’s health system and the regulatory environment in which the SFs and the MoH function. Its tight macro-budgetary framework has created substantial incentives for the SFs to contain costs. The introduction of NHI was the catalyst of major changes in reimbursement policy, bringing

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Table 1.

Changes in the Pharmaceutical Sector Environment.

Pricing – Related Government price control changed from fixed unified prices to maximum prices (recommended 1984/5, implemented 1993) Reduced import customs, retailer and importer margins (early 1990s to 1994) Parallel imports (proposed 1996, implemented 2001) OTC price controls abolished (initiated 1994, implemented and subsequently frozen in 2001, re-implemented in 2005) International price benchmarking (1998) to higher income European countries; extended to include three middle-income European countries (2007) Encouragement of generics Companies specialising in generics established (Unipharm 1976; Teva generics mid-1980s; GenMedix importer 1990 by CHS) Administrative drug approval processes speeded up (early 1990s onwards) Intellectual property rights and patent law changes (1998 and 2003) to facilitate earlier entry of generics De facto generic substitution by pharmacists; legislated for in 1999 Therapeutic substitution Therapeutic class effect introduced by MoH into management of national drug basket (late 1990s to early 2000); also administrative changes by sickness funds (e.g. CHS’s ‘pocket’ drug formulary and coercive letters from some sickness funds to physicians) Improved organisation/Delivery of services MHS opens its own network of pharmacies (1987/8) Restrictions on ownership and location of pharmacies abolished (1993) Establishment of government-owned company, Sarel, for distribution of medicines to hospitals (1995) Decentralisation of approvals for off-label uses from MoH to medical institutions (1994) General sales list of OTC medicines available through non-pharmacy outlets (2005) Pharmacist must inform patients of option of cheaper version (2005) Prescription co-payments Introduced by MHS (1976); CHS (1985) – mainly to reduce utilisation Government approves major increase (1998) – to increase sickness fund revenues Other recent proposals Nurse/pharmacist prescribing Government seeks legislation forbidding sickness funds to provide non-basket drugs through ‘premium’ supplementary insurance

in, for the first time, government as an active participant in national reimbursement decision-making. This was manifested in the establishment by the MoH in 1998 of a national process for health technology assessment (HTA), priority setting and rationing of new drugs and medical

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technologies, the product of which was the selective adoption of new technologies and drugs for public finance. Increased government involvement in health has arguably also meant ideas can make a difference more quickly and that the spread of ideas from other countries with similar types of health system has been facilitated. In the Israeli pharmaceutical context, this has led to the introduction of international drug price benchmarking, parallel imports and a general sales list of non-prescription medicines and proposals to introduce nurse prescribing (Table 1).

DRUG EXPENDITURES: LEVELS AND TRENDS Analysis of data on outpatient drug expenditures in Israel shows a considerable reduction in growth of expenditures (total and per capita) on medicines since 1997 (Sax, 2005) (Table 2). Growth in the share of total drug expenditure of national expenditure on health (NEH) and of gross domestic product (GDP) has also been constrained since 1997. More recent data on drug expenditures by the SFs shows that the longterm decline in expenditure growth rate continues and has even intensified (Sax, 2007) (Table 3). The extent to which government has been prepared to make additional funding to the SFs for the addition of new drugs to the NHI basket appears to have had no overall impact on these long-term spending patterns (Sax, 2005). Table 2. Spending on Medicines in Israel, 1992–2002a. Year

1992 1997 1998 1999 2000 2001 2002

Total Expenditure (NIS Million) 1,176 3,278 3,599 3,811 4,356 4,503 4,846

Per Capita Total Expenditure b

Total Expenditure as Share (%) of

NIS

$PPP

NEH

GDP

230 562 603 622 693 677 738

97 162 164 171 192 194 197

9.4 10.85 10.2 10.4 11.0 10.6 10.8

0.68 0.88 0.83 0.86 0.90 0.92 0.95

Source: Reprinted with permission from Sax (2005). Notes: GDP, gross domestic product; NEH, national expenditure on health; NIS, (new Israeli) shekels; PPP, purchasing power parity. a In the community by outpatients. b In nominal terms; the general consumer price index increased by 99.3% from 1992 to 2002.

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Table 3. Long-Term Trends in Overall Drug Expenditure by Sickness Funds. 2005–2007

2001–2004

1999–2001

1995–1998

1992–1994

5.6

6.5

10.2

15.8

28.0

Annual % increase

Source: Sax (2007). Updated by authors, based on data from Public Summary Reports 2005– 2007 on Sickness Fund Activities, Ministry of Health.

Table 4.

Spending Per Capita on Medicines 1992–2002a – International Comparison. Per Capita Total Expenditure ($PPP)

Total Expenditure as Share (%) of NEH GDP

Public Expenditure

Per Capita ($PPP)

Israel 21 country averageb

2002 2000 1997

1992

197 362

97 200

192 317

162 258

2002a 10.8 16.2

0.95 1.40

% of Total Expenditure

2002a 103 216

52.4 59.8

Source: Reprinted with permission from Sax (2005). For some of the countries, the original data refer to 2001. b Only countries with full data for the years listed are included in this measure; other OECD countries for which 2002 data are available: Mexico ($120) and Slovak Republic ($260). a

In the global scene, Israel, a medium-income country with a lower than average level of expenditure on health compared to OECD countries, has a particularly low level of expenditure on medicines (Table 4) (Sax, 2005). The substantive gap in spending on medicines between Israel and other countries has increased since 1997. The level of expenditure on medicines which is publicly financed is one of the lowest among OECD countries. The share of drug expenditure of NEH is also very low compared to other countries. Whereas the share of NEH of GDP in Israel is similar to the international average, the share of drug expenditure of GDP is well below the average (Sax, 2005). Such relatively low shares are more typical of countries with higher relative incomes than that of Israel (Jacobzone, 2000). The limited evidence available (anecdotal evidence from industry and SFs sources) as well as prescription market research (IMS Health) suggests that the low level of pharmaceutical expenditure reflects low prices overall for drugs in Israel rather than a relatively low volume consumption of


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pharmaceutical products. No evidence is available by which to assess whether Israelis tend to use a less expensive mix of products (e.g. older or less expensive therapeutic alternatives).

CONTAINMENT OF DRUG EXPENDITURES We now examine how pricing and reimbursement policies may have contributed to the achievement of cost containment. The regulation of prices in Israel has traditionally consisted of two separate processes. The first process is direct government regulation of listed maximum prices, ostensibly to protect purchasers and consumers against excessive prices, in particular, to offset the monopoly power of manufacturers of patented products. Subsequently, indirect price ‘regulation’, as a by-product of reimbursement policy, involves market-based negotiations between supplier and institutional purchasers, mainly the SFs. As will be shown, success in containing drug costs has been mainly achieved by means of a combination of leveraging the strong purchasing power of the SFs to obtain low prices with strong SF administrative control of the prescribing and supply of drugs, to ensure delivery of the lowest priced substitutable product within the tightly financed public health system.

Government Pricing and Reimbursement Policy The regulatory and institutional arrangements for prices and pricing were created and developed when Israel had a national pharmaceutical industry whose strength derived mainly from licensing-in and importing arrangements with foreign research-based industry. Government price controls involved a complex and opaque set of calculations based mainly on supplier costs. This process changed substantially in 2000 when government still concerned with ensuring prices that are deemed to be fair and not excessive, nevertheless decided to benchmark to high-income European countries – to Holland and as a result indirectly to Germany, UK, Belgium and France. Furthermore, some of these countries have no price control (UK, Germany) and are first or early launch countries as well as having powerful researchbased drug industry (UK, Germany and France). This somewhat generous benchmark was adjusted in 2007 with the addition of three lower income and later-launch countries: Spain, Portugal and Hungary. The impact of this benchmark adjustment on prices of new products appears to be marginal,

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probably as these three countries are member states of the EU, where industry keeps to uniformly high prices, and also as pricing in Israel may take place beforehand. The role of government pricing controls in attaining the goal of cost containment has been limited in Israel. Government has rarely used price controls, such as across-the-board price freezes or general price reductions on existing products, for explicit cost-containment endeavours as is the case in other countries. Government price intervention has to balance wider policy goals, such as ensuring financial stability of the SFs, which, to no small extent, have come to rely both on revenues from prescription copayments and on extra state funding for new drugs; both of these revenue sources are based on the maximum prices set by government and not on the actual prices paid by the SFs. Furthermore, there is also an implicit interest in a government list price that leaves plenty of margins for generic producers; the use of price freezes/ reductions could weaken the role of a thriving domestic generic industry. Government has preferred to use other policy levers to create the market conditions that encourage price competition. The effective monopoly life of a patented drug is often shortened in Israel due to relatively late regulatory market approval as well as a subsequently delayed and drawn-out reimbursement decision-making process. Furthermore, generic versions of many leading drugs have become available in Israel some years earlier than in many other countries. Patent expiry of a drug in Israel occurs as soon as the first patent expiry worldwide. There has long been a tradition of encouraging generics in Israel’s health institutions, in particular, through generic substitution by pharmacies. At first, this was more a case of institutional ‘flexibility’ on the part of SFowned pharmacies; subsequently, this found formal expression in legislation (Table 1). Partly as a result of this, prescribing by generic name is notably almost non-existent in Israel. Another policy reform was the introduction of parallel imports in 2001 after prolonged industry-wide opposition. A preliminary assessment based on an opinion survey among senior personnel in the health system claims that this has contributed to lower drug prices (Eldar-Lissai, Shani, & Shvarts, 2006). Other reforms have focused on deregulation to create wider distribution channels in the over the counter (OTC) market (Table 1); there is no evidence to indicate that these have attained the stated goal of increasing price competition. Apart from price regulatory policy, government affirms its commitment to pharmaceutical management actions through its administration of the


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NHI benefits package that includes a national drug formulary (the ‘drug basket’). The national formulary often limits the use of reimbursable drugs to certain indications and population subgroups. Where appropriate, therapeutic substitutes of reimbursed drugs are clustered in a group (‘therapeutic class effect’); in fact, this is a form of price regulation. The mechanism for updating this package involves budgetary control elements, mainly through the requirement that there be prior extra government funding for the addition of any new drugs and that the national procedure of rationing and priority setting is linked to assessment of budgetary impact of any new drug. However, the management of expenditures for drugs in the package, including expensive new drugs mandated by government to be added to the basket, is generally not a direct concern for the government which leaves this to the SFs. In Israel, government remains as the regulatory agency of the pharmaceutical market, but purchasing power and budgetary responsibilities are decentralised, belonging almost exclusively to the SFs acting as insurers and payers and also indirectly to the mainly publicly financed hospitals.

SFs’ Pricing and Reimbursement Policy Reimbursement regulation in Israel is mostly state-centralised, but some flexibility is crucially left to the SFs (partial decentralisation) (Gress, Niebuhr, May, & Wasem, 2007). This flexibility is limited to prescription drugs that can be substituted generically or therapeutically. As a result, the SFs have individual formularies for these products. However, stand-alone patented drugs need to be reimbursed by all SFs. In the case of hospitals, it is even more decentralised as the NHI basket is not incumbent upon them by law. Capturing the benefits from market forces and the bargaining power of the SFs has had a major impact on cost containment, greater than any government pricing controls. Price competition for generic and therapeutic substitutes is high (Sax, 2001). SFs are able to negotiate with industry about discounts, rather than prices, and can determine market shares for substitutes. The size of discounts usually depends on the prescription volume of the product. Particularly in the case of therapeutic substitution, market share may be determined by the outcome of a balance of preferences and restrictions set by each SF for the various drugs in a therapeutic group.

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Negotiations between SFs and manufacturers about rebates in return for preferred status on SF-specific formularies only makes sense as the former are able to oblige providers (physicians, pharmacies and hospitals) – or create sufficient incentives for them – to use preferred prescription drugs. With SFs able to selectively contract, they do not have a hard time to incentivize physicians to control expenditures, including prescribing. This capability has been facilitated by the widespread computerisation of practices, including prescribing. As a result, the SFs are able to reduce to a substantial extent the share of prescription drugs that are more expensive than their therapeutic or generic substitutes. A range of formulary care management tools are used by the SFs to influence expenditures by constraining the type and amount of drugs reimbursed. Many of the clinical protocols developed by the SFs to reduce costs and improve the quality of care are related to the use of medicines and are in addition to any restrictions set out earlier by the MoH when adding new drugs to the national basket. These may concern restrictions on the use of medicines as first-, second- or third-line therapy as well as guidelines regarding step-up (or step-down) therapy. Administrative tools include limiting prescribing to specialists, requiring prior approval by specialists in senior management positions at regional or national level (Dayan, 2007), and limiting purchase at a SF-owned pharmacy. Furthermore, some SFs monitor the prescribing behaviour of individual physicians and groups, by specialty, sending them periodic feedback regarding their prescribing pattern compared to others in the same specialty. Updates regarding suggested prescribing are sent out from their central offices, based on computerised systems, to register the SFs’ prescribing preferences. There are no formal or automatic financial penalties for physicians who overprescribe; they may be called in to discuss their prescribing patterns, give them a chance to explain and exhort them to be more careful in future (Rosen & Goldwag, 2003). Even those SFs that in the past had relatively open formularies have tightened them in recent years. Today, all four SFs have closed formularies for generic and therapeutic substitutes, that is, they will not reimburse other products prescribed by the physician. Notwithstanding this, the degree of success in cost containment appears to be related to SF purchasing power: per capita drug expenditures of SFs (age-adjusted) are a function of their size (CHS-NIS 406 (135 USDPPP), MHS-NIS 452 (150 USDPPP), MSF-NIS 517 (172 USDPPP) and LSF-NIS 519 (173 USDPPP); 2004 data) (Sax, 2007). Israel’s SFs make substantial use of government-approved patient costsharing requirements, which have had the effect of shifting a substantial and


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constantly growing part of the cost burden for prescription drug expenditures onto individuals. There is evidence to show that prescription co-payments are high enough to suppress demand in the more vulnerable sectors of the population (Gross, Brammli-Greenberg, & Matzliach, 2005). Distribution Costs Israel applies full value-added tax (VAT) (currently 16.5%) to all medicines including those reimbursed; many countries have either reduced VAT or have no VAT at least for reimbursed medicines. Apart from high taxes, direct distribution costs are overall quite low, due to the relatively limited involvement of retail and wholesale elements of the supply chain. Much of the logistics of supply from industry to patient are under the direct control of the SFs, in particular, the two largest (CHS and MHS). Where retail distribution is involved, differential pharmacy margins are applied: between 17 and 35% with lower margins for the more expensive products. Consignment-based supply arrangements between CHS, the largest SF, and retail pharmacy chains have extended that fund’s relatively low distribution costs while offering consumers better access to retail pharmacy services. A similar type of consignment arrangement has also been established between some SFs and selected hospitals for certain expensive drugs. Thus, although ex-manufacturer prices of stand-alone patented drugs are as high as in Europe and VAT rebates on medicines are non-existent, the relatively low direct distribution costs coupled with high generic penetration result overall in relatively modest prices and costs for drugs acquired by SFs.

FINANCING OF PHARMACEUTICALS: EQUITY AND SUSTAINABILITY It was hoped that the introduction of the NHI Law in 1995 would bring an end to the accumulation of financial deficits in the health system. In fact, budgetary deficits in all four SFs were eliminated for the first time only in 2004. With reduced government financing of the health care system, the main factor behind achievement of this policy goal, however, was increased funding from private financing. Private financing has grown to over onethird of total NEH (Central Bureau of Statistics [CBS], 2007). (Note – latest CBS data released 5/2009 and based on WHO methodology relates to 43% share.)

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A major driver of the growth in private financing of NEH has been the continual growth in prescription drug co-payments, whereby cost sharing by patients reached almost 43% of the expenditure on drugs in the basket in 2007 compared to about 39% in 2002 and less than 30% in the mid-1990s (Sax, 2007). There is evidence to suggest that the increased co-payments have created financial barriers to access, particularly for people with low incomes (Gross & Harrison, 2006). The increased purchase of SFs’ supplementary and commercial private insurance that allows private purchase of certain non-basket prescription drugs has also contributed to private expenditures devoted to pharmaceuticals growing faster than public expenditures. Mainly for reasons of equity, in 2007, the government thwarted attempts by SFs to extend coverage of drugs not in the basket only to those who elect to buy premium versions of supplementary insurance. Introduction of the NHI law instituted universal coverage and at the same time specifies the content of the drug basket. The updating of the cost of the basket for technological and demographic changes is a major policy issue. According to the SFs, there is continuing erosion in the real value of the NHI’s macro-budgetary framework (Zmora, Chernichovsky, & Shmueli, 2003). Uniquely, in Israel each year, as part of the annual budgetary process, the government determines how much money will be made available to fund new drugs and other technologies. In recent years, macroeconomic factors (i.e. economic/ business cycle) as well as political considerations (elections, coalition building) have influenced the outcome and timing of decisions regarding the size of this extra funding for new drugs. The latest funding proposals for the three years 2008–2010 are evidence for the first time of a multi-year commitment by government to fund new drugs at about a 1.5% annual increase in the budget of the package of benefits. In contrast, other countries that also have a national mechanism of adding new technologies to a basket of services publicly financed (England & Wales, Scotland, Canada, Australia, New Zealand, USA-Medicare, USA-VA) prefer to leave the annual health budget open and not known in advance.

EFFICIENCY OF EXPENDITURES IN THE PHARMACEUTICAL SECTOR Economic analyses in the literature point to prescription drugs producing benefits that far exceed costs, even when based on US prices and utilisation


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(Lichtenberg, 1996, 2001). In the Israeli context, with lower prices, there is arguably an even more favourable ratio overall between benefits and costs. In the absence of evidence that Israelis are on average hospitalised more than elsewhere or unnecessarily, the low proportion of health expenditures devoted to pharmaceuticals and the low pharmaceutical expenditure levels may be taken as indicators of efficiency of drug expenditures in Israel. However, the reliance on price benchmarking – external (other countries’ prices) and internal (prices for other products considered comparable) – rather than pharmacoeconomic assessment (value-for-money) as a basis for establishing prices suggest that Israel has scope to improve the effectiveness of its pharmaceutical expenditure. Many of Israel’s pharmaceutical policies can be considered as promoting efficient drug expenditures. The process by which generic products and parallel imports are eligible to be certified as substitutable for originals has been streamlined and has thus increased efficiency. Policies also provide strong administrative incentives for cost-effective dispensing: pharmacists are required to dispense the lowest cost product certified as substitutable. Generic products represent a relatively high share of the market in terms of volume and a relatively small share of the market in terms of value. However, even if brand-name prices make the Israeli market attractive for generics, generic price competition may still be limited in the case of low-volume therapeutic sub-markets in what is a small pharmaceutical market. With no patient incentives in place to choose generic alternatives, discounts and rebates are provided by manufacturers almost exclusively to third party insurers and payers and do not carry over to lower prices for patients. Efficiency can also be pursued within a macro-budget by guidelines, reimbursement rules and provider behaviour that is based on evidence of cost-effectiveness. There are incentives in place for SFs to promote the most cost-effective prescribing behaviour in as much that they have responsibility for financing drug expenditures and ensuring good outcomes; they also have strong administrative levers by which to influence the physicians employed by them. However, whether sufficient attention is in fact paid to the question of cost-effectiveness and value of pharmaceutical expenditures is questionable by the process by which new drugs are selected for adoption into the public package of services. There is little known about the policy levers used by the various SFs to promote cost-effective prescribing, dispensing or use by patients. In any case, whenever cost-effectiveness or value-for-money considerations may be applied in SF decision-making, the perspective will be that of the SF alone and not that of the state or society.

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Co-payments have increased steeply, and research is required to evaluate whether this has gone beyond what simple economic efficiency would have advocated to limit excess consumption. There is cause for concern that where drug use is forgone, this could lead to costly adverse events or to substitution and more expensive health services. In view of evidence that user charges have reduced use (Gross et al., 2005), attention should be given to using incentives to encourage use of cost-effective treatments by lowincome groups (Guiffrida & Torgerson, 1997).

New Drugs and Updating the Basket The Israeli method, based on a given additional annual budget fixed every year in the framework of state budgetary discussions, reflects the overall importance given to population health in the framework of the possible uses of resources. Although sponsors of new drugs requesting public reimbursement are encouraged to provide economic data in their submissions to the MoH, there is no formal requirement on government to use costeffectiveness as a decision criterion for reimbursement. On the contrary, by introducing a process of HTA, efficiency may potentially be enhanced by eliminating coverage of those products not found to be cost-effective, while retaining reimbursement for highly effective drugs. But in fact, technologies are not ranked according to cost of health production but according to other criteria such as type of perceived clinical benefit (whether life-saving, life-extending or QoL improvement), number of patients requiring them, short-term budgetary impact and feasibility of financing the technology out-of-pocket or through supplementary insurance (Shani, Shemer, Luxembourg, & Siebzehner, 2001). As drugs are not ranked according to their cost-effectiveness, companies are encouraged to price their new products at higher levels than otherwise would be the case. The MoH does not take into consideration in its budgetary impact of new technologies for inclusion in the basket indirect sources of cost savings such as decreased treatment costs (hospitalisation and other non-drug costs) and the decreased overall cost per patient from the introduction of generic versions of alternative drugs used to treat the same indication. Can we be sure that drugs that are relatively non-cost-effective, providing an unfavourable ratio between marginal costs and marginal benefits, have been excluded from reimbursement? Such drugs may have been included for reimbursement, for example, when the budgetary impact from the SF perspective justified it, or for example, where public pressure exerted by


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vested interests, and involving politicians and media, overrode unfavourable cost-effectiveness considerations. Partly in response to increased political meddling in reimbursement decision-making, the proposal for an advisory economic sub-committee, intended to increase the input from more objective cost-effectiveness criteria into this process, was established late 2005. This subcommittee reviews and refines the MoH staff’s (HTA) projections regarding the prices of proposed new drugs and other technologies, along with the volumes likely to be consumed. The subcommittee consists only of representatives of MoH, MoF and from the SFs. There are concerns regarding the lack of transparency of the committee’s work and decision-making. While the Public Committee that decides on which drugs enter the package of benefits does not use formal economic evaluations, an analysis of its 2006 decisions reveals that in general, medicines with very high cost per quality-adjusted life year (QALY) were rejected and most of those entered the package has relatively low cost per QALY. Exceptions (e.g. including Avastin for colorectal cancer despite its high cost per QALY) were caused by public outcry or care for orphan drugs (Shmueli, 2008). A policy initiative that could add to the efficiency of public funding would be the negotiation of a maximum reimbursement price before a coverage decision is made and not subsequent to it as is the procedure today. There is an incentive for manufacturers to overbid when submitting a price proposal to the national reimbursement agency (MoH) due to the fact that applications and decisions remain confidential. Efficiency could be strengthened by also having periodic retrospective reviews of newly introduced reimbursed drugs at defined intervals, in light of new evidence. A system for recouping overpayments from manufacturers in light of evidence that the price exceeded a cost-effective level could help to enhance efficiency further.

AVAILABILITY OF PHARMACEUTICALS An important policy question is the extent to which state-of-the art medicines are available for purchase in Israel. Factors affecting availability include product approval timing and outcomes, as well as manufacturers’ launch decisions and marketing strategies. Companies file applications for the Israeli market quite early. The Israeli regulator faces significant pressure: though Israel is a small country, the core national drug formulary is not smaller than in other countries and the public

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is believed by at least some stakeholders to have high expectations for the availability of new drugs. In comparison with other medium-income small countries, availability of medicines appears to be quite comprehensive. With the establishment of a system of price control based on European prices, instances of non-availability of new drugs in Israel are unlikely to be the consequence of government pricing decisions. Such instances may however be due to the manufacturer’s low expectations in a small competitive market. This may be particularly true for ‘me-too’ drugs that may have to compete in a crowded therapeutic segment, especially if there is a generic version of a therapeutic alternative in that segment. Although opinion is divided, a reasonable conclusion is that in general, most new drugs have been entered the market in Israel promptly. This is particularly so in the case of life-saving or life-extending drugs, for example, Herceptin or Avastin. This has not always been the case. The annual average lag between first global launch of a new drug and its launch in Israel ranged between four and eight years during the decade up to 1993 (Sax, 1989). It declined continuously thereafter so that in the early 2000s it was about 1.5 years, with possibly quicker approval times given for potentially life-saving drugs. It is global interdependencies that have largely dictated that the time taken to process market applications approximate international best practice (Vogel, 1998). This is reflected in an increased reliance by the Israeli regulator on overseas regulatory agencies – in particular, the USs FDA, the UK regulatory agency, and from 1995, the European agency (EMEA). The increased local presence and concerted lobbying activity of multinational research-based companies from the late 1990s have also been catalysts in this process. In a few instances, the Israel regulator has exceptionally ‘fast-tracked’ the market approval of new drugs independently of regulatory agencies abroad (e.g. locally developed products, Copaxone, Azilect) or in parallel with them in the case of potentially life-extending medicines (e.g. Glivec, Herceptin) and other technologies (Visudyne, drugeluting stents) (Tal, Nir, Hakak, Bakshi, & Fire, 2006). The reason for the approval lag is the Israel regulator’s procedure of commencing processing and evaluation of new product submissions only after marketing authorisation has been granted by one of the above overseas regulatory agencies. The Treasury has no real interest in cutting down the time for the MoH to approve a drug as this will only create more pressure on the government to fund new drugs for earlier addition to the basket. Bottlenecks in recent years at the MoH’s pharmaceutical administration (PA) may have contributed to delays in approvals. Following an appeal to the High Court from a patients representative organisation to reduce


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approval times, the MoH has complied with the court’s request and has for the first time published timetables for approvals according to type of product application (e.g. the maximum time to approve the marketing of a new drug entity should not exceed one year) (Ministry of Health [MoH], 2007). One side benefit of the delayed approval of new drugs has been that Israeli patients have been spared some, but not all (e.g. Vioxx), of the postapproval safety problems that have arisen in other countries with faster market authorisation. The low priority the FDA assigns to its responsibility for arbitrating drug safety has been highlighted in a recent Institute of Medicine (USA) report (The Future of Drug Safety), and in its subsequent response to this report, which the FDA itself commissioned (Smith, 2007). A relatively simple procedure, commonly in use in hospitals, exists at the individual patient level for facilitating access to drugs that are not registered or are registered but are not approved for a specific indication (off-label use). Off-label use of a registered drug is considered to be relatively limited in Israel medical institutions (H. Halkin, personal communication), possibly reflecting a low level of physician discretion in such circumstances. On the contrary, by means of the institutionalisation of a special procedure, the use of non-registered drugs is reportedly relatively common in Israel. This may be because of a greater reluctance in Israel than in some other countries to forgo the latest technology available, while abroad, there may be more emphasis on requiring clinical trial protocol (H. Halkin, personal communication). In the hope of containing the growing use of this special procedure, the MoH has recently decided to transfer administration checks of imported medicines that are not (yet) registered in Israel to the hospitals.

ACCESSIBILITY OF PHARMACEUTICALS Timely market availability is only one determinant of accessibility of drugs to patients; affordability is another one. The presence of universal coverage of medicines would suggest that there are no major limits on accessibility of pharmaceuticals in Israel and that most of the Israeli population is well protected against exposure to high drug expenditures. By including criteria other than cost-effectiveness in national reimbursement making such as needs-based criteria, Israel appears to have found a way that allows drugs that are not cost-effective, but important for patients with serious illnesses, to be reimbursed. Nevertheless, there is a considerable ‘gap’ in accessibility

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to new and often expensive drugs that are not (yet) covered by basic health insurance. Reimbursement decisions can only be made after a product is registered and following annual decisions on extra government funding for new drugs. As a result, delays in reimbursement decisions may often be substantial and patients often have to wait longer than those in other countries before new drugs become affordable, usually after a delay of some years after first world launch. Beyond national decision-making, Israelis may experience restricted affordable access to medicines to the extent that subsequent SF reimbursement policies often restrict coverage of certain medicines. Each SF fixes its own formulary that includes drugs in the basket as well as drugs that are added according to the criteria of each SF. Each SF has its own process of allocation of drugs to its members; more expensive drugs may require special approval of a specialist and regional or central administration. There is also a process in the SFs to approve drugs not in the basket at the individual patient level (‘committees for special needs’). The MoH has no information with regard to the formulary rules and guidelines that the SFs give to their physicians with regard to restrictions and on use of drugs in the national drug basket. Thus, the MoH is not able to supervise and to give the SFs, if need be, guidelines in these matters (State of Israel, Bureau of the Comptroller, 2007). Increased cost sharing for medicines is another source of concern as to drug accessibility. Co-payments act as hurdles to access to medicines for the most deprived part of the population because there is no exemption from co-payment until people reach a relatively high cap. While individuals on disability and income maintenance allowances are exempted from co-payment, there are no explicit exemptions for patients suffering from chronic diseases, raising the possibility that low-income people suffering from chronic conditions may experience financial barriers to access. A major impediment to any government initiative to improve affordability is that prescription co-payments have become a major source of revenue for the SFs in the financially constrained public health system.

OTHER POLICY GOALS Other policy goals that should be employed to assess the impact of drug regulation and policy relate to quality of care and health outcomes, public satisfaction with drug policies and outcomes, and industrial policy goals.


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Quality of Care There is very little data by which to assess quality of care in general and to the use of medicines in Israel in particular, much less to make the link between findings and policies. Influenza vaccination rates for persons aged 65 years and over are an important indication of health care quality; data from the Quality Indicators for Community Health Care Program shows that 51% of this population were vaccinated in 2003–2005 (MoH, 2006). Medication errors, as in most OECD countries, are probably common and in terms of patient safety and care costs are probably significant. With the widespread use of electronic prescribing, sources of paper-based prescribing error are limited. Little importance has been given in Israel to professional services supplied by pharmacists. However, this may be changing slowly as recent government proposals to allow a limited prescribing role to pharmacists, as well as to nurses, indicates (Table 1).

Public Satisfaction and Input There are no surveys that specifically reveal Israelis’ views and values in the area of drug policy. Pharmaceutical policy-making and decision-making is not particularly transparent nor are there many channels for involvement by patients or consumer groups. The MoH’s Pharmaceutical Administration does not publish any public reports about approved medicines, unlike the FDA and the EMEA. There is also a lack of transparency with regard to the procedures and outcomes in the process of national reimbursement decision-making, for example, no public information is available about manufacturers’ applications, nor about recommendations of the committee nor about negative decisions. Yet, such information could be useful for physicians and for patients, even when drugs are not excluded from the benefits package.

Industrial Policy Goals The objective of government policies is to make Israel a competitive, knowledge-based economy. The pharmaceutical industry has become an important contributor to Israel’s overall economy. Perhaps uniquely for a developed country with a strong R&D infrastructure, it is the generic drug industry that has led the way. Government pricing regulation, which allows

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high prices for new drugs, has not only clear benefits for research-based firms but it also provides opportunities subsequently for generic companies. Research-based manufacturers have not located their production and research in Israel. Although there are aspects of pharmaceutical regulation and policy which are problematic for them, there is little reason to believe that these have influenced decisions not to locate here.

KEY FINDINGS AND CONCLUSIONS This chapter has assessed the impact of Israeli pharmaceutical and regulatory policies on a range of key health system performance goals. The findings point to a number of successful accomplishments as well as outstanding challenges. The key findings include the following: Price referencing to countries of the EU does not provide a strong basis for regulating prices of products at market entry, given that Israel is a lower income country and that included in the reference countries are those that are often first-launch or early-launch countries or with non-regulated prices. Having prices of patented medicines in Israel roughly in line with European comparators raises the question as to whether price levels are at an appropriate level. The actual acquisition prices of generics are low, at least in cases where consumption and competition are substantial. Government price regulation operates more as a symbolic initiative representing consumer protection than a functional mechanism responsible for consumer savings. In effect, it operates as a mechanism responsible for SF savings, over which the state has no information or monitoring. This is an example of the information gap and asymmetry that exists between government and SFs, a gap that is a source of inefficiency and inequity in the health system. Decentralised reimbursement and pricing coupled with low direct distribution costs have very likely been the main drivers of the containment of (public) expenditure on medicines. This is in spite of a uniformly high VAT and in spite of relatively high level of drug consumption, at least when one considers the relatively young demographic profile of Israel. In Israel, as elsewhere, changes in the mix of medicines that are prescribed and consumed favouring new and higher priced products over old ones are contributing to expenditure growth. A wide range of drugs are available on the market and are affordable to most consumers and patients.


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Israel has established strong incentives for competition by generic alternatives once a product has gone off-patent. The incentives faced by physicians and pharmacists are aligned and this has allowed Israel to achieve high generic penetration of the market with a relatively low share of the value. There may be still scope for further improvement in this area – for example, by encouraging generic prescribing, creating patient incentives to choose lower cost products or by reforming government pricing of generics – thereby increasing the cost-effectiveness of pharmaceutical expenditure. There is little use of cost-effectiveness analysis for determining the public reimbursement status of new pharmaceuticals. The limitation of the Israeli reimbursement system lies in the lack of incentives for manufacturers to propose the lowest cost-effective price and in the inability of the state to recoup overpayments when later information shows that initial decisions on market-entry price and budgetary impact were incorrect. There is scope to get better value from pharmaceutical expenditures by making explicit assessments of costs relative to benefits when considering the reimbursement and pricing of pharmaceutical products. This could also provide an opportunity for making the whole process more transparent than it is now. New products are available on the Israeli market reasonably promptly, especially if they are life-extending, although there does appear to be an increase in the lag in recent years. Accessibility is enhanced by additional decentralised processes to make available products not (yet) on the market. Although the basket is extensive, substantial delays in reimbursement decisions and high levels of cost sharing mean that medicines are increasing unaffordable for patients, especially for low-income persons with chronic diseases. Access to new and costly medicines is often problematic. Differences in timeliness of formulary inclusion decisions and other factors result in differences in access across the SFs. There are no incentives for SFs that are successful in drug expenditure management (e.g. CHS) to use it as a competitive advantage, for example, to offer its members lower co-payments for preferred products or less restriction on new drugs.

REFERENCES Central Bureau of Statistics. (2007). National expenditure on health. Jerusalem (in Hebrew). Dayan, A. (2007). Medicines in the ‘‘stocks’’. Zman HaRefuah, 15–21 (in Hebrew).

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Eldar-Lissai, A., Shani, S., & Shvarts, S. (2006). Impact of parallel trade of pharmaceuticals on the Israeli health care system. Annual research report 2005–6. Israel National Institute of Health Policy & Health Services Research (in Hebrew). Gress, S., Niebuhr, D., May, W., & Wasem, J. (2007). Reform of prescription drug reimbursement & pricing in the German social health insurance market. Pharmacoeconomics, 25, 443–454. Gross, R., Brammli-Greenberg, S., & Matzliach, R. (2005). Public opinions on the level of service & performance of the health system in 2003 in comparison with previous years. Myers-JDC Brookdale Institute (in Hebrew). Gross, R., & Harrison, M. I. (2006). Responses of Israeli sickness funds to environmental change following the national health insurance law: Opening the black box. Health Policy, 76, 213–232. Guiffrida, A., & Torgerson, D. (1997). Should we pay the patient? Review of financial incentives to enhance patients’ compliance. British Medical Journal, 315, 703–707. Guillen, A. M., & Cabiedes, L. (2003). Reforming pharmaceutical policies in the EU: A ‘‘penguin effect’’? International Journal of Health Services, 33, 1–23. Jacobzone, S. (2000). Pharmaceutical policies in OECD countries: Reconciling social and industrial goals. Labour market and social policy. Occasional Papers No. 4. OECD, Paris. Lichtenberg, F. R. (1996). Do (more & better) drugs keep people out of hospitals? American Economic Review, 86, 384–398. Lichtenberg, F. R. (2001). Are the benefits of newer drugs worth their cost? Evidence from the 1996 MEPS. Health Affairs, 20, 241–251. Lofgren, H., & de Boer, R. (2004). Pharmaceuticals in Australia: Developments in regulation and governance. Social Science & Medicine, 58, 2397–2407. Ministry of Health. (2006). Quality indicators for community health care in Israel. State of Israel Public Report 2003–5. Jerusalem (in Hebrew). Ministry of Health. (2007). Regulation 72 for determination of timetable for product registration. State of Israel, Jerusalem (in Hebrew). Rosen, B., & Goldwag, R. (2003). Health care systems in transition: Israel. In: S. Thomson & E. Mossialos (Eds), European observatory on health care systems. Copenhagen: WHO. Sax, P. (1989). Availability & timing on new drugs in Israel: Analysis & international comparison. Journal of Clinical Pharmacology, 29, 967–974. Sax, P. (2001). Changes in drug economy in Israel’s health maintenance organizations in the wake of the national health insurance law. Israel Medical Association Journal, 3, 605–609. Sax, P. (2005). Spending on medicines in Israel in an international context. Israel Medical Association Journal, 7, 286–291. Sax, P. (2007). Sick funds and the Israel drug economy: Affordable medicines for whom? PHARMA Israel Drug Bulletin, 77, 1–7. Shani, S., Shemer, J., Luxembourg, O., & Siebzehner, M. (2001). Setting priorities for the adoption of health technologies on a national level – The Israeli experience. Health Policy, 54, 169–174. Shmueli, A. (2008). Economic evaluation of the decisions of the Israeli public committee for updating the national list of health services in 2006/2007. Value in Health, 12, 202–206. Shmueli, A., Achduth, L., & Sabag-Endeld, M. (2008). Financing the package of services during the first decade of the national health insurance law in Israel: Trends and issues. Health Policy, 87, 273–284.


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Smith, S. W. (2007). Sidelining safety – The FDA’s inadequate response to the IOM. New England Journal of Medicine, 357, 960–963. State of Israel, Bureau of the Comptroller. (2007). Annual report, Vol. 57B. Jerusalem (in Hebrew). Tal, O., Nir, A., Hakak, N., Bakshi, E., & Fire, G. (2006). Criteria for early adoption of medical technologies. Tel Hashomer: Gertner Institute for Epidemiology & Health Policy Research. Vogel, D. (1998). The globalization of pharmaceutical regulation. Governance, 11, 1–22. Zmora, I., Chernichovsky, D., & Shmueli, A. (2003). The Israeli capitation system. The Taub Centre for Social Policy Studies in Israel, Jerusalem (in Hebrew).

GLOBAL BUDGETS AND PROVIDER INCENTIVES: HOSPITALS’ DRUG EXPENDITURES IN TAIWAN Shin-Yi Chou, Mary E. Deily, Hsien-Ming Lien and Jing Hua Zhang ABSTRACT Purpose – This chapter examines how drug prescribing behavior in Taiwanese hospitals changed after the government changed reimbursement systems. In 2002, Taiwan instituted a system in which hospitals are reimbursed for drug expenditures at full price from a fixed global budget before the remaining budget is allocated to reimburse all other expenditures, often at discounted prices. Providers are thus given a financial incentive to increase prescriptions. Methodology – We isolate the effect of this system from that of other confounding factors by estimating a difference-in-difference model to analyze monthly drug expenditures of hospital departments for outpatients during the years 1999–2006. Findings – Our results suggest that hospital departments which use drugs more heavily as part of their regular medical care increased their drug Pharmaceutical Markets and Insurance Worldwide Advances in Health Economics and Health Services Research, Volume 22, 103–122 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1108/S0731-2199(2010)0000022008

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prescription expenditures after the implementation of the global budget system. In addition, we find that the response was stronger among for-profit than not-for-profit and public hospitals. Implications – Hospital doctors responded to the financial incentive created by the particular global budgeting system adopted in Taiwan by increasing expenditures on drug treatments for outpatients.

INTRODUCTION Many nations are assuming more responsibility for the health care of their citizens and as a result are struggling with rising health care expenditures. These expenditures are rising everywhere as health care becomes more technologically advanced. However, expenditures may also increase both because consumers demand more care when they face lower prices for the service, and because providers may supply more care in response to the particular reimbursement scheme adopted. Governments have experimented with a number of different strategies to address these two responses, such as co-pays for consumers and prospective payments for provider services, with the aim of controlling costs while still ensuring that citizens have access to necessary and good-quality health care. In this chapter we examine how adopting a particular strategy aimed at controlling provider costs, a global budget, affected the drug prescribing behavior of physicians in Taiwanese hospitals. In a global budget scheme, the government sets a fixed amount of money to be paid to providers for all health care they supply in the following period. The purpose is to give providers financial incentives to contain costs: ideally, given a fixed budget, providers will work to provide health care services as efficiently as possible so as to maximize their profit (or net revenues usable to pursue other goals, in the case of public or private-not-for-profit institutions). However, providers may also respond by adjusting the volume of their services, if possible, or by changing the quality of the care offered. Much depends on the details of the specific global budget plan adopted. In this chapter we study one aspect of health care providers’ response, hospital physicians’ drug prescription behavior, in Taiwan. Previous studies of the impact of global budget plans have focused primarily on the experiences of Canada and of various Western European countries. By examining the effect of global budgets in an Asian country with a very different history of government involvement in health care, we can get a

Global Budgets and Provider Incentives

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better idea of the universality of providers’ responses to these types of incentive schemes. Studying Taiwan has other advantages. Global budget schemes can be quite complicated, making it difficult to isolate provider responses. However, the incentive for hospital physicians to alter their drugprescription decisions is fairly clear in the system adopted in Taiwan. Further, we know exactly when hospitals switched to this new reimbursement scheme, have identified a treatment group and a control group, and have data for the groups both before and after the switch. We first provide some background with a brief discussion of global budgets and the specific global budget plan adopted for hospitals in Taiwan, particularly as it relates to reimbursement for drug prescriptions. We then discuss our methodology and specification, and describe the data sources. Finally, we present our results and end the chapter with our conclusions.

BACKGROUND Global Budget Systems A global budget is a target or limit set for overall spending (Poterba, 1994). Governments adopt global budgets as a response in rising health care expenditures for several reasons. First, such a budget allows the government to pre-specify the amount to be spent on health care in a period, giving it more control over its expenditures. Second, by simultaneously giving patients free access and setting the overall budget, the government forces health care providers to use their own judgment on how to use their limited budget to satisfy health care needs of their patients. Ideally, such budgets give health care providers the incentive to reduce costs by eliminating unnecessary services and increasing the efficiency with which necessary care is provided. Studies of the impact of global budgets have tended to focus on their effect on health expenditures and on whether any cost reductions realized were achieved by reducing the quality of the service provided.1 However, because the government cannot predetermine the amount of health care services that patients will demand when they fix the budget, they must adopt some mechanism to determine reimbursements that reconciles the fixed budget with fluctuating quantities of care, and the incentives that alternative mechanisms give to providers may be quite different. There are two basic approaches. In one, an expenditure target scheme, the government pays a fixed fee (or prospective payment) for predetermined

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quantities of different services. As discussed in Fan, Chen, and Kan (1998), under this approach the provider may be modeled as an individual optimizer, choosing an output level that maximizes their own profit or utility given the fee structured set by the government. In such an environment, governments must put some additional controls on supply decisions if they want to keep within their budget: for example, providers may be discouraged from supplying more than their quota if reimbursements are sharply discounted for services above the quota. However, in an expenditure cap system, the budget is fixed ex ante but the reimbursement prices are determined ex post: once the services have been provided, the prices of those services are adjusted so that the fixed budget will cover them. In this type of situation, providers are in a situation similar to Cournot competitors, having to make supply decisions before knowing the price they will receive for the service, because the price of the service will be determined by the summed supply decisions of all the providers. Each provider will have an incentive to increase their supply, ignoring their marginal impact on overall price levels. The greater the number of providers, the closer prices will approach marginal cost (Fan et al., 1998). Thus, providers in this type of global budget scheme are likely to earn lower profits, particularly as the number of providers increases. In some cases, however, reimbursement schemes contain elements of both ex ante fixed fees and ex post prices. For example, in the early 1990s Germany adopted a system to reimburse physicians in which fees for services were fixed ex ante during the first half of the year, but were reduced in the third- or fourth-quarters of the year if expenditures were exceeding the budget. In addition, physicians faced risk to their income if they, as a group, prescribed drugs whose cost exceeded the pharmaceutical budget. In such a case, the budget for physician income in the next year would be reduced by the amount the physicians had exceeded the drug budget. Physicians responded to the increased financial risk associated with prescribing drugs by reducing the number of prescriptions written and by referring patients to specialists or to hospitals, both of which were not affected by the global budget for pharmaceuticals (Henke, Murray, & Ade, 1994).

The Global Budget System in Taiwan In March 1995, Taiwan enacted a compulsory National Health Insurance (NHI) plan that covers virtually the entire population. The NHI is designed to accomplish two objectives: to provide equal access to health care for all


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citizens and to constrain total health spending to reasonable levels (Council of Economic Planning and Development, 1990). To achieve the first goal, the program reimburses providers for most medical services, including acute care, prescription drugs, dental care, and traditional Chinese medical care. Outpatients pay only a small share of the cost of their care, approximately $5 for visits to clinics and $8 for visits to hospitals, and inpatients pay 10% of the cost of their care, but with a maximum payment of 10% of the average national income per person. In addition, indigenous people qualify for exemption from all cost sharing (Department of Health, Taiwan). Furthermore, patients may seek care from virtually any of the clinics or hospitals under contract to the NHI, no matter if the provider is for-profit, not-for-profit, or public.2 In the first few years of the new program the Bureau of National Health Insurance (BNHI) reimbursed health care providers on a fee-for-service basis. As a result of this, plus the low cost to consumers for virtually unlimited access, government spending on health care increased rapidly: spending on outpatient services, for example, increased 25% between 1996 and 2000. To achieve their second goal of controlling overall health spending, the BNHI reacted quickly to the rapidly rising expenditures by adopting global budgets for each of the major sectors of the health care system, first for dental services in 1998, then traditional outpatient Chinese medical services in 2000, Western-based medical clinics in July 2001, and finally for hospitals, both inpatient and outpatient care, in July 2002. Taiwan’s global budgeting system uses the expenditure cap approach. Before the start of each fiscal year the NHI Expenditure Committee discusses the national budget for each major health care category (dental services, traditional Chinese medical care, clinics, and hospital services) with the BNHI and with representatives of health providers. Once the committee sets the overall national budget for a category, that budget is divided among six health care regions, with the share of each region determined by a combination of historical expenditure levels and risk adjustments. Each region then has its own subsidiary NHI Expenditure Committee to administer their budget for each health care category.

The Global Budget System and Providers’ Incentive to Prescribe Drugs The particular global budget system adopted in Taiwan affects the incentive of hospital physicians to prescribe drugs. Let the global budget for a region’s hospital sector be B. Because B is a fixed expenditure level set for the region

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before the start of the fiscal year, payments to individual providers must be adjusted according to the overall quantity of services provided. For instance, suppose Hospital i supplies qi service units during the fiscal year. The price that each hospital receives in reimbursement for each unit of service it provides is: p ¼ B=Q where Q ¼ Sqi is the total number of service units provided by all hospitals in the region during the fiscal year. The value p, known as the point value, fluctuates with the volume of service units supplied so that reimbursements do not exceed the fixed budget B. Specifically, if the number of service units supplied exceeds the amount expected when the budget was determined, the point value will be less than one. Thus, when making supply decisions providers know their region’s annual hospital budget, and they know the number of service units associated with supplying specific medical services and thus the quantity of service units that they are supplying, but they do not know the ex post point value at which service units will be reimbursed: providers must make supply decisions while uncertain of the final point value. In this situation individual hospitals have an incentive to increase their income by increasing the number of service units they generate (either by increasing the volume of patient visits or the intensity of treatment during a visit). Chen, Laditka, Laditka, and Xirasagar (2007) found significant increases in the number of discretionary, high-volume hospitalizations among many classes of patients after the imposition of global budgets in Taiwan, suggesting that hospitals did respond by attempting to increase their quantity of services. Collectively, however, hospitals will find that as they all increase the amount of service they provide, the point value will float downward. In fact, the average point value across all regions, which was first calculated in the second half of 2002 at approximately .96, decreased to .95 in 2003, then fell to .90 for 2004 and 2005, before rising again to .94 in 2006 (BNIH, 2009), suggesting that in every year hospitals have provided more service units than expected. However, the global budget scheme for hospitals treats reimbursements for drug prescriptions differently from reimbursements for other health services. Expenditures for drugs (D) are removed from the budget before the ex post point value is calculated, so that the point value is: p ¼ ðB DÞ=Q a practice known as the Pharmaceutical Benefit Scheme (PBS) (NHI, Global Budget Q&A Manual). Hospitals thus have an incentive to spend


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more on drug treatments so as to avoid the uncertain return associated with services reimbursed by the usually discounted point value. Further, the certain profit margin from drug expenditures is higher than that of the other medical services (Lee, Yang, Huang, Liu, & Chen, 2006).3 As might be expected, from 1996 to 2003 drug reimbursement grew 50%, from 62.2 billion Taiwan new dollars ($NT) to $NT 94.5 billion, and reimbursements for drugs as a percentage of total health expenditures are significantly higher in Taiwan than in other countries.4 The BNHI has adopted several strategies to contain drug expenditures by lowering drug prices.5 Nevertheless, drug expenditures are still reimbursed at cost before the remaining portion of the budget is divided to determine the floating point value used to reimburse other medical services. It thus remains the case that the removal of drug reimbursements from the global budget creates two potential sources of profit for hospitals, one for drug expenditures that is certain and relatively high, and one for all other expenditures that is uncertain and lower. Hospitals therefore have an incentive to increase use of drugs so as to increase profits from the certain source. It is the hypothesis of this chapter that hospitals in Taiwan have responded to the incentives built into the global budget scheme by increasing prescriptions written for their patients.

METHODOLOGY AND SPECIFICATION Previous studies of the impact of the PBS on drug prescriptions have looked at drug prescriptions before and after the implementation of the global budget. For instance, in a study of prescriptions for patients with hypertension or diabetes, Chou et al. (2008) found that hospital physicians increased prescriptions, particularly for diabetes patients, after the adoption of global budgeting. However, making before-and-after comparisons may be misleading because of the strategies that the BNHI adopted to control drug prices: such comparisons do not take changes in drug prices into account. Moreover, the BNHI increased its monitoring of utilization, including developing drug utilization guidelines, auditing more claims, and instituting provider profiling (comparing the costs and prescribing patterns among peer providers) and utilization review. These monitoring efforts may also have reduced the impact of the global budget. Consequently, we use difference-in-differences to isolate the effect of the budget system on the use of drugs. We assume that the effect of the global budget on drug-prescribing decisions varies with the extent that drugs are used

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for treating different types of illnesses, and that therefore doctors in different departments should have different responses to the policy. We thus separate hospital departments into treatment and control groups based on the importance of drugs in the care they provide, and estimate the following specification: Yj;h;t ¼ a þ b1 Tj þ b2 GBt þ b3 ðTj GBt Þ þ b4 Dj;t þ gj þ b5 Hh þ d þ t þ j;h;t where j is hospital departments, h the hospitals, and t the months. The dependent variable is the natural log of average drug expenditure per case in department j of hospital h in month t over the years 1999–2006.6 Since we are interested in changes in the propensity of doctors in different departments to prescribe drugs, we wanted to reduce heterogeneity in expenditures related to other aspects of patient care, and to do so we restricted the sample of cases used to calculate the expenditure variable. We first eliminated all records for inpatients. The case-mix of inpatient admissions is quite diverse and consequently the average expenditure per inpatient admission is more likely to be affected by extreme values. In addition, inpatient care involves extensive use of expensive equipment. By comparison, drugs are the principal method for treating outpatients. Finally, and importantly, although Taiwan’s NHI is noted for its generous coverage, it may not cover the most advanced medical treatment, especially for inpatient care. The inability to track the total health expenditures for such cases could bias our estimates on the impact created by financial incentives associated with global budgeting. Having restricted our sample to records of outpatient care, we made some additional eliminations: (1) patients treated at local clinics, because local clinics are reimbursed for drug use in a different system, (2) patients that paid a zero co-payment, to avoid possible bias, (3) patients receiving dialysis treatment and surgeries performed at outpatient visits, because these are quite expensive and likely to affect the average expenditures of a hospital substantially, and (4) outpatient records for dental care and for traditional Chinese medical treatments, as both represent very different patterns of care and drug use from the outpatient cases included in the sample. After our eliminations, the outpatient records we used to calculate drug expenditures are principally those of patients that did not suffer from major illnesses and that did not require repeated or expensive non-drug care (e.g., dialysis, surgery, or care for TB). We assigned each of these remaining cases to a hospital department, and calculated the department’s average expenditure per case each month.

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The variable T identifies the treatment group. We identify the treatment group by calculating for each department a drug ratio: the average drug expenditure per case as a fraction of total health care expenditure per case for patients treated in that department in a period before global budgeting was instituted (January 1999–June 2002). We then sort the departments into a control group, those departments that use relatively few drugs in their care of these patients and are thus much less likely to respond to the incentives created by the PBS, and a treatment group of departments that rely on drugs more heavily in their treatments (see Table 1). The variable T equals one if the department has a high drug ratio and is thus a part of the treatment group, and it equals zero if the department is part of the control group of departments with lower drug ratios. We expect the coefficient of this variable to be positive, because departments with higher drug ratios should prescribe relatively greater amounts of drugs each month than the control group, ceteris paribus. Table 1.

Treatment and Control Groupsa.

Treatment Group

Control Group

Department

Drug Ratiob

Department

Drug Ratiob

Renal medicine Cardiology

0.696 0.674

0.513 0.501

Endocrinology Thorax internal Cardiovascular surgery Psychiatrics Division of infectious diseases Neurology Rheumatoid immune branch Family medicine Internal medicine

0.667 0.666 0.650 0.649 0.648 0.646 0.630 0.608 0.601

Gastroenterology Neurosurgery Dermatology Urology

0.587 0.570 0.560 0.543

Blood cancer Combination of three small departments with medium drug ratios Orthopedics Gastrointestinal surgery Thoracic surgery General Ear, nose, and throat Pediatrics Obstetrics and gynecology Rectal surgery Combination of three small departments with low drug ratios Surgery Radiation oncology Ophthalmology Pediatric surgery

a

0.474 0.466 0.456 0.447 0.440 0.415 0.412 0.395 0.395 0.383 0.365 0.353 0.160

The average number of departments in a hospital is 7.76, with a standard deviation of 7.577 and a range of 1–33. Treatment and control groups are identified using average department drug ratios for the period 1999–June 2002. b Drug ratios are average for the period 1999–June 2002.

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The variable GB is a dummy variable that equals 0 for months before July 2002, the month that global budgeting began at hospitals, and equals 1 for months afterwards. We expect that the global budgeting system with the drug expenditure offset will be associated with greater drug expenditures, ceteris paribus, so the coefficient of this variable should be positive. We form our key variable of interest by interacting the treatment group dummy with the global budget dummy to examine the behavior of those departments that prescribed drugs more heavily after global budgeting began. Since these departments were already prone to use more drugs in their medical care, we expect that they may have more scope to respond to the global budgeting system compared to the control group and therefore expect the coefficient of this variable to be positive. The variable D represents the percentage of drug prescriptions written for chronic patients as opposed to the other patients in our sample, for a department in a month. We include this variable to control for differences in the ability of physicians to alter prescription behavior that may be represented by these two types of patients. We also include a set of department fixed effects, gj. The vector H represents variables included to control for variation in hospital characteristics that might affect prescribing behavior. We use two approaches to control for hospital characteristics. We first estimate the specification with 11 variables to control for the following hospital characteristics: type of hospital, hospital accreditation level, ownership type, and hospital size. We use five dummies to control for six different hospital types: ordinary hospitals (community hospitals that provide a limited set of general services), specialty hospitals (e.g., obstetrics hospitals), psychiatric hospitals, special hospitals (e.g., alcohol or drug abuse treatment), and hospitals for chronic care (e.g., TB). The base group is general hospitals (hospitals that offer a broad range of services). Three dummy variables are constructed to control for different hospital accreditation levels: regional hospitals (minor teaching hospitals), accredited community hospitals, and non-accredited community hospitals. The base group is medical centers (major teaching hospitals). We also include two dummies to control for hospital ownership type. Taiwan law classifies hospital owners into three types: public, private-notfor-profit, and for-profit (Lien, Chou, & Liu, 2008). The base group is public hospitals, which are hospitals managed by either the government, by public enterprises, or by public universities. We include a dummy for private not-for-profit hospitals (NFP), which may be owned by private universities or by charitable groups or groups funding medical research, and a dummy


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for for-profit hospitals (FP), which are owned and operated by physicians. Finally, we include the number of beds as a measure of hospital size. As an alternative to modeling hospital characteristics, we use fixed effects, replacing all variables representing hospital characteristics with one for each hospital. In all specifications we include five regional dummies (d) to control for the health service region in which the hospital is located. Hospitals located in Region One form the base group. Finally, we include year dummies (t) to control for time-related factors that may affect prescription behavior, including: the different drug cost containment strategies implemented by the National Health Insurance Bureau of Taiwan, the introduction of new drugs, which are typically more expensive, and advances in medical technology. In addition, however, we re-estimated the specification several times, omitting different years of the sample. First, we dropped observations from 2002 to 2003, 2002 because that was the year that hospitals switched to the global budget systems, and 2003 because the outbreak of Severe Acute Respiratory Syndrome (SARS) that year may have affected peoples’ willingness to go to the hospital and prescribing decisions. We also estimated the specification using just the year before global budgeting and the year after (2001, 2004, again excluding 2003), and again using just the two years before global budgeting (2000 and 2001) and the two years after (2004 and 2005, again excluding 2003). Table 2 gives descriptive statistics for the variables used in the specification.

DATA The data are from two sources, both from the National Health Insurance Database (NHID) that covers health utilization by all NHI enrollees in Taiwan. Our principal data are derived from the longitudinal claims of a random sample of one million NHI enrollees, approximately 5% of the 20 million people in Taiwan, between 1999 and 2006.7 Each claim includes diagnoses of diseases, dates of admission and discharge (for inpatient care) and treatment (for outpatient care), the department that provided the services, as well as a unique identifier for the health provider. More importantly, the claim contains a detailed description of the hospital’s claim for reimbursement, including a separate identification of the requested reimbursement for expenditures on drugs,8 which allows us to identify the contribution of drug expenses to the total expenditure for each outpatient treated at every hospital. We use the hospital identifier to merge these outpatient claim files with hospital basic files from NHID in the same period to obtain a hospital’s

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Table 2.

Sample Statisticsa.

Whole Sample

Dependent variable Mean of drug expenditure (NT$) log(Mean of drug amount) Explanatory variables Hospital type (%) General hospital Ordinary hospital Specialty hospital Psychiatric hospital Special hospital Chronic hospital Hospital accreditation (%) Medical center Regional hospital County hospital Clinic hospital Hospital ownership (%) For-profit Non-for-profit Public Health service region (%) Region 1 Region 2 Region 3 Region 4 Region 5 Region 6 Hospital size Number of beds Case type (%) Chronic cases Non-chronic cases Sample size a

Treatment Groupb

Control Groupb

Mean

Standard Deviation

Mean

Standard Deviation

Mean

Standard Deviation

431.44

730.12

649.93

487.12

321.12

803.84

5.60

1.06

6.17

0.91

5.31

1.01

59.8% 36.5% 2.0% 1.1% 0.1% 0.5%

0.49 0.48 0.14 0.10 0.04 0.07

62.7% 33.5% 1.2% 1.7% 0.2% 0.6%

0.48 0.47 0.11 0.13 0.04 0.08

58.3% 38.1% 2.4% 0.7% 0.1% 0.4%

0.49 0.49 0.15 0.09 0.03 0.06

11.7% 33.3% 54.5% 0.6%

0.32 0.47 0.50 0.07

11.9% 37.3% 50.4% 0.4%

0.32 0.48 0.50 0.06

11.6% 31.2% 56.5% 0.7%

0.32 0.46 0.50 0.08

41.4% 30.3% 28.2%

0.49 0.46 0.45

38.8% 33.5% 27.8%

0.49 0.47 0.45

42.7% 28.8% 28.5%

0.49 0.45 0.45

27.3% 14.1% 21.5% 15.1% 17.9% 4.1%

0.45 0.35 0.41 0.36 0.38 0.20

26.9% 15.1% 22.1% 15.1% 16.7% 4.1%

0.44 0.36 0.42 0.36 0.37 0.20

27.5% 13.5% 21.2% 15.2% 18.5% 4.1%

0.45 0.34 0.41 0.36 0.39 0.20

473.8

559.6

86.5% 13.5% 344,232

0.34 0.34

514.1

566.7

94.9% 5.1%

0.22 0.22

115,490

453.5

554.9

82.2% 17.8%

0.38 0.38

228,742

The number of hospitals decreased during the period of this study, from 634 hospitals in 1999 to 523 in 2006 (BNIH, 2007). b Treatment and control groups are identified using average department drug ratios for the period 1999–June 2002. See Table 1.


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accreditation level, ownership status, and its zip-code, which we use to identify its region. After eliminating some observations with missing data, the final sample size is 344,232 observations of average monthly hospitaldepartment drug expenditure over the period 1999–2006.

RESULTS We use OLS to estimate the specification, and cluster the standard errors by hospital. Table 3 shows the basic estimation results. Columns (1) and (2) represent results using all years of the data: column (1) includes variables for hospital characteristics and column (2) includes hospital fixed effects instead. Columns (3) and (4) repeat the estimations of columns (1) and (2) with the years 2002 and 2003 omitted. Inspection of the key variable, the interaction of the dummy for the global budgeting period and the treatment dummy, shows that average drug expenditures by departments in the treatment group were significantly higher, by 15–16%, after the imposition of global budgeting. The result is somewhat stronger with the years 2002 and 2003 omitted. The results also show that average drug expenditure by treatment group departments was significantly higher than expenditures by the control group departments, as would be expected. Interestingly, the global budget dummy shows that, after controlling for department, hospital, regional, and annual effects, average department drug expenditures were significantly lower after the imposition of global budgets, suggesting that the steps taken by the Taiwanese Department of Health to counteract the incentives created by the PBS may have been effective. Departments that wrote a higher percentage of prescriptions for chronic patients had significantly higher drug expenditures, ceteris paribus. All hospital types had lower average drug expenditures than the base group (general hospitals that offer a broad range of services), and hospitals with higher levels of accreditation were systematically more likely to make greater expenditures on drugs. These patterns likely reflect the greater breadth and specialization of care in the two base groups: the patients they treat may require more sophisticated and more expensive drug treatments. Interestingly, departments in FP hospitals had significantly lower claims for drug reimbursement. We pursue the issue of owner-type effects further below. Table 4 shows the estimation of the basic specification (with and without hospital fixed effects) looking at only the one-year before and after time

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Table 3.

SHIN-YI CHOU ET AL.

OLS Estimation Explaining the Log of Average Department Expenditure per Case on Drugs. 1999–2006

GBtreatment dummy Treatment dummy GB Percentage of chronic cases Hospital type Ordinary hospital Specialty hospital Psychiatric hospital Special hospital Chronic hospital Hospital accreditation Regional hospital County hospital Clinic hospital Hospital ownership NFP FP Number of beds Constant Observations R2

2002 and 2003 Omitted

(1)

(2)

(3)

(4)

0.155 [0.0134] 0.366 [0.0737] 0.0741 [0.00979] 1.371 [0.0377]

0.157 [0.00413] 0.510 [0.0167] 0.0728 [0.00607] 1.328 [0.00428]

0.189 [0.0165] 0.400 [0.0769] 0.275 [0.0238] 1.363 [0.0374]

0.189 [0.00476] 0.322 [0.0184] 0.240 [0.00511] 1.319 [0.00497]

0.103 [0.0274] 0.223 [0.0652] 0.0417 [0.0839] 0.674 [0.0824] 0.281 [0.128]

0.0972 [0.0271] 0.232 [0.0712] 0.0603 [0.0800] 0.138 [0.162] 0.260 [0.108]

0.110 [0.0628] 0.220 [0.0728] 0.0866 [0.163]

0.0899 [0.0579] 0.190 [0.0670] 0.0766 [0.156]

0.0218 [0.0319] 0.0827 [0.0296] 2.87e-05 [2.85e-05] 4.894 [0.0964] 344232 0.634

0.0303 [0.0320] 0.0726 [0.0295] 4.52e-05 [2.52e-05] 4.832 [0.0956] 257102 0.632

4.669 [0.0397] 344232 0.696

4.644 [0.0405] 257102 0.694

Notes: Standard errors are clustered by hospital and are reported in brackets. All regressions include department, region, and year fixed effects. Regressions (2) and (4) also include hospital fixed effects. po.01, po.05, po.1.

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Table 4.

OLS Estimation Explaining the Log of Average Department Expenditure per Case on Drugs for Selected Years. Before: 2001 & After: 2004

(1) GBtreatment dummy Treatment dummy GB Percentage of chronic cases Hospital type Ordinary hospital Specialty hospital Psychiatric hospital Special hospital Chronic hospital Hospital accreditation Regional hospital County hospital Clinic hospital Hospital ownership NFP FP Number of beds Constant Observations R2

0.0700 [0.0160] 0.340 [0.0848] 0.0219 [0.0168] 1.357 [0.0426]

(2) 0.0719 [0.00782] 0.262 [0.0319] 0.0115 [0.00599] 1.318 [0.00843]

Before:2000–2001 & After: 2004–2005 (3)

(4)

0.127

0.128 [0.00570] 0.343 [0.0227] 0.0874 [0.00524] 1.328 [0.00604]

[0.0163] 0.656 [0.0875] 0.0542 [0.0176] 1.360 [0.0405]

0.107 [0.0300] 0.258 [0.0730] 0.0764 [0.0977] 0 [0] 0.350 [0.131]

0.0986 [0.0281] 0.255 [0.0779] 0.0809 [0.0858] 0.868 [0.0753] 0.313 [0.106]

0.0750 [0.0751] 0.175 [0.0866] 0.0350 [0.178]

0.0801 [0.0667] 0.175 [0.0765] 0.0242 [0.163]

0.0172 [0.0361] 0.118 [0.0318] 3.68e-05 [3.43e-05] 4.785 [0.113] 87712 0.639

0.0246 [0.0338] 0.0886 [0.0302] 4.59e-05 [2.74e-05] 4.704 [0.109] 173408 0.635

4.659 [0.0765] 87712 0.716

4.569 [0.0501] 173408 0.703

Notes: Standard errors are clustered by hospitals and are reported in brackets. All regressions include hospital department dummies and year dummies. Regressions (2) and (4) also include hospital fixed effects. po.01, po.05, po.1.

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SHIN-YI CHOU ET AL.

period (columns 1 and 2), and the two-year before and after time periods (columns 3 and 4), again omitting 2003. The results show that the effect of the PBS on prescribing behavior appears to grow as physicians and hospitals have more time to adjust to the new reimbursement system. We investigated further the impact of owner-type by re-estimating the basic specification, this time with the base group as FP hospitals. We interact each owner type, NFP and public (GOV), with the key variable, the interaction between the global budget dummy GB and the treatment dummy T, as well as with the variables GB and T individually. Table 5 shows the results for the entire sample (column 1), for the sample omitting 2002 and 2003 (column 2), for the one year window (column 3) and for the two year window (column 4). The results for the treatment dummy T, the global budget dummy GB, and the interaction of the two continue to show that, overall, the treatment group departments spent more on drug treatments, that average expenditure of all departments fell with the imposition of global budgeting, and that treatment group departments spent more on drugs after global budgeting began. However, the two coefficients for the interactions of T, GB, and owner type show that treatment departments in NFP and public hospitals spent significantly less on drugs, approximately 12–13% less, than departments in FP hospitals, after global budgeting began. With all interactions included, the dummy variables for owner type now show that as a group both NFP and public hospital departments spent less on prescriptions than FP hospital departments, but the coefficients on the two variables interacting ownership type with the treatment dummy show that treatment departments in NFP and public hospitals spent more than treatment departments in FP hospitals over the entire period. The coefficients on variables interacting ownership type with the global budget dummy show that both NFP and GOV hospital departments increased their expenditure on drug treatments more than FP hospital departments.

CONCLUSION In this chapter we study drug prescribing behavior by hospital departments before and after global budgeting with the PBS offset was instituted. Our hypothesis was that providers would respond to the incentives created by the new system by increasing their prescriptions, and thus increasing the share of drug expenditure in the total health care expenditure on each case. We find some evidence to support our hypothesis: departments in the

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Table 5. OLS Estimation Explaining the Log of Average Department Expenditure per Case on Drugs with Owner-Type Interactions. 1999–2006


2001 & 2004

2000–2001 & 2004–2005

(1)

(2)

(3)

(4)

0.129 [0.0108] GOVGB treatment dummy 0.138 [0.0108] 0.227 GB treatment dummy [0.00689] Treatment dummy 0.257 [0.0164] GB 0.144 [0.00758] 0.246 NFP treatment dummy [0.00849] 0.176 GOV treatment dummy [0.00835] 0.144 NFPGB [0.00806] 0.111 GOV GB [0.00791] Percentage of chronic cases 1.377 [0.00415] Hospital type Ordinary hospital 0.107 [0.00324] Specialty hospital 0.236 [0.00829] Psychiatric hospital 0.0316 [0.0114] Special hospital 0.687 [0.0297] Chronic hospital 0.276 [0.0159] Hospital accreditation Regional hospital 0.110 [0.00511] County hospital 0.219 [0.00631] Clinic hospital 0.0943 [0.0161] Hospital ownership NFP 0.0769 [0.00656]

NFPGB treatment dummy

0.166 [0.0123] 0.171 [0.0124] 0.281 [0.00792] 0.290 [0.0187] 0.358 [0.00680] 0.253 [0.00927] 0.180 [0.00910] 0.181 [0.00921] 0.126 [0.00913] 1.368 [0.00479]

0.100 [0.0209] 0.116 [0.0210] 0.128 [0.0134] 0.227 [0.0323] 0.0876 [0.00972] 0.260 [0.0155] 0.170 [0.0153] 0.128 [0.0157] 0.112 [0.0155] 1.365 [0.00820]

0.136 [0.0149] 0.157 [0.0151] 0.207 [0.00958] 0.545 [0.0243] 0.134 [0.00759] 0.250 [0.0111] 0.178 [0.0110] 0.156 [0.0112] 0.136 [0.0111] 1.366 [0.00584]

0.101 [0.00374] 0.245 [0.00940] 0.0499 [0.0132] 0.106 [0.103] 0.254 [0.0183]

0.111 [0.00646] 0.271 [0.0163] 0.0656 [0.0217] 0 [0] 0.343 [0.0336]

0.102 [0.00459] 0.269 [0.0117] 0.0708 [0.0158] 0.846 [0.193] 0.307 [0.0234]

0.0896 [0.00581] 0.190 [0.00714] 0.0849 [0.0179]

0.0769 [0.0108] 0.177 [0.0133] 0.0227 [0.0335]

0.0820 [0.00724] 0.178 [0.00891] 0.0363 [0.0223]

0.0860 [0.00717]

0.0502 [0.0122]

0.0704 [0.00870]

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Table 5. (Continued )

FP Number of beds Constant Observations R2

1999–2006


2001 & 2004

2000–2001 & 2004–2005

(1)

(2)

(3)

(4)

0.0329 [0.00624] 2.77e-05 [3.53e-06] 4.858 [0.0146] 344232 0.635

0.0402 [0.00681] 4.41e-05 [3.97e-06] 4.806 [0.0165] 257102 0.634

0.000239 0.0327 [0.0116] [0.00828] 3.41e-05 4.31e-05 [7.72e-06] [4.95e-06] 4.716 4.669 [0.0289] [0.0206] 87712 173408 0.641 0.637

Notes: Standard errors in brackets. All estimations include department, region, and year fixed effects. po.01, po.05, po.1.

treatment group increased their average monthly drug expenditure per case after the global budget system was introduced in 2002. We also found this response to be more pronounced in treatment departments in FP hospitals as opposed to those in NFP or public hospitals.

NOTES 1. For example, Naito (2006) finds that the resource intensity of treatments chosen for patients with end-stage renal disease in Japan may be affected by the movement from fee-for-service to global-budget type reimbursement systems. Sepehri, Chernomas, and Akram-Lodhi (2005) find that in Vietnam the gradual growth of revenues from patients with private insurance, as opposed to revenues from a governmental global budget, has introduced differences in treatment patterns for insured vs. uninsured patients. Coyte et al. (1994) and Hamilton, Ho, and Goldman (2000) compare waiting times for surgery in Canada to waiting times in the United States. However, see also Feldman and Lobo (1997) who examine the theoretical basis for expecting health systems using global budget to experience excess demand. 2. The NHI has contracts with approximately 97% of hospitals and 93% of clinics (http://www.doh.gov.tw/statistic). 3. Lee et al. (2006) indicates that the profit rate earned on prescription drugs, in the range 5–40%, is greater than the profit earned from providing other medical services, which is less than 10%. 4. During the period 1997–2005, drugs represented 21–25% of overall national expenditures for health care in Taiwan compared to18.4–20.6% in Japan, 18–20.9% in France, 12.9–14.6% in Germany, and 9.7–12.3% in the United States (NHI Statistics).


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5. Their strategies to control prices include: (1) adjusting drug prices based on comparisons to prices of existing drugs (inter-brand comparisons) or to prices found in market price and volume surveys; (2) delegating the financial responsibility to the regional bureaus; (3) instituting a co-pay for outpatient drugs; (4) reducing the flat daily rate used to reimburse drug prescriptions by clinics; and (5) pricing by generic group, that is, reference pricing based on the similarity of the active ingredients of drugs (Lee et al., 2006). 6. Each case represents one patient visit. We also estimated all specifications with the log of the number of prescriptions written each month by each hospital department instead of the expenditure. As the results were very similar to those reported here, we do not report them. These results are available on request from the authors. 7. The original random sample of one million NHI enrollees was drawn from the 2005 claims data. We then extracted those patients’ complete claims records between 1997 and 2006. 8. Because few adjustments were made to the charged payments, the actual reimbursement that NHI paid was very close to the charged expense.

ACKNOWLEDGMENTS We are grateful to the National Health Research Institute for providing the data. Support from the National Science Council (NSC 97-2410-H-004-009MY2) for Hsien-Ming Lien is greatly appreciated. The usual disclaimers apply.

REFERENCES Bureau of National Health Insurance, Taiwan. (2007). National Health Insurance Statistical Trends 2007. Available at http://www.nhi.gov.tw/webdata/webdata.asp?menu ¼ 1& menu_id ¼ 4&webdata_id ¼ 2063&WD_ID Bureau of National Health Insurance, Taiwan. (2009). Global Budget Payment System. Available at http://www.nhi.gov.tw/english/webdata.asp?menu ¼ 11&menu_id ¼ 296& webdata_id ¼ 1942 Chen, F.-J., Laditka, J. N., Laditka, S. B., & Xirasagar, S. (2007). Providers’ responses to global budgeting in Taiwan: What were the initial effects? Health Services Management Research, 20(2), 113–120. Chou, C.-C., Hu, K.-Y., Wu, N.-R., Cheng, Y.-H., Loh, C.-H., & Yeh, M.-K. (2008). Changes in drug prescription utilization for diabetic and hypertensive outpatients after initiation of the National Health Insurance’s Global Budget Program in Taiwan. Medical Science Monitor, 14(5), PH33–PH39. Council of Economic Planning and Development. (1990). The Report of National Health Insurance Plan. Taipei, Taiwan: Council of Economic Planning and Development.

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Coyte, P. C., Wright, J. G., Hawker, G. A., Bombardier, C., Dittus, R. S., Paul, J. E., Freund, D. A., & Ho, E. (1994). Waiting times for knee replacement surgery in the United States and Ontario. New England Journal of Medicine, 331(16), 1068–1071. Fan, C.-P., Chen, K.-P., & Kan, K. (1998). The design of payment systems for physicians under global budget – an experimental study. Journal of Economic Behavior & Organization, 34(2), 295–311. Feldman, R., & Lobo, F. (1997). Global budgets and excess demand for hospital care. Health Economics, 6(2), 187–196. Hamilton, B. H., Ho, V., & Goldman, D. P. (2000). Queuing for surgery: Is the U.S. or Canada worse off? Review of Economics and Statistics, 82(2), 297–308. Henke, K-D., Murray, M. A., & Ade, C. (1994). Global budgeting in Germany-lessons for the United-States. Health Affairs, 13(4), 7–21. Lee, Y-C., Yang, M.-C., Huang, Y.-T., Liu, C.-H., & Chen, S.-B. (2006). Impacts of cost containment strategies on pharmaceutical expenditures of the National Health Insurance in Taiwan, 1996–2003. Pharmacoeconomics, 24(9), 891–902. Lien, H.-M., Chou, S.-Y., & Liu, J.-T. (2008). Hospital ownership and performance: Evidence from stroke and cardiac treatment in Taiwan. Journal of Health Economics, 27(5), 1208–1223. Naito, H. (2006). The Japanese health-care system and reimbursement for dialysis. Peritoneal Dialysis International, 26(2), 155–161. Poterba, J. M. (1994). A skeptic’s view of global budget caps. Journal of Economic Perspectives, 8(3), 67–73. Sepehri, A., Chernomas, R., & Akram-Lodhi, H. (2005). Penalizing patients and rewarding providers: User charges and health care utilization in Vietnam. Health Policy and Planning, 20(2), 90–99.

MEDICARE PART D TURNS FOUR: TRENDS IN PLAN DESIGN, ENROLLMENT, AND THE IMPACT OF THE PROGRAM ON BENEFICIARIES Jack Hoadley and Kosali Simon ABSTRACT Purpose – As Medicare Part D enters its fifth year, we assess how the supply side of the market has evolved and what research has shown about how Medicare drug coverage has affected consumers. Methods – We conduct descriptive data analyses to explore the varied nature of Medicare standalone prescription drug plans (in terms of both price and non-price features), examine features associated with high enrollment, and show trends over time in both plan design and enrollment patterns from 2006 to 2010. We also review existing evidence about Part D’s effects on drug access for beneficiaries and conclude with a discussion of current policy concerns. Findings – Medicare Part D has been successful in certain ways, but several areas of concern remain. Although it is a measure of success that 90% of Medicare beneficiaries now have drug coverage, efforts continue Pharmaceutical Markets and Insurance Worldwide Advances in Health Economics and Health Services Research, Volume 22, 123–147 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1108/S0731-2199(2010)0000022009

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to reach the vulnerable populations who are not yet signed up. Use of medications (and relative use of generics) has increased under the program, while out of pocket costs have fallen. Policymakers continue to question government’s role in areas such as negotiating prices directly with pharmaceutical manufacturers and limiting the number of plans offered. Results from data analysis indicate, among other things, high growth in premiums, whereas plans have become less generous by certain measures. Originality – This chapter brings together data on all plans offered in Medicare Part D standalone drug coverage market and shows new evidence on the landscape’s rapid evolution.

INTRODUCTION November 15, 2009, marked the start of the fifth open enrollment period for Medicare Part D. From January 2006 onwards, all 45 million Medicare beneficiaries had the option of receiving heavily subsidized drug coverage from a private plan sponsor as part of their Medicare package. A large number of private sponsors offer plans under a structure dictated by the Medicare Modernization Act of 2003 (MMA, Public Law 108–173, 117 Stat. 2066) and administered by the Centers for Medicare and Medicaid Services (CMS) (for prior reviews, see e.g. Duggan, Healy, & Scott-Morton, 2008; Goldman & Joyce, 2008). Beneficiaries could select coverage from a private standalone prescription drug plan (PDP), or through a Medicare Advantage prescription drug plan (MA-PD), which adds drug coverage to the Medicare private-sector alternative. Enrollment started out heavily concentrated in a few plans and it remains a fairly oligopolistic market despite scores of different companies offering plans. Take-up of Part D occurred at a rapid pace, and evidence showed substantial reductions in drug uninsurance and out of pocket drug spending among beneficiaries. Medicare beneficiaries who have retiree drug coverage from employers were allowed and encouraged (through subsidies to their employers) to maintain that coverage. Low-income beneficiaries with low assets receive assistance with premiums and cost sharing on a sliding scale. In total, all beneficiaries have a choice of at least several dozen options for receiving Part D drug coverage regardless of where they live. Because MA-PDs are bundled products that are difficult to describe separately from the larger Medicare Advantage plans of which they are a part, this chapter focuses mostly on PDPs that contain two-thirds of all Part D enrollees.

Medicare Part D Turns Four

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In 2010, 1,576 PDPs are being offered across 34 PDP regions nationwide. An additional 44 PDPs are offered in five regions covering the U.S. territories, but are not included in this analysis. The average 2010 beneficiary monthly premium for receiving coverage through a standalone plan is $47. Because there are many plans with low enrollment and high premiums, it is useful to also report the enrollment weighted average of the monthly premium. For 2010, this is $38.94 (using 2009’s enrollment numbers, as actual enrollment for 2010 is not yet known). In 2009, the unweighted monthly premium was $45.46 and the enrollment weighted premium was $35.09. In many ways, Part D has done well. Despite there being no automatic signing up of beneficiaries for the program (other than dually eligible Medicaid beneficiaries), 27 million enrollees are receiving Part D coverage currently; only approximately 10% of beneficiaries remain uninsured, relative to an estimated one-third of seniors being uninsured prior to Part D (Hoadley, Cubanski, Hargrave, Summer, & Neuman, 2009a). Beneficiaries were informed that if they failed to enroll when first eligible (unless they maintained creditable coverage from some other source), they would face a late enrollment penalty of 1% of the national average premium for each month of delay upon eventual enrollment. This penalty, which is imposed for as long as the beneficiary remains in Part D, was intended to reduce adverse selection of enrollment and may be one of the reasons for the high and rapid take-up of Part D. When the program first opened, the open enrollment period lasted until May 15; more than 60% of the enrollees had signed up by January 1 (CMS, 2009a, 2009b). Part D spending has actually come under projections that were made in the Actuary’s reports (CMS, 2004, Table IIC20; CMS, 2005, Table IIIC17). Medicare Part D total expenditures were approximately $49.3B in 2008 for benefits and administrative costs (CMS, 2009a, Table IIIC17), while projections for 2008 were $98.5B in the 2005 report and $101.9B in the 2004 report. Despite fears to the contrary when the MMA was being drafted, a large number of private plan sponsors have entered the market. The MMA called for a public fallback option to be offered if it turned out that no private plans were willing to participate. Seniors’ use of prescription drugs has increased (by approximately 12% to 5% or lower during the first year of Medicare Part D, depending on the study), whereas out of pocket costs have fallen by approximately 15%

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(Lichtenberg & Sun, 2007; Levy & Weir, 2007; Yin et al., 2008; Ketcham & Simon, 2008) But in some other respects, Part D could do better and there are calls for improvements to this program. Despite the success in getting many beneficiaries enrolled, there are still an estimated 10% of all beneficiaries who lack coverage. Debates continue on whether the government itself should negotiate with drug manufacturers and whether it should offer a public plan as one of the options. Critics question whether the private plan sponsors are able to obtain prices as low as what a government negotiator may obtain. As of this writing, the House of Representatives version of the health care reform bill that was passed on November 7, 2009 (HR 3962), contains a provision to require the Secretary of Health and Health Services to negotiate drug prices with manufacturers in Medicare Part D. The Senate version of health reform, however, contains no such provision. The complexity of plan design and the myriad of choices available may not be well understood by beneficiaries; beneficiaries may be unable to make the calculations necessary to find their ‘‘optimal’’ plan and end up in one that costs them more than the lowest possible total annual amount. For example, see Abaluck and Gruber (2009) and Kling, Mullainathan, Shafir, Vermeulen, and Wrobel (2009) for evidence on the degree of mismatch. When a beneficiary’s total consumption of drugs hits $2,830 in 2010, he or she faces a coverage gap (the so-called donut hole), which leaves them with no insurance assistance for the next $3,610 of their drug usage for the year. The duration of the gap is based on the standard benefit design. Since the actual gap is measured by total out of pocket spending, its duration varies for plans that deviate from the standard benefit. There is currently an effort by lawmakers in both the House and the Senate to eliminate or reduce this feature that was built into the original plan design. As of this writing, the House version contains language to eliminate the donut hole feature of the standard plan design by 2019. The Senate version (HR 3590 introduced November 18, 2009 in the Senate) aims to reduce (but not eliminate) the donut hole. In addition to the coverage gap, the relatively high share of out of pocket costs (both in theoretical terms as shown in Table 1 and in survey research, e.g., Neuman et al., 2007) suggests that Part D enrollees still report some problems in obtaining all the drugs they feel they need. In 2006, 7.8% of Part D enrollees reported spending more than $300/month

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Table 1.

The Design of Part D Drug Coverage in 2010.

Annual Retail Drug Expenses

0 310 (at deductible) 2,830 (at gap threshold) 6,440 (at catastrophic threshold) 8,000 9,000 30,000

Beneficiaries

Plan Sponsors

CMS

Amount paid

As percent of annual retail expenses

Amount received

Amount paid

Amount paid (premiums and reinsurance)

467 777 1,407 5,017

W100 W100 49.7 77.9

1,831 1,831 1,831 1,831

0 0 1,890 1,890

1,364 1,364 1,364 1,364

5,095 5,145 6,195

63.7 57.2 20.7

1,831 1,831 1,831

2,124 2,274 5,424

2,612 3,412 20,212

Notes: The average weighted premium per month is $38.90, as above, thus $467 per year. The total cost of the plan, on average is $467/0.255 ¼ $1,831, as by law, the premium faced by the beneficiary is on average 25.5% of the full cost. These are not meant to be exact calculations, but for sake of comparison, the average per beneficiary cost from federal spending has been $1,543 in 2006 and $1,517 in 2008 according to CMS 2009.

on prescription drugs out of pocket, substantially higher than the 4.8% in the case of employer prescription drug coverage (Neuman et al., 2007, Exhibit 2). Assessing how Part D has evolved from supply and demand sides, six years since its inception, is useful groundwork in considering future changes to this massive program.

SUPPLY SIDE ISSUES Institutional Rules In the MMA, the intention of Congress was to allow private plan sponsors to compete in offering coverage. The role of the government has been to set the rules for plan design and market operation, as well as to provide direct subsidies. The beneficiary premium is set by law to be on average 25.5% of the full cost of the plans. In June of each year, plan sponsors declare to CMS an intention to participate in the next year’s market. They submit their plan design and cost statements, from which CMS calculates the regional average of the ‘‘bids,’’ and assigns premiums to be on average 25.5% of the costs of

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the average plan plus 100% of the cost attributable to any benefits the plan may include beyond the basic benefit package. Under current guidelines, each plan sponsor could enter each market with up to three standalone drug plan products (and may offer MA products as well) as long as they offer meaningfully different options. Markets are defined as regions. Of the 34 regions (excluding the territories), 25 are single-state regions and 9 are multistate regions. According to the MMA, the government is not allowed to play any role in negotiating or setting the price of drugs. Plans are required to follow a specified standard benefit design for a basic plan or deviate from it in an actuarially equivalent way. The coverage gap feature of the standard plan was motivated primarily by the desire to keep the cost estimates for the legislation under a certain threshold level that was considered important in gaining the support necessary in Congress for its passage. Table 1 shows the features of the standard plan in 2010 depending on the annual drug expenses of an individual. This standard plan has a deductible of $310, beneficiary cost sharing of 25% for drug expenses in the range of $310 to $2,830, and a coverage gap that runs until total out of pocket expenses exceed $4,550 (thus total drug expenses of $2,830 to $6,440 under the standard benefit design), after which catastrophic coverage with 5% beneficiary cost sharing commences up to an unlimited amount of expenses. These are the thresholds of the standard benefit design and can vary somewhat under alternate designs; the vast majority of plans use something other than the standard design. For example, 21.3% of enrollees in 2009 are in 122 plans that do not impose a deductible but are actuarially equivalent to the standard design because some other cost sharing feature has been made less generous. Table 1 shows how the payments of different parties depend on the annual drug usage (retail pharmacy expenses) of a given beneficiary. For beneficiaries that sign up but do not incur any drug expenses, the beneficiary only pays the premium, CMS only pays the premium subsidy, and both amounts go to the plan sponsor who pays out no expenses. For beneficiaries who incur some spending but are under the deductible, the situation is similar except the beneficiary also pays the cost of the drugs used. In these two scenarios, the beneficiary incurs more than 100% of their drug spending as out of pocket costs (premiums and cost sharing), and CMS in total also pays more than the actual drug spending in payments to the plan sponsor. Thus, it is highly beneficial for plan sponsors to attract low-drug spending beneficiaries. When beneficiaries have drug expenses that are above the deductible but below the donut hole threshold ($2,830), plan sponsors have


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now paid out the 75% cost sharing above the deductible, and CMS expenses have been limited to the premium subsidy. During the donut hole, only the beneficiary pays, whereas plan sponsor and CMS payouts do not increase. After the catastrophic threshold level is met, the CMS liabilities increase at a fairly fast pace as CMS pays 80% of the costs (to provide reinsurance to the plan sponsor), the plan sponsor pays 15% and the individual pays 5%. This leads the relationship between beneficiary cost sharing and total drug expenses to have two local maximums – one at low levels of expenses when beneficiary cost sharing is over 100% and another at the exit of the donut hole ($6,440) at which point beneficiary cost sharing is almost 78%. The reinsurance described earlier is one of several features that reduce the risks faced by the private plan sponsors. Profit bands (risk sharing): CMS guaranteed protection from substantial losses and denied plans the ability to keep substantial profits. Plans that have actual costs in excess of their expected costs (after accounting for the reinsurance feature) by a sufficiently large amount, receive additional payments to compensate for those losses. In the same way, if plans make larger than expected profits due to lower actual than expected costs, the plans have to return those extra profits to the government. For 2006 and 2007, the plans bore all profits and losses within a band of 2.5% from their expected costs (CBO, 2004). If the actual costs were higher (lower) than the expected costs by more than 2.5% but less than 5%, then the government pays (receives) 75% of the amount of profit or loss in that range. If the actual costs differ with the expected costs by more than 5%, then the government pays 80% of the amount beyond 5% in the case of losses, and receives 80% of the amount beyond 5% in the case of profits. In later years, these bands widened. Reinsurance: CMS pays for 80% of the catastrophic costs directly, limiting the risk exposure of the plan sponsor to 15% of total drug costs over the catastrophic threshold (the remaining 5% is cost sharing by the beneficiary). The catastrophic threshold is $6,440 in 2010, based on the standard benefit design. In addition, plans are not responsible for any costs during the coverage gap unless they offer enhanced coverage. Risk adjustment of reimbursement: The direct subsidy is paid prospectively to the firm as a fixed up-front dollar amount, with some risk adjustments for age, sex, disability, and the presence of certain chronic conditions (CMS, 2009b). In addition, a late enrollment penalty of 1% of the national average premium for each month of delay in signing up for Part D is intended to reduce adverse selection of enrollment.

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Competition and Cost Control Features The design of the MMA assumed that competition between plan sponsors, leading to efficient formulary designs and negotiated prices with manufacturers, is the primary method by which the legislation aimed to control program costs. Plans are allowed to use standard formulary cost containment tools including prior authorization, step therapy, quantity limits, multiple ‘‘tiers’’ of cost sharing, mail order, and exclusion of certain drugs from the formulary (as long as they cover at least some drugs in some classes and all drugs in other classes). Economic theory suggests that a new insurance program for drugs could lead to increases in prices set by pharmaceutical firms, due to the anticipated increase in beneficiary demand from lowering out of pocket costs for consumers. Viewed another way, consumer demand becomes less sensitive to the full price when insurance covers the transaction, leading oligopolistic manufacturers to increase prices to maximize profits (Pavcnik, 2002). Prices could also increase because of the shift of the dual eligible population out of the Medicaid program. Medicaid receives, by law, the lowest transaction price that the manufacturer uses within the United States; the House health reform bill (HR 3962) contains a provision to reclaim Medicaid drug rebates to Part D drugs consumed by low-income beneficiaries. On the contrary, theory also suggests that negotiations between plan sponsors and drug manufacturers could exert downward pressure on prices as manufacturers vie to have their drugs placed on the lowest out of pocket tiers. There is prior descriptive evidence that the insured pay less for drugs than the uninsured (e.g., DHHS, 2000). Plan sponsors have an incentive to maximize price discounts from manufacturers to keep their premiums low and compete for customers. Thus, since there could be a price increase due to the lower elasticity of demand of insured patients and a price decrease from the negotiations that happen between plan sponsors and drug manufacturers, the net hypothesized effect of Part D on pharmaceutical prices is ambiguous.1 Duggan and Scott-Morton (forthcoming) provides a first test of the effect of Part D on pharmaceutical prices using national price data at the retail point of sale (without rebates). They argue that although they do not have data on the separate prices paid by Part D and non-Part D beneficiaries, national price trends for drugs which are predominantly taken by seniors (as measured before the start of Part D) relative to other drugs, are likely to display the causal effect of Part D. They find that drugs with a higher share of Medicare beneficiaries without prior drug coverage experienced a fall in

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prices rather than a rise. However, it is hard to know what the full price concessions may have been under Part D as rebate data are not available to researchers. Nevertheless, additional tests of these hypotheses could be conducted with data over time that contain prices under Part D relative to cash pay, to test the sensitivity of results to the methods used. There remain many questions of interest regarding the competitive effects of Medicare Part D on the supply side of pharmaceutical markets that are yet to be examined.

Cost Offsets How is the new drug coverage under Part D affecting Medicare’s total costs, beyond just drug costs? As President Bush said in motivating the need for Part D at his signing of the MMA, ‘‘Drug coverage under Medicare will allow seniors to replace more expensive surgeries and hospitalizations with less expensive prescription medicine’’ (CMS, 2003). Analysis of this question of offsets has been limited by delays in the availability of program claims data, although some results from more limited databases are starting to emerge. For example, Zhang, Donohue, Lave, O’Donnell, and Newhouse (2009a) show that there has been a reduction in non-drug costs for Part D MA enrollees who had minimal or no coverage before 2006 in the Medicare Current Beneficiary Survey. However, they find an increase in non-drug expenses of those who had relatively generous prescription coverage before Part D. Further work on this question of cost offsets will be an important part of assessing the full cost saving implications of Part D.

Market Structure When viewed in terms of number of entrants, the Part D market place looks highly dissipated in market power. Entry to the new market occurred from new entities as well as existing companies (Gold, 2006). Yet, right from the start, the enrollment was considerably consolidated. In 2006, United controlled 25% of the market, Humana 19%, and Wellpoint 6% (Cubanski & Neuman, 2007). There were 54 plan sponsors overall taking part in the PDP market. They found that 10 plan sponsors accounted for 72% of the market; they and Gold (2006) note that 7 of these 10 were present in both the PDP and the MA-PD markets and were present in the MA market prior to Part D. The smallest 13 plan sponsors in the PDP

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market account for 0.01% of all PDP enrollment in 2006. Cubanski and Neuman note that concentration in private Medicare plans is not something new; in 2005, MA market share was skewed, with almost 50% of enrollment in plans owned by seven firms: Kaiser, Pacificare, Humana, UnitedHealthcare, HealthNet, Aetna, and CIGNA.2 In 2009, the top 10 PDPs have 85.5% of enrollment (author calculations). How have the plans offered in Part D evolved over time? Taking all standalone PDPs that have been offered since the inception of this program, the next sections and accompanying tables describe different aspects of plan offerings, including premiums, enrollment number of plans, deductibles, and cost sharing.

Premiums Nominal unweighted average monthly premiums actually fell going from 2006 to 2007, partly due to miscalculations by plan sponsors in 2006 that caused 2006 bids for some plans to be higher than warranted by actual 2006 experience (a smaller number of plans found their bids to be lower than actual experience). But the enrollment weighted average premium rose by more than a dollar going from 2006 to 2007. Note that when weighting by enrollment, for 2010 plans, we used 2009 enrollment to arrive at a weighted premium since 2010’s new enrollment process has not yet finished. Since 2007, premiums have increased steadily in each year both as a straight average and as a weighted average. The weighted premium averages show that enrollment continues to be heavily concentrated in the cheaper plans since those means are always lower than the straight mean premium. This reflects both the automatic enrollment of low-income beneficiaries in only the lowest premium plans in every region (LIS plans), as well as the fact that many consumers were drawn to the least expensive plans. There has been a net increase of 50% in the weighted average premium from 2006 to 2010. In comparison, U.S. medical care prices have increased approximately 15% over this same time period3 (Table 2). Fig. 1(a) and (b) shows the distributions of these premiums and how they have changed over time for plans offering the basic benefit (both the standard benefit and benefits actuarially equivalent to the standard benefit). There continues to be wide dispersion in premiums, even for those not offering enhanced benefits. Some of this variation is based on geography. We have shown elsewhere that the 2010 weighted monthly premium varies from $26.76 in New Mexico to $46.66 in Idaho/Utah (Appendix 1 of

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Table 2.

Five-Year Comparison of Medicare Part D PDP Monthly Premiums.

Unweighted averages Overall Basic design Enhanced design LIS plans Non-LIS plans Gap coverage No gap coverage Plans with 0 deductibles Enrollment-weighted averages Overall Basic design Enhanced design LIS plans Non-LIS plans Gap coverage No gap coverage Plans with 0 deductibles

2006

2007

2008

2009

2010

37.43 33.11 43.27 25.85 42.07 49.71 35.20 39.61

36.79 28.83 45.66 23.99 41.23 53.24 30.17 41.37

40.02 30.14 49.63 24.74 45.72 63.05 30.62 46.92

45.46 33.79 55.89 27.71 49.43 75.45 37.90 54.98

46.57 36.70 56.28 29.33 50.75 79.43 38.82 60.94

26.04 24.16 35.35 23.24 35.79 55.69 24.17 28.90

27.39 24.05 40.42 21.89 34.85 58.46 24.50 32.54

29.95 26.76 40.95 24.22 35.88 66.87 27.06 35.53

35.08 31.08 46.13 26.64 41.22 74.33 32.36 41.54

38.97 34.85 52.75 28.76 44.45 81.15 35.84 47.98

Source: Author calculations from the CMS Landscape and Enrollment files, PDP market. Note that when producing enrollment weighted data for 2010, it is with the assumption that enrollment weights stay the same as in 2009. This means that the number of plans for 2010 with enrollees will look low because a pre-condition for being in this table is that the plan had enrollees in 2009. Definitions: ‘‘Basic’’ refers to plans that are not ‘‘Enhanced’’ plans. ‘‘LIS’’ plans are ones that are below the benchmark premium. ‘‘Gap coverage’’ refers to plans that cover at least generics in the gap.

(a)

(b) 2010 Distribution of Premiums, Without Enhanced Plans

2006 Distribution of Premiums, Without Enhanced Plans

0.15 Fraction of Plans

Fraction of Plans

0.15

0.1

0.05

0.1

0.05

0

0 0

20

40

60

80

0

20

Dollars

Fig. 1.

40 Dollars

Distribution of Premiums.

60

80

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Hoadley et al., 2009a). There is a considerable range of variation within each region; plans of identical actuarial value (basic-benefit plans) range in premium by twofold or more (Hoadley, Cubanski, Hargrave, Summer, & Neuman, 2009b). Over time, there have been large increases in premiums of some of the PDPs with high enrollment. For example, the average premium for AARP Preferred, with 2.9 million enrollees in 2009 (which is 17.8% of the total PDP market enrollment in 2009), has increased from $26.31 in 2006 to $39.39 in 2010 (a 50% increase); while the average premium for Humana PDP Enhanced, with 1.6 million enrollees in 2009, has nearly tripled from $14.73 in 2006 to $41.53 in 2010 (a 182% increase). By contrast, CCRx Basic (1.1 million enrollees) has a lower average premium in 2010 ($29.17) than in 2006 ($30.94), a 6% decrease (Exhibit 3 of Hoadley et al., 2009a). The highest premiums in the Part D market are found in plans with gap coverage, where weighted average premiums have consistently been more than double the average premium for all plans. The average is much higher, although gap coverage by 2008 was almost solely for generic drugs and typically covers only a subset of generic drugs on formulary (Hoadley, Cubanski, Hargrave, Summer, & Neuman, 2009c).

Number of Offerings and Enrollment by Plan Characteristics Table 3 shows the five-year comparison of the number of plans offered and median enrollment for plans with different characteristics, as well the share of enrollment in certain types of plans. It is noteworthy that the number of plans offered in more recent years has not diminished, as was expected to happen. In fact, the total number of plans offered rose after the first year and stayed around the same number in 2007 and 2008 before declining in 2009 and 2010 – in part as a result of corporate mergers and acquisitions. However, the 2010 number of plans is still above the number in 2006. As shown in Hoadley et al. (2009a), the number of PDPs per region ranges from 41 in Alaska and Hawaii, to 55 in Pennsylvania/West Virginia. In 2007, CMS considered limiting the number of plans per sponsor to two but decided instead to leave it at three at the time (CMS, 2006). In 2009, CMS published new proposed regulations and requested comments on how best to reduce the total number of offerings. Options include eliminating plans with low enrollment and plans without meaningful differences offered by the same sponsor. LIS eligible individuals are allowed to enroll in any Part D plan, but would have to pay the additional premium associated with that plan above

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Table 3.

Five-Year Comparison of Medicare Part D PDP Number of Plans and Enrollment. 2006

2007

2008

2009

2010

Number of plans Number of plans made available 1,429 1,875 1,824 1,687 1,576 Basic design 821 988 899 796 781 Enhanced design 608 887 925 891 795 LIS status 409 483 495 308 307 Non-LIS status 1,020 1,392 1,329 1,379 1,269 Gap coverage 220 538 529 340 301 No gap coverage 1,209 1,337 1,295 1,347 1,275 Plans with 0 deductibles 834 1,127 1,065 934 629 Median enrollment Median overall 1,682 1,335 1,835 2,264 2,450 Basic design 5,135 2,517 4,503 4,636 4,552 Enhanced design 767 901 931 1,444 1,567 LIS plans 15,781 5,057 7,943 11,681 8,379 Non-LIS plans 685 877 1,003 1,457 1,790 Gap coverage 829 2,328 487 1,119 1,168 No gap coverage 2,132 1,850 3,141 2,897 3,100 Plans with 0 deductible 1,428 1,170 1,491 1,795 2,170 Total enrollment in year 15,470,039 16,124,066 16,439,077 16,567,260 15,293,330 Share of total enrollment by these types of plans Basic design 0.83 0.80 0.78 0.73 0.77 LIS plan 0.78 0.58 0.51 0.42 0.35 Gap coverage 0.06 0.09 0.07 0.06 0.07 Plans with 0 deductible 0.56 0.54 0.51 0.48 0.40 Notes: CMS reports enrollment only for plans with more than 10 enrollees, thus not all these plans are used when enrollment numbers are needed. Also note that in 2008, ‘‘De minimis’’ plans (explained in the text) are considered LIS plans by CMS, whereas in 2007 they are not. In this analysis, we did not override this inconsistency of the CMS landscape files. Source: Author calculations from the CMS Landscape and Enrollment files, PDP market. Note that when producing enrollment weighted data for 2010, it is with the assumption that enrollment weights stay the same as in 2009. This means that the number of plans for 2010 with enrollees will look low because a pre-condition for being in this table is that the plan had enrollees in 2009. Definitions: ‘‘Basic’’ refers to plans that are not ‘‘Enhanced’’ plans. ‘‘LIS’’ plans are ones that are below the benchmark premium. ‘‘Gap coverage’’ refers to plans that cover at least generics in the gap.

the benchmark plan premium. Benchmark plan premiums are below a certain level that has been determined for that market based on the average of the bids that have been received for the region; beneficiaries who are eligible for the low-income subsidy and enroll in benchmark plans pay no

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premium. The number of LIS plans rose from 2006 to 2008, but has declined since. Note that in 2007 and 2008 there were 157 and 53 plans, respectively, that were called ‘‘De Minimis’’ plans. Under a Medicare demonstration, these plans were allowed to retain their LIS beneficiaries despite their premiums that year exceeding the regional threshold for LIS plans by $2/month in 2007 and $1/month in 2008 (Exhibit 7 of Hoadley et al., 2009a). In the counts above, these plans are included as LIS plans in 2008 but not in 2007. Since that demonstration ended, all LIS enrollees whose LIS plans do not meet the LIS threshold the next year must be reassigned to a new plan, or else must choose a new plan themselves or pay a premium to remain in their original plan. Of the total LIS enrollment in PDPs (7.9 million in 2009), 58% of these enrollees (4.6 million) are in plans that would meet the 2010 threshold, whereas 42% of enrollees (3.3 million) are in plans that would not meet the 2010 thresholds. For these 3.3 million enrollees, onethird are to be randomly reassigned by CMS, whereas the remaining two-thirds will receive a letter from CMS advising them that they can choose a new plan themselves or pay the premium differential. When examining LIS plans, a relevant question is how often plans change this designation as it means auto-enrollees would be reassigned. Hoadley, Cubanski, Hargrave, Summer, and Neuman (2008a) show that of the 483 plans that were benchmark plans as of 2007, 61.5% stayed as benchmark plans in 2008, whereas 32.9% no longer had this status and 5.6% left the market. Of the 505 other plans in 2007, 14.7% became benchmark plans in 2008. The number of plans that offer at least some coverage in the coverage gap rose in 2007 but has fallen since then; what the numbers do not reveal is that the relative generosity of the plans has fallen substantially. Plans that have gap coverage now cover only generic drugs, and even then it is often only a subset of generics that are covered. In the program’s first two years, a handful of plans offered coverage for brand-name drugs in the coverage gap, but these plans experienced substantial adverse selection and terminated this coverage after just one year. Exhibit 4 of Hoadley et al. (2009a) shows more details of how gap coverage generosity has evolved over time.

Deductibles and Cost Sharing Another plan characteristic that has been evolving over time is the annual deductible. The average deductible has risen overall, from $92.25 in 2006 to $142.89 in 2010. The deductibles remain very low among plans with gap coverage and other enhanced plans. Eliminating the deductible is one of the

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most popular types of enhanced benefit design. In 2010, 60% of PDPs will have a deductible, which is an increasing trend as only 42% of plans used a deductible in 2006 and only 45% in 2009 (Exhibit 6 of Hoadley et al., 2009a). Most of the increase in use of deductible in 2010, relative to prior years, comes from the use of partial deductibles that are less than the standard deductible amount (Table 4).

Table 4.

Five-Year Comparison of Medicare Part D PDP Annual Deductibles and Cost Sharing.

Overall Basic design Enhanced design LIS plans Non-LIS plans Gap coverage No gap coverage Plans with a deductible Enrollment weighted Overall Basic design Enhanced design LIS plans Non-LIS plans Gap coverage No gap coverage Plans with a deductible Cost sharing Median cost sharing per month’s supply Generic copay Preferred brand name copay Non-preferred brand name copay Specialty-tier coinsurance

2006

2007

2008

2009

2010

92.25 155.94 6.25 156.17 66.62 0.45 108.95 221.55

94.89 176.18 4.34 199.64 58.55 6.32 130.53 237.86

105.03 188.73 23.70 235.93 56.29 11.63 143.20 252.42

114.86 230.94 11.16 257.11 83.09 5.00 142.59 257.33

143.39 247.80 40.84 293.32 107.13 1.00 177.01 238.64

106.84 127.71 3.18 128.96 29.72 1.84 113.44 243.52

120.56 151.48 0.20 172.50 50.14 0.39 131.76 259.60

133.42 170.20 6.25 228.20 35.40 0.62 143.83 271.46

148.03 199.69 5.39 268.85 60.15 1.42 158.19 284.29

165.02 204.70 32.08 292.57 96.59 0.38 177.22 276.90

5 28 55 25%

5 28 60 30%

5 30 71.50 30%

7 37 74.75 33%

7 42 47 30%

Notes: Cost sharing numbers come from Hargrave, Hoadley, Summer, Cubanski, and Neuman (2009) Spotlight, where calculations are weighted by enrollment. For further notes regarding cost sharing calculations, please see source. Source: Author calculations from the CMS Landscape and Enrollment files, PDP market. Note that when producing enrollment weighted data for 2010, it is with the assumption that enrollment weights stay the same as in 2009. This means that the number of plans for 2010 with enrollees will look low because a pre-condition for being in this table is that the plan had enrollees in 2009. Definitions: ‘‘Basic’’ refers to plans that are not ‘‘Enhanced’’ plans. ‘‘LIS’’ plans are ones that are below the benchmark premium. ‘‘Gap coverage’’ refers to plans that cover at least generics in the gap.

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Although the standard benefit in law assumes 25% cost sharing for all drugs, only approximately 10% of all plans are using that benefit design in 2010. Most plans adopt a tiered cost sharing approach, most often with tiers for generics, preferred brand drugs, non-preferred brand drugs, and specialty drugs (typically, injectibles and others with a monthly cost of at least $600). In recent years, there has also been some increase in plans with more complicated tier structures, for example, adding preferred and non-preferred generic tiers. The cost sharing associated with these tiers has steadily increased since 2006, especially in the tiers for brand-name drugs. Cost sharing has risen by 50% for preferred brand-name drugs and 40% for the non-preferred tier. Plans have also increased their use of coinsurance, probably as an attempt to target higher cost sharing specifically for more expensive drugs (Hargrave, Hoadley, Summer, Cubanski, & Neuman, 2009).

Market Concentration Over time, the market did not see a large reduction in the numbers of plans, despite predictions of declines as the market matured. To see whether enrollment is heavily concentrated among a small subset of these plans, we turn to analyzing enrollment patterns. The data suggest a non-uniform distribution of plan enrollment. On the contrary, there are a few plans with relatively high market shares but there are a large number of plans with small market shares. We show the concentration of enrollment in one region, California, over time in the few figures below. The picture in other regions is similar. Fig. 2(a) and (b) shows that there continue to be several plans with a large share of the market in region 32 (California), but also a large number of (a)

Distribution of Enrollment, Region 32, 2006

(b)

Distribution of Enrollment, Region 32, 2009 0.8

Fraction of Plans

Fraction of Plans

0.8

0.6

0.4

0.2

0.6

0.4

0.2

0

0 0

50000 100000 150000 200000 250000 Plan Enrollment

Fig. 2.

0

50000

100000

Plan Enrollment

Distribution of Enrollment.

150000

200000

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plans with low enrollment that have not exited the market since 2006. The pattern is similar in most other regions. Although there have been a few exits and some acquisitions, it remains puzzling why so many plans remain in the market despite low enrollment even after four years. One possibility is that there are very low entry barriers to being listed as a plan on the ‘‘exchange.’’ Yet market share is driven by marketing expenditure, brand name (due to past marketing), and possibly due to negotiations between plan sponsors and pharmaceutical companies by which more popular plans obtain lower prices and thus may be able to offer lower premiums. To the extent that some plans have high enrollment in only a subset of regions, the marginal cost of operating in other regions seems relatively low, so that may account for some low-enrollment plans. Enrollment is heavily concentrated in some low cost plans, but also in some plans with recognized brand names (e.g., United Healthcare plans with the AARP label) that are higher in premiums. In Table 5, enrollment at the plan level is aggregated into the 34 regions to calculate the total market size for each plan for each year. The average plan in each region has only approximately 2% of the market, a level that has not changed over the program’s first four years. The maximum market share across regions is close to 40% in the first three years and then somewhat lower in 2009. Table 5 also shows the average market concentration across regions, as measured by the HHI. By this measure, the mean market concentration has dropped over time, particularly from the second to the third year. With HHI values under 1000 generally regarded as unconcentrated markets, these results suggest that the average region is somewhat un-concentrated, but there are least a few regions where enrollment is moderately concentrated. If only the national and Table 5. Year

2006 2007 2008 2009 2009 (LIS only) 2009 (Non-LIS only)

Market Structure by Year.

Market Share (% of Regional Enrollment)

HHI Overall

Mean

Minimum

Maximum

Mean

Minimum

Maximum

2.4 1.92 1.9 2.04 n/a n/a

0 0 0 0 n/a n/a

38.37 38.17 38.54 30.6 n/a n/a

1049.81 1027.66 877.96 864.79 1226.35 1349.87

645.72 637.06 581.5 485.53 682.27 843.59

1941.17 1746.19 1753.85 1465.13 4186.21 2706.22

Notes: Market Share is defined by plan within region (rather than by plan sponsor at the national level). HHI is calculated for each region and averaged for that year.

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near-national plan sponsors are used for 2009 enrollment (thus leaving out all the plans that are in a single region or a small set of regions), the HHI is 726 at the national plan level and 1466 at the plan sponsor level (taking into account all the most recent mergers) (author calculations). For 2009, we also show market concentration within the LIS and nonLIS beneficiaries separately. In a sense, these two populations operate in different environments. LIS beneficiaries have a strong incentive to enroll in the small subset of plans that qualify as benchmark plans. As shown earlier, benchmark plans have been a declining share of all plans, but a substantial subset of LIS beneficiaries are enrolled in non-benchmark plans. Although some non-LIS enrollees may seek out the benchmark plans for their low premiums, many have enrolled in other plans based on factors such as plan reputation. In addition, some of the current benchmark plans are new entries by existing plan sponsors. Because most non-LIS beneficiaries have tended to stay in their original plans, this trend may have encouraged the separation of the two groups into different plans. The market concentration measure confirms that enrollment of both subpopulations is moderately concentrated, more so than the combined population. In some regions, these subpopulations are highly concentrated. We also find evidence that enrollment share is correlated with premiums, in part because of the strong incentive for LIS beneficiaries to enroll in lowpremium plans. Fig. 3(a) and (b) shows that in both years, there is a negative association between enrollment share and premium of the plan. The slope has become somewhat steeper in 2009, moving from 138 in 2006 to 158 (b)

Premium vs Enrollment Share, 2006

(a) 100

Dollars

drug_prem Fitted values Dollars

Premium vs Enrollment Share, 2009

150

50

drug_prem Fitted values

100

50 0 0 0

0.2

0.4

Share of Enrollment

0.6

0

0.1

0.2

0.3

Share of Enrollment

Fig. 3. Enrollment Shares by Premium Category. Note: Each Dot Represents One Plan, Whose Enrollment Has Been Divided by the Total Regional Enrollment to Create Its Enrollment Share. This Regional Enrollment Share is Then Plotted against the Monthly Premium of the Plan. Since Premiums Are Generally Higher in 2009, the Scale of the Y-Axis Differs Across Graphs.

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in 2009, suggesting that there may be more competition between plans as price distributions are better understood by customers. The incentive for LIS beneficiaries to enroll in the increasingly small set of benchmark plans contributes to this relationship.

Multiyear Plans Another feature of the market is the entry and exit of plans. Overall, approximately 885 of the original 1,429 plans have remained in the market for the program’s first five years from 2006 to 2010.4 Approximately 537 plans that were in the market in 2006 had left by 2010 and approximately 684 plans newly entered the market between 2007 and 2010. Of these plans, the majority are from plan sponsors who were already in the market, either retiring existing plans or creating new additional plans. Sometimes, plan sponsors dropped an enhanced plan to introduce a new lower cost plan (Tables 6–8). Compared to the plans that stayed, exiting plans had lower enrollment and higher out of pocket costs (as measured by deductibles). New entrants had much higher deductibles than plans that were stayers. In work not reported in a Table, we regressed the premium increase from 2006 to 2010 Table 6.

Five-Year Stayers: Plans continuously in the market 2006–2010 (885 Plans).

Year Average premium Average deductible Percent of plans that are zero premium plans (for LIS) Percent of plans that cover at least generics in the gap Average enrollment

Table 7.

2006

2007

2008

36.94 91.6 27.5 16.4 13,048

34.88 80.03 26.12 21.2 14,214

39.45 80.88 22.98 25 13,653

2009

2010

46.74 49.58 82.22 117.87 14.01 13.9 21.2 21.3 14,061 14,061

Leavers: Plans Present in 2006 But Absent by 2010 (537 Plans).

Year

2006

2007

2008

2009

2010

Average premium Average deductible Percent of plans that are zero premium plans (for LIS) Percent of plans that cover at least generics in the gap Average enrollment # plans

38.23 93.34 30.54 13.78 7,400 537

34.92 95.74 30.03 22.9 9,620 323

32.24 131.17 40.72 2.71 10,430 221

46.11 92.48 5.26 3.01 7,532 133

N/A N/A N/A N/A N/A 0

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Table 8.


Entrants: Plans Present by 2010, Not Present in 2006 (684 Plans).

Year

2006

2007

2008

2009

2010

Average premium Average deductible Percent of plans that are zero premium plans (for LIS) Percent of plans that cover at least generics in the gap Average enrollment # plans

N/A 35.08 40.06 41.31 42.67 N/A 148.4 145.3 165.67 176.68 N/A 39.6 36.06 28.77 26.75 N/A 30.05 29.4 19.24 16.23 N/A 1158 3791 4885 n/a 0 366 513 577 684

Notes: These are not enrollment weighted numbers. Plans are defined as stayers based on having the same contract and plan ID from 2006 through 2010. Some cases could have been excluded where there is a fundamental change in design under the same ID numbers. Other cases might have been included where there is change in ID numbers, but no significant change in plan design.

against the enrollment of that plan in 2006, among the stayers. The objective was to see if higher 2006 enrollment was associated with larger premium changes over time. We found that plans with higher 2006 enrollment did have a higher premium change over time that was statistically significant, but only by a modest magnitude; 10,000 more enrollees in 2006 was associated with $9.25/yr more in premiums in 2010.

SUMMARY Where does this leave us? First: an assessment. Relative to predictions, the program’s cost growth has been modest. Whether this means the initial predictions were simply inaccurate or the program’s performance has been strong is uncertain. Factors suggested by different observers include the impact of an unusually large number of new generic drugs, robust competition leading to higher than expected rebates, and enrollment somewhat below expectations. Relative to the expectations of many observers, more plans have entered than anticipated (and some would suggest the number is higher than optimal). Enrollment has been rapid, presumably due in part to beneficiaries’ awareness of the late enrollment penalty and because the premium charged for the benefit is significantly subsidized by the government. Despite these incentives, not everyone without coverage has enrolled. Enrollment has been highly concentrated in a few national plan sponsors, yet many small plans have not left the program. Research has investigated the optimal choice strategies and the mixed consequences of choice (e.g. Winter et al., 2006; Domino, Stearns, Norton, &


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Yeh, 2008; Frank & Newhouse, 2007; Kling et al., 2009; Abaluck & Gruber, 2009; Lucarelli, Prince, & Simon, 2008). Rising premiums for plans with gap coverage strongly suggest the presence of adverse selection at least into those plans, and some studies have started to examine this question (e.g. Levy & Weir, 2007). Given about half of all beneficiaries are now in Part D, overall utilization effects appear low relative to what would be expected; Part D has resulted in approximately 12% to 6% or lower increases in quantity of medications used by seniors (Lichtenberg & Sun, 2007; Levy & Weir, 2007; Yin et al., 2008; Ketcham & Simon, 2008). One likely reason for this low utilization effect is that a majority of Part D beneficiaries had previous coverage through private insurance or Medicaid. Before Part D implementation, Pauly (2004) predicted that Part D may reduce elderly patients’ out of pocket costs by 14% overall, by halving costs for the quarter or so of seniors who were uninsured. If we use a price elasticity of demand of 0.4, Pauly estimated a 6% increase in utilization as a result of Part D. Relative to that prediction, the estimated increases in utilization do not look small. Studies on the impact of the coverage gap on utilization are emerging as well, suggesting that about one-fourth of beneficiaries subject to the gap (thus, excluding those with low-income subsidies) may have spending levels that reach the gap. When they do, some respond by switching to more generics or seeking other sources (such as physician samples) for drugs, while others appear to stop taking some of their drugs while in the gap (Hsu et al., 2008; Zhang, Donohue, Newhouse, & Lave, 2009b; Hoadley, Cubanski, Hargrave, Summer, & Neuman 2008b). Only approximately 15% of those hit the gap ever reach catastrophic coverage. There is relatively little research to date on the broader impact of Part D on the use of other health care services, drug prices, or innovation in the pharmaceutical sector, although studies are starting to emerge. Significant delays in the availability of Part D claims data have slowed research into these questions. This is a fertile area of research, especially now that claims data are beginning to be available to researchers. There are many papers on Part D topics that are not summarized here, but the collective findings to date give us a foundation to consider the next stages of policymaking issues in this program.

POLICY ISSUES ON THE HORIZON Even though Part D’s functioning has surpassed expectations of some observers, others are urging policymakers to address deficiencies.

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For example, the version of health reform legislation (Affordable Health Care for American Act) that passed the House on November 7, 2009, contains a provision that would require the Secretary of HHS to start negotiating part D prices with manufacturers from 2011 onwards. That legislation also has a provision to eliminate the Part D coverage gap in steps, by increasing the initial coverage zone (the area of 25% cost sharing) by $500 a year in 2010, and every year thereafter until the entire gap is eliminated by 2019. Before 2019, manufacturers have agreed to provide a 50% discount on brand name drugs. In addition, the House bill would extend the use of the Medicaid rebate system for drugs used by the dualeligible population in Part D plans. Many provisions are not in the Senate bill (under debate at this writing), so it remains uncertain whether they will emerge in the final legislation – should it become law. One issue of interest to policymakers, but not in the health reform bill at present, is the concern about the vast number of choices facing beneficiaries. CMS has proposed new rules that might limit the number of plans. Another outstanding issue is that many eligible low-income beneficiaries are not enrolled for the program’s subsidy (or possibly not enrolled in Part D at all). There has been low take-up by this vulnerable group. Although drug prices have risen more slowly in recent years, cost containment always remains a concern. In particular, there is pressure to control costs for specialty drugs, one of the most rapidly growing elements of the program. Plans have less incentive to manage costs for high spenders because they are substantially protected by the program’s risk sharing and reinsurance features. Medicare Part D represents a major improvement for beneficiaries not previously covered by insurance for prescription drugs and for some low-income beneficiaries not previously enrolled in Medicaid. Because it represents a major initiative and a new approach to structuring Medicare benefits, much more research is needed to assess its impact and implications.

NOTES 1. Lakdawalla and Sood (2009) point out another theoretical effect of insurance on the pharmaceutical market: since insured customers face low out of pocket costs (closer to marginal cost) after paying a lump sum premium, the quantities they demand are closer to what would be supplied by a more competitive market. This serves to reduce economic inefficiencies associated with monopoly power that are created by the existence of patents in the pharmaceutical industry 2. In 2005, United and Pacificare merged in 2006 but their plans were still being offered as if from two different plan sponsors.

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3. The Bureau of Labor Statistics medical care consumer price index CUUR0000SAM increased from 329.5 in January 2006 to 378.6 in October 2009. 4. Some entry and exit of plans may the effect of mergers and acquisitions; others may be decisions by continuing plan sponsors to restructure their plan offerings – for example, dropping a high-end plan and replacing it with a plan priced below its previous least expensive plan. Sponsors making changes vary in whether they have simply restructured existing plans under the same contract and plan ID numbers or dropped one plan in favor of a new one.

REFERENCES Abaluck, J., & Gruber, J. (2009). Choice inconsistencies among the elderly: Evidence from plan choice in the Medicare Part D program. NBER working paper 14759, February. Centers for Medicare and Medicaid Services (CMS). (2003). President signs Medicare legislation. Available at http://www.cms.hhs.gov/History/Downloads/BushSignMMA2003.pdf Centers for Medicare and Medicaid Services (CMS). (2004). Medicare trustees report 2004. Available at http://www.cms.hhs.gov/ReportsTrustFunds/ Centers for Medicare and Medicaid Services (CMS). (2005). Medicare trustees report 2005. Available at http://www.cms.hhs.gov/ReportsTrustFunds/ Centers for Medicare and Medicaid Services (CMS). (2006). Instructions for 2007 contract year to Medicare prescription drug plan sponsors. Baltimore, MD. Centers for Medicare and Medicaid Services (CMS). (2009a). Medicare trustees report 2009. Available at http://www.cms.hhs.gov/ReportsTrustFunds/ Centers for Medicare and Medicaid Services (CMS). (2009b). Part D payment and risk adjustment. Available at http://www.cms.hhs.gov/DrugCoverageClaimsData/02_RxClaims_ PaymentRiskAdjustment.asp Congressional Budget Office. (2004). A detailed description of CBO’s cost estimates for the Medicare prescription drug benefit. Technical Report; Congressional Budget Office. Available at http://www.cbo.gov/doc.cfm?index ¼ 5668&type ¼ 0 Cubanski, J., & Neuman, P. (2007). Status report on Medicare Part D enrollment in 2006: Analysis of plan-specific market share and coverage. Health Affairs, 26(1), w1–w12. Department of Health and Human Services (DHHS). (2000). Jack Hoadley, team leader, Prescription drug coverage, spending, utilization and prices. Report to the President, April. Available at http://aspe.hhs.gov/health/reports/drugstudy/index.htm Domino, M., Stearns, S., Norton, E., & Yeh, W. (2008). Why using current medications to select a Medicare Part D plan may lead to higher out of pocket payments. Medical Care Research and Review, 65(1), 114–126. Duggan, M., Healy, P., & Scott-Morton, F. (2008). Providing prescription drug coverage to the elderly: America’s experiment with Medicare Part D. Journal of Economic Perspectives, 22(4), 69–92. Duggan, M., & Scott-Morton, F. (forthcoming). The effect of Medicare Part D on pharmaceutical prices and utilization. American Economic Review. Frank, R., & Newhouse, J. (2007). Mending the Medicare drug benefit. Discussion Paper 2007-03, Hamilton Project, Brookings Institution. Gold, M. (2006). Landscape of private firms offering Medicare prescription drug coverage in 2006. Kaiser Family Foundation, (March).

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Goldman, D., & Joyce, G. (2008). Medicare Part D: A successful start with room for improvement. Journal of the American Medical Association, 299(16), 1954–1955. Hargrave, E., Hoadley, J., Summer, L., Cubanski, J., & Neuman, P. (2009). Medicare Part D 2010 data spotlight-benefit design and cost sharing. December. Kaiser Family Foundation. Available at http://www.kff.org/medicare/upload/8033.pdf Hoadley, J., Cubanski, J., Hargrave, E., Summer, L., & Neuman, P. (2008a). Medicare Part D 2008 data spotlight-low-income subsidy plan availability. April. Kaiser Family Foundation. Available at http://www.kff.org/medicare/upload/7763.pdf Hoadley, J., Cubanski, J., Hargrave, E., Summer, L., & Neuman, P. (2008b). The Medicare Part D Coverage gap: Costs and consequences in 2007. August. Kaiser Family Foundation. Available at http://www.kff.org/medicare/upload/7811.pdf Hoadley, J., Cubanski, J., Hargrave, E., Summer, L., & Neuman, P. (2009a). Medicare Part D spotlight: Part D plan availability in 2010 and key changes since 2006. November. Kaiser Family Foundation. Available at http://www.kff.org/medicare/ upload/7986.pdf Hoadley, J., Cubanski, J., Hargrave, E., Summer, L., & Neuman, P. (2009b). Medicare Part D 2010 data spotlight-premiums. December. Kaiser Family Foundation. Available at http://www.kff.org/medicare/upload/8032.pdf Hoadley, J., Cubanski, J., Hargrave, E., Summer, L., & Neuman, P. (2009c). Medicare Part D 2010 data spotlight: The coverage gap. November. Kaiser Family Foundation. Available at http://www.kff.org/medicare/8008.cfm Hsu, J., Fung, V., Price, M., Huang, J., Brand, R., Hui, R., Fireman, B., & Newhouse, J. (2008). Medicare beneficiaries’ knowledge of Part D Prescription drug program benefits and responses to drug costs. Journal of the American Medical Association, April 23/30, 2008, 299(16): 1929–1936. Ketcham, J., & Simon, K. (2008). Medicare Part D’s effects on elderly patients’ drug costs and utilization. American Journal of Managed Care, 14(November), 14–22. Kling, J., Mullainathan, S., Shafir, E., Vermeulen, L., Wrobel, & M. V. (2009). Misperceptions in choosing Medicare drug plans. Available at http://www.nber.org/Bkling/choosing.pdf Lakdawalla, D., & Sood, N. (2009). Innovation and the welfare effects of public drug insurance. Journal of Public Economics, 93(3–4), 541–548. Levy, H., & Weir, D. (2007). Take-up of Medicare Part D and the SSA subsidy. Michigan retirement research center research paper WP 2007-163. Available at http://papers.ssrn. com/sol3/papers.cfm?abstract_id ¼ 1082832 Lichtenberg, F., & Sun, S. (2007). The impact of Part D on prescription drug use by the elderly. Health Affairs, 26(6), 1–10. Lucarelli, C., Prince, J., & Simon, K. I. (2008). Measuring welfare and the effects of regulation in a government-created market: The case of Medicare Part D plans. National Bureau of Economic Research Working Paper. Neuman, P., Strollo, M. K., Guterman, S., Rogers, W. H., Li, A., Rodday, A. M. C., & Safran, D. G. (2007). Medicare prescription drug benefit progress report: Findings from a 2006 national survey of seniors. Health Affairs, 26(5), w630–w643. Pauly, M. V. (2004). Medicare drug coverage and moral hazard. Health Affairs, 23(1), 113–122. Pavcnik, N. (2002). Do pharmaceutical prices respond to potential patient out-of-pocket expenses? RAND Journal of Economics, 33(3), 469–487. Winter, J., et al. (2006). Medicare prescription drug coverage: Consumer information and preferences. Proceedings of the National Academy of Sciences, 103(20), 7929–7934.


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Yin, W., Basu, A., Zhang, J., Rabbani, A., Meltzer, D., & Alexander, C. (2008). The effect of the Medicare Part D prescription benefit on drug utilization and expenditures. Annals of Internal Medicine, 148(3), 169–177. Zhang, Y., Donohue, J., Lave, J., O’Donnell, G., & Newhouse, J. (2009a). The effect of Medicare Part D on drug and medical spending. New England Journal of Medicine, 361(1), 52–61. Zhang, Y., Donohue, J., Newhouse, J., & Lave, J. (2009b). The effects of the coverage gap on drug spending: A closer look at Medicare Part D. Health Affairs, 28(2), w317–w325.

PART III U.S. STUDIES – FROM MARKETS TO POLICY INNOVATIONS

DOES PRESCRIPTION DRUG ADHERENCE REDUCE HOSPITALIZATIONS AND COSTS? THE CASE OF DIABETES William E. Encinosa, Didem Bernard and Avi Dor ABSTRACT Purpose – To estimate the impact of diabetic drug adherence on hospitalizations, emergency room (ER) visits, and hospital costs. Methods – It is often difficult to measure the impact of drug adherence on hospitalizations since both adherence and hospitalizations may be correlated with unobservable patient severity. We control for such unobservables using propensity score methods and instrumental variables for adherence such as drug coinsurance levels and direct-to-consumer advertising. Findings – We find a significant bias due to unobservable severity in that patients with more severe health are more apt to comply with medications. Thus, the relationship between adherence and hospitalization will be underestimated if one does not control for unobservable severity. Overall, we find that increasing diabetic drug adherence from 50% to 100% reduces the hospitalization rate by 23.3% from 15% to 11.5%. ER visits reduce by 46.2% from 17.3% to 9.3%. Although such an increase in Pharmaceutical Markets and Insurance Worldwide Advances in Health Economics and Health Services Research, Volume 22, 151–173 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1108/S0731-2199(2010)0000022010

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adherence increases diabetic drug spending by $776 a year per diabetic, the cost savings for averted hospitalizations and ER visits are $886 per diabetic, a cost offset of $1.14 per $1.00 spent on diabetic drugs. Originality – Most of the drug cost-offset literature focuses only on the impact of cost-sharing and drug spending on cost-offsets, making it impossible to back-out the empirical impact of actual drug adherence on cost-offsets. In this chapter, we estimate the direct impact of adherence on hospitalizations and costs.

1. INTRODUCTION Patient cost-sharing in purchases of prescription drugs has been increasing dramatically. Between 2000 and 2006, copayments for generics, preferred brand drugs, and non-preferred drugs increased by 57%, 85%, and, 124% respectively in employer-based plans (Kaiser Family Foundation, 2006). Similar increases in cost-sharing were observed more recently under the Medicare Part D plan introduced in 2006. For instance, in the Part D standalone plans, median copayments increased by 40% for generics, 27% for preferred brand drugs, whereas coinsurance rates for specialty drugs increased by 10% in 2009 alone, despite the previous year’s sharp premium increases.1 Much of this is in response to drug price increases, which tend to have a bigger effect on the elderly. Between 2003 and 2006, prices of drugs purchased by elderly Medicare beneficiaries rose by 24.2% for drugs used more heavily by the elderly, compared to 18.8% for drugs less heavily used (Frank & Newhouse, 2008). At the same time, many employers as well as Medicare policy makers have begun to express concerns over the long-run effects of cost-sharing, especially in the case of prescription drugs that are used to control common chronic conditions such as hypertension, diabetes, or asthma. Specifically, excessive cost-sharing may lead to the underuse or inappropriate use of medications, thus leading to medical complications and ultimately higher medical costs. To address these concerns, some innovative employers have begun to experiment with value-based insurance design (VBID), whereby copayments for important preventive care drugs are reduced to zero, whereas copayments for drugs with less value are increased.2 Anecdotal evidence suggests that VBID has met with some success. For example, Pitney Bowes, a firm with 35,000 employees, dropped its coinsurance rate for diabetic drugs from 50% to 10%. According to the firm, this resulted in

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a 12% reduction in the overall cost of care for the median diabetic (Fuhrmans, 2004). The University of Michigan, in a 2006 pilot program, documented similar cost-offsets when it provided no-cost diabetes medications to its employees and their dependents (Chernew, Rosen, & Fendrick, 2007). In the broader policy arena, the Medicare Payment Advisory Commission recommended to Congress that Medicare explore the use of VBID in Part D (Medpac, 2009). In May 2009, a U.S. Senate bill (S. 1040) entitled the ‘‘Seniors’ Medication Copayment Reduction Act of 2009’’ was introduced to establish a Medicare Part D demonstration to test whether VBID increases adherence to prescribed drug regimens, improves outcomes, and reduces costs for fifteen conditions. Poor adherence appears to be endemic across many medical conditions despite its potentially severe consequences. In one study, only 42% of glaucoma patients complied after having been told they would go blind if they did not adhere. For patients who already had gone blind in one eye, adherence rates rose only to 58% (Cramer, 1991). Similarly, 18% of renal transplant patients facing organ rejection or even death from poor adherence with immunosuppressant therapy were not taking their medication (Rovelli et al., 1989). Of 49 randomized control trials of interventions to improve adherence, McDonald, Garg, and Haynes (2002) found that only 17 interventions led to improvements in treatment outcomes. Thus, there appears to be a need for further policy intervention. Although increases in patient cost-sharing have been shown to reduce adherence to prescribed drug regimens for important chronic conditions such as diabetes (Dor & Encinosa, 2004, 2010), the empirical relationship between pharmaceutical drug adherence and outcomes or costs has not yet been fully explored. Although many studies show that reducing drug copayments increases drug adherence (Gibson, Ozminkowski, & Goetzel, 2005), improves some outcomes (Hsu et al., 2006; Rice & Matsuoka, 2004; Goldman, Joyce, & Zheng, 2007), and reduces costs (Gaynor, Li, & Vogt, 2007; Chandra, Gruber, & McKnight, 2007; Shang & Goldman, 2007; Zhang et al., 2009; Deb, Travedi, & Zimmer, 2009), none of these recent papers have actually measured the direct effect of drug adherence on outcomes such as averted hospitalizations or their corresponding costs. Given that adherence could be improved through either direct intervention by the insurer or optimal benefit design (Dor & Encinosa, 2004, 2010), it is important to fill the gap in that literature. In this study, we address this issue by estimating the direct impact of adherence on hospitalizations and costs in the case of diabetes. In addition, we convert our adherence measure to a dollar measure by estimating the costs

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of all drug prescriptions under adherence. Hence, our chapter is a contribution to the drug cost-offset literature. Yet another motivation for examining the direct impact of adherence on costs (rather than indirectly through changes in cost-sharing and benefit design) is that adherence may be affected by various incentives other than removal of financial barriers. Many VBID programs currently being implemented involve much more than cost-sharing reductions. They often involve specific programs, such as the Medication Therapy Management Programs in Medicare Part D. To encourage adherence to medications, these programs offer interventions such as phone outreach, medication reviews, refill reminders, intervention letters, educational newsletters, and drug interaction screenings. Such incentives that go beyond the lowering of copayments are often needed to improve adherence. Decreasing cost-sharing is only one factor among many that might improve adherence. This may explain why some studies find little impact in decreasing cost-sharing on cost-offsets. In this chapter we broaden the scope of prior research. Thus, we do not examine cost-sharing per se, rather we will examine the direct impact of drug adherence on outcomes and costs, and focus on patients diagnosed with diabetes.

1.1. Why Diabetes? Diabetes is one of the most common chronic conditions for which prescription medications exist, with 23.6 million Americans, or 7.8% of the U.S. population estimated to have this diagnosis. It is the leading cause of adult blindness, kidney failure, and non-trauma amputations, and a leading cause of heart disease. 234,000 people die each year from diabetes in the United States, making it the seventh leading cause of death. The prevalence of diabetes in the United States increased by more than 30% over the last ten years. Moreover, the annual costs of diabetes in medical expenditures and lost productivity climbed from $98 billion in 1997 to $174 billion in 2007. As the incidence of diabetes reaches epidemic proportion, leading to spiraling costs, the need to undertake prevention measures is becoming even more pronounced. There are two major forms of the disease. Type 1 diabetes occurs in approximately 10% of cases; in this manifestation of the disease, a person is unable to produce insulin, the major hormone in the body that regulates blood sugar level. Persons with type 1 diabetes are dependent on daily insulin injections, but few oral prescription medications are available. In type 2 diabetes mellitus, either persons produce low levels of insulin or


Table 1.

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Pharmacological Treatment of Type 2 Diabetes Mellitus.

Major Metabolic Defect

Drug Therapy

Defective insulin secretion Pancreatic beta cells (decreased insulin secretion)

Secretagogue therapy Sulfonylureas (SU) Non-SU secretagogues (meglitinides)

Insulin resistance Skeletal muscle (decreased glucose uptake) Liver (increased glucose production)

Insulin sensitizer therapy Thiazolidinediones (TZD) Biguanides (metformin) TZD TZD

Adipose tissue (increased lipolysis) Carbohydrate absorption

Drug therapy

Small intestines

Alpha-glucosidase inhibitors (AGI)

Source: Inzucchi (2002).

the insulin produced is deficient in regulating blood sugars. For this variant of the disease, five types of oral prescription medications are available: sulfonylureas (SU), non-SU (meglitinides), metformin, thiazolidinediones (TZD), and alpha-glucosidase inhibitors (AGI). Each of these drugs targets a separate organ site in the body to control blood sugar levels, as shown in Table 1. These five pharmacological methods of controlling of blood sugar can substantially delay or prevent the costly medical complications arising from diabetes (Cohen, Neslusan, Conklin, & Song, 2003), such as blindness, kidney failure, non-trauma amputations, and heart disease. Compliance with these medications is of recent importance, since it was found that only 55% of diabetics keep their blood sugar under control (National Healthcare Disparities Report, 2008). A person is considered compliant if he or she adheres to the anti-diabetic drug regimen prescribed by a physician (Hughes et al., 2001; Dezii, 2000). Since these anti-diabetic medications are intended to be taken permanently, measurement of compliance is relatively straightforward when tracking such individuals. In this chapter, we examine patient compliance with all five anti-diabetic drugs in Table 1. Our main concern is that decreases in patient compliance with these drugs may cause costly, preventable hospitalizations and emergency room (ER) visits.

2. DATA In this analysis we use one of the largest available databases of privately insured individuals in the United States, the MarketScans Research

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Databases maintained by Thomson Reuters. These databases encompass five million individuals who are covered by employer-sponsored health insurance offered by approximately 40 large firms. These include both active employees and annuitants (retirees). The complete database contains various files with detailed information on medical conditions, insurance coverage, and payments for persons with any insurance claims for inpatient, outpatient, and prescription drug services. For purposes of this study, five different files from the 2001–2002 MarketScans were linked to create a single analysis file. The first file was the MarketScans drug file, which contains the insurance drug claims for all individuals who purchased prescription drugs. The second was the Employer Benefit Plan Design (BPD) database, with information on benefit design and drug copayment structure from some of the larger employers in MarketScans, offering a total of 50þ insurance plans with prescription drug benefits. The third was the MarketScans Enrollment File, which contains health plan enrollment history. The fourth and fifth files, respectively, were the MarketScans Hospital Inpatient File and the Outpatient Services File containing information on patients’ medical conditions and certain demographic characteristics. Finally, the 2001 and 2002 Redbooks (Medical Economics Company, 2002) were used to obtain additional information about the particular prescription drugs for diabetes. We focus on non-elderly adults over the age of seventeen with chronic type 2 diabetes who require oral anti-diabetic medications on an ongoing basis as described in Table 1. We include diabetics continuously enrolled during 2001–2002 with (1) at least one anti-diabetic drug claim between January 1 and April 1 in 2001 and (2) at least one anti-diabetic drug claim in 2002. Our final sample consists of 56,744 diabetics. We use any ICD-9 type 2 diabetes diagnosis on any inpatient or outpatient claim over a two-year period to identify type 2 diabetics. Access to detailed patient information allows us to track patient compliance, as measured by the sequence of prescription refills within a defined time interval. To adjust for patient heterogeneity (case mix), we controlled for age (five categories), sex, and 27 chronic conditions developed by in the AHRQ Comorbidity Software (www.ahrq.gov/data/hcup/comorbid.htm; Elixhauser, Steiner, Harris, and Coffey, 1998, and updated by McDonald et al., 2002). These comorbidities were obtained from the MarketScans Hospital Inpatient File and the Outpatient Services File. We control for the patient’s number of chronic conditions. The 27 conditions are congestive heart failure, arrhythmias, valvular disease, pulmonary circulation disease, peripheral vascular disease, hypertension, paralysis, other neurological


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disorders, chronic pulmonary disease, diabetes with chronic complications, hypothyroidism, renal failure, liver disease, peptic ulcer disease with bleeding, lymphoma, metastatic cancer, solid tumor without metastasis, rheumatoid arthritis collagen, coagulopathy, obesity, weight loss, fluid and electrolyte disorders, chronic blood loss anemia, deficiency anemias, alcohol abuse, psychoses, and depression. In addition, we include a binary variable for insulin use, as well as a binary variable called ‘‘drug abuse,’’ since drug abuse is a strong predictor of hospitalizations. If the patient was a dependent rather than the primary insurance policy holder, this is recorded in the variable ‘‘Dependent.’’ We also control for ‘‘Retiree.’’ Next, to control income, we used the log of the median household income of the patient’s county, taken from the Area Resource File (ARF). We also include a variable ‘‘Hourly’’ to indicate whether the worker earns an hourly wage versus a salary. County health maintenance organization (HMO) penetration is included from the InterStudy data. A large HMO penetration rate is known to reduce the hospitalization rate in an area. To control for unobservable variation in health plan benefits by firm, we include 12 firm fixed effects, as well as a binary indicator ‘‘small’’ for firms with less than 10,000 employees. Finally, in analyses of hospital ER use, we also control for the coinsurance rate for ER visits, since that may influence the decision to use the ER. In the hospitalization analysis, we do not use coinsurance rates since hospital coinsurance rates were too low (less than 2%) to have any effect on hospitalizations. Overall, we have 18 covariates and 12 firm fixed effects. The cost regressions do not use firm fixed effects since they were run on small samples consisting of only the patients with hospital admissions or positive ER visits. The descriptive statistics for all the covariates can be found in Table 2.

3. EMPIRICAL METHODS The main independent variable of interest is ‘‘non-compliance,’’ defined as the percentage of days in 2001 in which the patient did not possess a diabetic medication (i.e., 1 minus the medical possession ratio). In a series of analyses, we obtain estimates of the impact of non-compliance on patientlevel outcomes measures related to probabilities of hospital utilization (admissions and emergency room visits) and to levels of hospital care conditional on positive use (hospital spending) and hospital ER visits. As explained later, we use combinations of estimated parameters to calculate savings associated with improved compliance. Since reduced

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Table 2. Variables

Descriptive Statistics.

Raw Sample (All Patients)

Raw Subsamples

Matched Subsamples

50% 100% 50% 100% compliance compliance compliance compliance 2001 instrumental variables Direct-to-consumer $158 (390) advertising (spending per 100,000 capita) Firm’s average .292 (.103) coinsurance rate for diabetic drugs 90-day prescriptions .236 Mail order .127 Chain pharmacy .642

177

134

177

120

.314

.272

.314

.293

.058 .032 .748

.539 .299 .495

.058 .032 .748

.424 .247 .550

.675

0

.675

0

.168 .134

.107 .137

.168 .134

.108 .110

.978 98 585

1.003 75 701

.979 97 584

.638 52 485

2,801 20,019

3,079 22,491

2,803 20,019

2,420 21,834

379

1,422

379

1,462

.170 53.98 (7.516) .534 .002 .256 .310 .215 (.160)

.106 49.782 .533 .002 .297 .316 .197

.264 55.749 .430 .002 .311 .347 .239

.106 49.786 .533 .002 .297 .317 .197

.102 51.052 .521 .002 .313 .308 .201

$42,551 (11,211)

40,590

44,031

40,601

40,856

.027 .385 .094

.024 .255 .087

.029 .510 .109

.024 .255 .087

.021 .244 .087

2002 outcomes Patient’s non.176 compliance rate ER visit rate .121 Hospitalization rate .127 (non-maternity) Hospital days .938 (4.967) ER spending $72 (414) ER spending for ER $601 (1,055) visits Hospital expenditures $2,802 (15,495) Hospital expenditures $21,658 (38,053) on hospital admissions Drug expenditures $898 (938) Patient characteristics Insulin use Age Male Drug abuse Hourly wage worker Dependent County HMO penetration rate County median household income Small firm size Retiree ER coinsurance rate


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Table 2. (Continued ) Variables

Raw Sample (All Patients)

Raw Subsamples

Matched Subsamples

50% 100% 50% 100% compliance compliance compliance compliance Number of chronic conditions 1 .692 2 .199 3 .069 4 .025 5þ .016 N 56,744

.707 .191 .062 .027 .013 6,322

.673 .208 .073 .031 .016 5,724

.707 .191 .062 .027 .013 5,721

.721 .184 .058 .025 .012 5,721

Notes: Means reported. Standard deviations are in parentheses. The subsamples consist of the patients that either always complied or complied 50% of the time or less. Matching of subsamples was done by propensity score matching. Significant at 90%. Significant at 95%. Significant at 99%.

non-compliance is not costless (higher compliance implies higher spending on prescribed drugs), we obtain estimates of the impact of non-compliance on diabetic drug spending and use these to calculate the costs of improved compliance. Finally, we combine all the relevant estimates to calculate cost-offsets from compliance, using a simulation that compares partial compliance with full compliance. To allow for lagged effects, we analyze the impact of 2001 non-compliance on 2002 outcomes. Results from regression analyses on probabilities or rates are summarized in Table 3, whereas results pertaining to levels are summarized in Table 4. Five regression methods were employed: Probit and IV-Probit (Table 3 only), HOLS-GMM, IV-GMM, and propensity score OLS (Tables 3 and 4). We implement the instrumental variable models, IV-Probit and IV-GMM, because non-compliance may be correlated with unobserved patient severity. Patient severity refers to the patient’s relative severity of illness, potentially biasing the estimated effects of non-compliance on the various outcomes of interest. The IV-Probit implements the Amemiya Generalized Least Squares (AGLS) estimator based on Newey (1987). However, it maintains the same distributional assumption of the probit model (normally distributed errors), which can be viewed as somewhat restrictive. To address this we turn to the generalized method of moments (GMM), which allows for any general functional form on the error terms. In particular, we estimate the HOLS-GMM model due to Cragg (1983) that employs

160

Table 3.


The Estimated Impact of Non-Compliance on Hospital Use. Any Hospitalization .045 (.007) .058 (.032) .046 (.007) .070 (.033) .036 (.009)

PROBIT IV-PROBIT HOLS-GMM IV-GMM Propensity score OLS Over-identification test (Hansen J-stat P-value) First-stage F-statistic

.190

Any ER Visit .076 (.006) .153 (.038) .087 (.007) .162 (.035) .075 (.009) .460

699.18

703.26

Note: Standard errors are in parentheses.

Significant at 90%. Significant at 95%. Significant at 99%.

Table 4.

The Estimated Impact of Non-Compliance on Drug, Hospital, and ER Spending. Log (Diabetic Drug Spending)

HOLS-GMM IV-GMM Propensity score OLS Over-identification test (Hansen J-stat P-value) First-stage F-statistic

Log (Hospital Days|Hospital Admission)

2.147 (.023) – 2.422 (.028) –

–

Note: Standard errors are in parentheses. Significant at 90%. Significant at 95%. Significant at 99%.

.131 (.044) .565 (.209) .194 (.060) .209

150.15

Log (Hospital Spending|Hospital Admission) .024 (.062) .532 (.310) .047 (.083) .46

77.43

Log (ER Spending|ER Visit) .153 (.065) .336 (.424) .099 (.085) .00

83.60


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heteroskedastic OLS estimation using GMM. The fourth method is the IVGMM method due to Baum, Shaffer, and Stillman (2003), used in the Stata program ivreg2. The fifth method, which we label ‘‘propensity score OLS,’’ uses a matched sample design (for partial compliers versus full compliers) rather than instrumental variable techniques. Finally, the last three methods are also used to estimate the log expenditure models summarized in Table 4. To predict non-compliance, five excluded instrumental variables are used in the IV-Probit and IV-GMM models. These are the log of local and national direct-to-consumer-advertising (DTCA) spending level per 100,000 capita in 2001 for diabetic medications; the health plan’s average coinsurance rate for its diabetic medications; a dummy for 90-day prescriptions, a dummy for mail order; a dummy for whether the pharmacy used by the patient was a chain store. The first instrumental variable (IV for short) is drawn from Taylor Nelson Sofres’ (TNS) advertising data. This DTCA includes advertising on cable TV, network TV, magazines, newspapers, radio, Sunday ads, and syndication. Of all the patients in the raw sample, 72% used a diabetic drug that had been advertised through some venue. Wosinska (2005) and Bradford et al. (2006) have shown that DTCA is a good predictor of drug compliance. Unlike most drugs, DTCA for diabetic drugs is mainly local. We used this cross-sectional variation in DTCA across metropolitan areas to predict compliance. Similarly, for our second excluded IV, Dor and Encinosa (2004, 2010) show that the diabetic drug coinsurance rate is a strong predictor of compliance. Our firm–region level coinsurance rate for diabetic medications took on 48 distinct values (12 firms, 4 regions), ranging from 4 to 52%. The remaining three IVs are exogenous drug benefit characteristics that affect compliance but are uncorrelated with severity. Descriptive statistics for these instrumental variables, as well as all other variables are included in Table 2. We employ two accepted criteria for statistical identification: ‘‘relevance’’ and ‘‘validity.’’ To assess the relevance of our five instruments, we report the F-test of their combined effect in the first stage of the GMM estimation. To assess their validity we report the P-value of their Hansen J-statistic overidentification test. All our outcome regressions pass this validity test with the exception of the ER cost regression, where the Hansen test fails to reject the hypothesis that our model is correctly specified at the 1% level. This confirmed the IV hypothesis that drug copayments are not correlated with severity (since we averaged coinsurance rates over the firms to avoid selection effects), and similarly, DTCA is not correlated with severity. Finally, we ran separate regressions to verify that our excluded instrumental variables do not predict the second-stage outcomes related to hospital care. This procedure

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confirmed the assumption behind the IV models, namely that drug coinsurance rates, DTCA, and benefit design characteristics (days prescribed; if mail order; retail outlet is chain pharmacy) predict drug compliance behavior but have no direct impact on whether the patient is hospitalized. We label the last estimation method as ‘‘propensity score OLS,’’ specifically linear probability models in Tables 3 and ordinary least squares on levels in Table 4. Under this approach, we simply pool two matched groups of patients, namely full compliers and low compliers, as an alternative to identifying compliance in the outcome regressions using IV techniques. We match patients in the 10th percentile of non-compliance to patients in the 90th percentile of non-compliance. In our data, these groupings corresponded exactly to those who comply less than 50% of the time versus those who comply fully, that is, 100% of the time (ideally we would have wished to match 0% compliers to perfect compliers; in practice, however, we did not observe zero compliers in the data). That is, we match 5,721 of the 6,322 patients who comply less than 50% of the time with 5,721 patients that comply 100% of the time. The characteristics of the both the raw and matched subsamples are reported in Table 2. To construct our matched groupings, we used the propensity scorematching routine due to Leuven and Sianesi (2003) (pscore2, Stata version 3.0.0). Accordingly, we first create the propensity score with a logit regression for the probability that the patient has a hospitalization in 2002, controlling for the 18 covariates described above in the Data Section. Next, using the nearest-neighbor method (Dehejia, 2005; Dehejia & Wahba, 2002; Becker & Ichino, 2002), we create the matches by balancing propensity scores across all the covariates. Three patients in the 100% compliance subsample lacked a common region of support and were excluded (Becker & Ichino, 2002). As observed in Table 2, the resulting matched subsamples have very similar characteristics for all the covariates. The median absolute value of the bias in the covariates between 50% compliant patients and 100% compliant patients was reduced from 19.1 to 1.8 due to the matching (Rosenbaum & Rubin, 1985). Simulation results on the impact of changes from 50% compliance to 100% compliance on hospitalizations and ER visits are summarized in Table 5. Results for rates, costs conditional on positive use, and expected population costs are reported at the sample means. Expected costs were obtained using the standard two-part model (Mullahy, 1998). That is, E(COSTij ) ¼ Pij (COSTij|COSTijW0), where i indexes patient i, j indexes the expenditure category (admissions, ER), P is the probability of positive utilization in the expenditure category, and (COSTij|COSTijW0) is the

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Table 5.

Simulation of 2002 Outcomes Due to 2001 Diabetic Drug Compliance.

Variable

50% Compliancea Exogenousb

Distribution Any hospitalization Any ER visit Drug sendingW0

14.2% 14.9% 100.0%

IV

15.0% 17.3% 100.0%

100% Compliance Exogenousb

11.9% 10.6% 100.0%

IV

11.5% 9.3% 100.0%

Conditional costsc Hospital costs ER costs Drug costs

$11,880 $302 $284

$13,977 $283 $329

$11,738 $280 $832

$10,715 $297 $1,105

Expected costsd Hospital costs ER costs Drug costs

$1,687 $43 $284

$2,097 $49 $329

$1,397 $30 $832

$1,232 $28 $1,105

a

10% of the samples are 100% (perfect) compliers and 10% of the samples comply less than 50% of the time. b All ‘‘Exogenous’’ predictions are based on the HOLS-GMM estimates of Tables 3 and 4. The IV predictions are based on the IV-GMM estimates of Tables 3 and 4, except for the drug and ER costs, which are based on the propensity score OLS regressions. c Conditional costs are conditional upon positive use. d Expected costs are conditional costs multiplied by the probability of use in the top panel.

corresponding conditional cost obtained from transforming the dependent variables in the log regressions. In addition, Table 5 compares results from naı¨ ve models (columns labeled ‘‘Exogenous’’) and a reference IV estimator. For each dependent variable we define the reference IV model (columns labeled ‘‘IV’’) as either the IV-GMM model or the propensity score model depending on the specification test values reported in Tables 3 and 4. This comparison indicates the direction of the bias due to unobserved patient severity. Finally, Table 5 also reports expected drug costs for 50% and 100% compliance. To calculate cost offsets, we compare the savings in hospital costs from this incremental increase in compliance with the corresponding increase in drug costs. Note that we do not examine physician visits in this study. Often a visit to a doctor is correlated with improved adherence, yet what the doctor says or does in the office to improve adherence is not observed. In general, instrumental variables that control for unobservable physician behavior are not available in claims data such as ours.

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4. RESULTS 4.1. Outcomes As shown in Table 2, raw hospitalization rates among the 50% compliers and the 100% compliers were about equal. However, after controlling for the 18 covariates and 12 firm fixed effects (Table 3), all five of the regression methods indicate that non-compliance led to an increase in the likelihood of having any hospitalization; note that propensity score OLS yielded the lowest estimate for the coefficient on non-compliance at 0.036. Comparing Probit with IV-Probit, the coefficient for non-compliance increased from 0.045 to 0.058. Similarly, comparing HOLS-GMM with IV-GMM, the coefficient for non-compliance increased from 0.046 to 0.07. This suggests that without an IV correction, the effect of non-compliance on the rate hospitalization would be underestimated. Table 2 also shows that unlike hospitalizations, the raw rate of ER visits was substantially higher among the 50% compliers compared with the 100% compliers, that is, 16.8% versus 10.7%, respectively. Nevertheless, we find here the same pattern of underestimated compliance effects associated with the ‘‘naı¨ ve’’ models found in our analysis of hospitalization rates. Referring again to Table 3, comparing the Probit with IV-Probit for ER use, the coefficient for non-compliance increased from 0.076 to 0.153. Similarly, comparing the corresponding HOLS-GMM with IV-GMM, we observe that the coefficient for non-compliance increased from 0.087 to 0.162. A similar pattern is found in the conditional levels regressions summarized in Table 4. Thus, comparing HOLS-GMM with IV-GMM for the log of hospital days, the coefficient for non-compliance increased from 0.131 to 0.565. Comparing HOLS-GMM with IV-GMM for conditional hospital spending, the coefficient for non-compliance increases from 0.024 (P ¼ 0.7) to 0.53 (P ¼ 0.086). Note that an exception occurs in the ER spending models, where the non-compliance coefficient declines in the IV-GMM and the instrumental variables do not satisfy the overidentification test. We will therefore refer to the propensity score OLS model for purposes of simulation in this particular case. Additional interpretation of the IV and non-IV comparisons can be offered by noting that, by definition, very severely ill patients are more apt to require hospital services, but patient severity may be unobservable. Not controlling for unobservable severity gives rise to two possible biases: 1. Very severely ill patients have difficulties complying with their medications. This implies an overestimation of the effect of non-compliance on hospitalization when not controlling for unobservable severity.


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2. Very severely ill patients are more apt to comply. This implies an underestimation of the effect of non-compliance on hospitalization when not controlling for unobservable severity Given that both the non-IV models tended to underestimate the compliance effects relative to the IV methods as previously described, we can conclude that the latter (2) bias dominates.3 This inference is consistent with another one of our findings that pertains to an observable dimension of severity that is available in our data; the number of chronic conditions. A greater proportion of the 100% compliant patients have chronic conditions compared to the 50% compliant patients (Table 2). A similar result not shown in the tables is that the mean number of chronic conditions was significantly higher for the 100% compliant patients than for the 50% compliant patients, that is, 1.5 days versus 1.4 (Po0.01), respectively. The magnitude of this bias can be gauged in the top panel of Table 5, where we simulate the marginal effects of compliance on the probabilistic outcomes. Under HOLS-GMM (in the ‘‘Exogenous’’ columns of Table 5), not controlling for unobservable severity, moving from 50% compliance to 100% compliance reduces the probability of hospitalization from 14.2% to 11.9%, a 2.3 percentage point drop. However, under IV-GMM (in the ‘‘IV’’ columns of Table 5), moving from 50% compliance to 100% compliance reduces the probability of hospitalization from 15% to 11.5%, a 3.5 percentage point decline. The bias associated with unadjusted severity appears to be even higher in the case of ER visits. By moving from 50% to 100% compliance, the percentage decline in the probability of ER visits is 4.3 percentage points under the exogenous model (14.9% to 10.6%) versus 8 percentage points under IV-GMM (17.3% to 9.3%). Analogous findings apply to the conditional levels regressions. In particular, the decline in hospital costs is substantially greater under IV-GMM (middle panel of Table 5). Thus, using the regression coefficients obtained earlier from Table 4, increasing compliance from 50% to 100% reduces hospital costs from $13,977 to $10,715 for a hospitalized patient using the IV-GMM model, compared with virtually no change in these costs when the HOLS-GMM is used. Similar findings apply to side regressions we ran on the number of hospital days, conditional on hospital use (not shown in Table 5). Under HOLS-GMM, moving from 50% compliance to 100% compliance reduced the number of hospital days from 5.4 to 5.0 days, a drop of 7%. Under IV-GMM, moving from 50% compliance to 100% compliance reduced the number of hospital days from 7.4 to 5.6 days, a more dramatic decline of 24%. Overall, these findings indicate that failing to correct for unobservable severity leads to a substantial negative bias in measuring the impact of compliance.

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4.2. Drug Expenditures and Cost Offsets In our next set of results, we compare the incremental change in expected hospital costs associated with an increase from 50% to 100% compliance to the corresponding change in drug costs (bottom panel of Table 5). Note that expected hospital costs where estimated from the previously described two-part model. However, this procedure was not needed for calculating expected drug expenditures since all patients in our sample incurred positive drug expenditures (hence the conditional is equal to the expected in this case). Given our discussion earlier, we base this analysis on the estimates obtained from the IV estimators as summarized in Table 5, rather than the naı¨ ve models. Accordingly, increasing compliance from 50% to 100% reduces expected hospital costs for all patients in our sample from $2,097 per patient to $1,232 per patient. This implies expected annual savings of $865 ($2,097–$1,232) in hospital costs per diabetes patient.4 To examine whether these savings create cost offsets, we need to estimate the increase in drug costs associated with the increase in compliance. To select the appropriate model for these calculations, we followed the same general approach described above for the outcome regressions, with some changes. First, we based our assessment of the bias due to unobservable severity from the comparison of HOLS-GMM with the propensity score OLS rather than IV-GMM. Note that all our instrumental variables (drug coinsurance, DTCA, 90-day prescriptions, mail order, and the use of a pharmacy chain) directly predict drug spending as well as non-compliance; therefore, they cannot be used as instrumental variables in the model in which drug spending is the outcome. In Table 4, comparing the log of drug spending regressions in the HOLS-GMM and the propensity score models, we observe that the magnitude of the coefficient for non-compliance increases from 2.147 (Po0.01) to 2.422 (Po0.01), an 11% increase. Thus, not controlling for unobservable severity leads to an underestimation of the magnitude of drug savings due to increased non-compliance. The C-statistic from the HOLSGMM drug regression confirms that non-compliance is indeed endogenous with respect to drug costs (this C-statistic is based on the unrestricted and restricted Anderson-Rubin over-identification statistics and the test is a likelihood-ratio test; Hayashi, 2002). Given the results earlier, in Table 5, we define the propensity score OLS as the reference IV model for estimating drug costs. Next, we repeat our simulation of costs at 50% compliance and 100% compliance as with hospitalization outcomes; (again, we do not use the two part model for this variable since all patients in the data have positive expenditures on


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prescription drugs). From this we predict that increasing compliance from 50% to 100% increases diabetic drug costs from $325 to $1,105, an additional $766 per diabetic patient. The analogous change when no attempt is made to control for patient severity is made under HOLS-GMM (see the ‘‘Exogenous’’ column in Table 5) is somewhat smaller, at $548 per diabetic, from $284 to $832. Combining results for the models that control for unobservable severity, we find that an increase in compliance from the 50% level to the 100% level yields a cost offset of 1.12 (1.12 ¼ $865/$776) or 12%. Put differently, for every dollar spent on drug expenditures to increase compliance with diabetic drug prescriptions, payers incur savings of 0.12 dollars in hospital care, a net gain of 12%. These cost offsets are further increased if a reduction in ER costs, due to better compliance, is added. Here too, we take the propensity score OLS as the reference model for our final calculation in lieu of the IV-GMM. Note that while our instrumental variables for ER spending in Table 4 satisfy the threshold for the F-tests, they fall short of the threshold for the Hansen overidentification test (PW0.10), leading us to reject the hypothesis that the instruments are uncorrelated with unobservable severity (at Po0.01). Thus, comparing HOLS-GMM with the propensity score estimates for the log of ER spending per visit, we find a decrease in the coefficient for non-compliance, from 0.153 (P ¼ 0.019) to 0.099 (P ¼ 0.24). Thus, while controlling for unobservable severity increases the probability of an ER visit, it decreases the costs of ER visits once the patient makes an ER visit. Overall, by using the two-part model for expected ER cost (Table 5), we can estimate that increasing compliance from 50% to 100% reduces these costs from $49 ( ¼ .173 $283) to $28 ( ¼ .093 $297) for a diabetic. This yields an additional expected savings of $21 per diabetes patient annually. Combining hospital and ER cost savings results in an overall cost savings of $886 and a cost offset of 1.14 dollars for each additional dollar spent on diabetes drugs. Finally, it should be noted that repeating the full set of calculations described earlier using the parameter estimates of the Exogenous models rather than the IV models would have resulted in an overall dollar loss of 45% rather than a gain of 14% from the same incremental increase in compliance. Again, this indicates that failure to control for unobservable patient severity can lead to a large downward bias in estimating cost offsets associated with patient adherence with prescribed medications. In this study we used various estimation techniques to address that issue. Our adjusted estimates are relevant for evaluating the potential returns from insurer-based or employer-based programs designed to improve patient adherence.

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5. CONCLUSION Our research is one of the first demonstrations of a direct effect between medication adherence and hospitalizations after controlling for unobservable patient heterogeneity. Most other studies on the cost-offsets of prescription drugs have examined the impact of cost-sharing or prescription drug spending on inpatient and outpatient care rather than the pattern of use as would be reflected in adherence measures. Generally, such studies do not account for gaps in drug utilization or irregular use. Hence, our chapter fills an important gap in the literature. We focus particularly on the privately insured non-elderly. Our study is also one of the first to find inpatient cost-offsets in the nonelderly private sector. Most other papers in the literature focus on the elderly Medicare population. Hsu et al. (2006) found a cap on drug benefits in Medicare reduced drug use and costs, and increased inpatient and outpatient use. However, the added costs did not outweigh the drug costs savings. Similarly, Stuart et al. (2007) found no cost offset in Medicare 1999–2000 MCBS survey data. Shang and Goldman (2007) used a longer panel with 1992-2000 MCBS data and found a $1 increase in prescription drug spending was associated with a $2.06 reduction in Medicare spending. In a California Medicare sample, Chandra et al. (2007) generally found no net cost-savings (a 20-cent offset per one dollar drop in drug and doctor payments), but found net cost-savings for very ill patients with four or more chronic conditions (a 1.77 dollar offset per one dollar drop in drug and doctor payments). Zhang et al. (2009) found similar results among the elderly, with cost offsets ranging from 0.8 to 1.7 for those with modest or no drug coverage before enrolling in Medicare Part D. However, they also found that those with more generous coverage before Part D actually had cost increases (a $2.30 dollar increase in medical spending per dollar increase in drugs). It is important to note that most of this literature offers estimates of cost-offsets for comparisons of patients without any drug coverage and patients with such coverage. By contrast, our study pertains only to patients who had drug coverage, and everyone in our sample incurred some positive level of prescription drug usage. As a result, we were not able to examine uninsured diabetics who did not have access to any anti-diabetic medications during the year. Thus, our cost-offset of 1.14 dollars may be a conservative estimate. Although measurement issues required us to limit our analysis to hospitalrelated outcomes without considering office-related outcomes, we were able


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to obtain a rich set of results that yield important implications for optimal benefit design. First, we have shown a direct effect between medication adherence and either adverted hospitalizations or visits to the hospital emergency room. After controlling for unobservable severity, we can conclude that patients with higher (unobservable) severity are more apt to comply with their medications; conversely, patients with relatively lower severity are less likely to comply. We speculate that non-compliance among less severe patients may be due to the relative absence of easily recognizable symptoms during early stages of the disease. This suggests that health plans and employers might benefit (in terms of cost-offsets) from targeting younger, healthier diabetes patients in particular, with campaigns aimed at improving medication adherence. Similarly, patients in this group stand to reap the greatest benefit from adherence as a prevention strategy. Our estimated cost-offset of 1.14 dollars in averted hospital costs per dollar of additional drug spending is higher than that found in most of the recent research on the non-elderly. For instance, in another study of the non-elderly, Gaynor et al. (2007) found no net cost-savings. They found that the drug cost-savings from increasing copayments had no impact on inpatient hospital costs. However, they also found a 35-cent cost-offset for outpatient services (a dollar drop in drug spending increased outpatient spending by 35 cents). One limitation of our study, as well as prior related papers, is the lack of appropriate instrumental variables to address the endogeneity of adherence with respect to outpatient (office) utilization, stemming from the direct influence of prescribing physicians on the behavior of their patients. Future research should examine this issue by linking physician office data with patient data. By focusing on drug expenditures as a function of levels of adherence, our research findings shed light on the potential success of VBID, in which drug benefit packages are designed specifically to reward better adherence. Although VBID should avert hospitalizations and lead to better outcomes, it is not clear that the relatively modest cost-offsets reported in the literature (in our case $1.14 cost-offsets over a one year period from averted hospitalizations per dollar increase in drug spending.) will be large enough to justify the costs of implementing VBID programs without further policy intervention. There may be sufficiently large net-cost savings even if some outpatient office visits are in fact averted in ways not identified in prior research. One approach to ensure the viability of VBID is to combine it with valuebased purchasing of drugs. Under value-based purchasing, a shift towards

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generic medications in PBM formularies will aid in creating cost offsets that could be passed on to consumers or used to cross-subsidize VBID programs. Negotiating discounts would be particularly efficacious if drug manufacturers and pharmacy benefit managers (PBMs) already set certain drug prices just high enough to partly capture inpatient cost-offsets. We further note the need for future research to examine longer term benefits of adherence, using extended follow-up periods, where additional gains may occur.

NOTES 1. The average Part D premium increased sharply by 17% in 2008, followed by another 24% increase in 2009. 2. Economists have gone further to propose actual subsidies for preventive drugs and medical testing, for instance see Dor (2004), Dor and Encinosa (2004), Chernew, Encinosa, and Hirth (2000), Fendrick et al. (2001). 3. Our IV results could also traditionally be interpreted as an attenuation in OLS due to measurement error in noncompliance. However, we do not suspect there is much measurement error in noncompliance due to missing drug claims since our data has been taken only from the health plans in MarketScans with complete drug claims data. One other potential source of measurement error in non-compliance would be due to patients splitting pills (e.g., a patient ordering an extra strength 30-day prescription to split into a 60 day prescription). However, there are only four categories of drugs recommended for pill splitting: ace inhibitors, Angiotensin Receptor Blockers (ARBs), antidepressants, and lipid-lowering medications (see United Healthcare, 2007, Tablet Splitter, http://www.halftablet.com/faq.html). Diabetic medications are not to be split. Thus, we do not believe that the IV is correcting solely for measurement error; rather, it is controlling for unobservable patient severity. 4. In contrast, if one does not control for unobservable severity, this expected annual savings of $865 in hospital costs per diabetes patient declines to $290 ( ¼ $1,687 $1,397 from naı¨ ve ‘‘exogenous’’ columns of the bottom panel of Table 5, where $1,687 ¼ .142 $11,880 and 1,397 ¼ .119 $11,738). Thus, using the naı¨ ve HOLS-GMM model results in an underestimation of the hospital cost savings arising under improved compliance.

ACKNOWLEDGMENTS This work was funded by the Agency of Healthcare Research and Quality (AHRQ). The authors’ views do not necessarily represent the views or policies of AHRQ or DHHS.


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COST SHARING, BENEFIT DESIGN, AND ADHERENCE: THE CASE OF MULTIPLE SCLEROSIS Avi Dor, Maureen J. Lage, Marcy L. Tarrants and Jane Castelli-Haley ABSTRACT Purpose – The authors focus on understanding the relationship between costs and cost sharing on medication adherence for individuals who initiated a disease-modifying therapy (DMT) for the treatment of multiple sclerosis (MS). DMTs reduce the risk of relapse and are an essential component of MS treatment. Furthermore, the authors compare monthly payment levels for copayments versus coinsurance and estimate the effects on adherence. Methods – Using the MarketScan Commercial Claims and Encounters database evidence from July 1 2005 to March 31 2008, the authors employ a multivariate two-stage least-squares model (2SLS) to examine the impact of copayments or coinsurance on the medication possession ratio (MPR). Findings – Descriptive results show that the mean out-of-pocket (OOP) costs of DMT per month were higher for patients with coinsurance than Pharmaceutical Markets and Insurance Worldwide Advances in Health Economics and Health Services Research, Volume 22, 175–193 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1108/S0731-2199(2010)0000022011

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for patients with copayments. For the cohort of patients with copayment there was little difference in monthly copayments across adherence thresholds. Regression analysis shows that an increase in cost sharing reduces adherence overall, but this effect was small and insignificant in the copayment cohort. In contrast, in the coinsurance cohort increased cost sharing was significantly associated with decreased adherence to DMT medication; with a 10% increase in cost sharing leading to an 8.6% decline in adherence. Implications – Employers increasingly rely on coinsurance, despite evidence that reliance on coinsurance results in lower adherence. Our research findings suggest that coinsurance appears to be a greater obstacle to compliance, confirming predictions found in the theoretical literature. Originality – This research converted counts of injectable treatments into a continuous adherence measure. Previous literature on cost sharing did not examine MS.

INTRODUCTION Multiple sclerosis (MS) is the most common neurologically disabling condition among young adults in industrialized countries (Frohman, Frohman, Zee, McColl, & Galetta, 2005). Of the 2.5 million people around the world who have MS, an estimated 400,000 live in the United States (Noonan, Kathman, & White, 2002), where 200 individuals are newly diagnosed with the disease each week (National Multiple Sclerosis Society [NMSS], 2009). Characterized by progressive damage and destruction to the protective sheath surrounding nerve fibers of the central nervous system (CNS), MS is incurable and unpredictable in terms of precise symptoms and course of progression. Its negative impact on patient quality of life is well documented (Parkin et al., 2000; Murphy et al., 1998). The disease also imposes a heavy economic burden on patients, the U.S. health care system, and society, with the direct and indirect costs related to the disease estimated at over $47,000, in 2004 dollars (Kobelt, Berg, Atherly, & Hadjimichael, 2006) per person annually, translating into a conservative estimate of a national annual cost of $6.8 billion, and a total lifetime cost per case of $2.2 million (cost estimates based on 1994 data; Whetten-Goldstein, 1998). Since the early 1990s, disease-modifying therapies (DMTs) have been available for the treatment of relapsing forms MS1 and have been shown to reduce the frequency of relapses and to delay disability progression, as

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measured by the expanded disability status scale (EDSS) progression rate (Johnson et al., 1995; Jacobs et al., 1996; Prevention of Relapses and Disability by Interferon beta-1a Subcutaneously in Multiple Sclerosis [PRISMS] Study Group, 1998; The IFNB Multiple Sclerosis Study Group, 1993). More recently, long-term data on the use of these medications have become available and suggest that efficacy persists over an extended period of time (Johnson et al., 1998; The IFNB Multiple Sclerosis Study Group and The University of British Columbia MS/MRI Analysis Group, 1995; Kappos et al., 2009; Herndon et al., 2005; PRISMS Study Group and the University of British Columbia MS/MRI Analysis Group, 2001). Adherence to MS DMTs has been determined to be associated with various factors, including age and race (Lafata et al., 2008), education level, prior relapses (Tremlett et al., 2008), side-effect profile (Portaccio, Zipoli, Siracusa, Sorbi, & Amato, 2008; Mohr et al., 1998), level of self-efficacy (Fraser, Morgante, Hadjimichael, & Vollmer, 2004), and therapeutic expectations (Mohr et al., 1996). Research has also demonstrated that patient financial incentives can affect adherence to MS therapies (Lafata et al., 2008). Specifically, evidence indicates that as patient cost sharing increases, adherence to DMTs declines (Lafata et al., 2008; Ozminkowski et al., 2004). Likewise, for other classes of medications that are typically used to treat chronic conditions, such as diabetes, depression, hypertension, and chronic heart failure, increases in cost sharing have been found to be associated with decreased medication use (Cole, Norman, Weatherby, & Walker, 2006; Gibson, Ozminkowski, & Goetzel, 2005) and delayed initiation of drug therapy (Solomon, Goldman, Joyce, & Escarce, 2009). However, the relationship between patient financial incentives and medication adherence has not been consistent across all medication classes, patient samples, or disease states (Gibson et al., 2005). Some studies have indicated that higher income patients (Stuart & Grana, 1998; Smith & Kirking, 1992) or individuals with a greater need for medication (Blais, Couture, Rahme, & LeLorier, 2003; Pilote, Beck, Richard, & Eisenberg, 2002) are less sensitive to increases in cost sharing. Other studies have shown that, when faced with increases in cost sharing, chronically ill patients decrease their use of non-essential medications more so than essential chronic care drugs (Goldman et al., 2004; Harris, Stergachis, & Ried, 1990). Still other research, conducted among the elderly (Page, Barton, & Nair, 2008; Cole et al., 2006) and those on welfare (Tamblyn et al., 2001), has shown that increases in cost sharing can lead to significant reductions in the use of essential medications among these patient populations (Cole et al., 2006; Tamblyn et al., 2001), along with a resulting increase in the risk of adverse events (Page et al., 2008;

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Christian-Herman, Emons, & George, 2004). Finally, previous analyses have indicated that patients in coinsurance arrangements have lower rates of medication adherence relative to patients in copayment plans (Dor & Encinosa, 2004). In a coinsurance situation, the patient pays a percentage of the drug price and that percentage of any cost increase, whereas in a copayment situation, the patient pays a fixed dollar amount for medications and the employer pays 100% of the cost increase (Dor & Encinosa, 2004). Researchers have suggested that the greater economic uncertainty present under coinsurance relative to copayment situations leads to lower adherence among patients under coinsurance, even when the out-of-pocket (OOP) payments are the same between these two patient groups (Dor & Encinosa, 2004; Bymark & Waite, 2001). While the effects of cost sharing on adherence may vary according to patient demographics or drug type, research among patients with diabetes, hypercholesterolemia, hypertension, and congestive heart failure has shown that greater medication adherence can lead to decreased disease-related and all-cause costs and a significant return on investment (Sokol, McGuigan, Verbrugge, & Epstein, 2005). Other studies have indicated that increased cost sharing may be related to a greater use of health care resources other than medication (Page et al., 2008; Christian-Herman et al., 2004). In response to the evidence that improved medication adherence can reduce overall health-related costs, the U.S. corporations Pitney Bowes and Marriott have reduced or removed coinsurance on maintenance drugs for certain chronic conditions, such as diabetes and hypertension, and these changes have resulted in reduced per employee cost increases relative to benchmark companies (Capozza, 2008). Notwithstanding the complex relationships among cost sharing, adherence, and overall costs related to chronic health conditions, however, cost sharing for enrollees in employer-based health plans is increasing (Baker, 2004). Moreover, health plans are moving away from copayment and toward coinsurance (Encinosa, 2002), despite the evidence that adherence is lower among patients under coinsurance relative to copayment (Dor & Encinosa, 2004; Bymark & Waite, 2001). Given these recent trends, as well as the high cost of MS in this country, the objective of this study was to analyze the relationships between individual copayments or coinsurance and medication adherence among a population of patients with MS. To make this analysis most useful and to avoid ambiguity in the findings, drugs examined in this analysis were limited to DMTs for MS. This class of medications has demonstrated a significant reduction in the risk of relapse rate (Johnson et al, 1995; Jacobs et al., 1996; PRISMS Study Group, 1998;


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The IFNB Multiple Sclerosis Study Group, 1993) and thereby essential to the treatment of MS. The overarching goal of this research was to assist in the creation of health care incentives that lead to better patient outcomes and reduced costs for patients, employers, the health care system, and society as a whole.

DATA AND METHODS The MedStat MarketScan Commercial Claims and Encounters (CCE) database from July 1, 2005, through March 31, 2008, provided the data for these analyses. The CCE database includes private sector health data from approximately 100 payers and contains data on clinical utilization, expenditures, and enrollment across inpatient, outpatient, prescription drug, and carve-out services. The CCE database is fully Health Insurance Portability and Accountability Act (HIPAA) compliant and links paid claims and encounter data to detailed patient information across sites and types of providers over time. The analysis focused on the relationship between costs and cost sharing on medication adherence for individuals who initiated with a DMT for the treatment of MS. To be eligible for inclusion in the study, individuals had to receive an outpatient prescription or receive a procedure code for use of Avonexs, Betaserons, Copaxones, or Rebifs, with the first such date identified as the index date (all injectables). In addition, patients were required to have received at least one diagnosis of MS (ICD-9-CM of 340.xx) and to have continuous insurance coverage from 6 months prior to the index date (e.g., the pre-period) through 1.5 years post-index date (e.g., the postperiod). Finally, patients who were found to have negative total copayments, who had both copayments and coinsurance in the post-period, or had a recorded total non-positive number of days supplied of MS medication were excluded from the final analyses. These criteria result in a sample of 3,985 individuals. Table 1 illustrates how each of the inclusion/exclusion criteria impacted the final sample size. Given this sample, patients with copayments were matched to patients with coinsurance based on age, sex, and region of residence. This resulted in a final sample of 1,974 patients – 987 with copayments and 987 with coinsurance. Patient characteristics for this cohort were examined at the time of medication initiation. Such characteristics included patient age, sex, region of residence, employment status, and industry. In addition, patient general health status was proxied by both the Charlson score in the 6-month

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Table 1.

Inclusion/Exclusion Criteria.

Inclusion/Exclusion Criteria 1. Use of MS medication with first such date identified as index date and index date between January 1, 2006, and October 1, 2006 2. Receipt of at least one diagnosis of MS at any time 3. Continuous insurance coverage (including outpatient prescription drug coverage) from 182 days before index date through 564 days post-index date 4. Delete patients who initiated on Tysabri 5. Remove patients who have negative total copayments, negative total coinsurance, or negative total medication payments 6. Exclude patients who had both coinsurance and copayments in the first 6 months of the post-period 7. Exclude patients who have neither copayments nor coinsurance in first 6 months of post-period 8. Remove patients who did not have a positive MPR in last 12 months of the post-period

N 18,472 18,370 13,854 13,849 13,844 13,798 4,543 3,985

pre-period (D’Hoore, Sicotte, & Tilquin, 1993; Charlson, Pompei, Ales, & Mackenzie, 1987) and the total number of unique diagnoses (at the 3-digit level) the patient was diagnosed with during the same time period. Comorbid conditions examined for this cohort of patients included anxiety, depression, diabetes, hyperlipidemia, and hypertension. In addition, whether the patient received an outpatient prescription for a corticosteroid, anti-cholinergic, anticonvulsant, CNS stimulant, genitourinary medication, or musculoskeletal agent was also examined during the pre-period. To examine the relationship between medication costs and cost sharing on medication adherence, the 1.5-year post-period was divided into two periods. In the first 6 months, the type of insurance cost sharing a person had for MS DMTs was examined. Specifically, an individual was categorized as having copayments or coinsurance during the first 6 months post-index date. Costs were constructed over this same time period as the sum of all payments associated with the use of DMTs. Such costs were measured as gross payments to the provider and equaled the amount eligible for payment under the medical plan terms after applying rules such as discounts, but before applying coordination of benefits (COB), copayments, and deductibles. Copayment costs calculated as the sum of all copayments associated with use of DMTs were measured as payments made by the beneficiary to satisfy copayment plan provisions or apply to the stop-loss cap. Similarly, coinsurance costs were calculated as the sum of all


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coinsurance associated with the use of DMTs and were measured as payments made by the beneficiary to satisfy coinsurance plan provisions or apply to the stop-loss cap. All costs were converted to 2008 values using the medical component of the consumer price index. It was hypothesized that the amount of cost sharing associated with use of a medication in the first 6 months of the post-period affects patient adherence to such medication in the last year of the post-period. As a result, as patient copayments or coinsurance amounts increase, it is hypothesized adherence will decline. It was further hypothesized that, given coinsurance is associated with more uncertainty, in terms of patient expenditures, patients with coinsurance may be less adherent than those with copayments. Consistent with previous literature (Dor & Encinosa, 2004), the coinsurance rate squared is used as a proxy for patient uncertainty. To examine these issues, the relationship between costs and cost sharing in the first 6 months of the post-period and adherence in the last year of the post-period was examined. Consistent with previous research (Cooper, Hall, Penland, Krueger, & May, 2009; Ivanova et al., 2008), adherence to medication therapy was proxied by the medication possession ratio (MPR) – for example, the percentage of days any MS DMT medication was prescribed over the last year of the post-period. When examining unadjusted difference across insurance categories, chisquare statistics were used for categorical variables and t-statistics for continuous variables. A multivariate, two-stage, least-squares model (2SLS) was used to examine the impact of copayments or coinsurance on patient adherence. In the first stage, patient monthly copayments or coinsurance during the first 6 months of the post-period were estimated as a function of patient age, sex, region of residence, employment status, and industry of employment. Estimates of such costs were used in the second-stage regression that had medication adherence in the last year of the post-period as the dependent variable and also controlled for patient age, sex, region of residence, general health, comorbid conditions, and prior medication use. Slightly different specifications for the copayments and coinsurance adherence models were used; while both models included the monthly cost sharing amount as independent variables, the coinsurance rate was included only in the latter (copayment involves a flat cost sharing dollar amount). Note, the coinsurance term was squared, following Dor and Encinosa, who show that the squared term captures price uncertainty borne by patients in this regime. Finally, findings of p values r0.05 were considered to indicate statistically significant results. All analyses were conducted using SAS version 9.1.

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RESULTS AND DISCUSSION Table 2 presents the unadjusted descriptive statistics for patients with copayments and coinsurance. The typical patient was a 49-year-old female who was employed and resided in the North Central region of the United States. The most common comorbid condition in the 6 month period before MS medication initiation was hypertension, and the most commonly prescribed medications in such period were musculoskeletal agents, anticonvulsants, and corticosteroids. Comparing patients with copayments to those with coinsurance revealed no significant difference in these groups in terms of pre-period comorbid diagnoses, medication use, or general health. However, patients with coinsurance were significantly less likely to be employed, although when employed, they were found to be significantly more likely to be associated with the manufacturing of durable goods, compared to patients with copayments. Fig. 1 examines the relationship between patient adherence and patient OOP expenses for MS medications by type of insurance coverage. As expected, generally patients with copayment insurance had significantly lower monthly OOP expenses than those with coinsurance. In addition, patients with coinsurance who had low adherence thresholds (e.g., o0.50) were found to have higher monthly OOP expenses than similar patients with higher adherence threshold levels. For example, the mean OOP expenditure for patients in the coinsurance cohort with an adherence level between 0 and 0.25 was $1,116, while patients in the coinsurance cohort who had an adherence level of between 0.50 and 0.75 had a monthly mean OOP of $66. This relationship suggests that increased OOP may impact patient adherence to medication. In the copayment cohort, no relationship was found between adherence levels and copayment; however, there was also little variation found in copayment OOP levels in this dataset. Table 3 reports the results of the first stage of the 2SLS model. Specifically, the analysis focuses on how patient characteristics impact monthly copayments and coinsurance amounts during the first 6 months of the post-period. Note that among the independent variables in this regression are variables excluded from the second stage (instrumental variables). These include dummy variables for region and employment type.2 Results reveal that for both patients who had copayments or coinsurance, being employed was associated with significantly lower monthly payments. Patients with copayments who were associated with durable manufacturing, finance and real estate, or services industries also had significantly lower monthly copayments, while those in the non-durable

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Table 2.

Descriptive Statistics.

Variable

Frequencies Full sample

Copayment (N ¼ 987)

p Value Coinsurance (N ¼ 987)

%

N

%

N

%

188 380 820 586

9.52 19.25 41.54 29.69

94 190 410 293

9.52 19.25 41.54 29.69

94 190 410 293

9.52 19.25 41.54 29.69

422 1,552

21.38 78.62

211 776

21.38 78.62

211 776

21.38 78.62

106 1,338 420 108 2 1,697

5.37 67.78 21.28 5.47 0.10 85.97

53 669 210 54 1 904

5.37 67.78 21.28 5.47 0.10 91.59

53 669 210 54 1 793

5.37 67.78 21.28 5.47 0.10 80.34

745

37.74

178

18.03

567

57.45

86

4.36

84

8.41

2

0.20

235 86 298 496

11.91 4.36 15.10 25.13

229 48 287 161

23.40 4.86 29.08 16.31

6 38 11 363

0.61 3.85 1.11 36.78

General health (based on pre-period) Hospitalized with MS 38 diagnosis

1.93

17

1.72

21

2.13

0.5123

N Age (years) o35 35–44 45–54 55–64 Sex Male Female Region Northeast North Central South West Unknown Employed Industry Manufacturing – durable goods Manufacturing – nondurable goods Transportation Finance, real estate Services Missing/unknown

1.00

1.00

1.00

o0.0001 o0.0001

Pre-period comorbid diagnoses Anxiety Depression Diabetes Hyperlipidemia Hypertension

21 109 109 96 274

1.06 5.52 5.52 4.86 13.88

10 56 52 47 142

1.01 5.67 5.27 4.76 14.39

11 53 57 49 132

1.11 5.37 5.78 4.96 13.37

0.8263 0.7675 0.6222 0.8342 0.5151

Pre-period medication use Corticosteroid Anti-cholinergic Anti-convulsant CNS stimulant Genitourinary agent Musculoskeletal agent

399 71 480 345 349 509

20.21 3.60 24.32 17.48 17.68 25.79

198 42 240 185 174 262

20.06 4.26 24.32 18.74 17.63 26.55

201 29 240 160 175 247

20.36 2.94 24.32 16.21 17.73 25.03

0.8665 0.1161 1.00 0.1384 0.9530 0.4403

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Table 2. Variable

(Continued ). Means

Full sample Mean

SD

Patient general health (based on pre-period) Charlson score 0.28 0.77 Number of diagnoses 5.09 4.17 Adherence MPR 0.69 0.28

p Value

Copayment

Coinsurance

Mean

SD

Mean

SD

0.27 5.12

0.77 4.21

0.30 5.06

0.78 4.14

0.3209 0.7669

0.72

0.26

0.66

0.30

o0.0001

$1,400 $1,162

$1,200

ALL Copayments Coinsurance

$1,000 $800

$734

$600 $400

$316 $177

$200 $37

$31

$50 $34 $66

$44 $30 $58

$43 $42 $44

0 - 0.249*

0.25 - 0.499*

0.50 - 0.749*

0.75 - 0.99*

1+

$0

Fig. 1. Average Total Monthly Out-of-Pocket MS Medication Payments for Various MPR Levels. Note: Monthly out-of-pocket for MS payments based on first 6 months after medication initiation. po0.05.

manufacturing industry were found to have significantly higher copayments. In the coinsurance cohort, employment in the durable manufacturing industry was associated with lower monthly coinsurance payments, while employment in the transportation industry was associated with significantly higher monthly coinsurance payments. Table 4 reports the results of the second stage from the multivariate 2SLS regression. The analysis examines the impact of estimated copayments or coinsurance in the first 6 months of the post-period on medication adherence in the subsequent year, while controlling for patient characteristics, general health, comorbid conditions, and prior medication use. An examination of the copayment group revealed no significant impact of changes in monthly copayment amounts on medication adherence. However, patient age was

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Table 3.

Impact of Patient Characteristics on Copayments and Coinsurance Levels.

Variable

Copayment Coefficient

Intercept Female Age (years) 35–45 45–55 55–65 Region Northeast North Central West Employed Industry Durable manufacturing Non-durable manufacturing Transportation Finance, real estate Services Adjusted R2

Coinsurance

Standard error

Coefficient

Standard error

66.89 1.51

8.93 3.59

1,187.48 32.58

147.38 84.52

6.78 5.32 0.18

5.82 5.30 5.64

203.54 141.25 95.61

137.96 128.28 136.35

10.46 0.82 9.59 29.66

8.00 5.39 7.52 6.03

248.43 27.26 407.96 720.21

166.01 88.67 166.16 113.95

14.73 57.16 9.36 17.58 36.50

6.65 7.11 6.03 8.67 6.72

284.53 25.45 1,034.14 255.20 272.69

97.53 766.85 449.34 195.45 336.03

0.2051

0.1214

Notes: Result of OLS regression that examines the impact of patient sex, age, region of residence, employment status, and industry on average monthly copayment and coinsurance payments during the first 6 months of the post-period. This regression represents the first stage in the 2SLS model. po0.10; po0.05; po0.0001.

found to have a significant impact on adherence with older patients generally more adherent. In addition, the analysis revealed some evidence of a negative association between region of residence, pre-period diagnosis of depression, and pre-period use of anticonvulsants and medication adherence. In the coinsurance cohort, both the average monthly OOP coinsurance payments and the coinsurance rate squared were found to be inversely associated with medication adherence. This finding suggests that not only do the actual dollars of OOP payments impact medication adherence, but as patient uncertainty regarding such payments increases (as proxied by the coinsurance rate squared), patients are also significantly less likely to be adherent to the medication regimen. In addition, results from the coinsurance cohort revealed that patients who use anticonvulsants or CNS stimulants in the

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Table 4.

Impact of Copayments and Coinsurance on MPR.

Variable

Intercept Average copayment Average coinsurance Coinsurance rate squared Female Age (years) 35–44 45–54 55–64 Region Northeast North Central West Pre-period Charlson score Pre-period number of diagnoses Pre-period diagnosis of depression Pre-period diagnosis of hyperlipidemia Pre-period use of anticonvulsants Pre-period use of CNS stimulants R2

Copayment

Coinsurance

Coefficient

Standard error

Coefficient

Standard error

0.65 0.00006 – – 0.02

0.03 0.0004 – – 0.02

0.72 – 0.0001 3.86 0.02

0.04 – 0.0002 0.62 0.02

0.03 0.03 0.03

0.02 0.01 0.03

0.11 0.12 0.14

0.04 0.03 0.03

0.03 0.04 0.01 0.01 0.003 0.07 0.06

0.04 0.02 0.04 0.01 0.002 0.04 0.04

0.08 0.03 0.11 0.01 0.003 0.04 0.06

0.04 0.02 0.04 0.01 0.002 0.04 0.04

0.04 0.01

0.02 0.02

0.06 0.05

0.02 0.02

0.205

0.145

Notes: Result of 2SLS model where first stage examines the associations between monthly total copayments or coinsurance from the first 6 months of the post-period and patient characteristics (age, sex, region, employment status, and industry) and second stage examines the association between MPR in the last year of the post-period and estimated copayments or coinsurance from the first stage, while controlling for patient characteristics (age, sex, and region), general health (pre-period Charlson score and number of diagnoses), patient comorbidities (depression and hyperlipidemia), and prior medication use (anticonvulsants and CNS stimulants). po0.10; po0.05; po0.0001.

pre-period were generally less adherent. There was also some evidence of regional variation in medication adherence. This retrospective analysis was undertaken to answer several key questions related to financial incentives and medication adherence. First, to discover whether higher cost sharing would lead to lower adherence among a population of patients with MS, a disease that is both costly (Kobelt et al., 2006) and potentially devastating to patient quality of life (Parkin et al., 2000;


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Murphy et al., 1998). Second, to look at patients under coinsurance plans relative to patients under copayment plans to discover whether patients under coinsurance paid more per month for their medications. As expected, unadjusted statistics revealed that, within the cohort of patients under coinsurance, increased cost sharing was associated with decreased adherence to essential medications. Specifically, the patients within this cohort with the highest mean monthly payment ($1,162; SD $2,312) had the lowest level of adherence, filling their prescriptions less than 25% of the time, while patients under coinsurance who were completely adherent to the DMTs (i.e., they filled their prescription 100% of the time) paid an average of $44 per month (SD $60; see Fig. 1). As the mean monthly payment decreased among the coinsurance cohort, adherence to the DMTs steadily improved (see Fig. 1). This result is consistent with earlier research among patients with MS, which indicated that as patient cost sharing increases, adherence to DMTs declined (Lafata et al., 2008; Ozminkowski et al., 2004). It is not possible to say, given the constraints of the research model, whether the patients in this cohort with low adherence to DMTs chose to rely on symptomatic therapy (e.g., corticosteroids) or whether their OOP costs were so high that they avoided any type of treatment for MS. In either case, however, these results imply that there may be a threshold beyond which even those individuals who are privately insured and mostly actively employed (53.33%) are unwilling to commit to therapy with DMTs. These findings suggest that reducing coinsurance payments may lead to better adherence. While lowering patient cost sharing may increase drug costs for insurers, research (Sokol et al., 2005) and evidence from U.S. corporations have shown that greater medication adherence can lead to decreased disease-related and all-cause costs and a significant return on investment (Capozza, 2008). The results among the cohort of patients under copayment revealed a different pattern. Specifically, there was little difference in monthly copayments across alternative adherence thresholds. For example, patients who were the least compliant paid on average $37 per month (SD $94) while those who had the highest MPR paid $42 per month (SD $64). The finding from this database that monthly copayments were relatively stable over alternative adherence thresholds suggests other factors may be impacting patient adherence, such as good insurance and relatively low copayments. Other studies have shown adherence to MS DMTs to be associated with various factors, including education level, prior relapses (Tremlett et al., 2008), side-effect profile (Portaccio et al., 2008; Mohr et al., 1998), level of self-efficacy (Fraser et al., 2004), and therapeutic expectations (Mohr et al., 1996).

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The second set of questions related to differences in average monthly costs and adherence between the copayment and the coinsurance cohorts. Consistent with previous research in other disease settings (Dor & Encinosa, 2004), this analysis found that the mean OOP cost of DMT medication per month was higher for the coinsurance cohort at $284 (SD $1,148) relative to the copayment group cohort, at $32 (SD $51).3 However, in contrast with previous research (Dor & Encinosa, 2004; Bymark & Waite, 2001), multivariate analyses revealed no significant impact of monthly copayment amounts on patient adherence (see Table 4). This apparent discrepancy may arise from differences in the patient samples. The individuals in this sample were privately insured, and the majority of them were active, full-time employees (see Table 2), factors that may have made them less sensitive to differences in cost sharing. This result is therefore consistent with previous research that has indicated that higher income patients (Stuart & Grana, 1998; Smith & Kirking, 1992) are less likely to change their medication use due to increases in cost sharing. Finally, the patients in the copayment cohort may be flexible regarding cost sharing for DMTs based on their understanding that DMTs for MS are costly. Previous research has shown treatment with DMTs to cost upwards of $20,000 per patient per year (First DataBank, Inc., 2007). Consistent with these studies, this analysis revealed total costs for these drugs to be an average of $1,803 per month per user (SD $1,352) in the copayment group. Relative to the costs of those therapies without insurance, the patients in this study may have considered a monthly copayment of $32 to be a relative bargain, particularly considering the cost burden born by their insurer. While estimated copayment amounts were not found to statistically impact patient adherence, both estimated patient monthly coinsurance payments and the measure of patient risk among the cohort of patients with coinsurance were found to be statistically significant. Specifically, as patient monthly coinsurance payments increased, medication adherence declined. This finding, in contrast to the result regarding the impact of monthly copayment on adherence, may be due to the wider variability and higher mean of coinsurance payments in this database. While average monthly copayments were $32 per month (SD $51), average monthly coinsurance payments were $283 (SD $1,148). In addition, as patient risk, as proxied by the coinsurance rate squared, increased medication adherence declined. This finding suggests that the method of OOP payment impacts medication adherence independently of the amount paid by the patient. As with any research, the findings presented here should be interpreted within the context of the limitations of the study’s design. First, this analysis


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was conducted using an administrative claims database and included only patients with medical and outpatient prescription benefit coverage. The results, therefore, may not generalize well to other populations. Second, it is less rigorous to rely on diagnostic codes rather than formal diagnostic assessments for identifying patients. Third, although the study controlled for differences in patient characteristics, general health, and comorbidities, severity of disease could not be controlled for. Fourth, the use of medical claims data precluded the verification of medication adherence if no claim was filed. Fifth, this investigation examined adherence to specific DMTs and did not account for prescribed changes in treatment protocol. Thus, patients switched by their physicians from a DMT to a different type of drug during the study period would have been viewed as non-adherent, even if they were fully compliant with their prescribed therapy.

CONCLUSIONS In conclusion, this analysis found that in the setting of MS, the behavior of patients under coinsurance arrangements differed significantly from the behavior of patients under copayment insurance plans: under coinsurance, patient adherence to DMTs decreased as cost sharing levels increased. In addition, patient risk due to price uncertainty, as proxied by the coinsurance rate squared, was also found to be negatively associated with medication adherence. In contrast, no relationship was found between monthly average copayments and medication adherence, suggesting that forces other than the level of cost sharing may affect adherence in this group. It should be noted that in an earlier study focusing on diabetes, Dor and Encinosa (2004) did find that copayment reduced adherence to diabetes; however, they also found these effects were substantially smaller in this payment regime as compared with coinsurance regime. Moreover, their theoretical model showed that because of price uncertainty, the non-adherence effect of higher payments would always be higher under coinsurance. It is interesting to note that the research findings with respect to two very different diseases models, namely MS and diabetes, are both consistent with the theoretical predictions, despite having very different treatment patterns. The findings of this study as well as the prior literature on adherence and cost sharing show that the conditions leading to adherence are complex, variable, and likely to involve more than just the patients’ financial incentives. Further research is needed to investigate the ways in which insurance companies may incentivize the use of less costly therapies for

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chronic illness, as well as the impacts of such incentives on patient outcomes and total costs. Finally, future research should also examine whether patient outcomes are worse under coinsurance, independently from the dollar amount that is paid for copayments or coinsurance.

NOTES 1. Most MS patients (approximately 85%) are initially diagnosed with the relapsing-remitting form of the disease, in which acute attacks are followed by periods of complete or nearly complete remission; over time, about 50% of these patients move into secondary progressive MS with superimposed relapses on disease progression. The other 15% of MS patients are initially diagnosed with a continuously progressive form of MS for which there is no approved treatment. 2. Following, we conducted F-tests to examine the validity of the instruments. Under copayments, the joint F-test for the region indicators in the first-stage regression was F ¼ 1.61, p ¼ 0.1855, while the joint F-test for industry variables was F ¼ 49.02, po0.0001. Under coinsurance, the corresponding test scores were F ¼ 3.56 and F ¼ 3.69, both highly significant; the test score for all instruments combined was 7.01. Thus, the industry variables satisfy rules-of-thumb for instrumental variables described in Staiger and Stock (1997) and Bound, Jaeger, and Baker (1995) under copayments, while under coinsurance, the region indicators are also required. 3. The relative magnitudes of the copayment and coinsurance effects on MPR in Table 4 can be gauged from converting the regression coefficients to elasticities. The elasticity of MPR with respect to copayment was negligible and statistically insignificant (0.003). The elasticity of MPR with respect to coinsurance was 0.086 and highly significant (holding the coinsurance rate constant), meaning that a 10% increase in the coinsurance rate leads to an 8.6% decline in adherence. Note that the MPR rates for copayments and coinsurance are 0.71 and 0.66 respectively.

REFERENCES Baker, C. A. (2004). Cost sharing in medical insurance plans, 31 March. Available at http:// www.bls.gov/opub/cwc/content/cm20040326ar01p1.stm. Accessed on 5 October 2009. Blais, L., Couture, J., Rahme, E., & LeLorier, J. (2003). Impact of a cost sharing drug insurance plan on drug utilization among individuals receiving social assistance. Health Policy, 64(2), 163–172. Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association, 90(430), 443–450. Bymark, L., & Waite, K. (2001). Prescription drug use and expenditures in California: Key trends and drivers. Oakland, CA: California HealthCare Foundation.


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Capozza, K. (2008). First-dollar coverage for chronic disease care: can it save money and improve patient outcomes? UC Berkeley Center for Labor Research and Education Policy Brief, UC Berkeley Labor Center, September. Available at http://laborcenter. berkeley.edu/healthcare/chronic_disease_coverage08.pdf Charlson, M. E., Pompei, P., Ales, K. L., & Mackenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40, 373–383. Christian-Herman, J., Emons, M., & George, D. (2004). Effects of generic-only drug coverage in a Medicare HMO. Health Affairs (Millwood), July/December, Suppl., Web exclusives, W455–W68. Cole, J. A., Norman, H., Weatherby, L. B., & Walker, A. M. (2006). Drug copayment and adherence in chronic heart failure: Effect on cost and outcomes. Pharmacotherapy, 26(8), 1157–1164. Cooper, J., Hall, L., Penland, A., Krueger, A., & May, J. (2009). Measuring medication adherence. Population Health Management, 12(1), 25–30. D’Hoore, W., Sicotte, C., & Tilquin, C. (1993). Risk adjustment in outcome assessment: The Charlson comorbidity index. Methods of Information in Medicine, 32, 382–387. Dor, A., & Encinosa, W. (2004). Does cost sharing affect compliance? The case of prescription drugs. NBER Working Paper series, Working Paper no. 10738. National Bureau of Economic Research, Cambridge, MA, August. Available at http://www.nber.org/ papers/w10738 Encinosa, W. (2002). Pharmacy benefit design options available to employers. Expert Review of Pharmacoeconomics and Outcomes Research, 2(4), 389–396. First DataBank, Inc. (2007). Wholesale acquisition cost, November. Available at http:// www.firstdatabank.com/Products/price-point-rx.aspx. Accessed on November 2007. Fraser, C., Morgante, L., Hadjimichael, O., & Vollmer, T. (2004). A prospective study of adherence to glatiramer acetate in individuals with multiple sclerosis. Journal of Neuroscience Nursing, 36(3), 120–129. Frohman, E. M., Frohman, T. C., Zee, D. S., McColl, R., & Galetta, S. (2005). The neuroophthalmology of multiple sclerosis. The Lancet Neurology, 4(2), 111–121. Gibson, T. B., Ozminkowski, R. J., & Goetzel, R. Z. (2005). The effects of prescription drug costs sharing: A review of the evidence. The American Journal of Managed Care, 291, 2344–2350. Goldman, D. P., Joyce, G. F., Escarce, J. J., Pace, J. E., Solomon, M. D., Laouri, M., Landsman, P. B., & Teutsch, S. M. (2004). Pharmacy benefits and the use of drugs by chronically ill. The Journal of the American Medical Association, 291(19), 2344–2350. Harris, B. L., Stergachis, A., & Ried, L. D. (1990). The effect of drug co-payments on utilization and cost of pharmaceuticals in a health maintenance organization. Medical Care, 28(10), 907–917. Herndon, R. M., Rudick, R. A., Munschauer, F. E., Mass, M. K., Salazar, A. M., Coats, M. E., Labutta, R., Richert, J. R., Cohan, S. L., Genain, C., Goodkin, D., Toal, M., & Riester, K. (2005). Eight-year immunogenicity and safety of interferon beta-1a-Avonex treatment in patients with multiple sclerosis. Multiple Sclerosis, 11(4), 409–419. Ivanova, J. I., Birnbaum, H. G., Heish, M., Yu, A. P., Seal, B., van der Molen, T., Emani, S., Rosiello, R. A., & Colice, G. L. (2008). Adherence to inhaled corticosteroid use and local adverse events in persistent asthma. The American Journal of Managed Care, 14(12), 801–809.

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Page, R. L., Barton, P., & Nair, K. (2008). Effect of cost-sharing for prescription medications on health outcomes in older adults: A critical review of the literature and potential implications for managed care. The Consultant Pharmacist, 23(1), 44–54. Parkin, D., Jacoby, A., McNamee, P., Miller, P., Thomas, S., & Bates, D. (2000). Treatment of multiple sclerosis with interferon beta: An appraisal of cost-effectiveness and quality of life. Journal of Neurology, Neurosurgery, and Psychiatry, 68(2), 144–149. Pilote, L., Beck, C., Richard, H., & Eisenberg, M. J. (2002). The effects of cost-sharing on essential drug prescriptions, utilization of medical care and outcomes after acute myocardial infarction in elderly patients. Canadian Medical Association Journal, 167, 246–252. Portaccio, E., Zipoli, V., Siracusa, G., Sorbi, S., & Amato, M. P. (2008). Long-term adherence to interferon beta therapy in relapsing remitting multiple sclerosis. European Neurology, 59(3–4), 131–135. PRISMS Study Group. (1998). Randomised double-blind placebo-controlled study of interferon beta-1a in relapsing/remitting multiple sclerosis. The Lancet, 352(9139), 1498–1504. PRISMS Study Group and the University of British Columbia MS/MRI Analysis Group. (2001). PRISMS-4: Long-term efficacy of interferon-beta-1a in relapsing MS. Neurology, 56(12), 1620. Smith, D. G., & Kirking, D. M. (1992). Impact of consumer fees on drug utilisation. Pharmacoeconomics, 2(4), 335–342. Sokol, M. C., McGuigan, K. A., Verbrugge, R. R., & Epstein, R. S. (2005). Impact of medication adherence on hospitalization risk and healthcare cost. Medical Care, 43(6), 521–530. Solomon, M. D., Goldman, D. P., Joyce, G. F., & Escarce, J. J. (2009). Cost sharing and the initiation of drug therapy for the chronically ill. Archives of Internal Medicine, 169(8), 740–748. Staiger, D., & Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica, 65(3), 557–586. Stuart, B., & Grana, J. (1998). Ability to pay and the decision to medicate. Medical Care, 36(2), 202–211. Tamblyn, R., Laprise, R., Hanley, J. A., Abrahamowicz, M., Scott, S., Mayo, N., Hurley, J., Grad, R., Latimer, E., Perreault, R., McLeod, P., Huang, A., Larochelle, P., & Mallet, L. (2001). Adverse events associated with prescription drug cost-sharing among poor and elderly persons. The Journal of American Medical Association, 384(4), 421–429. The IFNB Multiple Sclerosis Study Group. (1993). Interferon beta-1 b is effective in relapsingremitting multiple sclerosis. I. Clinical results of a multicenter, randomized, doubleblind, placebo-controlled trial. Neurology, 43(4), 655–661. The IFNB Multiple Sclerosis Study Group and The University of British Columbia MS/MRI Analysis Group. (1995). Interferon beta-1b in the treatment of multiple sclerosis: Final outcome of the randomized controlled trial. Neurology, 45(7), 1277–1285. Tremlett, H., Ven der Mei, I., Pittas, F., Blizzard, L., Paley, G., Dwyer, T., Taylor, B., & Ponsonby, A. L. (2008). Adherence to the immunomodulatory drugs for multiple sclerosis: Contrasting factors affect stopping drug and missing doses. Pharmacoepidemiology and Drug Safety, 17(6), 565–576. Whetten-Goldstein, K. (1998). A comprehensive assessment of the cost of multiple sclerosis in the United States. Multiple Sclerosis, 4(5), 419–425.

GENERIC UTILIZATION AND COST-SHARING FOR PRESCRIPTION DRUGS Teresa Bernard Gibson, Catherine G. McLaughlin and Dean G. Smith ABSTRACT Purpose – The purpose of this study is to estimate the own- and crossprice elasticity of brand-name outpatient prescription drug cost-sharing for maintenance medications and to estimate the effects of changes in the price differential between generic and brand-name prescription drugs. Methodology/approach – We first review the literature on the effects of an increase in brand-name drug patient cost-sharing. In addition, we analyze two examples of utilization patterns in filling behavior associated with an increase in brand-name cost-sharing for patients in employersponsored health plans with chronic illness. Findings – We found that the own-price elasticity of demand for brandname prescription drugs was inelastic. However, the cross-price elasticity was not consistent in sign, and utilization patterns for generic prescription fills did not always increase after a rise in brand-name cost-sharing. Research limitations – The empirical examples are limited to the experience of patients with employer-sponsored health insurance. Pharmaceutical Markets and Insurance Worldwide Advances in Health Economics and Health Services Research, Volume 22, 195–219 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1108/S0731-2199(2010)0000022012

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Practical implications – The common practice of increasing brand-name prescription drug patient cost-sharing to increase consumption of generic drugs may not always result in higher generic medication use. Higher brand-name drug cost-sharing levels may result in discontinuation of chronic therapies, instead of therapeutic switching. Originality/value of chapter – The value of this chapter is its singular focus on the effects of higher brand-name drug cost-sharing through a synthesis of the literature examining the own- and cross-price elasticity of demand for brand-name medications and two empirical examples of the effects of changes in brand-name cost-sharing.

1. INTRODUCTION In an era of rapidly rising prescription drug expenditures, health plans and employers in the United States have offered financial incentives to enrollees to select less expensive generic medications in lieu of brand-name drugs with higher ingredient costs. Over time, within employer-based health plans, costsharing levels for generic medications have increased by 25% from $8 to $10 between 2000 and 2009 (Kaiser Family Foundation and the Health Research and Educational Trust, 2009). In contrast, cost-sharing levels for preferred and nonpreferred brand-name medications in tiered pharmacy benefit plans have increased at a much higher rate, rising 80% from $15 to $27 and 59% from $29 to $46, respectively, during the same time (Kaiser Family Foundation and the Health Research and Educational Trust, 2009). Even in light of these trends, little is known about the own- and crossprice elasticity of demand for brand-name and generic medications. To some extent, generic incentive policies have been instituted based on the assumption that consumers see generic medications as good financial substitutes for brand-name medications. However, the strength and extent of the evidence that this assumption is true is less clear. We add to the body of evidence by analyzing the effects of an increase in brand-name medication cost-sharing amounts for maintenance medications. Our research has two aims: first, to estimate the own- and cross-price elasticity of demand for brand-name outpatient prescription maintenance medications and, second, to estimate the effects of changes in the price differential between generic and brand-name prescription drugs. To accomplish these aims, we first exploit a natural experiment with data from two large firms. In the late 1990s, the first firm implemented higher

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cost-sharing levels for brand-name drugs purchased in pharmacies. The second firm serves as a comparison group, as it made no changes to their drug benefits during the same period. We focus on chronically ill early retirees within these two firms who used maintenance medications because they were likely to be regular purchasers of prescription drugs. Second, we provide a more recent example using 2003–2006 data from employersponsored health plans in the Medstat MarketScan Database, examining the utilization of prescription drugs among chronically ill patients with type 2 diabetes on two classes of oral antidiabetic medications. Unlike many previous studies of the impact of changes in cost-sharing on the utilization of prescription drugs, this study focuses on changes in the utilization of maintenance medications that are indicated for chronic conditions, drugs that are to be taken regularly over time. We focus on these drugs for several reasons. One, in contrast to acute care drugs that are taken for short periods and are typically used to treat illness shocks and acute exacerbations of chronic conditions, maintenance drugs are meant to be taken regularly over a long period. The cost impact of a change in cost-sharing for maintenance medication is long term as well, and therefore, the price elasticity of demand is likely to differ. Second, adherence to maintenance drugs can affect both long- and short-term health outcomes for patients with chronic conditions, increasing the importance of understanding how financial incentives affect the demand for maintenance drugs. Improved estimates of the response of patients with ongoing health problems to changes in costsharing can inform improved benefit design. This chapter is organized as follows. In Section 1.1, we review the relevant literature on the effects of prescription drug cost-sharing on the utilization of brand and generic drugs. Section 2 contains an economic and conceptual framework. Sections 3 and 4 describe the two empirical studies we conducted and their results. In Section 5, we offer some conclusions.

1.1. Review of the Relevant Literature There are a number of observational studies that provide insights into the effects of cost-sharing for brand-name prescription drug on brand-name and generic medication utilization (Christian-Herman, Emons, & George, 2004; Gibson, McLaughlin, & Smith, 2005; Joyce, Escarce, Solomon, & Goldman, 2002; Motheral & Henderson, 1999; Gilman & Kautter, 2008; Landon et al., 2007; Motheral & Fairman, 2001; Nair et al., 2003; Roebuck & Liberman, 2009). These studies are summarized in Table 1.

Enrollees in employersponsored health plans (1994–1997) Enrollees in employersponsored Medicare supplemental plans (2002) Commercially insured 18–64 years (1997–1999)

Commercially insuredo64 years (2000–2001)

Gibson et al. (2005)

Landon et al. (2007)

Joyce et al. (2002)

Gilman and Kautter (2008)

Medicare HMO (2001–2002)

Study Population

Type of Medication

Plans instituting a copayment change in 2001 (analyzed six pairs of plans where only brand-name copayments increased)

One-tier/two-tier/three-tier plans

One-tier/three-tier plans

All

Decrease

Mostly lower brand Highest spending in spending for two- and one-tier plans, twothree-tier plans (not all tier lower spending , effects are significantly three tier also lower different from one-tier than one tier (but not plans) always lower than two tier)

No change

Maintenance medications

Decrease (multi-source and single-source brand-name medications) Decrease

Decrease

Decrease

Decrease

Brand-name fills

Increase

Generic fills

Percent generic higher for four copayment comparisons, no different for two copayment comparisons

Higher percent generic prescriptions Higher percent generic prescriptions

–

Increase in GFR larger in cases compared with control group

Generic fill ratio (GFR)/ percent generic

Effect of Cost-Sharing Increase in Brand-Name Prescription Drugs

All

All Introduction of a generic-only drug benefit to Medicare HMO enrollees from a plan covering both brand and generic medications All One-tier ($2 all medications) to two-tier ($2/$7) plan

Cost-Sharing

Review of the Literature on Cost-Sharing and Brand and Generic Utilization.

Christian-Herman et al. (2004)

Reference

Table 1.

Enrollees in a large managed care plan (HMO, PPO and Medicare þ Choice members) with prescription utilization in at least one of five classes of maintenance medications PBM clients serviced by CVS Caremark (2005–2007)

Nair et al. (2003)

Out-of-pocket cost within tiers (generic/brand/ nonpreferred brand)

Brand increase $10 to $15, generic $4 to $5 or $5 to $7 Two-tier ($7/$12 retail, $10 mail order) to three-tier plan ($8/$15/$25, double copayments for mail order) Shift from a two-tier to a three-tier plan

Note: PBM, Pharmacy Benefit Manager.

Roebuck and Liberman (2009)

Commercially insuredZ18 years (1996–1997) Enrollees in a Midwestern PPO

Motheral and Henderson (1999) Motheral and Fairman (2001)

All

Two-tier plans: No change Three-tier plans: Small decrease with preferred brand out-of-pocket cost squared but no change with nonpreferred out-of-pocket cost

No change

All

All

No change

All

Three-tier plans: Decrease with nonpreferred brand-name out-ofpocket cost and but not preferred brand out-ofpocket cost

Two-tier plans: Decrease

Decrease

Decrease

Increase in generic dispensing rate

Increase in generic utilization ratio

No change

Increase

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Here, we review both studies reporting the effects of brand-name copayments and studies measuring the effects of brand-name copayments that occur along with small changes in generic copayments. Most of these studies focus on utilization of acute and maintenance medications combined in terms of the volume of prescriptions filled, although one study presents findings on maintenance medications alone (Gilman & Kautter, 2008). All of these studies reported lower brand-name drug utilization associated with higher brand-name copayments with one slight exception. When comparing two- and three-tier plans to one-tier plans with the same generic copayment, Joyce and colleagues (2002) found a decrease in single-source brand-name drug spending (drugs remaining on patent) in two- and threetier plans relative to one-tier plans but no difference in multi-source brandname drug spending (drugs with generic substitutes available). The effects of changes in brand-name prices on generic utilization are mixed although only one study found an increase in generic use concurrent with an increase in cost-sharing for brand-name prescription drugs. Christian-Herman et al. (2004) examined the effects of a large increase in the brand/generic price differential due to the introduction of a generic-only drug benefit for Medicare health maintenance organization (HMO) enrollees who previously had a brand/generic benefit that covered both brand and generic drugs. This change in plan design was associated with a larger (20%) increase in the volume of generic drugs dispensed when compared to a 13.7% rise in the volume of generic dispensing in a control group that maintained brand/generic coverage. Three studies report a decrease in generic utilization associated with higher brand-name copayments. First, an analysis of a natural experiment – the change to a brand/generic copayment differential ($2/$7) from a single copayment amount ($2) for active employees in a single employer – found a drop in generic utilization with an estimated cross-price elasticity of .032 (Gibson et al., 2005). Second, Joyce et al. (2002) studied changes in the health care utilization of privately insured individuals under various copayment scenarios. They estimated the effects of a brand/generic copayment differential for a one-tier ($5 flat copayment) compared to a two-tier ($5 generic/$10 brand) or three-tier ($5 generic/$10 preferred brand/$15 nonpreferred brand) plan and found lower generic spending per member in tiered plans (Joyce et al., 2002). Similar results were found comparing a $10 one-tier plan to a $10/$20 two-tier or to a $10/$20/$30 three-tier plan. Third, a cross-sectional study of Medicare beneficiaries with employer-sponsored supplemental coverage revealed lower total generic utilization for enrollees in three-tier plans (with mixed benefit structures) compared to enrollees in onetier plans with a $5 or $10 copayment (Gilman & Kautter, 2008).


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Finally, four studies report no statistically significant changes in generic utilization associated with higher brand-name copayments. First, in an early study by Motheral and Henderson (1999), an increase in the brand/generic differential (from $4/10 to $5/15 for one commercial health plan and from $5/ 10 to $7/15 for a second commercial health plan) was not associated with a significant change in generic claims. Second, Motheral and Fairman (2001) studied preferred provider organization (PPO) enrollees moving from a twotier ($7/$12 retail, $10 mail order) to a three-tier plan ($8/$15/$25, double copayments for mail order) and again found no changes in generic utilization. Third, assessing the independent effects of brand-name patient costs on generic use, Roebuck and Liberman (2009) reported that retail brand-name costs in two-tier plans were not associated with changes in generic utilization. In three-tier plans, nonpreferred brand-name costs (and its squared term) and preferred brand-name costs were not associated with changes in generic utilization, although the square of preferred brand-name costs was associated with a very small decline in generic use. Fourth, in the aforementioned study of Medicare beneficiaries with employer-sponsored supplemental coverage, generic utilization for maintenance medications for chronic conditions was no lower in three-tier plans (with mixed benefit structures) than in one-tier plans (Gilman & Kautter, 2008). A number of studies estimate the effects of changes in cost-sharing for brand-name drugs on generic drug utilization by measuring changes in the generic fill rate, a widely cited measure that is defined as the percent generic prescriptions of total prescriptions (generic prescriptions/brand plus generic prescriptions). As it is a ratio, this measure is imperfect, because the generic fill rate can increase with a decline in the volume of brand-name prescription fills, as long as the drop in brand-name fills exceeds the change in generic fills. Also, the generic fill rate can rise absent any change in the volume of generic medications (Gibson, Ozminkowski, & Goetzel, 2006). Most of these studies report an increase in the generic fill rate with higher levels of cost brand-name cost-sharing (Christian-Herman et al., 2004; Gilman & Kautter, 2008; Motheral & Henderson, 1999; Nair et al., 2003; Roebuck & Liberman, 2009). However, two studies report mixed effects. In the same study that found no change in generic utilization when moving from a two-tier to a three-tier plan, Motheral and Fairman (2001) reported no change in the generic fill rate. In another study, when comparing six pairs of plan designs (each one-tier plan design was paired with a tiered plan with the same generic copayment and a higher brand-name copayments (e.g., $5 versus $5/$10)), four of the plans with higher brand-name copayments

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had a higher generic fill rate, although two plans with higher brand-name copayments had a generic fill rate that was no different (Landon et al., 2007). In three of these studies, the authors parse out the components of the generic fill rate. As summarized above, Christian-Herman et al. (2004) analyzed the effects of introducing a generic-only benefit for a Medicare HMO population. In addition to finding an increase in the utilization of generic drugs, they found a drop in brand-name utilization and a concomitant rise in the generic dispensing rate. In the other two studies, however, the increase in the generic dispensing rate did not appear to be due to an increase in generic utilization but rather was driven by a decline in brand-name utilization (Gilman & Kautter, 2008; Motheral & Henderson, 1999).

1.2. Study Contribution This study contributes to the literature in several ways. We examine issues related to changes in generic utilization associated with a change in brandname prescription drug patient cost-sharing amounts and provide an economic framework surrounding these issues. We also use two different patient samples to estimate the magnitude and direction of effects of changes in brand-name prescription drug cost-sharing when there is no concurrent change in generic prices. In each case, identification of effects is primarily based on changes in cost-sharing over time.

2. MODELING THE RELATIONSHIP BETWEEN BRAND-NAME COST-SHARING AND GENERIC UTILIZATION Generic medications can substitute for brand-name medications in two ways. First, after patent expiration (17 years), a generic medication can substitute for the original brand-name medication with the same active ingredient. This type of substitution has been the focus of mandatory generic substitution policies that require use of a generic equivalent after patent expiration without explicit direction from the provider to fill the brand-name medication. Griliches and Cockburn (1994) cite a ‘‘putative loss,’’ whether real or imagined, related to a perceived loss to the consumer in security or quality associated with generic drug purchases compared to an equivalent brand-name drug. Using a linear utility framework, patients will switch to a generic medication when the copayment differential between


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brand-name (pb) and generic drugs (pg) exceeds an individual-specific brand premium (bh, where h is an individual) required to compensate for this perceived loss when choosing a generic drug, pb W pg þ bh. A second type of substitution would occur when a brand-name medication is still under patent protection and no generic substitutes exist. Any substitution must be made with a generic drug with a different active ingredient, which, by definition, will be older. One would posit that the putative loss associated with this type of substitution may be larger on average as patients substitute a drug with a newer active ingredient with a drug with an older active ingredient. Consistent with this concept, studies report a higher percent generic fill rate associated with an increase in the brand/generic differential (ChristianHerman et al., 2004; Motheral & Henderson, 1999; Gilman & Kautter, 2008; Landon et al., 2007; Motheral & Fairman, 2001; Nair et al., 2003, Roebuck & Liberman, 2009). To our knowledge, no studies report the effects of the size of the differential on the rate of generic utilization. In our second study presented here, we analyze these effects. In addition, no studies compare the size and sign of the own- and cross- price elasticities of brandname and generic medications. We make this comparison for two classes of diabetes medications. If brand-name and generic maintenance medications are gross substitutes, in conjunction with higher cost-sharing for brand-name medications, we would see a rise in the utilization of generic medications for chronic conditions when cost-sharing for brand-name medications rose. The studies reviewed above give conflicting results, estimating positive, negative, and zero cross-price elasticities. We examine the Slutsky equation for cross-price elasticity @xðP; IÞ=@Py to analyze the income and substitution effects of a change in the price of brand-name prescription (y) drugs on generic drug (x) use. h i ZxPy ¼ ZxPy f y x U

If generic medications are a normal good ex W 0, and a gross substitute for brand-name drugs, the cross-price elasticity of demand for generic drugs with a change in the price of brand-name drugs ZxPy will be positive, that is, the substitution effect exceeds the income effect. Similarly, if generic medications are a gross complement for brand-name drugs, the cross-price elasticity will be negative, that is, the income effect exceeds the substitution effect. To observe the expected cross-price elasticity, one would need to assign a brand-to-generic cost-sharing differential exceeding the putative loss.

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Alternatively, certain generic medications may be inferior goods, when, for example, when bn is large. Given that we observe aggregate data, not individual consumption data, we may not observe the distribution of bn and may wrongly assume it to be close to zero. In fact, in individual data, we observe the specific price changes associated with the patient’s plan, not the full range of price changes and resulting behavior (use or no use), and therefore, it may be difficult to estimate bn using administrative claims. This warrants further study. Given the results presented so far, the income-compensated price elasticity is not known, and we cannot infer whether generic and brandname maintenance drugs are true substitutes or true complements. In the studies presented here, we provide further evidence of the extent of substitution of generic medications for brand-name medications.

3. STUDY NO. 1: NATURAL EXPERIMENT 3.1. Methods The first study exploits a natural experiment examining data from two large firms that contributed data to the Medstat MarketScan Database. Employer A increased copayments for brand-name drugs from $2 to $7 per prescription. The copayment for generic drugs remained at $2 per prescription. Employer B’s copayment remained unchanged at $4 per prescription during the study period. The prescription drug plans for Employer A and Employer B were administered by the same pharmacy benefits manager. The study period is 20 months, 10 months before and 10 months after the Employer A copayment increase. The 10-month windows were selected for two reasons. First, the intent was to focus on the short-term effects of the copayment increase. Second, Employer B made several changes to their benefit plan design a year after the copayment change that may confound the results. Both employers limited retail refills to a one-month supply, and therefore, changes in prescription utilization were more likely to reflect filling behavior than changes in the amount supplied. Early retirees and dependents of Employer A could receive up to a 90-day supply of maintenance medications at one time through a mail order program, with no copayment assessed ($0). Employer B’s early retirees and dependents could also receive up to a 90-day supply of maintenance medications through mail order for one copayment amount ($4). Employer A offered a comprehensive medical plan (indemnity coverage) to its early retirees and dependents, including a carved-out drug plan. The


205

comprehensive plan had 10% coinsurance for most medical services after a nominal deductible was met. HMOs were offered in a few limited geographic areas but had low enrollment, and examination of monthly attrition rates showed that there was no evidence of excess attrition out of the fee-for-service plan around the time of the copayment increase. HMO data were not available for study. Employer B offered a comprehensive medical plan and a PPO to its early retirees and their dependents. Both plans also had a 10% cost-sharing provision for most medical services after a nominal deductible was met. Employer B’s PPO plan assessed a 20% coinsurance amount for outof-network services. Most PPO services (98.2% of claims) were delivered in-network. Enrollees switching between Employer B’s PPO and comprehensive plan at any time during the study period were included in the study. Both employers raised their calendar-year medical plan (not prescription drug) deductible amount at the beginning of the year. Employer A raised its annual medical deductible from $50 to $100 a year, and Employer B raised its annual medical deductible from $95 to $125 a year. Each deductible increase is small and similar in size, and therefore, the difference-indifferences modeling approach is likely to account for common effects, if any, of the deductible increase. In addition, sensitivity testing (not reported) was performed and revealed that the deductible increase did not have had an effect on subsequent utilization. Employer A had several important attributes: availability of enrollment, drug, and medical data across several years and the existence of a carved-out drug benefit that was offered in all fee-for-service plans to lessen the likelihood of selection due to the generosity of the drug benefit. Employer B was found to be the most appropriate employer in the MarketScan Database that could serve as a control group, because Employer B had data during the same period, used the same drug vendor as Employer A, did not change drug copayments, and offered health benefits to a large group of early retirees. In addition, pre-period (before the cost-sharing increase) trends in utilization and spending were similar between the two employers.

3.2. Patient Selection Continuously enrolled early retirees and their spouses who were between 40 and 64 years during the study time frame were selected. Among these, the study was limited to enrollees with at least one irreversible chronic illness before the copayment increase. Patients had to fill at least one prescription

206


for a maintenance drug in the pre-period (the first 10 months of the study). This resulted in 2,507 enrollees from Employer A and 4,607 patients from Employer B. A panel dataset was created containing 20 monthly observations for each of the 7,114 individuals meeting the study criteria.

3.3. Dependent Variables Total prescription drug utilization was measured by counting the number of prescriptions filled in each month. Maintenance prescription drugs were identified using a drug type indicator of ‘‘chronic–only’’ on each drug claim (versus acute only or acute/chronic). Prescriptions also were classified as either generic, single-source brand name (patent-protected with no generic substitutes available) or multi-source brand name (patent has expired and generic substitutes are available), according to the status of the drug on the date the prescription was filled. Each drug was classified as a maintenance drug or as a generic drug at the National Drug Code level using information from the Redbook drug information database. Prescriptions were classified as retail when filled in a retail pharmacy or mail order when filled by a mail order pharmacy. 3.4. Explanatory Variables Three explanatory variables are included in the models to estimate the effects of the copayment (price) increase. The first variable is a fixed effect for Employer A, representing the unmeasured health plan and other characteristics of Employer A distinguished from Employer B (Employer A). The second is an indicator variable for the period after the copayment increase (Post), and the third is the interaction of these two variables (Employer A Post). Determinants of the demand for health, such as patient characteristics, prices, and income, are likewise determinants of the demand for medical care (Grossman, 2000). Many determinants of health that have been used as determinants of prescription drug utilization have been included as explanatory variables including patient demographic characteristics representing female gender, residence in the North Central Census region, employee status (versus spouse), and patient age in years (e.g., Gibson et al., 2005; Goldman et al., 2004; Motheral & Fairman, 2001). Income is also a determinant of prescription drug utilization and, because actual income was not available, median household income from the U.S. Census files was


207

assigned based on the patient’s ZIP code of residence. A natural log transformation of income in the area of residence allows interpretation of the estimated coefficients as income elasticity. Chronic diseases were identified for each person by applying the disease staging algorithm (Gonnella, Hornbrook, & Lewis, 1984; Gonnella, Lewis, Gozum, Callahan, & Barnes, 2005) to all diagnosis codes occurring on at least two different dates in the pre-period, to eliminate rule-out diagnosis codes. Within disease staging, chronic diseases are defined as generally irreversible diseases that were grouped into 12 chronic categories: Artery Disorders, Asthma, Cancer, Chronic Cardiac Problems, Chronic Coronary Artery Disease, Chronic Liver Disease, Chronic Respiratory Disease, Diabetes Mellitus, Immune Disorders and HIV, Metastatic Malignancies, Psychiatric Disorders, and Renal, Urinary and Bladder Disease. The number of chronic conditions per person was also included. While all enrollees in Employer A’s health plan were enrolled in a single type of plan, an indicator variable was created to distinguish Employer B’s PPO plan from its comprehensive medical plan, controlling for plan-related differences, including physician cost-sharing, which can be associated with prescription drug utilization (Hillman et al., 1999). A linear time trend was included to allow for secular changes in utilization over time. Several specifications were tested to incorporate contemporaneous effects of time (e.g., log transformation); however, over such a short period, the time trend was relatively negligible, and the specification of the time variable did not affect the elasticity estimates. 3.5. Estimation Prescription drug utilization was modeled (Useit) as a function of demographic, plan, chronic disease, income, and trend variables (Eq. (1)). Utilization was also a function of three indicator effect variables: Employer A, Post, and Employer A Post. Generalized estimating equations with a negative binomial family and a log link were used to estimate the following models. We estimate total prescription drug utilization and produce separate estimates for categories of maintenance drugs: multi-source brand-name drugs, single-source brand-name drugs, and generic drugs. Useit ¼ expða1 þ a2 Demographicit þ a3 Planit þ a4 Chronici þ a5 Incomeit þ a6 Trend t þ b1 EmployerAi þ b2 Postt þ b3 EmployerA Postit Þ

ð1Þ

208


where i is patient and t is month. Robust standard errors were estimated and standard errors were adjusted for repeated observations by patient. The effects of the copayment increase were estimated using a differencein-differences estimator (DDUse). ^

^

^

^

DDUse ¼ ½UseEmpAPost UseEmpAPre ½UseEmpBPost UseEmpBPre

(2)

Using a DD approach, the effects of the copayment increase are identified by the change in Employer A’s utilization trends before and after the copayment increase, net of changes in the comparison employer during the same period. A strong assumption is that the Employer B trends represent contemporaneous trends for firms without a change in cost-sharing. 3.6. Results Characteristics of Employer A and Employer B are compared in Table 2. In general, the employee samples within two employers are relatively similar to Table 2.

First-Month Patient Characteristics by Employer.

Female (%) Age (years) Household income of residence (ln) Employee (%) North Central Region (%) Number of chronic conditions Cancer (%) Metastatic malignancies (%) Immune disorders and HIV (%) Chronic respiratory diseases (%) Non-acute coronary artery diseases (%) Other non-acute coronary problems (%) Other artery disorders (%) Chronic liver disease (%) Diabetes mellitus (%) Renal, urinary, and bladder disease (%) Asthma (%) Psychiatric disorders (%)


Employer A, n ¼ 2,507

Employer B, n ¼ 4,607

p

0.597 56.3 (5.05) 10.437 (.32) 0.682 0.959 1.232 (.52) 0.084 0.011 0.015 0.342 0.377 0.040 0.077 0.010 0.116 0.007 0.146 0.018

0.610 58.0 (3.93) 10.564 (.35) 0.648 0.916 1.219 (.5) 0.074 0.010 0.014 0.292 0.433 0.033 0.094 0.007 0.114 0.007 0.135 0.016

0.299 o.01 o.01 o.01 o.01 0.152 0.142 0.769 0.614 o.01 o.01 0.113 0.020 0.022 0.828 0.826 0.169 0.601

209


each other in terms of patient sociodemographic characteristics and health status. Where significant differences exist, they are typically not large; for example, the average age of employees in Employer A is 56.3 years, while the average age for employees in Employer B is 58 years. The percent of patients with each chronic condition is similar for 8 of 12 conditions. Of the four conditions where the samples are different, the treated prevalence of two (other artery disorders and chronic liver disease) are within two percentage points of each other. In the two remaining conditions, Employer A has a higher prevalence of chronic respiratory diseases (34.2% compared to 29.2% for Employer B), and Employer B has a higher prevalence of nonacute coronary artery diseases (43.3% versus 37.7% for Employer A). Fig. 1 and Table 3 show the unadjusted changes in utilization between the pre- and post-periods for Employers A and B. The comparison employer, Employer B, shows a significant increase in use in all categories of prescription drugs in the post-period. However, Employer A, the employer subject to an increase in brand-name drug copayments, had a significant increase in overall utilization, generic utilization, and single-source brandname utilization. In contrast, total brand-name drug utilization was unchanged, and multi-source brand-name utilization dropped significantly in the post-period.

3.5 Pre

*

Post

3

No. of Prescription Fills

*** 2.5

***

2

*** 1.5

***

* ***

1

*** ***

0.5

0 Maintenance Employer A

Brand Name

Single Source Brand Name

Generic

Multi-Source Brand Name

Fig. 1. Monthly Pre- and Post-Utilization of Generic and Brand-Name Maintenance Prescription Drugs. Note: po0.01, 0.01opo0.05, 0.05opo0.10.

210


Table 3. Monthly Pre- and Post-Utilization of Generic and Brand-Name Maintenance Prescription Drugs. Employer A, n ¼ 2,507

Maintenance prescription drugs Generic Brand name Multi-source brand Single-source brand

Employer B, n ¼ 4,607

Pre

Post

p

Pre

Post

p

2.86 (3.67) 0.873 (1.54) 1.984 (2.76) 0.724 (1.43) 1.260 (1.88)

2.94 (4.28) 0.943 (1.7) 1.995 (3.23) 0.635 (1.51) 1.360 (2.26)

0.022

2.28 (3.35) 0.703 (1.47) 1.582 (2.50) 0.401 (1.04) 1.181 (1.97)

2.57 (3.54) 0.813 (1.6) 1.759 (2.57) 0.494 (1.15) 1.265 (1.97)

o.001

0.039 0.667 o.001 o.001

o.001 o.001 o.001 o.001


Table 4.

Effects of Cost-Sharing on the Utilization on Maintenance Prescription Drugs.

Magnitude of effect (no. of prescription fills) Price elasticity estimate

Generic

Brand Name

Multi-Source Brand

Single-Source Brand

0.0478 (.016) 0.0219 (.007)

0.1800 (.027) .0364 (.005)

0.1992 (.016) 0.1101 (.009)

0.0045 (.021) 0.0014 (.007)

Note: Standard errors are in parentheses. po0.001. po0.05.

The multivariate estimates are presented in Table 4, and these results echo the descriptive results with a significant decline in utilization of brand-name prescription drugs and multi-source brand-name prescription drugs. The copayment increase was not associated with a change in utilization of singlesource brand-name prescription drugs, where no generic equivalent is available. Even though Employer A had an increase in generic use in the post-period, the multivariate results show that the increase in generic utilization was lower than the contemporaneous trend in Employer B, and the net effect of the cost-sharing increase was a drop in generic utilization.


211

The own-price elasticity of demand for multi-source brand-name prescription drugs was largest at .11 (sign is preserved) and .036 for all brand-name prescription drugs. Similarly, the cross-price elasticity of demand for generic prescription drugs was 0.022 (all po0.01).

4. STUDY NO. 2: DIABETES MEDICATION ANALYSIS 4.1. Methods In the second study, we used the January 2003 through September 2006 Medstat MarketScan Database representing the health experiences of over 120 large and medium sized firms in the United States for the analysis. A retrospective study was conducted among commercially insured patients with type 2 diabetes who were 18–64 years old. To be selected, patients had to fill at least two prescriptions for an antidiabetic agent within 180 days of each other and to have a diagnosis of diabetes (ICD-9-CM code 250.x) through either an emergency room visit, one inpatient hospitalization, or two outpatient visits separated by at least 30 days. Patients with a diagnosis of gestational diabetes (ICD-9-CM code 648.8x) at any time during the study period were excluded from the study. Also excluded from this sample were patients with type 1 diabetes and patients filling prescriptions in antidiabetic medication classes where generic substitutes did not exist during this time frame (e.g., insulin, alpha-glucosidase inhibitors, and meglitinides). Patients filling a prescription for at least one of two classes of antidiabetic medication where generic substitutes existed – biguanides and sulfonylureas – were selected for the study. We focused on patients with diabetes who were enrolled in plans where the generic copayments for biguanides and sulfonylureas did not change over their time of enrollment. This resulted in 30,660 patients with type 2 diabetes filling a prescription for a biguanides or a sulfonylureas; of these patients, 9,325 patients filled prescriptions only for biguanides; 7,944 filled prescriptions only for sulfonylureas; 13,391 filled prescriptions for both biguanides and sulfonylureas. 4.2. Patient Selection In this study, we followed a cohort of patients from the first observed fill of a biguanide or sulfonylurea after January 2003, the beginning of the study

212


period, through September 2006 or through time of disenrollment from the employer’s health coverage if before the end of the study period. If a patient changed health plans within an employer, they could be followed over time, but when they left the employer, they were disenrolled from the study. We used a cross-section/time series design with patient as the cross-section and calendar quarter as the unit of time. Because we focus on filling behavior in (at most) two medication classes, we aggregate experience to a calendar quarter, resulting in fewer zero-valued cells.

4.3. Dependent Variables Total prescription drug utilization was measured by counting the number of prescriptions filled in each quarter. Brand-name and generic prescriptions were summarized into separate measures for each medication class. The total number of brand-name and generic medications filled in either class was also accumulated in each quarter.

4.4. Explanatory Variables A model of prescription drug utilization was estimated with the following categories of explanatory variables: cost-sharing, sociodemographic characteristics, health plan type, health status, and time. The key explanatory variable is the brand-name prescription drug cost-sharing amount for the patient’s health plan offering. Cost-sharing amounts, whether copayments or coinsurance, are expressed as a dollar amount (2006 dollars) per fill and are measured in each quarter of the study. To calculate the cost-sharing amount, the copayment or coinsurance amount on each pharmaceutical claim record was expressed in dollars per prescription fill within each plan offering. Relative prescription drug cost-sharing amounts were expressed as the difference between brand and generic cost-sharing amounts in dollars per fill and the ratio of brand-name cost-sharing amount to generic costsharing amount. As higher physician office visit copayments have been associated with a reduction in prescription drug utilization (Joyce et al., 2002), plan-level office visit copayments were also included in the models. Sociodemographic variables included the patient’s gender, age in years, geographic census region of residence, and an employee versus dependent/ spouse indicator. Median household income (log transformed) was assigned by patient ZIP code from the 2000 U.S. Census. Insurance plan type was


213

classified as Comprehensive, Health Maintenance Organization, Preferred Provider Organization, Capitated Point-of-Service (POS) Plan, and Noncapitated POS Plan. As an indication of health status, the following measures were assessed in a rolling one-year lag before each quarter of the study: the Charlson comorbidity index (Deyo adaptation) and an indicator for a specialist visit to a cardiologist or endocrinologist. The specialist indicator serves as a proxy for higher disease severity and increased complexity of comorbidities such as heart disease. Calendar time was incorporated using an array of indicator variables for each quarter, with the first quarter (1Q) of 2003 as the reference quarter.

4.5. Modeling Approach Prescription utilization (Yit) was modeled as a function of the covariates: Y it ¼ expðb1 sociodemographicit þ b2 incomeit þ b3 plan typeit þ b4 health statusip þ b5 cost-sharingit þ b6 timet Þ

ð3Þ

where i represents patients, t represents a calendar quarter, and p represents a rolling 12-month lag. Generalized estimating equation models with a negative binomial outcome and a log link were employed to model counts of prescription drug fills. Standard errors were adjusted for clustering by patient over time using robust standard errors to reduce the effects of unknown heteroscedasticity or specification errors.

4.6. Results In the index quarter, patients with type 2 diabetes were about 52 years old on average, and over 70% were employees (versus spouses or dependents) (Table 5). Generic cost-sharing amounts in the index quarter were $7–$8, and brand-name cost-sharing ranged from an average of $18.97 for patients on sulfonylureas only to $23.22 for patients on biguanides only. The brand– generic price differential ranged from $11.43 for patients on sulfonylureas only to $15.53 for patients on biguanides only, and the brand–generic price ratio was lowest for patients on sulfonylureas only at 2.61 and was highest for patients on biguanides and sulfonylureas at 3.16.

214

Table 5.


Characteristics of Patients with Diabetes in the Index Quarter. Biguanides Sulfonylureas Biguanides and Biguanides or Only Only Sulfonylureas Sulfonylureas (n ¼ 9,325) (n ¼ 7,944) (n ¼ 13,391) (n ¼ 30,660) Mean/% Mean/% Mean/% Mean/%

Age (years) Female (%) Employee (%) (versus spouse/ dependent) Census region North Central (%) South/North East (%) West(%) Median income in ZIP code (log) Health plan Comprehensive (%) HMO/EPO (%) Point of service plan (%) Preferred provider organization (%) Health status Charlson comorbidity index Saw endocrinologist (%) (lagged) Saw cardiologist (%) (lagged) Cost-sharing Brand-name cost-sharing Generic cost-sharing Brand/generic difference Brand/generic ratio Office visit copayment Measures Number of brand-name prescriptions in medication class Number of generic prescriptions in medication class

51.220 (9.102) 0.556 0.690

53.025 (7.557) 0.440 0.711

52.437 (7.523) 0.489 0.713

52.219 (8.075) 0.497 0.706

0.222 0.562 0.216 3.694 (.318)

0.203 0.626 0.171 3.644 (.309)

0.196 0.499 0.306 3.706 (.322)

0.206 0.551 0.243 3.686 (.318)

0.091 0.194 0.123 0.593

0.072 0.193 0.136 0.598

0.067 0.307 0.118 0.508

0.076 0.243 0.124 0.557

1.265 (1.069) 0.071

1.417 (1.315) 0.034

1.227 (1.028) 0.043

1.288 (1.124) 0.049

0.141

0.181

0.126

0.139

$23.22 (9.161) $7.69 (2.645) $15.53 (7.723) 3.092 (1.167) $14.46 (5.766)

$18.97 (6.168) $7.54 (2.408) $11.43 (4.897) 2.614 (.897) $14.18 (5.637)

$21.42 (6.983) $7.16 (2.483) $14.26 (5.917) 3.159 (1.169) $13.74 (5.443)

$21.29 (7.188) $7.42 (2.482) $13.87 (5.925) 2.996 (1.083) $14.07 (5.602)

0.815 (1.298)

1.659 (1.471)

1.453 (1.844)

1.312 (1.635)

1.380 (1.518)

0.665 (1.299)

2.221 (2.307)

1.562 (1.967)

Notes: EPO, exclusive provider organization. Standard deviations are in parentheses.


215

On average, the patients in the sample filled almost equal numbers of brand-name and generic medications each quarter (1.3 for brand name and 1.6 for generic). However, in the index quarter, patients on sulfonylureas only filled 1.7 brand-name prescriptions, which was 2.5 times the amount of generic sulfonylureas (0.67). Patients on biguanides only filled 1.4 generic prescriptions per quarter, which was close to 70% more than the amount of brand-name prescriptions filled (0.82). Table 6 presents the results of the multivariate estimates and reveals that for the full sample a change in brand-name copayments was associated with a significant drop in brand-name utilization (price elasticity 0.151) and a rise in generic use (price elasticity 0.149, both po0.01). As the difference between brand and generic copayments increased, patients reduced consumption of brand-name prescription drugs and increased consumption of generic prescription drugs. Similar results were found for patients on biguanides only (own-price elasticity 0.012, cross-price elasticity 0.114, both po0.01) and patients on biguanides and sulfonylureas (own-price elasticity 0.197, cross-price elasticity 0.351, both po0.01). However, patients on sulfonylureas only had a different response to an increase in brand-name prescription drug copayments. While brand-name utilization decreased (price elasticity 0.186), generic utilization remained unchanged. Figs. 2(a) and (b) plot the estimated contemporaneous quarterly trends in generic and brand-name drug utilization relative to first quarter consumption (1Q03) in the biguanides-only and sulfonylureas-only patient cohorts. These estimates are net of cost-sharing effects. The underlying trend in utilization for patients on sulfonylureas shows a large increase in generic utilization of up to two prescriptions per quarter and a smaller decline in brand-name utilization of 0.5 prescriptions per quarter. The underlying trends in utilization are much less extreme for patients on biguanides only – an increase in generic prescriptions of up to 0.5 prescriptions per quarter and a decrease in brand-name utilization of up 0.25 prescriptions per quarter.

5. DISCUSSION AND CONCLUSIONS An increase in brand-name cost-sharing led to a decline in brand-name utilization in all of the patient samples examined. However, we found various effects on generic utilization. While the utilization of generic medications rose over time in all patient samples, in the first study of early retirees with chronic conditions, we saw a small decline in generic utilization

216


Table 6.

Estimated Effects of Cost-Sharing on the Utilization of Antidiabetic Medications. Generic

Biguanides only (n ¼ 9325) Brand-name copayments Price elasticity estimate

0.002 (0.001) 0.090 (0.026)

Brand/generic copayment difference Brand/generic copayment ratio Sulfonylureas only (n ¼ 7944) Brand-name copayments 0.002 (0.001) Price elasticity estimate 0.042 (0.026) Brand/generic copayment difference Brand/generic copayment ratio

Generic

0.002 (.001)

Generic

Brand Name 0.010 (0.002) 0.273 (0.050)

0.023 (.004) 0.015 (0.003) 0.186 (0.038)

0.001 (.001)

Biguanides and sulfonylureas (n ¼ 13,391) Brand-name copayments 0.005 (0.001) Price elasticity estimate 0.351 (0.039) Brand/generic copayment 0.006 difference (0.001) Brand/generic copayment ratio Biguanides or sulfonylureas (n ¼ 30,660) Brand-name copayments 0.003 (0.000) Price elasticity estimate 0.149 (0.022) Brand/generic copayment 0.004 difference (0.000) Brand/generic copayment ratio

Brand Name

0.010 (.002)

0.012 (.003)

0.008 (.007) 0.019 (0.002) 0.197 (0.022) 0.040 (.004) 0.015 (0.002) 0.151 (0.016) 0.030 (.003)

0.017 (0.002)

0.014 (0.001)

Brand Name

0.062 (.011)

0.077 (.017)

0.099 (.013)

0.088 (.009)

Notes: Cells contain estimated coefficients (or elasticity estimates) and standard are given in parentheses. po0.01. 0.01opo0.05. 0.05opo0.10.


217

Contemporaneous Trends in Biguianide Only Utilization (n=9,325) Number of Prescription Fills

2

(a)

1.5

Generic

Brand Name

1 0.5 0 -0.5

2Q03 3Q03 4Q03 1Q04 2Q04 3Q04 4Q04 1Q05 2Q05 3Q05 4Q05 1Q06 2Q06 3Q06

-1 -1.5

Contemporaneous Trends in Sulfonylurea Only Utilization (n=7,944) Number of Prescription Fills

2

(b)

1.5

Generic

Brand Name

1 0.5 0 -0.5

2Q03 3Q03 4Q03 1Q04 2Q04 3Q04 4Q04 1Q05 2Q05 3Q05 4Q05 1Q06 2Q06 3Q06

-1 -1.5

Fig. 2. (a–b) Estimated Underlying Trends in Brand and Generic Utilization of Antidiabetic Medications (Relative to 1Q03). Note: Bars represent the 95% confidence interval.

associated with a brand-name copayment increase net of the contemporaneous trend. In the second study, we found an increase in generic consumption associated with a rise in brand-name copayments for two of the patient samples, and no significant change in generic consumption in the third sample. It is still unclear whether generic medications are true substitutes for brand-name medications. There may be sizeable income effects for many medications that warrant investigation, specifically regarding whether generic drugs are normal or inferior goods. As is often the case, actual patient income, much less household income, was not available for the studies presented here. The use of ZIP code level information is sufficient for control of sociodemographic characteristics of patients but is insufficient to use as a proxy for income when examining income/substitution effects. Future studies should address this phenomenon.

218


Copayment incentives may have less of an effect in situations where there is a strong underlying trend toward generic prescribing. We see this underlying trend in the first study, where generic consumption rose in both employers, although the rise in generic utilization for the employer with an increase in brand-name prescription drug cost-sharing (0.07 fills per month) was not larger than the rise in generic utilization for the employer without a change in cost-sharing (0.11 fills per month), and therefore, the net effect attributable to the change in cost-sharing was negative. We also see this phenomenon in the second study, when the rise in brand-name copayments was not significantly associated with a change in generic prescribing for sulfonylureas over and above the very strong underlying trend toward generic consumption. We found significant effects of the brand–generic differential and brand– generic ratio on the utilization of brand-name prescriptions which lends support to the existence of a brand-name premium. Due to the rise in the number of generic drug introductions in the past 20 years, and the introduction of mandatory generic substitution policies, it is possible that generic drugs have become more acceptable to patients and the size of the brand-name premium has declined over time. These two trends are especially likely when a generic drug with the same active ingredient exists reducing the putative loss associated with generic substitution. Campaigns to promote generic substitution may have succeeded in lowering the putative loss associated with generic substitution, but likely there is more work to be done. The brand premium may also differ by medication class, according to the amount of generic penetration in the class, and whether good perceived substitutes exist. Further research determining the size and distribution of the brand premium would help inform benefit plan design and copayment levels. When altering copayments, consideration should be made to factors such as the number and percentage of generic introductions anticipated within each class and the number and percentage of generic substitutes. In cases where there is a strong trend toward generics, these factors may dominate financial incentives. When generic formulations dominate use, such as biguanides, generic prescription drug cost-sharing incentives may have a greater effect on generic utilization.

REFERENCES Christian-Herman, J., Emons, M., & George, D. (2004). Effects of generic-only drug coverage in a medicare HMO. Health Affairs, W4, 455–468.


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Gibson, T. B., McLaughlin, C. G., & Smith, D. G. (2005). A copayment increase for prescription drugs: The long-term and short-term effects on use and expenditures. Inquiry, 42(3), 293–310. Gibson, T. B., Ozminkowski, R. J., & Goetzel, R. Z. (2006). The effects of prescription drug cost sharing: A review of the evidence. American Journal of Managed Care, 11(11), 730–740. Gilman, B. H., & Kautter, J. (2008). Impact of multitiered copayments on the use and cost of prescription drugs among medicare beneficiaries. Health Services Research, 43(2), 278–295. Goldman, D. P., Joyce, G. E., Escarce, J. J., Pace, J. E., Solomon, M. D., Laouri, M., Landsman, P. B., & Teutsch, S. M. (2004). Pharmacy benefits and the use of drugs by the chronically Ill. The Journal of American Medical Association, 291(19), 2344–2350. Gonnella, J. S., Hornbrook, M. C., & Lewis, D. Z. (1984). Staging of disease: A case-mix measurement. The Journal of American Medical Association, 251(5), 637–644. Gonnella, J. S., Lewis, D. Z., Gozum, M. V., Callahan, C. A., & Barnes, C. A. (2005). Disease staging: Clinical criteria (5th ed.). Ann Arbor, MI: Thomson Medstat. Griliches, Z., & Cockburn, I. (1994). Generics and new goods in pharmaceutical price indexes. The American Economic Review, 84(5), 1213–1232. Grossman, M. (2000). The human capital model. In: A. J. Culyer & J. P. Newhouse (Eds), Handbook of Health Economics (Vol. IA, pp. 347–408). New York, NY: Elsevier. Hillman, A. L., Pauly, M. V., Escarce, J. J., Ripley, K., Gaynor, M., Clouse, J., & Ross, R. (1999). Financial incentives and drug spending in managed care. Health Affairs, 18(2), 189–200. Joyce, G. F., Escarce, J. J., Solomon, M. D., & Goldman, D. P. (2002). Employer drug benefit plans and spending on prescription drugs. The Journal of American Medical Association, 288(14), 1733–1739. Kaiser Family Foundation and the Health Research and Educational Trust. (2009). Kaiser/ HRET survey of employer-sponsored health benefits. Available at http://ehbs.kff.org Landon, B. E., Rosenthal, M. B., Normand, S. L., Spettell, C., Lessler, A., Underwood, H. R., & Newhouse, J. P. (2007). Incentive formularies and changes in prescription drug spending. American Journal of Managed Care, 13(6, pt.2), 360–369. Motheral, B., & Fairman, K. A. (2001). Effect of a three-tier prescription copay on pharmaceutical and other medical utilization. Medical Care, 39(12), 1293–1304. Motheral, B. R., & Henderson, R. (1999). The effect of a copay increase on pharmaceutical utilization, expenditures and treatment continuation. The American Journal of Managed Care, 5(11), 1383–1394. Nair, K. V., Wolfe, P., Valuck, R. J., McCollum, M. M., Ganther, J. M., & Lewis, S. J. (2003). Effects of a 3-tier pharmacy benefit design on the prescription purchasing behavior of individuals with chronic disease. Journal of Managed Care Pharmacy, 9(2), 123–133. Roebuck, M. C., & Liberman, J. N. (2009). Impact of pharmacy benefit design on prescription drug utilization: A fixed effects analysis of plan sponsor data. Health Services Research, 44(3), 988–1009.

DRUG PRICES, OUT-OF-POCKET PAYMENTS, AND INSURER COSTS: HOW DO PAYERS VARY? Jie Chen and John A. Rizzo ABSTRACT Purpose – To examine how drug prices for specific diseases vary across payers in the United States and how insurer and patient out-of-pocket (OOP) costs vary by payer type. Methodology – This study uses data from the Medical Expenditure Panel Survey (MEPS) from 1996 to 2006. We estimate multivariate price regressions for four major drug product classes (antihypertensive, antidepressant, antiasthma drugs, and non-steroidal anti-inflammatory drugs (NSAIDs)). Separate models are estimated for brand and generic drugs within each of these drug product classes. In addition to estimating overall transaction price equations for brands and generics, the study estimates patient OOP payments and insurer payments for drugs. Findings – We find relatively modest differences among payers in terms of total prices (e.g., insurer plus OOP). The main difference is in terms of how prices were shared between insurers and patients. Medicaid paid significantly more than other payers for each drug class, while Medicaid beneficiaries paid significantly less.


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Research implications – Our results shed light on how drug prices vary by different payers and how drug prices are shared by third party payers and patients. The relatively modest differences in total drug prices across payer type suggest that these payers do not differ greatly in terms of their ability to negotiate price concessions from their suppliers. Instead, larger differences emerge in terms of how total costs are shared among the payer and their patients. Understanding the reasons for these variations, and their implications for health outcomes, are important directions for further research.

1. INTRODUCTION Pharmaceuticals account for a significant share of total health care expenditures in the United States. In 2007, expenditures for prescription drugs were $227.5 billion and are expected to reach $453.7 billion by 2018 (Sisko et al., 2009). Importantly, the rate of spending growth for prescription medications outpaced other areas of medical care from 1995 to 2003. Prescription drugs promote the efficient health care delivery and can significantly increase health care quality and life expectancy of patients while reducing total health care costs (Lichtenberg, 1996, 2001; Lichtenberg & Virabhak, 2007). The determinants of pharmaceutical drug prices have been examined extensively (Berndt & Seley, 2000; Berndt, Cockburn, & Griliches, 1996; Berndt, 2002). Market regulation, advertising/detailing, and product innovation have all been found to be associated with pharmaceutical pricing strategies (Rizzo, 1999; Lu & Comanor, 1998; Lichtenberg, 2001; Lichtenberg & Virabhak, 2007). Pavcnik (2000) found that patients’ insurance would impact drug pricing significantly using a unique data set from Germany. In the United States, however, people are covered by different health insurance plans, including public plans such as Medicaid and Medicare or private health insurance plans. These plans vary in their degrees of market power and ability to set payment rates for pharmaceutical products. Hence, we might expect to see substantially different prices for drugs across different types of health insurance plan. For example, public insurers have considerable negotiation power with the drug manufacturers, for example, OBRA 1990 regulates the ‘‘best price’’ for Medicaid enrollees. Private insurers can also negotiate prices with pharmaceutical manufactory by directing consumers using formularies (Feldstein, 2006). Few studies

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have examined how drug prices for specific diseases vary across payers in the United States. Moreover, there is little evidence on insurer, and patient out-of-pocket (OOP) costs vary by payer type. Cost sharing is a key mechanism to control the rapid growth in pharmaceutical expenditures, by forcing consumers to shoulder some fixed (copay) or proportionate (coinsurance) payments for each drug prescription. Although cost sharing may control pharmaceutical expenditures by increasing consumers’ price sensitivity, it has also been shown to have unintended effects in terms of increasing health care utilization, such as hospital admissions and emergency room visits (Tamblyn et al., 2001; Winkelmann, 2004; Dor & Encinosa, 2004; Chandra, Gruber, & McKnight, 2007; Goldman, Joyce, & Zheng, 2007). Given this trade-off, how do third party payers decide on the copayment structure that balances the current pharmaceutical costs and future health care costs? There is very little evidence on how insurers and consumers share in the cost of pharmaceuticals (Chen & Rizzo, forthcoming). The present study also explores how the cost sharing differs across different health plans. Given the high and rising costs of pharmaceuticals, understanding drug pricing disparities and its cost sharing implications is important for policymakers in promoting more effective mechanisms to control these costs and reduce inequities. We use nationally representative data to investigate how drug prices vary by payer type in the United States. We examine these relationships for four major drug product classes (antihypertensive, antidepressant, anti-asthma drugs, and nonsteroidal anti-inflammatory drugs (NSAIDs)). Separate price regressions are estimated for brand and generic drugs within each of these drug product classes. In addition to estimating overall transaction price equations for brands and generics, this study also estimates patient OOP payments and insurer payments for drugs. The price equations control for insurance status (Medicaid, Medicare, private non-managed care, and private managed care) and for various sociodemographic, health, and market characteristics that could affect drug pricing. Using these price equation results, we quantify how much prices vary by payer type. The study will help quantify the role of third party payers in accounting for price disparities and shed light on how the cost burden of drugs is shared between patients and third party payers. The remainder of this chapter is divided into four parts. Section 2 briefly summarizes previous research in this area. Data and estimation issues are discussed in Section 3 and the results presented in Section 4. Section 5 summarizes the results and their policy implications.

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2. PREVIOUS WORK Pharmaceutical pricing has been examined extensively in the literature (Berndt & Seley, 2000; Berndt et al., 1996; Berndt, 2002; Santerre & Neun, 2007). Some studies show that patients’ insurance can impact the pharmaceutical manufacturers’ price behavior significantly. Pavcnik (2000) used a unique data set from Germany to examine the responsiveness of drug pricing after patients cost sharing increases. She found that prescription drug prices decreases sharply after this change, particularly for brand-name drugs. Studies also show that the purchasers’ negotiation power can impact the drug price. Frank (2001) found that uninsured individuals paid significantly more for prescription drugs than did people with private health insurance. But there remains little systematic evidence on how drug prices differ by payer type. However, a related literature has examined whether increased patient drug cost sharing reduces total health care costs and vice versa. Here, the results have been mixed. For example, Mahoney (2005) finds that direct health care costs per patient with diabetes decreases by 6% following a reduction in patient OOP payments for drugs. Dor and Encinosa (2004) also find that $6–$10 increases in copayments would reduce national drug expenditures for diabetes by $125 million directly but would increase other health care costs from diabetic complications by $360 million. Gaynor, Li, and Vogt (2006) found that patients respond to reductions in their expenditures on drugs by increasing outpatient and inpatient care. They note that 35% of the expenditure reduction in drugs is offset by increases in other health care spending. Chandra et al. (2007) report modest offset effects in a population of retired public employees from California. More specifically, they find that hospital utilization increased following increases in drug copayments such that overall cost savings were not sufficient. There is even less evidence on how the burdens of drug costs are shared among insurers and consumers. Chen and Rizzo (forthcoming) examined how pharmaceutical costs for higher quality drugs are shared among insurers and consumers and found that patients’ cost burden, hence access, to higher quality drugs varies across drug product classes.

3. DATA AND METHODS 3.1. Data We use data from the 1996–2006 Medical Expenditure Panel Survey (MEPS) conducted by the Agency for Healthcare Research and Quality

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(AHRQ) (Agency for Healthcare Research and Quality, 2008). The MEPS database consists of a number of files. We use two sub-files of MEPS for this study. The consolidated file is a person-year level database and provides detailed individual information on health care utilization and expenditures as well as individuals’ demographics, socioeconomic characteristics, health, and health insurance status. The prescribed medicines file is an event-level file that includes detailed information on the utilization and payments for each prescribed drug used by survey respondents.1 We convert the prescribed medicines file to the person-year level and then merged it with the consolidated file. We focus on four widely prescribed therapeutic classes: antidepressants, NSAIDs, antiasthma drugs, and antihypertensive drugs (including angiotensin-converting enzyme (ACE) inhibitor, beta blocker, calcium channel blockers, and diuretics). Pharmaceutical pricing strategies for brand-name medications and generics are likely to differ, because the nature of product differentiation and the competitive conditions in the relevant products markets are often so different between these two types of drugs. Thus, we examine the drug prices for brand and generic drugs, respectively, for each of these four therapeutic classes, using the drug brand/generic status provided by the FDA’s Orange Book. Our sample includes subjects aged 18–64 who had any private or public health insurance coverage during the survey year. All of the individuals in our sample used one or more prescription drugs, and both third party payers and individuals incurred positive amounts of payments for drugs.

3.2. Methods We estimate price equations for each of drug classes of the form: LnðtotalÞit ¼ a0 þ a14 health planit þ a5 X it þ it Lnðthird partyÞit ¼ a0 þ a14 health planit þ a5 X it þ it LnðOOPÞit ¼ a0 þ a14 health planit þ a5 X it þ it The dependent variables are the natural logarithm of the average total, individual, and third party drug payment for a particular drug per person per year, respectively. Health plan includes Medicaid, Medicare (such as Medigap for disabled people under 65 years), other public health plan (such as veteran’s administration, Tricare, and other federal/ state/local government plans), private managed care health plans, and private non-managed care health plans.

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The vector X includes other characteristics that could affect transactions price, such as age, gender, race/ethnicity, health status, education, family income, employment status, health care access, metropolitan statistics area (MSA), and US census regions (Andersen & Newman, 1973, Andersen, 1995). Binary indicators for each survey year are also included to capture intertemporal effects on price. All the regressions are adjusted using personal sampling weights provided by MEPS to make our results nationally representative (Wooldridge, 2008). We perform cluster analysis on people who had used multiple medications during the year. Prices are converted to 2006 dollars using the Medical Care Component of the Consumer Price Index.

4. RESULTS 4.1. Summary Statistics for Prices Table 1 summarizes the total drug price, third party payment, and OOP for each therapeutic class. Prices vary substantially across payer and drug type. Total prices for Medicaid drugs tend to be the lowest, although not for brand-name NSAIDs and asthma drugs. OOP costs are consistently lower for Medicaid. If anything, total drug prices under private non-managed care appear to be somewhat higher than prices for managed care. The ratio of OOP to third party payment for brand-name drug is the highest for private managed care ranking from approximately 1 to 4 for anti-asthma drugs to 1 to 2 for anti-hypertensive drugs. Medicaid has the lowest OOP to third party ratio ranking from 1 to 25 for NSAIDs to 1 to 6 for antihypertensive drugs. Total prices of generic drugs do not vary a lot across different payers. Medicaid still has the lowest OOP to third party payment ratios among all payers.

4.2. Multivariate Regressions 4.2.1. Brand-Name Drugs Table 2 provides the results of the multivariate price regressions. In the interest of brevity, the results are reported just for the different insurance types. However, all price regressions are adjusted for the covariates described earlier. Interestingly, total prices of brand-name drug vary relatively little by insurance type. Private non-managed care plans do have

N ¼ 5,928

N ¼ 8,914

92.95 89.00 121.25 87.94 73.09

8.52 10.36 51.09 22.64 19.15

N ¼ 3,357

Anti-Asthma Drugs

7.42 101.46 14.10 99.36 12.08 172.34 23.45 110.57 19.11 92.25

52.97 49.92 69.43 58.66 55.24

46.39 42.06 41.76 48.24 45.46

6.58 7.86 27.67 10.42 9.78

30.48 45.60 32.33 40.00 38.54

26.21 38.55 23.00 30.75 29.57

4.27 7.05 9.33 9.25 8.97

31.28 38.26 52.49 32.74 32.42

25.77 30.83 45.62 22.57 22.62

5.50 7.43 6.87 10.16 9.79

Total Thid party OOP Total Third party OOP Total Third party OOP

N ¼ 8,154

N ¼ 3,266

94.63 87.79 39.68 81.90 73.03 NSAIDS

9.64 102.05 13.83 101.90 34.55 51.76 23.50 105.35 21.06 92.14

Antidepressants

87.65 84.04 75.59 79.59 82.84

Source: MEPS 1996–2006 for all insured persons from 18 to 64 years.

Medicaid Medicare Other public health plan Private non-managed care Private managed care

Generic Drugs

N ¼ 5,147

Anti-Asthma Drugs

57.60 55.78 57.35 47.93 50.45

9.17 14.33 15.06 21.79 19.13

OOP

32.38 37.62 44.57 40.80 43.17

Total

26.25 30.13 33.15 30.46 33.11

Thrd party

N ¼ 10,830

6.12 7.49 11.43 10.34 10.05

OOP

Anti-Hypertensive Drugs

66.78 70.11 72.41 69.72 69.59

Third party

N ¼ 8,923

Anti-Hypertensive Drugs

Total Third party OOP Total Third party OOP Total Third party OOP Total

NSAIDS

Antidepressants

Summary Statistics of Drug Price ($ in 2006) across Brand-Name and Generic Drugs.

Medicaid 97.29 Medicare 97.87 Other public health plan 110.14 Private non-managed care 103.09 Private managed care 103.90

Brand-Name Drugs

Table 1.

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0.01 0.05 0.74 0.07

0.01 0.03 0.24 0.07

0.03 0.03 0.08 0.01

NSAIDs Private managed care Medicaid Medicare Other public health plan Private non-managed care

Anti-asthma drugs Private managed care Medicaid Medicare Other public health plan Private non-managed care

Anti-hypertensive drugs Private managed care Medicaid Medicare Other public health plan Private non-managed care

Ref

Ref

Ref

Ref

0.30 0.41 0.72 0.57

0.89 0.44 0.48 0.00

0.88 0.52 0.30 0.00

0.72 0.71 0.63 0.89

p

0.27 0.12 0.02 0.03

0.21 0.00 0.17 0.07

0.27 0.13 1.16 0.07

0.16 0.05 0.51 0.02

Ref

Ref

Ref

Ref

Coefficient

0.00 0.03 0.96 0.19

0.00 0.94 0.57 0.01

0.00 0.07 0.12 0.03

0.00 0.27 0.39 0.19

p

Third party

1.26 0.07 0.46 0.06

1.47 0.07 0.48 0.09

1.72 0.30 0.69 0.07

1.51 0.02 0.10 0.05

Ref

Ref

Ref

Ref

Coefficient

OOP

0.00 0.39 0.24 0.01

0.00 0.50 0.32 0.00

0.00 0.01 0.21 0.01

0.00 0.84 0.87 0.01

p

0.25 0.02 0.14 0.02

0.01 0.01 0.68 0.02

0.37 0.21 0.47 0.02

0.03 0.13 0.91 0.07

Ref

Ref

Ref

Ref

Coefficient

Total

0.00 0.58 0.48 0.27

0.89 0.84 0.20 0.54

0.00 0.01 0.23 0.47

0.69 0.11 0.00 0.18

p

0.01 0.06 0.08 0.04

0.33 0.09 1.10 0.07

0.07 0.13 0.51 0.02

0.30 0.23 0.83 0.09

Ref

Ref

Ref

Ref

Coefficient

0.85 0.34 0.76 0.20

0.00 0.27 0.06 0.16

0.35 0.19 0.29 0.60

0.00 0.04 0.03 0.19

p

Third party

Generic Drugs

1.08 0.01 0.06 0.02

1.15 0.09 0.13 0.02

1.37 0.14 0.09 0.03

0.93 0.03 1.11 0.04

Ref

Ref

Ref

Ref

Coefficient

OOP

0.00 0.87 0.80 0.31

0.00 0.38 0.30 0.41

0.00 0.12 0.76 0.23

0.00 0.74 0.04 0.27

p

Notes: All the regressions controlled the population characteristics (e.g., age, gender, race/ethnicity, health status, education, family income, employment status, health care access, MSA, and US census regions). For the anti-hypertensive drugs, we also included four dummy variables for ACE inhibitor, beta blocker, calcium channel blockers, and diuretics. The significance of bold values is p ¼ 0.0001.

0.01 0.01 0.11 0.00

Antidepressants Private managed care Medicaid Medicare Other public health plan Private non-managed care

Coefficient

Total

Multivariable Results.

Brand-Name Drugs

Table 2. 228 JIE CHEN AND JOHN A. RIZZO

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significantly higher total prices for NSAIDs and asthma, compared to private managed care plans. However, the main differences came in terms of how prices are shared between insurers and patients. Medicaid pays significantly more than other payers for each drug class, while Medicaid beneficiaries pay significantly less. Subjects with private non-managed care plans pay significantly more OOP than do persons covered by managed care plans. Private non-managed care plans also have higher third party payments for NSAIDs and antiasthma drugs. 4.2.2. Generic Drugs We again find that total prices vary little across insurance types. Similar to brand-name drugs, people covered by Medicaid pay significantly less OOP for generics. Other differences are generally small and statistically insignificant across different payer types.

5. CONCLUSION In this study, we examine how drug prices vary by different payers. Although the summary statistics show that public health plans have relatively lower total drug prices, after controlling for other covariates, these differences are no longer significant. Only private non-managed care plans have higher total costs in NSAIDs and anti-asthma drugs than do private managed care plans. Our results are consistent with the literature that managed care plans have been more successful in controlling drug prices than have non-managed care plans (Feldstein, 2006). We also find that insurer drug prices and cost sharing vary by different payers. People enrolled in Medicaid pay the lowest OOP compared to other plans, while Medicaid, as the third party payer, pays significantly more than other plans. People enrolled in private non-managed care plans pay significantly high OOP for all the medications we examined. However, the total drug prices and the third party payments are similar to those of other plans, at least for antidepressants and anti-hypertensive drugs. Our results shed light on how drug prices vary by different payers, and how drug prices are shared by third party payers and patients. The relatively modest differences in total drug prices across payer type suggest that these payers do not differ greatly in terms of their ability to negotiate price concessions from their suppliers. Instead, larger differences emerge in terms of how total costs are shared among the payer and their patients.

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Compared to other plans, Medicaid has shouldered more of the financial responsibility for drugs, while managed care has been more effective in constraining drug prices than have non-managed care plans. The OOP burden varies substantially across patients with different types of insurance. For brand-name drugs, OOP costs vary from twofold to more than threefold depending on drug product; similar, though less pronounced, variation exists for the generic drugs examined. Understanding the reasons for these variations, and their implications for health outcomes, is an important direction for further research.

NOTE 1. Household respondents provided information on the names of all outpatient medications used by each household member and the names and locations of the pharmacies where medications were obtained. They were also asked for permission to request records from these pharmacies. Pharmacy providers were asked to provide the data necessary to assign a national drug code, which is specific for manufacturers, ingredients, strength, package size, quantity dispersed, total charge, and sources of payments. The AHRQ performed detailed matching, imputation, consistency checks, sensitivity checks, and reconciliation algorithms in assembling the prescribed medicines database.

REFERENCES Andersen, R. (1995). Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health and Social Behavior, 36, 1–10. Agency for Healthcare Research and Quality. (2008). MEPS HC-105: 2006 full year consolidated data file. Rockville, MD. Available at http://www.meps.ahrq.gov/mepsweb/ data_stats/download_data/pufs/h105/h105doc.pdf. Accessed on June 29, 2009. Andersen, R., & Newman, J. (1973). Societal and individual determinants of medical care utilization in the United States. The Milbank Memorial Fund Quarterly, 51(1), 95–124. Berndt, E. R. (2002). Pharmaceuticals in US health care: Determinants of quantity and price. Journal of Economic Perspectives, 16, 45–66. Berndt, E. R., Cockburn, I. M., & Griliches, Z. (1996). Pharmaceutical innovations and market dynamics: Tracking effects on price indexes for antidepressant drugs. Brookings Papers on Economic Activity. Berndt, E. R., & Seley, L. (2000). International comparisons of pharmaceutical prices: What do we know, and what does it mean? Journal of Health Economics, 19, 283–287. Chandra, A., Gruber, J., & McKnight, R. (2007). Patient cost-sharing, hospitalization offsets, and the design of optimal health insurance for the elderly. NBER Working Paper no. 12972. National Bureau of Economic Research, Cambridge. Chen, J., & Rizzo, J. (forthcoming). Who pays for drug quality? Eastern Economic Journal.

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Dor, A., & Encinosa, W. (2004). Does cost sharing affect compliance? The case of prescription drugs. NBER Working Paper no. 10738. National Bureau of Economic Research, Cambridge. Feldstein, P. (2006). Health policy issues: An economic prospective (4th ed). Chicago: AUPHA/ HAP. Frank, R. (2001). Prescription drug prices: Why do some pay more than others do? Health Affairs, 20, 115–128. Gaynor, M., Li, J., & Vogt, W. (2006). Is drug coverage a free lunch? Cross-price elasticities and the design of prescription drug benefits. NBER Working Papers 12758. National Bureau of Economic Research. Goldman, D., Joyce, G. F., & Zheng, Y. (2007). Prescription drug cost sharing. The Journal of American Medical Association, 298, 61–69. Lichtenberg, F. (1996). Do (more and better) drugs keep people out of hospitals? The American economic review. Papers and Proceedings of the Hundredth and Eighth Annual Meeting of the American Economic Association, San Francisco, CA, January 5–7, 86(2), 384–388. Lichtenberg, F. (2001). Are the benefits of newer drugs worth their cost? Evidence from the 1996 MEPS. Health Affairs, 20, 241–251. Lichtenberg, F., & Virabhak, S. (2007). Pharmaceutical-embodied technical progress, longevity, and quality of life: Drugs as equipment for your health. Managerial and Decision Economics, 28(4–5), 371–392. Lu, J., & Comanor, W. (1998). Strategic pricing of new pharmaceuticals. Review of Economics and Statistics, 80(1), 108–118. Mahoney, J. (2005). Reducing patient drug acquisition costs can lower diabetes health claims. The American Journal of Managed Care, 11, S170–S176. Pavcnik, N. (2000). Do pharmaceutical prices respond to insurance? NBER Working Paper no. W7865. National Bureau of Economic Research (NBER). Rizzo, J. (1999). Advertising and competition in the ethical pharmaceutical industry: The case of antihypertensive drugs. The Journal of Law and Economics, 42, 89–116. Santerre, R., & Neun, S. (2007). Health economics: Theories, insights and industry studies, 4th ed. Ohio: Thomson South-Western. Sisko, A., Truffer, C., Smith, S., Keehan, S., Cylus, J., Poisal, J. A., Clemens, K., & Lizonitz, J. (2009). Health spending projections through 2018: Recession effects add uncertainty to the outlook. Health Affair, 28(2), w346–w357. Tamblyn, R., Laprise, R., Hanley, J. A., Abrahamowicz, M., Scott, S., Mayo, N., Hurley, J., Grad, R., Latimer, E., Perreault, R., McLeod, P., Huang, A., Larochelle, P., & Mallet, L. (2001). Adverse events associated with prescription drug cost-sharing among poor and elderly persons. The Journal of American Medical Association, 285, 421–429. Winkelmann, R. (2004). Co-payments for prescription drugs and the demand for doctor visits – Evidence from a natural experiment. Health Economics, 13, 1081–1089. Wooldridge, J. (2008) Introductory econometrics: A modern approach (4th ed.). Cincinnati: South-Western College Publishing.

ADVERSE SELECTION AND THE EFFECT OF HEALTH INSURANCE ON UTILIZATION OF PRESCRIBED MEDICINE AMONG PATIENTS WITH CHRONIC CONDITIONS Yuriy Pylypchuk ABSTRACT Purpose – To examine the effects of health insurance types on the use of prescribed medication that treat patients with hypertension, diabetes, and asthma. The study distinguishes between individuals with private health maintenance organization (HMO) plans and private non-HMO plans. The study also distinguishes between people with health insurance and drug coverage and people with health insurance and no drug coverage. Methods – Joint discrete factor models are estimated to control for endogeneity of each type of coverage. Findings – The main findings suggest that the effect of health insurance varies across patients with different conditions. The strongest and most significant effect is evident among patients with hypertension while the weakest and least significant is among patients with asthma. These findings suggest that patients with asymptomatic conditions are more Pharmaceutical Markets and Insurance Worldwide Advances in Health Economics and Health Services Research, Volume 22, 233–272 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1108/S0731-2199(2010)0000022014

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likely to exhibit moral hazard than patients with conditions that impose immediate impairment. Additional results suggest that, relative to the uninsured and people with health insurance but no drug coverage, patients with drug coverage are more likely to initiate drug therapy and to consume more medications. Originality – The results of the study indicate that moral hazard of drug utilization is condition specific. The variation in ‘‘silence’’ of conditions’ symptoms could be a key reason for difference in insurance effects among patients with hypertension, diabetes, and asthma.

1. INTRODUCTION In the past 10 years, the U.S. population witnessed a sharp rise in prices and expenditures for prescribed medicine (PM). Berndt (2002) reports that expenditures for PM as a share of total health expenditures increased from 5.9 percent in 1996 to 9.7 percent in 2000. Moeller, Miller, and Banthin (2004) report that between 1997 and 2001, the price of prescription drugs rose by 26.3 percent, the amount of drugs consumed by users increased by 11.7 percent, and total drug expenditures increased by 71.6 percent. Berndt (2002) also suggests that increased generosity of drug coverage is one of the potential explanations for such large increases in drug expenditures. In 1965, only about 3.5 percent of expenditures for prescription pharmaceuticals were paid by private insurance. In the late 1990s, the share of drug expenditures covered by private insurance exceeded 50 percent. Why might health insurance contribute to the rise in expenditures on prescribed medication? The classical argument for a positive association between health insurance and medication use is that health insurance reduces the net price of medical care which decreases consumers’ incentives to economize on health care costs (Pauly, 1968). Insured consumers do not usually bear all of the cost for medication; hence, their incentives to economize on costs are limited. However, there are factors that may reduce the size of moral hazard or even make relationships between health insurance and drug expenditures negative. For instance, health maintenance organizations (HMOs) create strong incentives for physicians to provide preventive care and for patients to purchase inexpensive drugs. HMOs also use strong bargaining power to negotiate low prices with pharmaceutical companies, which further reduces drug expenditures (Dranove, Hueghs, & Shanley, 2003). Finally, proper therapeutic treatment of chronically ill

Adverse Selection and the Effect of Health Insurance

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patients is likely to reduce the progression of disease and, subsequently, reduce drug expenditures. As insured patients are more likely to visit physicians and take medication on a regular basis, the association between health insurance and drug expenses may be negative in the long run.1 The net effect of health insurance on drug expenditures is an empirical question. The aim of this chapter is to investigate the relationship between types of health insurance and medication use. I specify a model that consists of two components, one for modeling insurance choice and another for modeling expenditures on prescribed medication. I allow a consumer to choose from four types of health insurance: uninsured, private insurance without drug coverage (HINDC), private HMO with drug coverage (HMODC), and private non-HMO with drug coverage (HIDC). The expenditure component is specified as a two-part model where the first part considers if a person spent any money on medication and the second part examines the amount of money spent conditional on positive expenditures. I define expenditures on prescribed medication as the yearly amount (in U.S. dollars) a person spent on medication. To control for the endogeniety of health insurance, I jointly estimate equations from the first and second components using a finite mixture model (Mroz, 1999). The study uses 2000–2003 data from the Medical Expenditures Panel Survey (MEPS) which is a nationally representative survey of the U.S. noninstitutional population. The survey provides detailed data on drug expenditures, health characteristics of each condition, and a person’s risk preferences. The models are estimated separately for patients with hypertension, diabetes, and asthma. There are two main reasons for studying these conditions. First, in recent years, the United States has witnessed a disproportionate growth of patients with these conditions and the rise in prices for the associated medications. The prevalence of diabetes and hypertension in the United States has increased by more than 30 percent over the past decade, although the prevalence of asthma has remained relatively stable. Moeller et al. (2004) report that annual medication expenditures per person using diabetic and cardiovascular drugs increased by 114 and 55 percent, respectively, during 1997–2001. The data of this study suggests that annual medication expenditures of asthmatic drugs per user have increased by 15 percent in real dollars over four years (2000– 2003). Therefore, it is important to know which incentive schemes significantly change patients’ behavior. Second, the key feature that distinguishes these conditions is the variation in perceptibility of the conditions’ symptoms which creates an interesting

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experiment to study. For instance, most people with hypertension may not feel any symptoms. People with high blood pressure rarely experience even headaches or dizziness, which is why the condition is nick-named the ‘‘silent killer’’ (Rapport, 1999) or simply a silent condition. On the contrary, asthma produces symptoms such as difficulty breathing, shortness of breath, sweating, and rapid pulse. Clearly, it is not a silent killer! Diabetes symptoms consist of unusual weight loss, irritability, blurry vision, increased fatigue, and so on (National Diabetes Association, 2005). In terms of silent conditions, it probably falls somewhere in between. Although all the patients in my sample have been diagnosed with these conditions, it is still possible that patients’ sensitivity to cost sharing is likely to vary with the noticeability of patient’s symptoms. In clinical literature, a number of studies find that symptoms of asthma impose greater constraints on people’s behavior and lifestyle than symptoms of hypertension and diabetes. Lam and Lauder (2000) find that patients with asthma are more likely to report having difficulty conducting daily and social activities and experiencing emotional problems and overall poor health than patients with hypertension and diabetes. Adams et al. (2001) construct a quality of life index (QLI)2 based on responses to 36 questions about functional health, well-being, and psychometrically based physical and mental health. They find that asthma has the most detrimental impact on this index compared to other chronic conditions. There were few studies that separately examined the effects of diabetes and hypertension on patients’ quality of life. Wandell’s (2005) literature review indicates that research consistently finds that that QLI is lowered by the comorbidities associated with diabetes, such as depression and psychiatric disorders, but not the symptoms that are directly attributable to diabetes. Finally, in a Stein et al. (2002) study, individuals with normal blood pressure and clinicians were asked to answer QLI questions pretending that this population had hypertension. The effects of hypertension on QLI were then compared among patients with normal blood pressure, clinicians, and patients who actually had hypertension. The study found that the negative effect of hypertension on QLI was the smallest among patients with hypertension. This result suggests that the symptoms of the ‘‘silent killer’’ appear to be even more silent to patients with hypertension than to those who were asked to assume that they have hypertension. The results of these studies reinforce the idea that among the three conditions, symptoms of hypertension are the least noticeable. I hypothesize that the behavior of patients with silent conditions is more likely to respond to insurance status than if patients’ conditions have


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perceptible symptoms. If uninsured, patients with silent conditions may be more likely to procrastinate undertaking treatment and less likely to comply with medication regimes, than patients with noticeable conditions. If insured, patients with silent conditions may be less willing to engage in preventive care than patients with noticeable conditions. The effect of health insurance on drug expenditures is expected to be larger among patients with hypertension than among patients with diabetes and asthma. The rest of the chapter is organized as follows: Section 2 provides literature review; Section 3 discusses the theoretical framework; Section 4 specifies empirical model; Section 5 describes the data set; Section 6 discusses results, and Section 7 presents conclusions.

2. LITERATURE REVIEW There has been much empirical literature on the effects of health insurance on medical utilization (see Zweifel & Manning, 2000, for a review). The Rand Health Insurance Experiment (RHIE) produced two papers that explicitly identify the effects of drug coverage on expenditures for prescribed medication (Leibowitz, 1989; Leibowitz, Manning, & Newhouse, 1985). These studies found that people are sensitive to cost sharing, and, as a result, insured individuals are likely to spend more on medication than are the uninsured. Although the RHIE was a randomized controlled trial, it excluded high income individuals and was limited to six cities in the United States. More importantly, the RHIE was conducted in the mid-1970s. Since then, the U.S. health care system has undergone many changes. Few studies identify the effects of drug coverage on demand for prescribed medication using non-experimental data. Coulson and Stuart (1995) and Coulson, Terza, Neslusan, and Stuart (1995) estimate the impact of the Pharmaceutical Assistance Contract for the Elderly (PACE) program on drug expenditures. The studies use Medicare claims data for elderly Pennsylvanians and find significant effects of drug coverage on drug use even after controlling for endogeneity of drug coverage. Yang, Gilleskie, and Norton (2004) specify a dynamic model where drug coverage affects drug expenditures and, subsequently, health status over time. The results indicate a positive and significant association of health insurance with expenditures on prescribed medication and improvements in health status. The main limitation of those studies is the focus on the elderly population. Additionally, the data was not nationally representative.

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Several studies have examined utilization patterns of medication that treats hypertension, diabetes, or asthma. Dor and Encinosa (2004) examined utilization behavior of diabetics. Their main finding is that patients are more sensitive to cost sharing if out-of-pocket expenditure is a direct function of total expenditures than if out-of-pocket cost depends only on flat deductible. Crown, Berndt, Baser, Finkelstein, and Witt (2003) use MarketScan claims data to identify the effect of health insurance on treatment patterns of asthmatic drugs among asthma patients. The study classifies drugs as controller medications, reliever medications, and controller and reliever medications. The main findings suggest that copayments have no effect on types of medication consumed. Finally, Rice, Lavarreda, Ponce, and Brown (2005) use data from the California Health Interview Survey to identify the effect of public and private coverage on utilization of specific drugs that treat hypertension, diabetes, and asthma. The study does not control for endogeneity of health insurance and uses data that is not nationally representative. Additionally, Rice et al. (2005) examine only specific drugs that treat each condition, not the entire set of medication types that patients use for their treatment. The results of the study suggest large effects of private and public insurance on utilization of prescribed medication.

3. THEORETICAL FRAMEWORK The theoretical discussion for this analysis is based on the models developed by Cameron, Trivedi, Milne, and Piggott (1988) and Koc (2004). These models use a two-period framework in which utility in each period depends on his/her health status and consumption of composite and health goods. Health status is generated by a health production function. Utility and the health production function are both increasing in their arguments. Most studies also specify a risk-averse utility function (Hubbard, Skinner, & Zeldes, 1995; Koc, 2004; Cameron et al., 1988). This specification helps to justify why individuals may choose to insure against losses in financial and health capital. As mentioned, the individual faces a two-period planning horizon. The individual initially forms expectations about the distribution of health status in period two conditional on his current health status and chooses the optimal type of health coverage based on these expectations. The insurance contract is chosen based on its co-pay and premium.


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At the beginning of period two, the individual learns his new health status and decides how much of the remaining income should be spent on consumption of health and non-health goods. This decision-making process implies that the individual has some known, ex ante, level of health in period one and knows the probability distribution of future health. The ex post level of health is realized in period two after the individual chooses health coverage. The individual also faces budget constraints across the two periods. In period one, the share of income to be spent on health insurance premiums is determined. In period two, the individual uses the remaining income to purchase the non-health good and out-of-pocket medical expenses, which will be a function of the insurance policy chosen in the initial period. The model is solved by backwards induction. First, the individual solves for the optimizing values of health and non-health care goods consumed conditional on the insurance policy chosen in period one and each possible realization of the ex post level of health. The solution to the health care decision problem gives a set of optimal values of medical care as a function of the co-pay for care and ex post health status. Next, substituting these optimums in the utility function and integrating this function over each possible level of ex post health status give the indirect conditional expected utility function associated with the co-pay of the insurance policy. Given the premium and the co-pay structure of health coverage, the individual maximizes this function with respect to the co-pay that yields the optimal insurance policy. The first-order conditions of this program yield the result that insurance will be purchased up to the point where its marginal benefit equals its marginal cost. The marginal cost of insurance appears as forgone consumption when the individual uses some of his income to purchase more insurance. The marginal benefit of additional insurance appears as the gain in marginal utility from extra health care and risk protection, because the model assumes the consumer is risk averse. The consumer with insurance utilizes more health care services because health insurance decreases the net price of medical care. Pauly (1968) names this change in behavior as moral hazard and shows that it represents a welfare loss to society. The main argument is simple. Although the net price of health care is reduced, the cost of providing health services has not changed. Therefore, society incurs a welfare loss because additional health care is consumed, which costs society more than its value to the consumer. In theory, a healthy patient is expected to be more sensitive to changes in prices of medical care than the patient with poor health status because the gain in utility from an increase in one unit of health is higher when the

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patient is ill than when the patient is healthy (Koc, 2004). Results of empirical studies confirm this theory. For instance, Wedig (1988) and Manning et al. (1987) found that individuals in fair or poor health are about twice less responsive to the price for physician visits than patients in good health. However, the existing literature does not articulate the exact meaning of poor or fair health from the patient’s point of view. In this study, I suggest that the variation in noticeability of conditions’ symptoms is an alternative dimension of patients’ health and that this dimension is likely to effect patients’ utilization behavior. The outlined model explains utilization of health care if an individual signs a contract with a health insurance plan. However, health insurance plans may also be obtained from health care providers contracts with different characteristics to affect the final demand for medical care. For instance, HMOs use different payment schemes to reimburse physicians for the services they provide (Glied, 2000). A conventional fee-for-service (FFS) contract reimburses physicians for every unit of medical care provided, which means that physicians have no financial incentives to restrain the supply of health services. This reimbursement scheme is called as a fully retrospective payments system. A typical HMO contract consists of one part that is independent of health care cost (the prospective amount) and another part that is a function of health care cost (the retrospective amount). In a fully prospective or a capitated system, the physician is reimbursed by the number of patients served and the reimbursement is not a function of the quantity of health care provided. Frank, Glazer, and McGuire (2000) show that physicians require greater expected health benefits for their patients before providing medical care under the prospective system than under traditional FFS insurance contracts. This suggests that in theory, the prospective system should reduce the quantity of medical care. Of particular relevance for this study, HMOs can restrain expenditures on prescribed medication through several channels. First, the prospective amount paid to the physician may be a function of the medication that the physician prescribes. For instance, HMOs may deduct payments if physicians prescribe only branded medication or may reward them if they prescribe generic drugs.3 Second, HMOs are more generous in covering patients’ expenditures on inexpensive or generic drugs than on expensive and branded medication. This creates incentives for patients to use inexpensive medication (Dranove et al., 2003). Third, HMOs use their strong bargaining power to negotiate low drug prices with pharmaceutical companies, which further reduces expenses on prescribed medications among HMO enrollees (Dranove et al., 2003).


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The empirical part of the chapter investigates whether (i) there is an association between health insurance types and the use of prescribed medication; (ii) the size and significance of any insurance effect varies across the three conditions; (iii) HMO and drug coverage, non-HMO and drug coverage, and health insurance and no drug coverage differently impact medication use.

4. ECONOMETRIC SPECIFICATION I estimate multivariate models of the determinants of the probability of using any prescribed medication and the amount of medication used by adults with hypertension, diabetes, or asthma. The model consists of two parts. The first is a specification of the choice of insurance status and the second is a specification of utilization equations conditional on insurance plan choice. The decisions to purchase insurance and medication are interdependent because the individual may have information about the distribution of his ex post level of health that is not observable to the insurer, which implies that our empirical specification should account for common unobserved factors across insurance and utilization equations. I estimate the system using a discrete factor model that controls for the simultaneous relationship between the equations by approximating the effects that are common but unobservable across equations (Mroz, 1999). Similar models have been used by Goldman (1995) and Mello, Stearns, and Norton (2002) in health economics literature. 4.1. Insurance Decision The insurance choice set consists of four mutually exclusive insurance categories: uninsured, FFS and drug coverage, HMO and drug coverage, and any private health insurance and no drug coverage.4 The specification of this choice set enables one to investigate the significance of drug coverage and HMOs on utilization of prescribed medication – something that has not been done in the literature. I specify the approximation to the indirect utility function associated with insurance choice j as V ji ¼ b1j X i þ d1j E i þ n1ji

(1)

where V ji is an indirect utility of individual i associated with the jth insurance category ( j ¼ 0, y, 3). Xi denotes exogenous covariates, b1j and d1j are

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vectors of coefficients for Eq. (1), and n1ji is a residual. The variables that enter vector Ei are assumed to affect health insurance choice, but these variables are not directly associated with expenditures. The probability that an individual chooses insurance j is Prð jÞ ¼ Prðv1ij v1ir 4X i ðB1j B1r Þ þ E i ðd1j d1r Þ8rajÞ which is specified as a multinomial logit model.

4.2. Utilization Decision I use a two-part model to estimate the utilization of prescribed medication. The technique enables one to distinguish the effect of variables on the decision to consume any medication and the amount of goods consumed. The patient’s propensity to use any prescribed medication and the amount spent on drugs conditional on using them are specified as ProbðExp40Þ ¼ b02 X i þ

m X

g02j hij þ ni2

(2)

j¼1

ðLn½ExpjExp40Þ ¼ b03 X i þ

m X

g03j hij þ vi3

(3)

j¼1

The dependent variable of Eq. (2) equals 1 if a person spent any money on prescribed medication; otherwise, the variable is coded as 0. The dependent variable of Eq. (3) equals the natural logarithm of expenditures on prescribed medication conditional on positive expenditures. Xi is a vector of sociodemographic characteristics and health status variables, hij is a binary variable representing the observed insurance choice, b2, b3 are vectors of coefficients, and g2j, and g3j are insurance effects. In our model, identification of the parameters is secured through covariance restrictions, which are explained subsequently. Additionally, Eq. (1) is identified from Eqs. (2) and (3) through a vector of variables E which are assumed to affect utilization only through health insurance choice. Hence, the system of three equations is over-identified. The variables that enter vector E should provide exogenous variation in characteristics of the health insurance plans, and these variables should not be directly related to health care use. The specific variables in Ei include (i) the county level HMO penetration rate, (ii) the county level malpractice


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insurance component of the geographic practice cost index (GPCI) which is developed by Medicare Payment Advisory Commission (see Table 1 for more details) and (iii) interaction of HMO penetration with indicator if married. The HMO penetration rate is assumed to reflect intensity of competition among HMO plans that should affect prices and quality of health coverage. Similarly, areas with high malpractice GPCI are likely to have higher premiums for health coverage, which is likely to affect the insurance take-up rate. I assume that these variables are independent of the distribution of drug utilization of a consumer. The interaction of marriage and number of HMOs indicates whether a response to availability of HMO plans is different for married people. According to the theoretical model, the consumer makes a decision to purchase health insurance based on the expected level of health status and attitudes toward risk. These characteristics may affect the choice of insurance and future consumption of health care, but they cannot be fully captured by observable data. To control for unobservables, the residuals from Eqs. (1–3) have the following specification: v1ij ¼ r1j ui þ e1ij , v2i ¼ r2 ui þ e2i , and v3i ¼ r3 ui þ e3i , where ui is common component that appears in all three equations, r1, r2, and r3 are equation-specific factor loadings, and e1ij, e2i, and e3i are independently distributed error terms of Eqs. (1–3), respectively. The residual ui represents unobserved heterogeneity that causes correlation across all three equations. The distribution of ui can be approximated P using the following discrete specifications: Prðui ¼ mk Þ ¼ pk ; k ¼ 1; :::; K; k pk ¼ 1, where K indicates the number of points of support, mk is a point of support, and pk is a weight or probability. Finally, I normalize parameter r1j to 1. Mroz (1999) suggests that such a normalization does not affect results of the model, but at least one of the parameters should be normalized to secure proper identification of the remaining parameters.

5. DATA I use data from the MEPS which is a nationally representative survey of the non-institutionalized U.S. population. The MEPS provide information on demographics, health status, utilization of health care services, health insurance coverage, income, and employment. The survey is designed as an overlapping panel so that individuals are interviewed five times over a two-year period, and each year, a new panel is started. In 2000–2003,

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Table 1. Variable Name

Means and Definitions of Explanatory Variables.

Variable Description

Sociodemographic characteristics Age Age at the end of the year

40.29 (0.398) 0.686 (0.015) 0.104 (0.011) 0.081 (0.009) 0.318 (0.009) 0.553 (0.017) 0.142 (0.011) 2.85 (0.052)

37.53 (0.102) 0.499 (0.003) 0.101 (0.008) 0.135 (0.011) 0.331 (0.004) 0.562 (0.004) 0.100 (0.003) 3.10 (0.021)

1 if graduated high school or GED at the end of the year 1 if completed BA at the end of the year 1 if completed Masters degree or higher at the end of the year

0.537 (0.008) 0.167 (0.006) 0.094 (0.005)

0.522 (0.015) 0.161 (0.011) 0.072 (0.009)

0.503 (0.016) 0.185 (0.014) 0.093 (0.010)

0.504 (0.006) 0.185 (0.005) 0.083 (0.003)

1 if employed the whole year

0.756 (0.006) 0.176 (0.006) 0.114 (0.004) 0.1256 (0.008)

0.705 (0.011) 0.211 (0.010) 0.092 (0.007) 0.125 (0.010)

0.738 (0.014) 0.155 (0.010) 0.097 (0.010) 0.115 (0.011)

0.785 (0.003) 0.109 (0.003) 0.109 (0.002) 0.097 (0.004)

0.195 (0.028) 0.234 (0.029) 0.397 (0.034) 0.801 (0.020)

0.208 (0.032) 0.218 (0.031) 0.401 (0.037) 0.781 (0.024)

0.248 (0.039) 0.237 (0.033) 0.315 (0.035) 0.859 (0.018)

0.229 (0.030) 0.232 (0.027) 0.349 (0.032) 0.838 (0.016)

Black

1 if Black

Hispanic

1 if Hispanic

Income

Total annual person’s income/100,000 1 if married at the end of the year 1 if divorced at the end of the year Number of persons living in the family at the end of the year

Household

Education High school College MA

Employment Employed Unemployed Selfemp Union Regions West Midwest

Remaining Population (Age 18–64 years)

50.19 (0.312) 0.458 (0.014) 0.153 (0.013) 0.135 (0.016) 0.305 (0.008) 0.693 (0.014) 0.139 (0.009) 2.87 (0.045)

1 if female

Divorced

Asthma

49.36 (0.158) 0.478 (0.006) 0.142 (0.010) 0.073 (0.007) 0.364 (0.005) 0.681 (0.008) 0.140 (0.005) 2.710 (0.021)

Female

Marry

Hypertension Diabetes

1 if unemployed the whole year 1 if self employed any time during the year 1 if employed by union any time during the year 1 if resides in western region

South

1 if resides in midwestern region 1 if resides in southern region

MSA

1 if resides in metro area

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Table 1. (Continued ) Variable Name


Health characteristics Vgood 1 if self-reported very good heath Good 1 if self-reported good heath Fairp Limit Chddx Angidx Midx

Ohrtdx Strkdx

Kidn Eye Kidne Flowa

1 if self-reported fair or poor heath 1 if reported having ADL or IADL 1 if had been diagnosed with a coronary heart disease 1 if had been diagnosed with angina or angina pectoris 1 if had been diagnosed with heart attack or myocardial infarction 1 if had been diagnosed with any other heart condition 1 if had been diagnosed with stroke or transient ischemic attack 1 if diabetes caused kidney problems 1 if diabetes caused eye problems 1 if diabetes caused kidney or eye problems 1 if keeps peak flow meter at home

Risk preference Risk 1 if a risk taker (see text) Others HMOp Homev/ 100,000 GPCI

A county level HMO penetration rate (mean) A county level median home value (mean) A Geographic Practice Cost Index (GPCI) is an index which adjusts the relative value of malpractice expense across geographic locations. It reflects the cost of malpractice expense in each county relative to national average


Asthma


0.310 (0.006) 0.349 (0.006) 0.194 (0.006) 0.048 (0.003) 0.049 (0.003) 0.038 (0.002) 0.046 (0.003)

0.179 (0.009) 0.396 (0.012) 0.354 (0.012) 0.065 (0.006) 0.086 (0.008) 0.049 (0.005) 0.072 (0.007)

0.326 (0.014) 0.314 (0.013) 0.217 (0.014) 0.054 (0.007) 0.023 (0.005) 0.026 (0.005) 0.022 (0.004)

0.358 (0.004) 0.225 (0.004) 0.061 (0.002) 0.014 (0.001) –

0.086 (0.004) 0.031 (0.002)

0.088 (0.007) 0.044 (0.005)

0.091 (0.008) 0.025 (0.005)

–

0.014 (0.001) 0.027 (0.002) 0.032 (0.002) 0.024 (0.002)

0.082 (0.008) 0.169 (0.009) 0.207 (0.010) 0.031 (0.005)

0.008 (0.002) 0.015 (0.003) 0.015 (0.003) 0.312 (0.015)

0.167 (0.005)

0.163 (0.009)

0.184 (0.011)

0.215 (0.003)

0.266 (0.010) 1.30 (0.035) 0.967 (0.030)

0.259 (0.011) 1.28 (0.045) 0.968 (0.030)

0.288 (0.011) 1.41 (0.046) 0.983 (0.038)

0.284 (0.010) 1.404 (0.041) 0.982 (0.038)

– –

–

– – – –

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Table 1. (Continued ) Variable Name


HMOpm

HMOp marry

N

Number of observations


Asthma


0.173 (0.006)

0.172 (0.008)

0.148 (0.007)

0.156 (0.006)

6,439

2,310

912

51,360

Source: Author’s calculations from the Medical Expenditures Panel Survey (MEPS), years 2000–2003. Notes: ADL, activities of daily living; IADL, instrumental activities of daily living. Standard errors in parentheses.

direct questions about chronic conditions were asked. I pool these years and extract people who had hypertension, diabetes, or asthma. I create a separate sample for each condition and treat each sample as crosssectional data.5 In the third and the fifth waves of interviews, MEPS asked separate questions if a respondent was diagnosed with each condition of interest. Respondents who answered ‘‘no’’ or refused to answer these questions were excluded from the study. The asthma sample consists of people who indicated they had asthma attacks in the last 12 months to exclude respondents who only had asthma in their childhood. The total number of people who said yes to having hypertension was 21,148, diabetes 6,530, and asthma 4,366. I then limit the sample to the working age population (18–64 years), which reduces the total number of people with hypertension, diabetes, and asthma to 13,011, 3,871, and 2,266, respectively. Finally, after eliminating people who had any public coverage during a year or who did not have information on drug coverage, the sample consists of 10,758 people with hypertension, 2,917 with diabetes, and 1,680 people with asthma. The information about the type of medication consumed and total expenditures on prescribed medication is provided by the MEPS PM file. The file contains event-level data that is collected five times during the twoyear period. The file also contains medication purchase dates, medication names, strength, and quantity dispensed. In addition to financial information, the file contains National Drug Codes that enable a researcher to obtain additional information about drug characteristics. To reduce the amount of underreported detailed financial information, the MEPS construct drug expenditure variables from information supplied by pharmacies. A respondent can choose to sign a permission form that


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allows MEPS to request information about the respondent’s medication from his pharmacy. About 73 percent of respondents who purchased any medication during a year signed the permission form. The MEPS offered pharmacies two options of responding: (1) send a computerized printout for identified patients, or (2) fill in the information on forms supplied by the MEPS.6 The respondents had to list the number and names of the drugs they obtained during each round, the condition treated by each drug, and the addresses of pharmacies that filled each prescription. Records sent by pharmacies had to be matched with medication reported by respondents. The MEPS developed the following matching procedure. First, the program matched the records by person’s name, Generic Product Identifier (GPI),7 and the round in which the drug was purchased. All pharmacy records that were matched with individual records by these characteristics were considered as an exact match. Because 73 percent of drug users signed the pharmacy permission form, some pharmacies chose not to reply, some pharmacies provided incomplete information, and some records supplied by pharmacies did not have a complete match; only about 50 percent of drug purchasers reported by respondents found an exact match. The expenditure information for the remaining purchasers had to be imputed from the records that had a complete match. The following list of variables was used during the imputation process: GPI, indicators for family, Medicare, Medicaid, private, Tricare, other federal and non-federal insurance, person’s rated health, person’s self-reported health, race, age, sex, region, urbanization, conditions, and employment status. The records were matched in descending order of importance: first, the records were matched by GPI, then by health insurance, and so forth. If the GPI number was missing, health insurance became a priority variable, then person’s health, and so forth. The matching process was done with replacement, that is, each record from a complete match could be used more than once for imputation. The problems that arise from this procedure are that the data may be inaccurate and that health insurance and a few other variables that were used to impute expenditures are among the key explanatory variables of this study. This may create a spurious correlation between insurance and expenditures and may bias estimates. It should be noted that the results may be biased only in Eq. (3) (where the dependent variable is the log of expenditures conditional on positive expenditures) because the information about whether a respondent used any drugs is not imputed. To check if results from Eq. (3) are sensitive to the use of imputed data, I will conduct three tests. First, using imputed and not imputed data, I define the dependent variable as the log of total positive annual

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expenditures8 spent on drugs that treat each condition9 and estimate the model described in Section 4. Second, I estimate the same model using the same definition of the dependent variable on observations with non-imputed data. Individuals were excluded if at least one medication record per individual did not find a complete match. Third, I define the dependent variable as the number of prescriptions purchased during the year conditional on positive expenditures and estimate exactly the same model specified in Section 4. The number of prescriptions purchased during a year is defined as the number of new medications or refills the respondent obtained from a pharmacy. One of the main disadvantages of this variable is that the respondent may purchase different number of tablets per prescription, which would not be captured by this variable. The number of medications purchased is reported by the respondent and it is not imputed in MEPS data. These three specifications illustrate if the results from Eq. (3) are sensitive to the imputed data. In case of diabetes, I excluded records that indicated purchases of diabetes appliances such as insulin injectors, glucometers, and lancets. I identified about 15 percent of records or six percent of total diabetic expenditures that are diabetic appliances. There are reasons why the model with those three specifications may produce qualitatively different results, but we still should not conclude that the imputed data causes this difference. The population with non-imputed data is not nationally representative; hence, the difference between the results in the first and second specifications may be due to selection issues, not to the imputation problem.10 The definition of the dependent variable in specification three does not capture as many details of drug consumption as the definition of the dependent variable in specification one. For instance, the insured and uninsured consumers may purchase equal quantities of medication during the year, but the insured may purchase more branded drugs than the uninsured. The third specification will not capture this issue. Clearly, if all three models produce similar results, we may surely conclude these results are not sensitive to the imputed data. Most insurance variables and sociodemographic explanatory variables were obtained from the consolidated full year file (CF). The information about whether a person has drug coverage and whether the drug coverage is missing was obtained from a Person Round Plan file. The file has a complex structure that provides information about privately insured respondents, the firms, or establishments that insure respondents and the type of insurance used by respondents during each wave of interview. I define four types of health insurance. If a respondent reported having drug coverage and nonHMO private insurance during a year (HIDC), I assume the respondent has


249

HIDC. The respondent is assumed to have drug coverage and a private HMO plan (HMODC) if the respondent indicated having HMODC any time during a year and not having HIDC. There was about one percent of population who indicated having one type of drug coverage and then another at different waves during the year. In this study, private non-HMO trumps private HMO because respondents in MEPS are known to slightly over-report enrollment in HMO plans (Zuvekas & Hill, 2004).11 However, this assumption does not make any difference for any of the results. If the respondent indicated having any private health coverage but no drug coverage during the year, the respondent is assumed to have private insurance and no drug coverage (HINDC). The remaining population is coded as uninsured. The survey provides very rich information not only about people’s general health status but also about the severity of each condition. For instance, patients with hypertension were asked if they had a heart attack or myocardial infarction, coronary heart disease, and angina or angina pectoris. Similarly, patients with diabetes were asked if diabetes caused eye or kidney problems. Patients with asthma were asked if they have a peak flow meter. Answers to these questions were used to construct conditionspecific measures of health, which are listed in Table 1. As hypertension and diabetes are known to occur together and become more severe in patients with both of these conditions than in patients with only one of these conditions (Grant & Meigs, 2005), I also include health measures of hypertension as indicators for severity of diabetes and health measures of diabetes as indicators for severity of hypertension. The variable RISKA was created to measure the individual’s attitudes toward risk. It was coded as 1 if a person disagrees with the statement: ‘‘I am more likely to take risks than the average person.’’ The definitions and means of remaining explanatory variables are presented in Table 1. Finally, I obtain HMO penetration rate variable from the Area Resource File (ARF) and GPCI malpractice cost index from the Medicare Payment Advisory Commission.

6. RESULTS 6.1. Descriptive Analysis Table 1 presents means of the 18–64-year-old population with and without the three diagnoses.12 People with diabetes and hypertension are more likely

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to be older and to report having poor health status than a comparable subpopulation without these conditions. Additionally, blacks are disproportionately represented in the diabetes and hypertension groups, while Hispanics are disproportionately represented in the diabetes and healthy groups. Perhaps surprisingly, there are no large disparities in educational attainments across chronic conditions. The other interesting characteristics are that people without chronic conditions are less likely to be unemployed, more likely to be married, and more likely to take risks than people with chronic conditions. Table 2 presents the distribution of patients by health insurance status. The population with chronic conditions has much lower rates of uninsurance (12–14 percent) than the overall working population (over 22 percent), indicating evidence of adverse selection. The distribution of insurance types across the three conditions is almost identical although people with diabetes have slightly lower rates of drug coverage than people with hypertension. Table 3 presents distribution of medication expenditures conditional on positive spending by health insurance types and chronic conditions. Among the three conditions, the drugs that treat diabetes are most costly, with an average value of over 800 dollars for patients with drug coverage. The difference in expenditures by insurance status varies considerably across conditions. Diabetics with HMODC or HIDC plans are likely to purchase more medication than uninsured diabetics. For patients with hypertension, Table 2.

HMODC HIDC HINDC Uninsured N

Distribution of Patients by Health Insurance Status. Hypertension

Diabetes

Asthma

0.450 (0.011) 0.409 (.011) 0.061 (0.004) 0.120 (0.006)

0.408 (0.016) 0.383 (0.016) 0.066 (0.006) 0.143 (0.012)

0.445 (0.017) 0.349 (0.016) 0.060 (0.09) 0.146 (0.011)

10,758

2,917

1,680

Source: Author’s calculations from the Medical Expenditures Panel Survey (MEPS), years 2000–2003. Notes: HIDC, non-HMO and drug coverage; HINDC, health insurance and no drug coverage; HMODC, HMO and drug coverage. Standard errors in parentheses.

251


Table 3.


Expenditure for Prescribed Medication, Conditional on any. Hypertension

Diabetes

Asthma

439.72 (11.86) 497.24 (13.69) 455.40 (39.20) 406.02 (21.42)

852.92 (43.97) 914.97 (46.26) 803.96 (94.57) 699.73 (53.39)

588.61 (51.3) 570.58 (42.06) 523.32 (132.23) 636.10 (199.36)

6,439

2,310

912

Source: Author’s calculations from the Medical Expenditures Panel Survey (MEPS), years 2000–2003. Notes: HIDC, non-HMO and drug coverage; HINDC, health insurance and no drug coverage; HMODC, HMO and drug coverage. Standard errors in parentheses. Statistically different from uninsured at the .05 level, two-tailed test. Statistically different from uninsured at the .01 level, two-tailed test.

the difference in conditional expenditures is only significant between HIDC and uninsured. Surprisingly, uninsured patients with asthma spend more on medication than insured, but the difference between expenditures for insured and uninsured is not significant for any of the insurance categories. Table 4 presents the distribution of propensities to use any medication. The results from this table are more consistent with the moral hazard story; the uninsured are significantly less likely to use any medication than the insured for each type of chronic condition.

6.2. Regression Results 6.2.1. Insurance Coverage Identification of the model is secured through covariance and exclusion restrictions. Table 5 reports joint significance of instruments for each type of health insurance and the overall significance of instruments relative to the uninsured. Overall, instruments are strong predictors of health insurance types, with at least a 0.05 level of significance for any type of health coverage. Table 6 presents estimates of instruments and other determinants of health insurance types from the multinomial logit model. The HMO penetration rate is the most consistent instrument; respondents living in counties with high HMO penetration rate are significantly more likely to choose HMODC in the

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Table 4. Probability Medication Expenditures are Positive. Hypertension

Diabetes

Asthma

0.625 (0.011) 0.616 (0.012) 0.572 (0.026) 0.462 (0.018)

0.814 (0.014) 0.813 (0.017) 0.800 (0.038) 0.751 (0.021)

0.561 (0.025) 0.576 (0.026) 0.476 (0.061) 0.445 (0.034)

10,758

2,917

1,680


Source: Author’s calculations from the Medical Expenditures Panel Survey (MEPS), years 2000–2003. Notes: HIDC, non-HMO and drug coverage; HINDC, health insurance and no drug coverage; HMODC, HMO and drug coverage. Standard errors in parentheses. Statistically different from uninsured at the .05 level, two-tailed test. Statistically different from uninsured at the .01 level, two-tailed test.

Tables 5.

Joint Significance of Instruments.

HMODC HIDC HINDC Joint significance of instruments relative to uninsured

Hypertension

Diabetes

Asthma

14.48 3.01 2.84 16.73

4.48 3.46 2.85 6.69

5.71 5.87 2.38 6.25

Source: Author’s calculations from the Medical Expenditures Panel Survey (MEPS), years 2000–2003. Notes: HIDC, non-HMO and drug coverage; HINDC, health insurance and no drug coverage; HMODC, HMO and drug coverage. Standard errors in parentheses. Reported values from Wald test. The Wald test is performed on a multinomial logit model where the uninsured is a reference category and the remaining covariates are sociodemographic and health characteristics listed in Table 6. Statistically significant at the .05 level, two-tailed test. Statistically significant at the .01 level, two-tailed test.

three samples and significantly less likely to enroll in HIDC in the hypertension and diabetes samples. The instrument in most models is insignificant in explaining HINDC, but the GPCI and the interaction of HMO penetration rate and marriage variables have expected signs, and at least one of these variables is a significant determinant of HIDC or HINDC.

Unemployed

Employment Employed

MA

College

Education High school

Household

Divorced

Marry

Income

Hispanic

Black

Female

Demographic Age

0.875 (0.123) 0.087 (0.139)

0.381 (0.086) 0.621 (0.139) 0.449 (0.191)

0.000 (0.004) 0.275 (0.076) 0.088 (0.102) 1.13 (0.116) 2.89 (0.175) 1.39 (0.100) 0.148 (0.119) 0.100 (0.027)

HMODC

0.609 (0.120) 0.049 (0.013)

0.368 (0.086) 0.791 (0.139) 0.760 (0.189)

0.002 (0.004) 0.138 (0.076) 0.414 (0.103) 1.39 (0.126) 3.10 (0.173) 1.26 (0.099) 0.094 (0.117) 0.120 (0.028)

HIDC

Hypertension

Table 6.

0.022 (0.173) 0.200 (0.191)

0.430 (0.128) 0.878 (0.189) 0.142 (0.263)

0.011 (0.005) 0.281 (0.108) 0.171 (0.151) 0.731 (0.182) 2.45 (0.229) 0.430 (0.135) 0.377 (0.169) 0.058 (0.038)

HINDC

0.873 (0.239) 0.042 (0.258)

0.533 (0.160) 0.750 (0.286) 0.194 (0.340)

0.014 (0.007) 0.288 (0.152) 0.279 (0.197) 1.48 (0.196) 2.42 (0.358) 1.42 (0.193) 0.011 (0.236) 0.113 (0.047)

HMODC

0.377 (0.229) 0.178 (0.246)

0.194 (0.157) 0.681 (0.286) 0.186 (0.339)

0.013 (0.007) 0.309 (0.152) 0.646 (0.196) 1.55 (0.200) 3.42 (0.350) 1.34 (0.189) 0.285 (0.236) 0.161 (0.048)

HIDC

Diabetes

0.341 (0.376) 0.103 (0.394)

0.457 (0.250) 1.13 (0.407) 0.814 (0.453)

0.013 (0.013) 0.191 (0.226) 0.047 (0.284) 0.904 (0.305) 1.76 (0.667) 0.588 (0.255) 0.223 (0.334) 0.017 (0.062)

HINDC

0.395 (0.276) 0.271 (0.331)

0.228 (0.213) 0.442 (0.336) 1.02 (0.884)

0.005 (0.009) 0.059 (0.202) 0.628 (0.277) 0.557 (0.291) 2.68 (0.554) 0.947 (0.242) 0.064 (0.315) 0.106 (0.057)

HMODC

Regression Results: Health Plan Choice Equation.

0.018 (0.276) 0.120 (0.323)

0.365 (0.223) 0.234 (0.346) 1.51 (0.884)

0.002 (0.010) 0.120 (0.208) 1.34 (0.304) 1.12 (0.337) 3.33 (0.564) 0.961 (0.249) 0.067 (0.321) 0.109 (0.062)

HIDC

Asthma

0.062 (0.478) 0.224 (0.558)

0.039 (0.391) 0.036 (0.598) 0.069 (0.501)

0.005 (0.015) 0.150 (0.342) 0.231 (0.427) 0.407 (0.494) 1.64 (0.875) 0.989 (0.400) 0.482 (0.505) 0.014 (0.096)

HINDC

Adverse Selection and the Effect of Health Insurance 253

0.043 (0.127) 0.315 (0.121) 0.631 (0.134) 0.280 (0.159) 0.247 (0.202) 0.411 (0.222) 0.277 (0.196)

Health characteristics Vgood 0.107 (0.128) Good 0.265 (0.122) Fairp 0.493 (0.134) Limit 0.100 (0.162) Chddx 0.025 (0.210) Angidx 0.472 (0.228) Midx 0.054 (0.197)

MSA

South

Midwest

0.526 (0.141) 0.117 (0.147) 0.382 (0.140) 0.293 (0.098)

1.36 (0.101) 1.24 (0.201)

1.54 (0.105) 1.18 (0.199)

0.569 (0.138) 0.471 (0.147) 0.574 (0.138) 0.404 (0.102)

Regions West

Union

Selfemp

HIDC

HMODC

Hypertension

0.308 (0.173) 0.476 (0.166) 0.662 (0.185) 0.489 (0.202) 0.171 (0.295) 0.163 (0.326) 0.391 (0.295)

0.486 (0.201) 0.034 (0.203) 0.388 (0.194) 0.330 (0.147)

0.657 (0.153) 0.921 (0.255)

HINDC

0.126 (0.310) 0.023 (0.276) 0.064 (0.282) 0.292 (0.295) 0.256 (0.379) 0.357 (0.437) 0.043 (0.354)

0.508 (0.281) 0.547 (0.281) 0.992 (0.269) 0.704 (0.214)

1.55 (0.228) 1.18 0.370()

HMODC

0.106 (0.316) 0.195 (0.282) 0.079 (0.287) 0.002 (0.279) 0.723 (0.367) 0.293 (0.419) 0.472 (0.345)

0.358 (0.292) 0.048 (0.302) 0.524 (0.278) 0.619 (0.202)

1.37 (0.221) 1.10 (0.382)

HIDC

Diabetes


0.001 (0.479) 0.019 (0.437) 0.279 (0.439) 0.356 (0.350) 0.224 (0.528) 0.511 (0.541) 0.704 (0.532)

1.20 (0.409) 0.562 (0.404) 0.954 (0.374) 0.062 (0.281)

0.896 (0.331) 0.951 (0.485)

HINDC

0.091 (0.324) 0.180 (0.317) 0.806 (0.339) 0.178 (0.376) – – –

– –

0.262 (0.353) 0.503 (0.368) 0.305 (0.356) 0.687 (0.301)

1.35 (0.308) 1.24 (0.518)

HIDC

0.271 (0.316) 0.423 (0.306) 0.725 (0.322) 0.583 (0.391) –

0.377 (0.329) 0.228 (0.354) 0.734 (0.341) 0.349 (0.298)

1.24 (0.289) 1.40 (0.509)

HMODC

Asthma

–

–

0.196 (0.473) 0.301 (0.491) 0.903 (0.533) 0.998 (0.642) –

0.609 (0.508) 0.148 (0.514) 0.636 (0.489) 0.069 (0.501)

0.358 (0.457) 0.787 (1.22)

HINDC

254 YURIY PYLYPCHUK

2.23 (0.298) 0.310 (0.585) 0.475 (0.440) 0.091 (0.617) –

0.516 (0.090)

(0.120) 0.164 (0.228) –

0.828 (0.302) 0.949 (0.591) 1.08 (0.437) 0.745 (0.620) 2.15 (1.23)

0.596 (0.089)

0.168 (0.120) 0.175 (0.221) –

–

–

0.414 (0.132) 0.206 (0.214) –

0.125 (0.423) 0.199 (0.863) 0.662 (0.640) 0.086 (0.905) 1.44 (1.71)

0.522 (0.136)

0.176 (0.170) 0.270 (0.296) –

–

–

0.135 (0.184) 0.653 (0.253) –

2.19 (0.629) 1.67 (1.07) 1.96 (0.868) 1.33 (1.15) –

0.452 (0.179)

–

–

0.581 (0.319) 0.450 (0.471) 0.033 (0.264) 0.404 (0.187) 0.248 (0.152) –

0.947 (0.635) 2.82 (1.15) 1.41 (0.866) 3.17 (1.20) 2.26 (0.958)

0.372 (0.180)

–

–

0.737 (0.313) 0.664 (0.458) 0.017 (0.252) 0.157 (0.183) 0.215 (0.153) –

2.47 (0.853) 6.86 (1.67) 0.561 (1.31) 3.95 (1.74) 0.711 (4.20)

0.591 (0.290)

–

–

0.171 (0.430) 0.935 (0.516) 0.106 (0.368) 0.261 (0.273) 0.048 (0.235) –

1.88 (0.692) 0.753 (1.82) 2.64 (0.816) 1.67 (1.72) –

0.488 (0.222)

1.13 (0.744) 0.918 (1.87) 2.65 (0.546) 1.29 (1.73) 0.856 (1.26)

0.698 (0.225)

0.049 (0.213)

0.459 (0.232) 0.719 (0.428) –

0.398 (0.223) 0.690 (0.426) – 0.138 (0.207)

–

–

–

–

–

–

–

–

0.916 (1.33) 0.959 (2.74) 2.94 (1.20) 0.630 (2.42) 0.569 (0.874)

0.248 (0.365)

0.227 (0.328)

0.238 (0.409) 0.910 (0.548) –

–

–

–

–

Source: Author’s calculations from the Medical Expenditures Panel Survey (MEPS), years 2000–2003. Notes: HIDC, non-HMO and drug coverage; HINDC, health insurance and no drug coverage; HMODC, HMO and drug coverage. Standard errors in parentheses. Statistically significant at the .10 level, two-tailed test. Statistically significant at the .05 level, two-tailed test. Statistically significant at the .01 level, two-tailed test.

Factor loading

Constant

HMOpm

GPCI

Instruments HMOp

Risk preferences Risk

Flowa

Kidne

Diabetes

–

Hypertension

0.258

–

0.165 (0.228) 0.237 (0.217) –

Eye

Kidn

Strkdx

Ohrtdx


256

YURIY PYLYPCHUK

The signs of remaining coefficients generally have expected directions and are significantly identified. Males are significantly less likely to enroll in any health insurance plan than females. Hispanics appear to be one of the most disadvantaged groups in terms of having any health coverage, for the coefficient on Hispanics is consistently negative and large across all types of health insurance and chronic conditions. Not surprisingly, the educated population is much more likely to have health insurance than the population who failed to obtain high school diploma. People who are unemployed during the year, self-employed, or not unionized are much less likely to enroll in private coverage than their counterparts. As expected, respondents who indicated being risk takers are less likely to have health coverage, and this effect is highly significant across every condition. There is mixed evidence of adverse selection into health insurance. For every condition, some health characteristics indicate positive selection into health coverage while others indicate that selection is negative. Koc’s (2005) study finds conceptually similar results. For instance, in the hypertension sample, people who reported having diabetes, disability (Limit), angina (Angidx), and any other heart condition (Ohrtdx) are more likely to have private coverage. However, respondents who reported having poor or fair health are less likely to have coverage than respondents with excellent selfreported health status. The situation is even less clear in the diabetes sample as most health measures are not precisely identified. The indicators for whether diabetes caused eye or kidney problems are significant and negative for HMODC and HIDC. However, respondents who reported having other heart problems are more likely to enroll in HMOD and HIDC. None of the health characteristics are significant predictors of HINDC in the diabetes sample. In the asthma sample, the respondents with poor or fair health status are less likely to have health coverage, while respondents who indicated having hypertension and diabetes are more likely to enroll in private coverage, although the estimates of these health indicators are mostly insignificant. In the aggregate, these estimates seem to favor a positive adverse selection into health coverage. The other interesting result is that self-reported poor health status decreases the likelihood of having health coverage while more objective measures of poor health increase probability of enrollment in health coverage.

6.2.2. Expenditures Table 7 presents estimates from expenditure equations. The coefficients on propensity to spend any money should be interpreted as regular probit

Household

Divorced

Marry

Income

Hispanic

Black

Female

Demographic Age

HINDC

HIDC

Health insurance HMODC

0.034 (0.002) 0.181 (0.028) 0.072 (0.041) 0.116 (0.054) 0.079 (0.052) 0.169 (0.042) 0.056 (0.049) 0.010 (0.011)

0.439 (0.065) 0.372 (0.067) 0.161 (0.051)

Prob(ExpW0)

0.007 (0.002) 0.125 (0.028) 0.020 (0.038) 0.072 (0.057) 0.105 (0.056) 0.077 (0.042) 0.053 (0.050) 0.034 (0.011)

0.110 (0.073) 0.144 (0.085) 0.152 (0.118) 0.014 (0.004) 0.147 (0.064) 0.088 (0.090) 0.173 (0.106) 0.241 (0.135) 0.135 (0.092) 0.088 (0.116) 0.067 (0.024)

0.281 (0.124) 0.253 (0.119) 0.178 (0.198)

Prob(ExpW0)

0.005 (0.003) 0.055 (0.051) 0.114 (0.066) 0.168 (0.077) 0.193 (0.107) 0.062 (0.072) 0.018 (0.086) 0.050 (0.016)

0.111 (0.106) 0.152 (0.118) 0.038 (0.173)

Ln(Exp)|ExpW0

Diabetes

Regression Results: Expenditure Equations.

Ln(Exp)|ExpW0

Hypertension

Table 7.

0.015 (0.004) 0.172 (0.097) 0.223 (0.169) 0.033 (0.152) 0.008 (0.188) 0.123 (0.120) 0.017 (0.147) 0.009 (0.031)

0.305 (0.192) 0.281 (0.203) 0.098 (0.316)

Prob(ExpW0)

0.006 (0.005) 0.238 (0.104) 0.003 (0.164) 0.447 (0.194) 0.200 (0.199) 0.041 (0.135) 0.144 (0.180) 0.080 (0.037)

0.197 (0.200) 0.200 (0.200) 0.199 (0.317)

Ln(Exp)|ExpW0

Asthma


South

Midwest

Regions West

Union

Selfemp

Unemployed

Employment Employed

MA

College

Education High school

0.013 (0.045) 0.142 (0.043) 0.151 (0.040)

0.075 (0.057) 0.009 (0.063) 0.024 (0.044) 0.035 (0.042)

0.027 (0.036) 0.062 (0.047) 0.045 (0.056)

Prob(ExpW0)

0.089 (0.045) 0.032 (0.043) 0.003 (0.040)

0.043 (0.054) 0.048 (0.060) 0.079 (0.044) 0.008 (0.041)

0.039 (0.034) 0.020 (0.046) 0.034 (0.056)

Ln(Exp)|ExpW0

Hypertension

0.126 (0.104) 0.087 (0.102) 0.038 (0.095)

0.095 (0.119) 0.005 (0.134) 0.027 (0.108) 0.002 (0.100)

0.016 (0.076) 0.017 (0.106) 0.009 (0.040)

Prob(ExpW0)

0.161 (0.078) 0.078 (0.076) 0.182 (0.070)

0.137 (0.085) 0.072 (0.098) 0.073 (0.089) 0.109 (0.071)

0.050 (0.058) 0.123 (0.081) 0.092 (0.108)

Ln(Exp)|ExpW0

Diabetes


0.267 (0.127) 0.031 (0.128) 0.354 (0.127)

0.126 (0.152) 0.131 (0.185) 0.230 (0.179) 0.165 (0.139)

0.020 (0.113) 0.006 (0.141) 0.240 (0.184)

Prob(ExpW0)

0.120 (0.147) 0.067 (0.145) 0.194 (0.149)

0.144 (0.163) 0.270 (0.188) 0.179 (0.191) 0.297 (0.174)

0.237 (0.117) 0.233 (0.148) 0.094 (0.196)

Ln(Exp)|ExpW0

Asthma

258 YURIY PYLYPCHUK

0.115 (0.040)

0.293 (0.046)

Diab

–

–

Hyp

–

0.117 (0.044) 0.182 (0.043) 0.184 (0.050) 0.010 (0.060) 0.151 (0.071) 0.088 (0.076) 0.055 (0.071) 0.089 (0.047) 0.312 (0.064) –

0.047 (0.034) 0.068 (0.038)

–

0.146 (0.043) 0.271 (0.043) 0.399 (0.051) 0.054 (0.065) 0.126 (0.076) 0.021 (0.080) 0.0767 (0.074) 0.041 (0.049) 0.0161 (0.079) –

0.019 (0.034) 0.077 (0.037)

Eye

Kidn

Strkdx

Ohrtdx

Midx

Angidx

Chddx

Limit

Fairp

Good

Health characteristics Vgood

Risk

MSA

0.235 (0.123) 0.531 (0.119) 0.616 (0.126) 0.124 (0.142) 0.277 (0.160) 0.126 (0.209) 0.423 (0.169) 0.039 (0.123) 0.435 (0.116) 0.169 (0.127) 0.504 (0.096) 0.008 (0.069) –

0.139 (0.078) 0.133 (0.087) 0.014 (0.120) 0.069 (0.115) 0.148 (0.116) 0.149 (0.102) 0.104 (0.097) 0.237 (0.126) 0.017 (0.100) 0.092 (0.079) 0.142 (0.132) 0.116 (0.092) 0.144 (0.063) 0.093 (0.050) –

0.090 (0.058) 0.066 (0.061)

0.189 (0.107) 0.388 (0.174)

–

0.014 (0.119) 0.111 (0.211)

–

–

–

– –

–

–

–

0.338 (0.166) 0.355 (0.170) 0.524 (0.180) 0.042 (0.179) –

0.357 (0.126) 0.178 (0.139)

–

–

–

0.226 (0.150) 0.413 (0.151) 0.596 (0.167) 0.061 (0.184) –

0.066 (0.127) 0.318 (0.127)


0.089 (0.073) – 4.98 (0.222) 11.23 (5.16) 0.135 (0.112) 0.029 (0.003) 0.117 (0.031)

0.021 (0.084) –

2.23 (0.162) 6.73 (4.10) 0.151 (0.080) 0.029 (0.005) 0.099 (0.022)

Ln(Exp)|ExpW0

1.32 (1.80) 8.69 (5.52) 0.742 (0.286) 0.311 (0.143) 0.788 (0.054)

–

–

Prob(ExpW0)

7.19 (0.614) 9.23 (7.62)

–

–

Ln(Exp)|ExpW0

Diabetes

– 0.301 (0.097) 1.85 (1.05) 5.09 (3.38)

0.874 (0.094) 6.72 (2.64) 9.2 (8.5)

Ln(Exp)|ExpW0

–

Prob(ExpW0)

Asthma


Weight

Support point 2

Support point 1

Factor loading

Constant

Flowa

Kidne

Prob(ExpW0)

Hypertension


260 YURIY PYLYPCHUK


261

coefficients, and coefficients on medication expenditures, conditional on positive spending, should be interpreted as regular OLS coefficients. The results indicate large positive and significant effects of HMODC and HIDC on propensity to use any medication for patients with hypertension and diabetes. Having HINDC does not have a large and significant effect on propensity to use medication for patients with any of the three conditions, which implies that having drug coverage really matters for this subpopulation. It should also be noted that insurance estimates are about 30 percent higher for patients with hypertension than for patients with diabetes and asthma. None of the insurance estimates in the asthma sample were significantly identified. The factor loadings are only significant in the hypertension sample. This is evidence that unobserved heterogeneity is not a significant factor in diabetes and asthma models. The estimates of most remaining determinants have expected signs and magnitude. Health characteristics increase the likelihood of using drugs and are significant in most models. Employment status, the type of employment, and education do not have a large effect on propensity to use any medication. People with preferences for risk aversion are more likely to use drugs that treat each condition, although the estimate in the diabetes sample is insignificantly identified. The insurance estimates from the conditional expenditures equation were significantly identified only in the hypertension sample. The estimate on HMODC plan is slightly lower than HIDC, but this difference is not statistically significant for any of the conditions. Given the results from Eqs. (2) and (3), it appears that patients with hypertension are more likely to exhibit moral hazard than patients with diabetes and asthma.

6.2.3. Sensitivity Results Table 8 presents summery of insurance estimates from models that do and do not control for unobserved heterogeneity. The estimates from probit models that control for unobserved heterogeneity are strikingly similar to those that do not control for unobservables. However, controlling for unobserved heterogeneity in the hypertension sample makes a large difference in the conditional utilization equations. If endogeneity is taken into account, insurance effects are about twice smaller in the hypertension sample than in the model that does not control for unobservables. In the diabetes and asthma sample, the estimates from conditional utilization equation preserve consistency in models that do and do not control for unobserved heterogeneity.

0.289 (0.120) 0.308 (0.127) 0.016 (0.184) 1,680

0.375 (0.099) 0.230 (0.095) 0.166 (0.153) 2,917

0.410 (0.054) 0.323 (0.055) 0.189 (0.077) 10,758

0.208 (0.215) 0.149 (0.221) 0.016 (0.306) 912

0.105 (0.093) 0.210 (0.094) 0.040 (0.137) 2,310

0.204 (0.064) 0.288 (0.072) 0.085 (0.097) 6,439

Ln Exp|ExpW0

0.305 (0.192) 0.281 (0.203) 0.098 (0.316) 1,680

0.281 (0.124) 0.253 (0.119) 0.178 (0.198) 2,917

0.439 (0.065) 0.372 (0.067) 0.161 (0.051) 10,758

Prob ExpW0

0.197 (0.201) 0.200 (0.198) 0.199 (0.317) 912

0.111 (0.106) 0.152 (0.118) 0.038 (0.173) 2,310

0.110 (0.073) 0.144 (0.085) 0.152 (0.118) 6,439

Ln Exp|ExpW0

Total

0.225 (0.305) 0.526 (0.305) 0.016 (.553) 664

0.404 (0.196) 0.322 (0.205) 0.385 (0.264) 993

0.177 (0.156) 0.271 (0.156) 0.130 (0.196) 3,447

Ln Exp|ExpW0

Not Imputed

With Unobserved Heterogeneity

Summary of Insurance Estimates.

0.027 (0.121) 0.162 (0.201) 0.024 (0.0.165) 912

0.414 (0.631) 0.805 (0.668) 0.277 (0.910) 2,310

0.285 (0.398) 0.772 (0.417) 0.164 (0.685) 6,439

# Medications|ExpW0

Count


N

HINDC

HIDC

Asthma HMODC

N

HINDC

HIDC

Diabetes HMODC

N

HINDC

HIDC

Hypertension HMODC

Prob ExpW0

Total

Without Unobserved Heterogeneity

Table 8. 262 YURIY PYLYPCHUK


263

This result is also consistent with the fact that factor loadings in the hypertension samples are highly significant and large, but they are mostly insignificant for patients with asthma and diabetes. As the data used to identify medication expenditures was partially imputed, the results from Eq. (3) may be biased. As described in Section 5, to check for sensitivity of these results, I estimated Eq. (3) using alternative definitions of the dependent variable. The last two columns of Table 8 present results of insurance estimates where the dependent variable was defined as the log of non-imputed expenditures, conditional on any and the number of times medication was purchased during the year, conditional on any. The significance levels of estimates are almost identical across the columns; however, some differences emerge in magnitudes of the coefficients. The estimates are consistently higher in the hypertension and diabetes samples when the dependent variable indicates the number of medications purchased than the estimates identified in the conditional expenditure equations. Additionally, the estimates on HIDC are noticeably larger if non-imputed data is used.

6.2.4. Simulations The effects of health insurance on medication expenditures are easiest to interpret if the results are expressed in terms of probabilities and dollars. In this section, I simulate the effect of each insurance type on medication expenditures and illustrate if the model is effective in predicting medical expenditures. The simulated effects of each insurance type were identified in the following way. For each observation, I draw a permanent component ui from a uniform distribution. I use the same draw for Eqs. (2) and (3). If the draw is greater than the estimated probability of the first point of support, I assume that the residual equals the second point of support. If the draw is less than or equal to the estimated probability of the first point of support, I assume that the residual equals the first point of support. I also draw residuals from a normal distribution for Eqs. (2) and (3) and treat these residuals as independent across equations. In Eq. (2), I switch the uninsured to insured, add the residuals, obtain the value of the dependent variable, and map that value to the unit interval using the cumulative normal distribution. I then switch the insured to uninsured and repeat the process. The difference in values between the insured and the uninsured populations provides marginal effects. The effects of insurance types on medication expenditures, conditional on any, were obtained in the same manner except that

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YURIY PYLYPCHUK

I exponentiated each prediction and conducted this analysis only on observations that had positive expenditures. The simulation results are presented in Table 9. If a respondent has HMODC or HIDC, his probability of spending a positive amount on medication that treats hypertension, diabetes, and asthma increases by about 10, 5, and 7 percentage points, respectively. For patients with all three conditions, the effect of health insurance and no drug coverage on use of any medication is positive, but noticeably lower than the effect of any type of drug coverage. The numbers in parentheses represent a percent change in drug expenditures due to a change in a specific type of health insurance.13 The results clearly indicate that patients with hypertension are more likely to purchase medication (45.1 percent) if they had drug coverage than patients with diabetes (15.7 percent) and asthma (30.4 percent.) The results of the conditional expenditures equation indicate that enrollment in the HMODC plan increases conditional medication expenditures by $65.21, $103.48, and $115.70 for patients with hypertension, diabetes, and asthma, respectively. The effect of HIDC on conditional expenditures is about 25 percent higher than the effect of HMODC among patients with diabetes and hypertension. The effects of health insurance types for patients with asthma are almost identical.

Table 9. Simulation Results: Probability of Medication Use, Medication Expenditures, Conditional on Any, and Total Expenditures.

Total Drug coverage HMODC HIDC HINDC

Hypertension

Diabetes

Asthma

Prob Exp|ExpW0 Total ExpW0 Exp



0.212 (45.1%) 0.125 (26.3%) 0.107 (22.5%) 0.053 (11.2%)

0.109 (15.7%) 0.060 (8.7%) 0.054 (7.8%) 0.038 (5.5%)

139.9 (33.1%) 65.2 (15.4%) 81.2 (19.2%) 85.1 (20.2%)

202.4 (54.5%) 93.5 (25.1%) 101.3 (27.2%) 81.5 (21.8%)

265.2 (30.1%) 103.5 (11.7%) 144.7 (16.4%) 33.2 (3.8%)

298.6 0.155 (39.3%) (30.4%) 124.9 0.078 (16.3%) (15.5%) 158.4 0.072 (20.9%) (14.1%) 8.1 0.025 (1.07%) (4.9%)

258.4 (50.4%) 115.7 (22.6%) 116.4 (22.8%) 113.5 (22.2%)

148.6 (42.8%) 112.3 (32.4%) 110.0 (31.8%) 66.3 (19.1%)

Source: Author’s calculations from the Medical Expenditures Panel Survey (MEPS), years 2000–2003. Notes: HIDC, non-HMO and drug coverage; HINDC, health insurance and no drug coverage; HMODC, HMO and drug coverage. The numbers in parentheses represent the percent increase in outcome if the population had a corresponding type of health insurance.


265

To compare the quantitative differences of total expenditures between insurance plans, I simulate the expected total expenditures as a product of probability of spending and the conditional expenditures conditional on positive spending. I use a smearing estimator to correct for the transformation from the expected log (expenditures) to the expected expenditures (Goldman, 1995; Duan, 1983). These results are presented in Table 9. The results indicate that drug coverage increases medication expenditures by 54.5, 39.3, and 42.8 percent for patients with hypertension, diabetes, and asthma, respectively. The results are not sensitive to the type of drug coverage; in the hypertensions and diabetes samples, patients with HMODC are likely to purchase slightly less medication than patients with HIDC. The opposite is true for patients with asthma; however, all the differences are relatively small. The other result is that patients with drug coverage spend noticeably more on medication than patients with HINDC. It is useful to know how the results of this study compare with findings from papers that simulated private insurance effects on medical expenditures for the entire population. Hadley and Holohan (2003) and Miller, Banthin, and Moeller (2004) used MEPS data to simulate the insurance effect on total health expenditures for the uninsured U.S. population. Their results indicate that extension of private coverage to the uninsured increases total health expenditures by about 90 percent per capita. Short, Hahn, Beauregard, Harvey, and Wilets (1997) found that if the total uninsured population had private coverage, their expenditures on prescribed medication would increase by about 60 percent, which is still higher than the response to insurance status among people with chronic conditions. Finally, to illustrate how well the model explains medication expenditures, Figs. 1 and 2 plot predicted and actual outcomes versus age of patients with each condition. Fig. 1 shows results from Eq. (2). For people with hypertension, the results indicate an upward trend with age for both predicted and actual spending. For people with diabetes and asthma, the lines are less steep and have larger gaps between the predicted and the actual probabilities than for people with hypertension. The results from the conditional expenditures equation indicate similar patterns for all three conditions with the exception that the gaps between predicted and actual outcomes are relatively larger than the predictions of the probability equation. Overall, the model fits these outcomes fairly well, keeping in mind that the sample size becomes very small at ages under 25 years.

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YURIY PYLYPCHUK

Prob of EXP>0

Hypertension 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Actual Simulated

15

25

35

45

55

65

Age Diabetes 1

Prob EXP>0

0.8 0.6

Actual

0.4

Simulated

0.2 0 15

25

35

45

55

65

Age Asthma 1

Prob EXP>0

0.8 0.6

Actual

0.4

Simulated

0.2 0 15

25

35

45

55

65

Age

Fig. 1. Actual and Simulated Probability of Using Prescribed Medication, by Age.

267


Medication Expenditures, conditional on any

Hypertesion 700 600 500 400 300 200 100 0

Actual Simulated

15

25

35

45

55

65

Age


Diabetes 2500 2000 1500

Actual

1000

Simulated

500 0 15

25

35

45

55

65


Age Asthma 2000 1500 Actual

1000

Simulated

500 0 15

25

35

45

55

65

Age

Fig. 2.

Actual and Simulated Expenditures, Conditional on Any, by Age.

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YURIY PYLYPCHUK

7. CONCLUSIONS The objective of this research was to investigate the role of private coverage in people’s decision to purchase medication that treats hypertension, diabetes, and asthma. The study makes several contributions to the literature examining the association between health insurance and medical expenditures. First, the study distinguished among four types of health insurance (uninsured, health insurance and no drug coverage, HMO and drug coverage, and non-HMO and drug coverage) and identifies the effect of these types on propensity to purchase medication and expenditures on medication, conditional on any. Because the symptoms of hypertension are more silent than the symptoms of the remaining two conditions, examination of the relationship between health insurance and drug expenditures suggests the interesting hypothesis of whether patients with silent conditions are more likely to exhibit moral hazard. The results from the probability to use any medication equation strongly support this idea; the insurance effects in the hypertension sample were much larger than those in the diabetes and the asthma samples. Additionally, the findings suggest that drug coverage is a very important determinant of expenditures on medication; having drug coverage increases the likelihood of using any medication by over 45, 15, and 30 percent for patients with hypertension, diabetes, and asthma, respectively. The drug coverage also increases conditional expenditures for prescribed medication by over 250 dollars per patient per year with diabetes or asthma and by 140 dollars per patient per year with hypertension. Perhaps the most important findings of this paper are that chronically ill patients are much less sensitive to insurance status than the overall population and that patients with silent chronic conditions are more responsive to insurance status than patients with noticeable chronic conditions. Total drug coverage increases expenditures for prescribed medication by 54.5, 39.3, and 42.8 percent for patients with hypertension, diabetes, and asthma, respectively. The results from other studies suggest that insurance increases total and prescribed medication expenditures by 90 and 60 percent, respectively. The study consistently finds small or no difference in effects of HMODC or HIDC on expenditures for prescribed medication, but findings suggest that patients with drug coverage are likely to spend more on medication than patients with HINDC. This study suffers from several limitations. Because the MEPS expenditures data is partially imputed, I checked for sensitivity of results by estimating models that use only non-imputed data and for the number of


269

purchases made during the year. Qualitatively, the results are almost identical; however, one consistent difference remained. The effect of nonHMO plans and drug coverage was much higher on non-imputed expenditures and the number of medications purchased than on expenditures that used imputed information. Additionally, the analysis does not include people with public coverage. In general, individuals covered by public insurance are less sensitive to incentives. Inclusion of this population would confound the main findings of this chapter. The analysis does not distinguish between temporarily insured and full-year insured. Although desegregation of detailed health insurance choices would definitely be informative, we would jeopardize the computational feasibility of the model. The study still makes strong assumptions about the distribution of the uncorrelated error terms. Additional work on semi-parametric and non-parametric estimations would be extremely useful to check the stability of results in this chapter.

NOTES 1. For example, Dor and Encinosa (2004) show that among patients with diabetes, medication compliance varies across different insurance regimes. 2. QLI measures quality of life in terms of satisfaction with life, physical functioning, and overall health status. The definition and design of QLI widely varies among different studies and surveys (Bowling, 2001). 3. A branded drug is a drug that is protected by a patent, which means that a drug can be produced and sold only by the company holding a patent. 4. A more complete set would further disaggregate health insurance and no drug coverage choice into HMO and no drug coverage and FFS and no drug coverage. However, in practice and as the data of this study reveal, only a very small percentage of HMO enrollees do not have drug coverage. In this case, I include the choice of HMO and no drug coverage into health insurance and no drug coverage category. 5. In all models, I account for repeated observations using appropriate weights and MEPS’ stratification design. 6. For each medication, pharmacies were asked to provide: the data the prescription was filled or refilled, the National Drug Code (NDC), the medication name, the strength of the medicine, the quantity dispensed, the total charge, the sources of payment, and the amount of payment made by each type of health insurance. 7. GPI is a 14-digit identifier of each drug in the United States. 8. I define prescribed medication expenditures in real 2003 U.S. dollars. The expenditures from years before 2003 were adjusted for inflation using Consumer Price Index (CPI). 9. For each drug purchased, the respondent had to name the condition that is being treated by that drug. MEPS assigned International Classification of Disease

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Code (ICD9) to each condition named by the respondent. I used ICD9 codes to identify drugs that treat hypertension (ICD9 ¼ 401), diabetes (ICD9 ¼ 250), and asthma (ICD9 ¼ 493). 10. For example, the people who do not sign permission forms may be more ill, immigrants, and Hispanics, which can be related to health care use and health insurance. 11. I suspect that inaccurate reporting of health insurance type is less of a problem in this study because patients with chronic conditions use more health services and, subsequently, should be more aware about their health insurance types than overall population. 12. In Table 1 and subsequent tables, all results are weighted and standard errors account for complex survey design. 13. The results are estimated as the difference in the predicted outcome when insured minus the predicted outcome when uninsured divided by the predicted outcome when uninsured.

ACKNOWLEDGMENTS The author is grateful for research support from Agency for Health Care Research and Quality and Social Scientific Systems. I am very grateful to Robert Goldfarb, Steve Hill, Edward Millar, David Ribar, Barbara Schone, Thomas Selden, and Robert Trost for helpful comments and suggestions.

REFERENCES Adams, R., Wakefield, M., Wilson, D., Parsons, J., Campbell, D., Smith, B., & Ruffin, R. (2001). Quality of life in asthma: A comparison of community and hospital asthma patients. Journal of Asthma, 38(3), 205–214. Berndt, E. (2002). Pharmaceuticals in U.S. health care: Determinants of quality and price? Journal of Economic Perspectives, 16(4), 45–66. Bowling, A. (2001). Measuring health: A review of quality of life measurement scales. Philadelphia, PA: Open University Press. Cameron, P., Trivedi, P., Milne, F., & Piggott, J. (1988). A microeconometric model of the demand for health care and health insurance in Australia. Review of Economic Studies, 55, 85–106. Coulson, N., & Stuart, B. (1995). Insurance choice and the demand for prescription drugs. Southern Economic Journal, 61(4), 1146–1157. Coulson, E. N., Terza, J. V., Neslusan, C., & Stuart, B. C. (1995). Estimating the moral hazard effect of supplemental medical insurance in the demand for prescription drugs by the elderly. American Economic Review, 85(2), 122–126.


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Crown, W. H., Berndt, E. R., Baser, O., Finkelstein, S. N., & Witt, W. P. (2003). Benefit plan design and prescription drug utilization among asthmatics: Do patient copayments matter? National Bureau of Economic Research. Dor, A., & Encinosa, W. (2004). Does cost sharing affect compliance? The case of prescription drugs. Working Paper. National Bureau of Economic Research. Dranove, D., Hueghs, E., & Shanley, M. (2003). Determinants of HMO formulary adoption decisions. Health Services Research, 38(1), 169–190. Duan, N. (1983). Smearing estimate: A nonparametric retransformation method. Journal of the American Statistical Association, 78(383), 605–610. Frank, R. G., Glazer, J., & McGuire, T. G. (2000). Measuring adverse selection in managed health care. Journal of Health Economics, 19(6), 829–854. Glied, S. (2000). Managed care. In: A. J. Culyer & J. P. Newhouse (Eds), Handbook of health economics. Amsterdam: Elsevier. Goldman, D. (1995). Managed care as a public cost-containment mechanism. RAND Journal of Economics, 26(2), 277–295. Grant, R., & Meigs, J. (2005). Management of the metabolic syndrome. Panminerva Medica, 47(4), 219–228. Hadley, J., & Holohan, J. (2003). Covering the uninsured: How much would it cost. Health Affairs, W3, 250–265. Hubbard, G., Skinner, J., & Zeldes, S. (1995). Precautionary savings and social insurance. Journal of Political Economy, 103(2), 360–399. Koc, C. (2004). The effects of uncertainty on the demand for health insurance. Journal of Risk and Insurance, 71(1), 42–61. Koc, C. (2005). Health-specific moral hazard effects. Southern Economic Journal, 72(1), 98–118. Lam, C., & Lauder, I. (2000). The impact of chronic diseases on the health-related quality of life (HRQOL) of Chinese patients in primary care. Family Practice, 17(2), 159–166. Leibowitz, A. (1989). Substitution between prescribed and over-the-counter medication. Medical Care, 27(1), 85–94. Leibowitz, A., Manning, W., & Newhouse, J. (1985). The demand for prescribed drugs as a function of cost-sharing. Social Science and Medicine, 21(10), 1063–1069. Manning, W., Newhouse, J. P., Duan, N., Keeler, E. B., Benjamin, B., Leibowitz, A., Marquis, M. S., & Zwanziger, J. (1987). Health insurance and the demand for medical care: Evidence from a randomized experiment. American Economic Review, 77(3), 251–277. Mello, M., Stearns, S., & Norton, E. (2002). Do medicare HMOs still reduce services use after controlling for selection bias? Health Economics, 11(4), 323–340. Miller, E., Banthin, J., & Moeller, J. (2004). Covering the uninsured: Estimates of the impact on total health expenditures for 2002. AHRQ Working Paper. Moeller, J., Miller, E., & Banthin, J. (2004). Looking inside the nation’s medicine cabinet: Trends in outpatient drug spending by medicare beneficiaries, 1997 and 2001. Health Affairs, 23(4), 217–226. Mroz, T. (1999). Discrete factor approximations in simultaneous equation models: Estimating the impact of a dummy endogenous variable on a continuous outcome. Journal of Econometrics, 92(2), 233–274. National Diabetes Association. (2005). Diabetes symptoms. Available at http://www.diabetes. org/diabetes-symptoms.jsp Pauly, M. (1968). The economics of moral hazard. American Economic Review, 58(3), 231–237. Rapport, S. (1999). Hypertension. Silent killer. New Jersey Medicine, 96(3), 41–43.

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Rice, T., Lavarreda, S. A., Ponce, N. A., & Brown, E. R. (2005). The impact of private and public health insurance on medication use for adults with chronic diseases. Medical Care Review, 62(2), 231–249. Short, P. F., Hahn, B. A., Beauregard, K., Harvey, P. H., & Wilets, M. L. (1997). The effects of universal coverage on health expenditures for the uninsured. Medical Care, 60(2), 95–113. Stein, J. D., Brown, G. C., Brown, M. M., Sharma, S., Hollands, H., & Stein, H. D. (2002). The quality of life of patients with hypertension. Journal of Clinical Hypertension, 4(3), 181–188. Wandell, P. (2005). Quality of life of patients with diabetes mellitus. An overview of research in primary health care in the Nordic countries. Scandinavian Journal of Primary Health Care, 23(2), 68–74. Wedig, G. (1988). Health status and the demand for health: Results on price elasticities. Journal of Health Economics, 38(2), 151–163. Yang, Z., Gilleskie, D., & Norton, E. (2004). Prescription drugs, medical care, and health outcomes: A model of elderly health dynamics. Working Paper. National Bureau of Economic Research. Zuvekas, S., & Hill, S. (2004). Does capitation matter? Inquiry, 41(3), 316–335. Zweifel, P., & Manning, W. (2000). Moral hazard and consumer incentives in health care. In: A. Culyer, & J. Newhouse (Eds), Handbook of health economics. Amsterdam: Elsevier.

THE EFFECT OF DRUG VINTAGE ON SURVIVAL: MICRO EVIDENCE FROM PUERTO RICO’S MEDICAID PROGRAM Frank R. Lichtenberg ABSTRACT Using micro data on virtually all of the drugs and diseases of over 500,000 people enrolled in Puerto Rico’s Medicaid program, the impact of the vintage (original FDA approval year) of drugs used to treat a patient on the patient’s three-year probability of survival, controlling for demographic characteristics (age, sex, and region), utilization of medical services, and the nature and complexity of illness are examined. It is found that people using newer drugs during January–June, 2000, were less likely to die by the end of 2002, conditional on the covariates. The estimated mortality rates are strictly declining with respect to drug vintage. For pre-1970 drugs, the estimated mortality rate is 4.4%. The mortality rates for 1970s, 1980s, and 1990s drugs are 3.6%, 3.0%, and 2.5%, respectively. The actual mortality rate is about 16% (3.7% vs. 4.4%) lower than it would have been if all of the drugs utilized in 2000 had been pre-1970 drugs. Estimates for subgroups of people with specific diseases display the same general pattern.


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FRANK R. LICHTENBERG

Clinical studies of specific new drugs have shown that these drugs increase survival rates. Here are three examples: Stenestrand and Wallentin (2001) studied the impact on survival of statin treatment following acute myocardial infarction. They found that oneyear mortality was 9.3% in the no-statin group and 4.0% in the statin treatment group. Grier et al. (2003) found that adding two experimental drugs to the standard four-drug chemotherapy regimen has significantly improved survival in patients with non-metastatic Ewing’s sarcoma, a highly malignant bone cancer of children and young adults. The overall survival rate increased from 61 percent to 72 percent for Ewing’s sarcoma patients with localized disease who underwent the experimental six-drug chemotherapy. The journal Medical News (2004) reported that patients suffering from advanced metastatic melanoma who were treated with a combination of an investigational agent, Ceplene, and interleukin-2 (IL-2) had twice the survival rate as patients who were treated with IL-2 only. The patients were enrolled in a three-year study. The study also showed that the Ceplene/IL-2 combination significantly increased survival in a subpopulation group of advanced metastatic melanoma patients with liver metastases. The rate of survival in this group was six times that of the group given IL-2 only. Also, I have performed several studies using aggregate data (Lichtenberg, 2003a, 2003b, 2003c, 2004, 2005) that indicated that the introduction of new drugs has increased longevity. The objective of the present study is to examine the impact of the vintage (original FDA approval year) of drugs used to treat a patient on the patient’s probability of survival, using micro data on virtually all drugs and diseases from Puerto Rico’s Medicaid program, which covers about 1.5 million people.1,2

1. ECONOMETRIC FRAMEWORK To determine the effect of the vintage distribution of a person’s prescribed medicines on probability of death, conditional on demographic characteristics (age, sex, and region), utilization of medical services, and the nature and complexity of illness, I will estimate the following model: DIEDi ¼ b1970 POST1970i þ b1980 POST1980i þ b1990 POST1990i þ gZi þ i

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The Effect of Drug Vintage on Survival

where ¼ 1 if individual i died during the period 2000–2002 ¼ 0 otherwise POST1970i ¼ the fraction of individual i’s prescribed medicines whose active ingredients were approved by the FDA after 1970 POST1980i ¼ the fraction of individual i’s prescribed medicines whose active ingredients were approved by the FDA after 1980 POST1990i ¼ the fraction of individual i’s prescribed medicines whose active ingredients were approved by the FDA after 1990 ¼ a vector of covariates Zi ¼ a disturbance ei DIEDi

Suppose individual A consumed only medicines approved in 1985. For that individual, POST70 ¼ POST80 ¼ 1, and POST90 ¼ 0. Hence, E(DIEDA| ZA) ¼ b1970 þ b1980. Suppose individual B consumed only medicines approved in 1995. For that individual, POST70 ¼ POST80 ¼ POST90 ¼ 1. Hence E(DIEDB| ZB) ¼ b1970 þ b1980 þ b1990, and E(DIEDB|ZB) E(DIEDA|ZA) ¼ b1990. The parameter b1990 may be interpreted as the difference between the death probability of people consuming only post-1990 medicines and that of people consuming only pre-1991 medicines. More generally, if all the drugs consumed by individual i contained ingredients approved in the years 1965, 1975, 1985, 1995, the E(DIEDi|Zi) would be as follows: Years

(DIEDi|Zi)

1965 1975 1985 1995

k k þ b1970 k þ b1970 þ b1980 k þ b1970 þ b1980 þ b1990

where k is a constant. Hence, the parameters may be interpreted as the difference given below: Parameters

Difference

b1970 b1980 b1990

1970s drugs versus pre-1970 drugs death probability 1980s drugs versus 1970s drugs death probability 1990s drugs versus 1980s drugs death probability

276


2. DATA AND MEASUREMENT Puerto Rico Health Insurance Administration (ASES) provided all medical and pharmacy claims of ASES beneficiaries during the period January 1– June 30, 2000. There were about 12.2 million claims.

2.1. Demographic Information The following demographic variables could be determined from the claims records: The person’s sex The person’s age The geographic region in which services were provided Unfortunately, data on other personal attributes, such as education and income, were not provided. But unobserved heterogeneity with respect to income is limited by the fact that, to be eligible for Medicaid in Puerto Rico, annual income of a family of four could not exceed $16,440 (in the year 2002).

2.2. Person’s Utilization of Services The person’s number of medical claims (physician encounters) The person’s number of hospital admissions The person’s number of pharmacy claims

2.3. Vintage Distribution of Pharmacy Claims Each pharmacy claim3 included the National Drug Code (NDC). I determined the active ingredient(s) contained in each NDC from Multum’s Lexicon. I determined the earliest FDA approval date of each active ingredient from standard commercial pharmaceutical databases, that is, Gold Standard Multimedia’s Clinical Pharmacology 2000 and Mosby’s Drug Consult. Using this information, I calculated, for each pharmaceutical claim, the values (0 or 1) of POST70, POST80, and POST90. I then calculated, for each individual, the average values of POST70, POST80, and

277


POST90, that is, the fraction of the individual’s Rx’s that were for drugs approved after 1970, 1980, and 1990.

2.4. Nature of Person’s Illnesses The medical claims include ICD9 (diagnosis) codes. I grouped these codes into the following 15 broad disease groups:4 Disease Category

ICD9 Codes

Disease Group

1 2 3

001–139 140–239 240–279

4 5 6 7 8 9 10 11 12

280–289 290–319 320–389 390–459 460–519 520–579 580–629 680–709 710–739

13 14

740–759 760–779

15

780–799

Infectious and parasitic diseases Neoplasms Endocrine, nutritional and metabolic diseases, and immunity disorders Diseases of the blood and blood-forming organs Mental disorders Diseases of the nervous system and sense organs Diseases of the circulatory system Diseases of the respiratory system Diseases of the digestive system Diseases of the genitourinary system Diseases of the skin and subcutaneous tissue Diseases of the musculoskeletal system and connective tissue Congenital anomalies Certain conditions originating in the perinatal period Symptoms, signs, and ill-defined conditions

I then calculated DISEASE_SHAREij ( j ¼ 1, 2, y, 15): the fraction of person i’s diagnoses that were in each disease category. For example, if all of person i’s diagnoses were diabetes, then DISEASE_SHAREij ¼ 1 if j ¼ 3 and DISEASE_SHAREij ¼ 0 if j6¼3. If person i had three circulatory diagnoses and one digestive diagnosis, then DISEASE_SHAREij ¼ 0.75 if j ¼ 7, DISEASE_SHAREij ¼ .25 if j ¼ 9, and DISEASE_SHAREij ¼ 0 for all other j. In addition to measuring the shares of diagnoses in each disease category, I calculated the person’s ‘‘effective number’’ of diseases. Rather than simply

278


counting the number of disease categories in which a person’s diagnoses fell, I computed the following index: N_DISEASEi ¼ 1=Sj DISEASE_SHARE2ij If all of a person’s diagnoses fell in one disease category, then N_DISEASEi ¼ 1. If half of a person’s diagnoses fell in one disease category, and half fell in a second category, then N_DISEASEi ¼ 2. If 90% of a person’s diagnoses fell in one disease category, and 10% fell in a second category, then N_DISEASEi ¼ 1/(.92 þ .12) ¼ 1.22. 2.5. Mortality The Department of Health provided a list of (encrypted) social security numbers of all Puerto Rican residents who died during the period 2000– 2002. I merged this list with the January 1–June 30, 2000, ASES claims data; this allowed me to determine whether or not an ASES beneficiary who had utilized services during January 1–June 30, 2000, had died by the end of 2002: DIEDi ¼ 1 if person i died by the end of 2002 ¼ 0 otherwise 2.6. Descriptive Statistics Sample means of the variables are shown in the following table.5 Variable Male Age DIED POST70 POST80 POST90 Number of medical claims Number of pharmacy claims Number of hospital claims N_DISEASE

Sample Mean 40.7% 34.7 3.1% 63.1% 29.7% 8.2% 4.2 4.6 0.24 1.76

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Variable Infectious and parasitic diseases Neoplasms Endocrine, nutritional and metabolic diseases, and immunity disorders Diseases of the blood and blood-forming organs Mental disorders Diseases of the nervous system and sense organs Diseases of the circulatory system Diseases of the respiratory system Diseases of the digestive system Diseases of the genitourinary system Diseases of the skin and subcutaneous tissue Diseases of the musculoskeletal system and connective tissue Congenital anomalies Certain conditions originating in the perinatal period Symptoms, signs, and ill-defined conditions

Sample Mean 4.3% 1.1% 6.9% 1.0% 4.7% 5.6% 9.0% 16.9% 4.6% 6.8% 2.8% 6.8% 0.4% 0.1% 7.5%

The three-year mortality rate is 3.1%. This seems consistent with published mortality data for Puerto Rico. According to the United Nations, the crude (annual) death rate in Puerto Rico during 2000–2005 under ‘‘medium variant’’ mortality assumptions is 8.3 per 1,000 population.6 This implies a three-year mortality rate of approximately 2.49% ( ¼ 3 0.83%). Mortality of ASES beneficiaries may be somewhat higher than that of other residents of Puerto Rico. The vintage distribution of drugs used by ASES beneficiaries to the vintage distribution of drugs used by all Americans and by American Medicaid beneficiaries using the 2000 Prescribed Medicines file of the Household Component of the Medical Expenditure Panel Survey (MEPS) can be compared, which is conducted by the Agency for Healthcare Research and Quality. This comparison is shown in Fig. 1. The fraction of ASES Rx’s that were approved after 1970 (63.2%) is similar to the fractions of U.S. Medicaid Rx’s that were approved after 1970 (61.7%). However, the fractions of ASES Rx’s that were approved after 1980 and 1990 (29.7% and 8.2%) are much smaller than the fractions of U.S. Medicaid Rx’s7 that were approved after 1980 and 1990 (48.4% and 25.5%). Use of older drugs in Puerto Rico’s Medicaid program may be partly attributable to the fact that,

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70.0% 63.2% 59.6%

61.7%

60.0%

ASES U.S.--total U.S.--Medicaid

49.0% 48.4%

50.0%

40.0% 29.7%

28.1%

30.0%

25.6%

20.0%

8.2%

10.0%

0.0% post70

Fig. 1.

post80

post90

Comparison of Vintage Distributions of ASES Rx’s, All U.S. Rx’s, and U.S. Medicaid Rx’s.

in Puerto Rico, the physician bears the costs of the drugs – the cost is deducted from the physician’s capitation payment.

3. EMPIRICAL RESULTS OLS estimates of the mortality model based on the entire population of ASES beneficiaries with pharmaceutical claims (N ¼ 539,750) are provided in Table 1.8 The dependent variable is DIED: a dummy variable equal to 1 if the person died during 2000–2002, otherwise equal to 0.9 In addition to the three drug-vintage variables (POST70, POST80, and POST90), the model includes 3 utilization variables (number of medical, pharmacy, and hospital claims), 15 diagnosis category variables, the index of the number of diseases (N_DISEASE), 9 region dummy variables, and 200 single-year-of-age-bysex dummy variables (not shown to conserve space). The coefficients of all three drug-vintage variables are negative and highly significant ( p-valueo.0001), which is consistent with the hypothesis that ASES beneficiaries using newer drugs during January–June, 2000, were less likely to die by the end of 2002, conditional on the covariates. Before considering the implications of the drug-vintage coefficients in detail, salient features of the coefficients on the covariates will be discussed.

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Table 1.

Estimates of Mortality Model, Full Sample.

Variable

Estimate

Standard Error

Drug-vintage variables POST70 POST80 POST90

0.0075 0.0056 0.0057

Utilization variables Number of medical claims Number of pharmacy claims Number of hospital claims Diagnosis category variables Infectious and parasitic diseases Neoplasms Endocrine, nutritional and metabolic diseases, and immunity disorders Diseases of the blood and blood-forming organs Mental disorders Diseases of the nervous system and sense organs Diseases of the circulatory system Diseases of the respiratory system Diseases of the digestive system Diseases of the genitourinary system Diseases of the skin and subcutaneous tissue Diseases of the musculoskeletal system and connective tissue Congenital anomalies Certain conditions originating in the perinatal period Symptoms, signs, and illdefined conditions N_DISEASE Region dummy variables Aguadilla Arecibo Bayamon Este Fajardo

t Value

Pr W |t|

0.0009 0.0010 0.0014

8.29 5.48 3.98

o.0001 o.0001 o.0001

0.0031 0.0011 0.0053

0.0000 0.0000 0.0001

72.20 28.59 49.05

o.0001 o.0001 o.0001

0.0157 0.1558 0.0003

0.0017 0.0029 0.0014

9.28 54.48 0.19

o.0001 o.0001 0.8487

0.0350

0.0035

10.03

o.0001

0.0011 0.0049

0.0014 0.0015

0.77 3.35

0.4386 0.0008

0.0048

0.0013

3.78

0.0002

0.0096

0.0010

9.25

o.0001

0.0092 0.0091

0.0017 0.0014

5.55 6.39

o.0001 o.0001

0.0121

0.0019

6.34

o.0001

0.0229

0.0014

16.28

o.0001

0.0012 0.0157

0.0051 0.0109

0.23 1.45

0.8191 0.1479

0.0096

0.0014

6.73

o.0001

0.0014

0.0002

8.42

o.0001

0.6358 0.6304 0.6357 0.6428 0.6421

0.0382 0.0382 0.0382 0.0382 0.0382

16.63 16.49 16.62 16.81 16.79

o.0001 o.0001 o.0001 o.0001 o.0001

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Table 1. (Continued ) Variable Guayama Mayaguez Montana Suroeste

Estimate 0.6298 0.6390 0.6285 0.6573

Standard Error 0.0382 0.0382 0.0382 0.0383

t Value

Pr W |t|

16.47 16.71 16.43 17.18

o.0001 o.0001 o.0001 o.0001

Notes: N ¼ 539,750. The dependent variable is a dummy variable equal to 1 if the person died during 2000–2002, otherwise equal to 0. The model also included 200 single-year-of-age-by-sex dummy variables.

The coefficients on all three utilization variables are positive and highly significant: People who used more medical services during January–June, 2000, were more likely to die by the end of 2002. For example, one additional medical claim (physician visit) is associated with a .0031 (about 8%) increase in the probability of death. Although utilization of medical services presumably reduces mortality, given initial (pre-treatment) health status, people in the worst initial health utilize the most medical services. Including the three utilization variables (as well as the diagnosis category and demographic variables) is therefore likely to control for initial health status, which is unobserved and might be correlated with drug vintage (although the sign of the potential correlation is not clear). The coefficients on the diagnosis category variables indicate differences in mortality rates associated with different diagnoses. They are plotted in Fig. 2. By a large margin, people diagnosed with neoplasms had the highest mortality rate. Diseases of the blood and blood-forming organs and diseases of the skin and subcutaneous tissue are two other high-mortality conditions. The lowest mortality conditions include mental disorders, diseases of the nervous system and sense organs, and diseases of the musculoskeletal system and connective tissue. The coefficient on N_DISEASE is negative and highly significant, which is somewhat surprising. This indicates that, conditional on the distribution of diagnoses a person had (and other covariates), the greater the effective number of diseases the person had, the lower the probability of death. The coefficients on the region dummy variables indicate differences in mortality rates associated with different regions. They are plotted in Fig. 3. The mortality rate in Suroeste is almost 1.5 percentage points higher than the mortality rate in the second highest region, Este. The regions with the lowest mortality rates are Montana, Guayama, and Arecibo.

Fig. 2.

Estimated Coefficients on Diagnosis Category Variables.

diseases of the musculoskeletal system and connective tissue

certain conditions originating in the perinatal period

diseases of the nervous system and sense organs

mental disorders

endocrine, nutritional and metabolic diseases, and immunity disorders

congenital anomalies

diseases of the circulatory system

diseases of the genitourinary system

diseases of the digestive system

diseases of the respiratory system

symptoms, signs, and ill-defined conditions

diseases of the skin and subcutaneous tissue

infectious and parasitic diseases

diseases of the blood and blood-forming organs

neoplasms

-0.0400 -0.0200 0.0000 0.0200 0.0400 0.0600 0.0800 0.1000 0.1200 0.1400 0.1600 0.1800

The Effect of Drug Vintage on Survival 283

284

FRANK R. LICHTENBERG 0.6100

0.6150

0.6200

0.6250

0.6300

0.6350

0.6400

0.6450

0.6500

0.6550

0.6600 0.6573

Suroeste 0.6428

Este

0.6421

Fajardo

0.6390

Mayaguez Aguadilla

0.6358

Bayamon

0.6357 0.6304

Arecibo

0.6298

Guayama

0.6285

Montana

Fig. 3.

Estimated Coefficients on Region Dummy Variables.

3.1. Estimates by Condition In addition to estimating the model using data on the entire population of ASES beneficiaries with pharmaceutical claims, I also estimated the model separately for people with three specific diagnoses: (1) diseases of the circulatory system; (2) endocrine, nutritional and metabolic diseases, and immunity disorders (primarily diabetes); and (3) neoplasms. All three of these conditions exhibit relatively high mortality rates (above 6%), and the first two are highly prevalent (affecting at least one-sixth of the population). There were more than 2,600 deaths in each group of people. Rather than reporting the complete set of estimates (as in Table 1) for each group, just the coefficients of the three drug-vintage variables, as well as means of key variables for each group, in Table 2, are reported. The first column of Table 2 reports estimates for the entire population (copied from Table 1). The coefficients of the three drug-vintage variables (lines 11, 12, and 13) indicate differences between the mortality rates of people using drugs of different vintages. By combining these coefficients with the average mortality rate (line 3) and the vintage distribution of drugs (lines 7–10), the (levels of ) mortality rates of people using drugs of different vintages can be inferred.10

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Table 2. Line

Drug-Vintage Coefficients and Related Estimates, by Disease Group.

Column

1 Entire population

1

3

4

People with People with diseases People with diseases of of the endocrine, neoplasms the nutritional and circulatory metabolic diseases, system and immunity disorders

539,750

114,656

91,087

15,758

19,820

9,486

5,513

2,634

Means 3 Died 4 Post-1970 5 Post-1980 6 Post-1990

3.7% 63.2% 29.7% 8.2%

8.3% 69.2% 39.7% 10.9%

6.1% 67.5% 37.5% 9.9%

16.7% 65.9% 34.4% 9.5%

Vintage distribution 7 Pre-1970 8 1970s 9 1980s 10 1990s

36.8% 33.5% 21.5% 8.2%

30.8% 29.5% 28.8% 10.9%

32.5% 30.1% 27.6% 9.9%

34.1% 31.5% 24.9% 9.5%

0.0075 0.0009 8.29 o.0001

0.0311 0.0037 8.42 o.0001

0.0154 0.0035 4.36 o.0001

0.0339 0.0113 2.99 0.0028

2

N (Number of beneficiaries with pharmacy claims) Number of deaths

2

Vintage coefficients 11 Post-1970 Standard error t-Statistic p-Value 12

Post-1980 Standard error t-Statistic p-Value

0.0056 0.0010 5.48 o.0001

0.0075 0.0037 2.05 0.0405

0.0095 0.0035 2.68 0.0074

0.0359 0.0122 2.94 0.0032

13

Post-1990 Standard error t-Statistic p-Value

0.0057 0.0014 3.98 o.0001

0.0143 0.0046 3.14 0.0017

0.0109 0.0047 2.32 0.0204

0.0092 0.0172 0.53 0.5929

4.4% 3.6% 3.0% 2.5%

10.9% 7.8% 7.0% 5.6%

7.6% 6.0% 5.1% 4.0%

Implied mortality rates 14 Pre-1970 15 1970s 16 1980s 17 1990s

20.1% 16.7% 13.1% 14.0%

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5.0%

Probability of death during 2000-2002

4.5%

4.4%

4.0% 3.6% 3.5% 3.0% 3.0% 2.5%

2.5% 2.0% 1.5% 1.0% 0.5% 0.0% pre 1970

1970s

1980s

1990s

Vintage of drugs consumed, Jan-June 2000

Fig. 4.

Estimated Vintage-Specific Mortality Rates, Entire ASES Population.

The vintage-specific mortality rates are shown in lines 14–17 of Table 2 and plotted in Fig. 4. The estimated mortality rates are strictly declining with respect to drug vintage. For pre-1970 drugs, the estimated mortality rate is 4.4%. The mortality rates for 1970s, 1980s, and 1990s drugs are 3.6%, 3.0%, and 2.5%, respectively. The differences in mortality rates are highly statistically significant ( p-valueo.0001). These estimates can be used to compare the actual mortality rate in the ASES population (resulting from the actual vintage distribution of drugs) to what the mortality rate would have been, given alternative hypothetical vintage distributions of drugs. Two such alternative distributions are considered: POST1970 ¼ 0%: this would have characterized the distribution of drugs in 1970 The vintage distribution of U.S. Medicaid Rx’s in 2000 The results of these calculations are shown in Fig. 5. The actual mortality rate is about 16% (3.7% vs. 4.4%) lower than it would have been if all of the drugs utilized in 2000 had been pre-1970 drugs. There would have been almost 3,800 more deaths in the ASES population during 2000–2002 if all of the drugs utilized in 2000 had been pre-1970 drugs. This suggests that new

287

The Effect of Drug Vintage on Survival 4.5%

4.4%

4.0% 3.7% 3.5%

3.5%

3.0%

2.5%

2.0% If all drugs pre-1970

Fig. 5.

Actual

If same vintage distribution as U.S. Medicaid in 2000

Actual versus Hypothetical ASES Mortality Rates.

drugs introduced during 1970–2000 reduced the mortality rate by about 0.58% ( ¼ (1/30) ln(4.4%/3.7%)) per year. I would like to compare this figure to time-series mortality data for Puerto Rico during the period 1970–2000. I do not have time-series data on the ageadjusted mortality rate of Puerto Rico during this period, but I do have data on the age-adjusted mortality rate of the United States during the period 1979–1998 (see Fig. 6).11 The age-adjusted mortality rate of the United States declined at an annual average rate of 0.71% during the period 1979– 1998 (and, due to the large jump between 1979 and 1980, it declined at an annual average rate of 0.92% during the period 1980–1998). Moreover, in both Puerto Rico and the United States, life expectancy increased much more rapidly in the 1970s than it has done since 1980.12 Therefore, the estimates’ implication that new drugs introduced during 1970–2000 reduced the mortality rate by about 0.58% per year is not implausible. At the same time, the estimates imply that the introduction of new drugs accounted for a significant fraction of the long-run decline in Puerto Rican mortality. As noted earlier, percentages of post-1980 and post-1990 Rx’s are much lower in ASES than they are in U.S. Medicaid. The estimates imply that if the ASES vintage distribution were the same as U.S. Medicaid’s, ASES’s mortality rate would have been 5.3% lower (3.5% vs. 3.7%), and there would have been almost 1,100 fewer deaths in the ASES population during 2000–2002.

288


0.03

0.029

0.028

0.027

0.026

0.025

0.024

0.023

0.022 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998

Fig. 6. Age-Adjusted 3-Year Death Rate, United States, 1979–1998.

3.2. Estimates by Disease Group Columns 2–4 of Table 2 present estimates of the model for each of three groups: (1) people with diseases of the circulatory system; (2) people with endocrine, nutritional and metabolic diseases, and immunity disorders (primarily diabetes); and (3) people with neoplasms. Estimated vintagespecific mortality rates, by condition, are shown in Fig. 7. With only one exception (post-1990 drugs for people with neoplasms), within each group the coefficients of all three drug-vintage variables are negative and highly significant, which is consistent with the hypothesis that ASES beneficiaries using newer drugs during January–June, 2000, were less likely to die by the end of 2002, conditional on the covariates.

4. SUMMARY The ASES contracts with private managed care organizations to provide health care services for approximately 1.5 million people, or 40% of the population of Puerto Rico. The impact of the vintage (original FDA approval year) of drugs used to treat a patient on the probability of survival,

289

The Effect of Drug Vintage on Survival People with diseases of the circulatory system 12.0%

10.9%

10.0% 7.8%

8.0%

7.0% 5.6%

6.0% 4.0% 2.0% 0.0% pre 1970

1970s

1980s

1990s

People with endocrine, nutritional, and metabolic diseases, and immunity disorders 8.0% 7.0% 6.0% 5.0% 4.0% 3.0% 2.0% 1.0% 0.0%

7.6% 6.0% 5.1% 4.0%

pre 1970

1970s

1980s

1990s

People with neoplasms 25.0%

Difference is not statistically significant

20.1% 20.0%

16.7%

15.0%

13.1%

14.0%

1980s

1990s

10.0% 5.0% 0.0% pre 1970

Fig. 7.

1970s

Vintage-Specific Mortality Rates, by Condition.

conditional on demographic characteristics (age, sex, and region), utilization of medical services, and the nature and complexity of illness, using ASES data covering over half a million people, were examined. I could not control for education and income, but to be eligible for Medicaid in Puerto Rico,

290


annual income of a family of four could not exceed $16,440 (in the year 2002). It was found that ASES beneficiaries using newer drugs during January– June 2000 were less likely to die by the end of 2002, conditional on the covariates. The estimated mortality rates are strictly declining with respect to drug vintage. For pre-1970 drugs, the estimated mortality rate is 4.4%. The mortality rates for 1970s, 1980s, and 1990s drugs are 3.6%, 3.0%, and 2.5%, respectively. The differences in mortality rates are highly statistically significant ( p-valueo.0001). The actual mortality rate is about 16% (3.7% vs. 4.4%) lower than it would have been if all of the drugs utilized in 2000 had been pre-1970 drugs. This suggests that new drugs introduced during 1970–2000 reduced the mortality rate by about 0.58% per year. This is not implausible, in light of the time-series data on mortality. The introduction of new drugs appears to have accounted for a significant fraction of the long-run decline in Puerto Rican mortality. Percentages of post-1980 and post-1990 Rx’s are much lower in ASES than they are in U.S. Medicaid. The estimates imply that if the ASES vintage distribution were the same as U.S. Medicaid’s, ASES’s mortality rate would have been 5.3% lower (3.5% vs. 3.7%). Use of older drugs in Puerto Rico’s Medicaid program may be partly attributable to the fact that, in Puerto Rico, the physician bears the costs of the drugs – the cost is deducted from the physician’s capitation payment. In addition to estimating the model for the entire ASES population, the model was estimated separately for three groups: (1) people with diseases of the circulatory system; (2) people with endocrine, nutritional and metabolic diseases, and immunity disorders (primarily diabetes); and (3) people with neoplasms. With only one exception, within each group, the coefficients of all three drug-vintage variables were negative and highly significant. In this study, I did not control for the effect of the vintage of medical products and services other than drugs on survival, and this may have affected our estimates of the effect of drug vintage. I plan to address this issue in future research.

NOTES 1. I am grateful to the Puerto Rico Health Insurance Administration (ASES) for providing me access to the data. ASES does not necessarily endorse or accept the conclusions of this study.


291

2. I hypothesize that survival also depends on the vintage (year of invention or market introduction) of medical products and services other than drugs, such as laboratory tests and radiological and surgical procedures. Unfortunately, the ability to measure the vintage of these other products and services is much more limited than the ability to measure the vintage of drugs. I plan to address this issue in future research. 3. I examined outpatient pharmacy claims (which contain NDC codes), but not hospital and medical claims for drugs administered by providers (e.g., chemotherapy), which contain J-codes and other HCPCS codes. In future research, I plan to account for all drug claims. 4. I excluded women with diagnoses of complications of pregnancy, childbirth, and the puerperium (ICD9 codes 630–677) from the sample. 5. For all variables except POST70, POST80, and POST90, the sample size is approximately 794,000. For POST70, POST80, and POST90, the sample size is approximately 542,000: about a third of beneficiaries had no pharmaceutical claims. 6. Source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2002 Revision and World Urbanization Prospects: The 2001 Revision, Available at http://esa.un.org/ unpp, 25 May 2003; 12:23:46 PM. 7. We consider any Rx for which Medicaid paid at least part of the cost to be a Medicaid Rx. 8. Due to the large number of regressors (described below), I estimated a linear probability model, rather than a probit model. 9. The mean of the dependent variable is 3.7%. This is higher than the 3.1% figure reported above, which included ASES beneficiaries with no pharmacy claims. 10. As argued above, if the mortality rate of people using pre-1970 drugs is equal to k, then the mortality rate of people using 1970s drugs is (k þ b1970), the mortality rate of people using 1980s drugs is (k þ b1970 þ b1980), and so on. To determine these mortality rates, we simply need to solve for k. The overall mortality rate is the weighted average of these mortality rates, weighted by the percentage of people using drugs from each period: MORTAVG ¼ ðDRUG%PRE1970 kÞ þ ðDRUG%1970s ðk þ b1970 ÞÞ þ ðDRUG%1980s ðk þ b1970 þ b1980 ÞÞ þ ðDRUG%1990s ðk þ b1970 þ b1980 þ b1990 ÞÞ where MORTAVG ¼ mean (DIED) DRUG%PRE1970 ¼ mean (1–POST70) DRUG%1970s ¼ mean (POST70–POST80) DRUG%1980s ¼ mean (POST80–POST90) DRUG%1990s ¼ mean (POST90) k ¼ MORTAVG ððDRUG%1970s b1970 Þ þ ðDRUG%1980s ðb1970 þ b1980 ÞÞ þ ðDRUG%1990s ðb1970 þ b1980 þ b1990ÞÞÞ

292


11. Source: CDC Wonder 1979–1998 Compressed Mortality data. 12. Life expectancy at birth, both sexes: Period 1970–1975 1975–1980 1980–1985 1985–1990 1990–1995 1995–2000

Puerto Rico

United States

72.2 73.4 73.8 74.6 73.9 74.9

71.5 73.3 74.0 74.4 74.9 76.2

Source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2002 Revision and World Urbanization Prospects: The 2001 Revision, Available at http://esa.un.org/unpp, 25 May2003; 5:45:45 PM.

REFERENCES Grier, H. E., Krailo, M. D., Tarbell, N. J., Link, M. P., Fryer, C. J. H., Pritchard, D. J., Gebhardt, M. C., Dickman, P. S., Perlman, E. J., Meyers, P. A., Donaldson, S. S., Moore, S., Rausen, A. R., Vietti, T. J., & Miser, J. S. (2003). Addition of ifosfamide and etoposide to standard chemotherapy for Ewing’s sarcoma and primitive neuroectodermal tumor of bone. New England Journal of Medicine, 348(8), 694–701. February 20. Lichtenberg, F. (2003a). The impact of new drug launches on longevity: Evidence from longitudinal disease-level data from 52 countries, 1982–2001. International Journal of Health Care Finance and Economics, 47–73. Lichtenberg, F. (2003b). The effect of new drugs on HIV mortality in the U.S., 1987–1998. Economics and Human Biology, 1, 259–266. Lichtenberg, F. (2003c). Pharmaceutical innovation, mortality reduction, and economic growth. In: K. M. Murphy & R. H. Topel (Eds), Measuring the gains from medical research: An economic approach (pp. 74–109). Chicago: University of Chicago Press. Lichtenberg, F. (2004). Sources of U.S. longevity increase, 1960–2001. Quarterly Review of Economics and Finance, 44(3), 369–389. Lichtenberg, F. (2005). Pharmaceutical knowledge-capital accumulation and longevity. In: C. Corrado, J. Haltiwanger & D. Sichel (Eds), Measuring capital in the new economy, Studies in income and wealth (Vol. 65, pp. 237–274). Chicago, IL: University of Chicago Press. Medical News. (2004). FDA approves advanced malignant melanoma drug. Available at http:// www.news-medical.net/news/2004/04/14/494.aspx Stenestrand, U., & Wallentin, L. (2001). Early statin treatment following acute myocardial infarction and 1-year survival. Journal of the American Medical Association, 285(4), 430–436, January 24–31.

PART IV SPECIAL TOPICS

RETAIL PHARMACY MARKET STRUCTURE AND INSURERINDEPENDENT PHARMACY BARGAINING IN THE MEDICARE PART D ERA Yang Xie, John M. Brooks, Julie M. Urmie and William R. Doucette ABSTRACT Objective – To examine whether local area pharmacy market structure influences contract terms between prescription drug plans (PDPs) and pharmacies under Part D. Data – Data were collected and compiled from four sources: a national mail survey to independent pharmacies, National Council for Prescription Drug Programs (NCPDP) Pharmacy database, 2000 U.S. Census data, and 2006 Economic Census data. Results – Reimbursements varied substantially across pharmacies. Reimbursement for 20 mg Lipitor (30 tablets) ranged from $62.40 to $154.80, and for 10 mg Lisinopril (30 tablets), it ranged from $1.05 to $18. For brand-name drug Lipitor, local area pharmacy ownership Pharmaceutical Markets and Insurance Worldwide Advances in Health Economics and Health Services Research, Volume 22, 295–316 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1108/S0731-2199(2010)0000022016

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concentration had a consistent positive effect on pharmacy bargaining power across model specifications (estimates between 0.084 and 0.097), while local area per capita income had a consistent negative effect on pharmacy bargaining power across specifications(0.149 to 0.153). Few statistically significant relationships were found for generic drug Lisinopril. Conclusion – Significant variation exists in PDP reimbursement and pharmacy bargaining power with PDPs. Pharmacy bargaining power is negatively related to the competition level and the income level in the area. These relationships are stronger for brand name than for generics. As contract offers tend to be non-negotiable, variation in reimbursements and pharmacy bargaining power reflect differences in initial insurer contract offerings. Such observations fit Rubinstein’s subgame perfect equilibrium model. Implication – Our results suggest pharmacies at the most risk of closing due to low reimbursements are in areas with many competing pharmacies. This implies that closures related to Part D changes will have limited effect on Medicare beneficiaries’ access to pharmacies.

INTRODUCTION The retail pharmacy market has experienced dramatic change over the past 20 years as a result of continuous expansion in the percent of prescriptions covered by prescription drug insurance. These prescriptions are often referred to as ‘‘third-party’’ prescriptions. In 2006, the initiation of Medicare Part D under the Medicare Modernization Act 2003 (MMA) furthered this change as the majority of Medicare beneficiaries that were previously cash-paying customers of retail pharmacies joined Part D plans. The percentage of retail prescriptions paid for by a third party rose from 85% in 2004 to 91% in 2006 (NCPA-Pfizer Digest, 2007). For third-party prescriptions, the price is determined by the reimbursement rate specified in the contract between the pharmacy and the third party. As a result, pharmacies currently have limited ability to set prices outside of contracts with payer groups and the profitability and survival of pharmacies heavily depends on the terms of third-party contracts. Researchers studied the impact of third-party contracts (Raisch, Larson, & Bootman 1989; National Association of Retail Druggists, 1994;

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Carroll, Miederhoff, & Waters, 1996) on pharmacies before the Medicare Part D era and found third-party contracts added revenue but curtailed profitability. With the enactment of the MMA, many are concerned with the impact that Part D has on independent pharmacies, especially those in rural areas. For example, one study (Fraher et al., 2005) suggests that MMA may have negative impact on rural, independent pharmacies. Pharmacy trade associations and many pharmacists perceive that independent pharmacies have no ability to bargain with insurers over reimbursement contracts (NCPA-Pfizer Digest, 2007) and have been advocating a collective bargaining act (Ukens & Rosendahl, 1994; Congressional Budget Office [CBO], 2007; Crouch, 2004; Rep. Anthony Weiner [D-NY], 2007, 2009) that would exempt independent pharmacies from anti-trust laws and allow them to collectively bargain with insurers. There are increasing concerns and ongoing debates over how Medicare Part D impacts the survival of independent pharmacies, especially those in rural areas. Yet, previous studies (Brooks, Doucette, & Sorofman, 1999, 2002; Doucette, Brooks, Sorofman, & Hetrick, 2001) have shown that pharmacy reimbursements from third-party payers vary substantially. Such seemingly contradictory observations of non-negotiable contract offers and reimbursement variation may be in fact consistent with the theory of subgame perfect equilibrium in Economics bargaining literature (Brooks et al., 2002). Insurers, on the contrary, accuse independent pharmacies of price-fixing by taking advantage of the bargaining power provided by Medicare Part D regulations and have called for a Federal Trade Commission investigation (Medicine & Health News, 2005). A letter from the Pharmaceutical Care Management Association (PCMA), the lobbying group for pharmacy benefit managers, to the Federal Trade Commission, alleges that ‘‘since PDPs (Prescription Drug Plans) must meet a geographic network access standard for retail pharmacies, pharmacies may well be tempted to withhold their participation in the networks of Pharmacy Benefit Managers or health insurer-sponsored PDPs in order to gain an unfair competitive advantage for chain drugstore and independent pharmacy-sponsored PDPs’’ (Medicine & Health News, 2005). Despite these contentions, there is a dearth of evidence describing the interactions between PDPs and independent pharmacies in the Part D era. To start to fill this information gap, the objectives of this study are (1) to assess whether variation exists in pharmacy–PDP contracts under the Part D and (2) to understand whether market structure influences the contract terms between PDPs and pharmacies. Specifically, we assess whether PDPs tailor contracts to markets and whether pharmacies have

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bargaining power based on their market circumstances under the MMA. This knowledge will greatly assist policy making and can provide justification for policies that will have a profound impact on pharmacy practice and survival, such as the collective bargaining law.

BACKGROUND Beginning on January 1, 2006, the Medicare Prescription Drug, Improvement, and Modernization Act of 2003 (MMA) added outpatient prescription drug coverage to Medicare (Medicare Part D). The drug benefit is provided to Medicare beneficiaries through private plans that contract with Medicare. Enrollment in Part D is voluntary and beneficiaries choose from two types of Medicare Part D plans: Prescription drug plans (PDPs) and Medicare advantage plans with prescription drug coverage (MA-PDs). PDPs offer only prescription drug benefits, with beneficiaries obtaining other Medicare benefits through the original Part A and Part B programs. The MA-PDs include prescription drug coverage along with their Medicare Parts A and B coverage. The Centers for Medicare and Medicaid Services (CMS) established regions of service for both PDPs and MA-PDs. Regions vary in size from one state to seven states and differ somewhat for PDPs and MA-PDs. Plan sponsors may participate in one or more regions but are not allowed to offer coverage in only part of a region. Each PDP and MA-PD plan sponsor establishes a pharmacy network that their Part D enrollees must use to obtain their outpatient prescription drugs. Plan sponsors may refuse to pay for prescriptions obtained at nonnetwork pharmacies but must have exceptions for emergency situations and extended out-of-area travel. According to MMA rules, plan sponsors must accept into their networks any pharmacy provider willing to accept their standard contract terms and conditions but may have preferred pharmacies within their networks. Plan sponsors must have sufficient pharmacies in their retail networks to ensure convenient access to covered Part D drugs. The convenient access requirement is further specified by type of area. For urban areas, at least 90% of Medicare beneficiaries in the sponsor’s service area must live within two miles of a retail pharmacy participating in the sponsor’s network. For suburban areas, at least 90% of Medicare beneficiaries in the sponsor’s service area must live within five miles of a retail pharmacy participating in the sponsor’s network. In rural areas, at least 70% of Medicare beneficiaries in the sponsor’s service area must live within 15 miles of a retail pharmacy participating in the sponsor’s network.


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Plan sponsors must meet or exceed these requirements for each state in which they operate. The inclusion of mail order pharmacies is optional, but mail order pharmacies may not substitute for retail pharmacies in meeting the convenient access requirements (CMS Medicare Prescription Drug Benefit Manual). Before the Part D, pharmacy trade associations and trade press have documented numerous cases that health plans offer ‘‘take it or leave it’’ contracts to retail pharmacies. One case study (Fridy, DeHart, & MonkTutor, 2002) reported insurance companies rejected all changes proposed by an independent pharmacy. But another study (Doucette et al., 2001) found in some cases third parties responded to contract negotiations. Brooks et al. (1999, 2002) found significant variation in contract terms negotiated between insurers and independent pharmacies. Pharmacies in areas with less competition had more bargaining power as revealed by third-party contracts with higher reimbursement rates. Considering the widely reported practice of health plans offering ‘‘take it or leave it’’ contracts to retail pharmacies, Brooks et al. suggested that this behavior is suggestive of a subgame perfect bargaining equilibrium from the bargaining literature. Rubinstein’s (1982) model assumes that bargaining is costly and each bargainer has distaste for the bargaining process. Therefore, instead of going through the bargaining process, one party constructs an initial offer that would be the minimum acceptable offer to the other party, who then accepts the offer, and the bargaining stops. In this case, insurers may have taken into account the market characteristics and the bargaining power of retail pharmacies, and observed differences in contract terms reflect differences in initial contract offerings. However, it is an open question whether PDPs continue the practice of tailoring contracts to pharmacies given their market circumstances and whether pharmacies maintain bargaining power based on pharmacy network definitions within the MMA. Surrounding the issue of the impact, several recent studies that estimated the impact of Medicare Part D on independent pharmacies ignored the potential of varied bargaining power. In a study before the launch of Part D, Fraher et al. (2005) suggested that the implementation of Medicare Part D could hurt rural seniors by negatively affecting independent pharmacies, especially rural independent pharmacies. Carroll (2008) estimated the impact of Medicare Part D on the profitability of independent community pharmacies and found that even though Part D increased the utilization of prescription drugs, more than half of the increase was lost to mail order. Using a financial model with parameter inputs drawn from existing literature, Carroll estimated a small decline in the typical independent

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pharmacy’s gross margin percent for prescriptions from Part D but a significant decrease in the net income, which is primarily due to an assumed consistent lower reimbursement rate from insurers. Another recent survey study (Spooner, 2008) also reported that the utilization increase caused by Part D is less than anticipated while the gross margin of independent community pharmacies went down. Most recently, Radford et al. (2009) reported in a survey study that the low reimbursement and administrative burden brought by Part D could threaten the viability of independent pharmacies, especially those who are the sole retail providers in the communities. The study led them to conclude policymakers should consider taking action to help sole community pharmacies deal with the financial and administrative challenges of Medicare Part D. In all the above-mentioned studies, though, the parameters used in estimation were based on the underlying assumption that retail pharmacies are treated the same by PDPs. Considering the findings from earlier Brooks et al. (1999, 2002) studies, it raises the question that whether the implementation of Part D affects retail pharmacies consistently. These studies reflect a general sense of frustration and disappointment toward Part D among independent retail pharmacies, which also led some researchers to suggest that independent pharmacy is the only group with a direct interest in Part D who are not benefiting from the program (Spooner, 2008). Independent pharmacies are important access points for rural elderly because they represent a large proportion of the rural pharmacies (Freeman, Richardson, & Slifkin, 2009). In addition, independent pharmacies have better patient satisfaction than chain pharmacies, suggesting better service levels. Given these factors, there is growing concern that Medicare Part D contracts could lead to increasing pharmacy access problems for Medicare beneficiaries and decreasing service quality in some areas (Doucette et al., 2001; Radford et al., 2009; Gellad et al., 2009; Lopes, 2007; Reid, 2007; Rineheart, 2006; Steil, 2008; Cherveny, 2006). Such concern prompted significant lobbying effort from the National Community Pharmacists Association (NCPA) to provide independent pharmacies with legislative relief. The Community Pharmacy Fairness Act (Rep. Anthony Weiner [D-NY], 2007, 2009) was introduced to create an exemption in the anti-trust law to allow independent community pharmacies to collectively negotiate contract terms with health plans. It argues that large chain pharmacies are able to use their bargaining power from the size in the marketplace to obtain favorable contract terms with the insurers, while the contracts offered to community pharmacies are often nonnegotiable.


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Following the introduction of the bill, the Congressional Budget Office (CBO) also conducted study on the potential impact of the bill. The CBO (2007) study estimated that enacting the bill would significantly increase federal direct spending for health benefits and reduce federal tax revenues based on the notion that independent community pharmacies charge higher retail prices than chain pharmacies. Other recent studies also reported that retail prescription prices are higher in poorer areas (Gellad et al., 2009; The Council of The City of New York, 2004; Cave, 2006) that create concerns among some researchers that an independent pharmacy exemption to the anti-trust law would make medications even less accessible to people living in those lower income areas and thereby further increases health disparity between the rich and the poor. However, given that more than 90% of the pharmacy revenue now comes from third-party reimbursement, it does not provide the complete picture by only studying the retail drug prices in pharmacies. Without examining the variation in reimbursement rates between pharmacies and third-party payers and understanding the factors that affect the contract terms, we will not fully understand the impact of Part D on independent pharmacies. Furthermore, to provide policymakers with evidence on whether legislative relief to the retail pharmacy industry should be provided, studies have raised the importance of studying the reimbursement between health plans and independent pharmacies. However, to our best knowledge, no study has examined the bargaining and contract terms between Medicare Part D PDPs and independent community pharmacies.

THEORETICAL MODEL In this study, we employ a bargaining model that was adapted by Brooks, Dor, and Wong (1997, 1998) (BDW) from a Svejnar (1985) wage determination model. The Svejnar model is a generalization of the Nash–Zeuthen– Harsanyi model (N-Z-H) (Zeuthen, 1930; Nash, 1953; Nash, 1950; Harsanyi, 1956). Svejnar operationalized the N-Z-H model by theorizing that the bargaining outcome is determined by the threat points of each bargainer, and bargaining power based on exogenous factors related to the bargaining environment and respective bargainer ‘‘fear of disagreement’’ that influence the bargaining outcome. The BDW model was employed to empirically estimate the hospital– insurer (Brooks et al., 1997, 1998) and the pharmacy–insurer (Brooks et al., 1999) bargaining problem. In this chapter, pharmacies sell prescriptions and

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the PDPs buy prescriptions for their beneficiaries. The pharmacy and PDP arrive at a mutually agreed upon transaction price. Both the pharmacy and the PDPs are assumed to be profit maximizers. Following the BDW and the Svejnar models, the bargaining outcome is the prescription price that maximizes: hY Y ig hY Y i1g (1) V¼ P P I I where g represents the pharmacy bargaining power and 1g is the PDP Q Q and (insurer) bargaining power. P and I are the Q Q theQPDP’s Q pharmacy’s disagreement profit levels, respectively, and ½ P P and ½ I I are their corresponding net profit gains from bargaining. The net profit gain of the PDP can be written as follows: Y Y ¼ ðR K PNÞ ðR K PH NÞ (2) I I

where R is the PDP’s revenue, K is its cost of production, N is the number patients insured by the PDP, PH is the price that the insurer must pay for a drug if the insurer has no bargaining power, and P is the reimbursement to be determined in negotiations. The net gain of the pharmacy can be written as follows: Y Y ¼ ½NðP CÞ ½NðPL CÞ (3) P P where C is the average pharmacy acquisition cost of drugs, P and N are as previously defined, and PL is the minimum reimbursement rate that the pharmacy would accept. Substituting Eqs. (2) and (3) into Eq. (1) and simplifying yields the following: V ¼ ½NðP PL Þg ½NðPH PÞ1g

(4)

Maximizing Eq. (4) with respect to P and rearranging yields the following: P PL ¼ g ðPH PL Þ

(5)

(PH PL) is the potential gain from the transaction to be divided between the buyer and the seller, and (P PL) is the portion of the potential gain acquired by the seller. The measure of seller’s bargaining power, g, is the share of the potential gain that a seller keeps as a result of the bargaining. If g equals one, the seller has complete bargaining power and does not discount its price from PH, whereas if g equals zero, the buyer has complete


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bargaining power and is able to extract the maximum discount from the seller. To explore whether bargaining power is influenced by exogenous factors such as local market structure, g is parameterized as follows: P PL ¼ ða þ bZÞ ðPH PL Þ

(6)

In this case, Z is a set of exogenous factors. If b equals zero, then a equals g and bargaining power does not vary with Z. In summary, we specify pharmacy bargaining power as the share of the pharmacy retail margin per prescription that the pharmacy retains from the PDP (prescription retail price-prescription cost)/(insurer reimbursementprescription cost). This measure of bargaining power is between zero and one. Note that pharmacy bargaining power is highest (one) when the insurer pays the pharmacy an amount greater than or equal to the retail price and lowest (zero) when the insurer reimburses at a level less than or equal to pharmacy cost. In this study, we will measure pharmacy bargaining power by asking the pharmacy to report the estimated acquisition costs (prescription cost), retail prices (prescription retail price) of two popular drugs, and the reimbursement rates from two of their PDP contracts. Using the calculated ‘‘bargaining power’’ measure, we are able to examine how pharmacy characteristics and local market structure affect pharmacy bargaining power.

DATA SOURCES There is a large body of theoretical literature on bargaining problems in Economics. Brooks et al. (1998) reviewed many of these papers in their hospital–insurer bargaining paper. However, the empirical literature on this topic is sparse, mainly due to the lack of data. In their hospital–insurer bargaining paper, BDW provides a straightforward, ready-to-use model to estimate the bargaining problem and exogenous factors that determine the bargaining power and the bargaining outcome. The challenge lies in finding measurements for the elements specified in the BDW model. Reimbursement rates for prescription drugs specified in contracts between pharmacies and health plans are confidential information and extremely difficult to acquire. The scarcity of such data greatly limited the empirical research on this important question. To tackle this challenge, we collected and compiled data from four sources: a national mail survey to independent pharmacies, National Council for Prescription Drug Programs (NCPDP) Pharmacy

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database, 2000 U.S. Census data, and 2006 Economic Census data, to specify our empirical model. For the mail survey, a two-stage area sampling technique was used to select a random sample of 1,650 independent pharmacies across the nation. First, six geographically dispersed Medicare regions were chosen: Southeast (Florida), Southwest (Arizona), South (Tennessee, Alabama), Northwest (Washington, Oregon), Midwest (Minnesota, Iowa, Wyoming, Montana, South Dakota, North Dakota, Nebraska), and Northeast (Pennsylvania, West Virginia). Each region (combination of states) has a common set of PDPs offering plans. Then a systematic random sample of 1,650 independent pharmacies was selected from the chosen regions. Because such information is highly confidential, we expected low response rate to our survey and used a large sample frame to try to acquire sufficient number of observations. The NCPDP Pharmacy database was used as the sampling frame. The NCPDP Pharmacy database contains over 74,000 pharmacies. It includes the name, address, type, and chain affiliation code of all licensed pharmacies in the United States on October 2008. The NCPDP Pharmacy database was used in two ways. First, we used it as the sampling frame for the mail survey. Secondly, we used it to calculate the pharmacy ownership concentration index in each of the five-digit zip code. The surveys were addressed to the pharmacy owners and managers who are most likely to be the ones making decisions regarding third-party contracts. We collected information on independent pharmacies’ bargaining activity, characteristics of the pharmacy, and satisfaction with the contracts offered by PDPs. We also asked pharmacies to report the estimated acquisition costs, retail prices, and the reimbursement rates from their most and least favorable contracts for two common pharmaceutical products: one brand-name drug Lipitor, 20 mg tablets (30 tablets), and one generic drug, Lisinopril 10 mg tablets (30 tablets). In 2008, these two drugs were among the top five drugs by number of prescriptions dispensed (IMS Health, 2008). Some may be concerned with the ability to generalize the results in our study because we have only one drug to represent brand-name drug and one to represent generic drug. However, reimbursement rates in PDP–pharmacy contracts are typically not determined on a drug-by-drug basis. Instead, they are determined by separate formulae for brand-name drugs and generic drugs. The brand-name drug reimbursement formula consists of an estimate of each product’s acquisition cost (published average wholesale price (AWP) for the product minus a fixed percentage) plus a fixed dispensing fee (Anthony, 1998). Therefore, the formula used to calculate the reimbursement rate for Lipitor in a contract would be the same formula used to


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calculate the reimbursement rates for any other brand-name drugs for that specific contract. Some third parties also use an AWP-based reimbursement formula for generic drugs while other third parties set maximum allowable costs (MACs) for each generically available drug. Although the MAC rates are set for individual products, it is likely that a third party would tend to have either generally high MACs or generally low MACs so that the reimbursement rate for Lisinopril should be fairly representative. We constructed other zip code level market structure measures and local socioeconomic characteristics from the 2000 U.S. Census data and 2006 Economic Census data. Other measures and variable specification will be discussed in detail in the next section.

VARIABLE SPECIFICATIONS To estimate Eq. (2) in the theoretical model, we need to specify P, PL, PH, and Z for pharmacy/PDP combinations. PH is the highest price the PDP would be willing to pay for a prescription of a particular medication; PL is the minimum price that the pharmacy would be willing to accept for that prescription. PL and PH together frame the bargaining outcome for each individual product, while P is the bargaining outcome (PL r P r PH). In the bargaining problem between pharmacies and PDPs, we define PN as the reimbursement amount for a certain product; PH is the retail prices that the pharmacy charges for a product. We assume insurers would not be willing to pay more that the retail price charged for that product at the pharmacy. This assumption is further validated by the fact that contracts often specify that the insurer pays the lower of the usual and customary (retail cash) price and the price determined by the insurers’ reimbursement rate formula. PL is the pharmacy’s acquisition cost for the product, assuming that in the long run a pharmacy cannot accept a reimbursement amount lower than the acquisition cost of the product. The key independent variables in this model are the measures of local area pharmacy market structure. Our hypothesis is that the bargaining between PDPs and independent pharmacies fits a Rubinstein subgame perfect equilibrium model, in which PDPs offer customized contracts to pharmacies based on structure of the local pharmacy market. We initially attempted to specify the ‘‘Z’’ variables in our bargaining model using the set of market structure variables used by Brooks et al. (1999). In the fivedigit zip code containing each independent pharmacy in our sample, we estimated a pharmacy ownership concentration Herfindahl index, number

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of pharmacies per 10,000 population, number of pharmacy employees per 10,000 population in the five-digit zip code area, and a set of local socioeconomic characteristics: per capita income, percentage of elderly population, and percentage of rural population. The market structure variables capture different aspects of the market condition. The pharmacy ownership concentration Herfindahl index is based on the shares of individual pharmacy locations in a market maintained by separate owners. A larger Herfindahl index means either fewer distinct owners are represented in the market or, given a number of distinct owners, pharmacy locations are concentrated among fewer owners. As such, a larger Herfindahl index in a market indicates less pharmacy competition. The number of pharmacies per 10,000 population measures the density of pharmacy outlets in a given area, regardless of ownership, and the number of pharmacy employees per 10,000 population measures the scale of pharmacy sizes in an area. Caution should be taken when interpreting the number of pharmacies and pharmacy employees per 10,000 population measures. A larger number does not necessarily mean there are more pharmacies or more pharmacy employees in an area. The number could also be large because the pharmacy in that area is simply serving a smaller population. For analysis, we calculated the pharmacy bargaining power for both of the PDP contracts reported by each pharmacy for each drug and analyzed the predictors for pharmacy bargaining power for each drug. In our study we have several measures describing local area pharmacy market characteristics and socioeconomic status. As such, we were concerned with multicollinearity. Therefore, we estimated several model specifications to assess the robustness of our estimates. The reimbursement rates for the most favorable and least favorable contracts were pooled together for the analysis because the market characteristics hypothesized to effect bargaining power would not differ across the pharmacy’s Part D contracts. We did include a ‘‘most favorable contract’’ dummy variable because the designation of ‘‘most favorable’’ and ‘‘least favorable’’ contract would be a proxy for PDP characteristics that may influence bargaining outcomes.

RESULTS The mail survey yielded a response rate of 19.5%. We had a 10% response rate from Southeast (Florida); 12% from Southwest (Arizona); 20% from South (Tennessee, Alabama), Northeast (Pennsylvania, West Virginia), and Northwest (Washington, Oregon); and 31% from the Midwest (Minnesota,

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Iowa, Wyoming, Montana, South Dakota, North Dakota, Nebraska). Ten percent of the respondents reported unusually high values for the price and reimbursement rates for Lisinopril, which led us to suspect that brand-name versions of Lisinopril (Zestril, Prinivil) were reported by mistake. Reported cash prices and reimbursement rates higher than $40 for Lisinopril were dropped from the analyses. Graphs 1 and 2 show the distribution of pharmacy bargaining power we calculated for Lipitor and Lisinopril. Pharmacy bargaining power is relatively evenly distributed between zero and one. When pharmacy bargaining power equals to zero, it indicates that the pharmacy has no bargaining power against this PDP and the pharmacy accepts a contract with the reimbursement rate equal to its acquisition cost for the drug. In other words, the PDP in this case extracts the maximum discount from the pharmacy. When the bargaining power is equal to one, it indicates that the pharmacy has complete bargaining power and gets a contract where the reimbursement rate is equal to the cash price it charges for the drug. Our data clearly show that some pharmacies are able to extract the maximum profit from the contracts with PDPs while some other pharmacies have no bargaining power and have to accept reimbursement rates close to the acquisition costs.

Frequency

60

40

20

0 0

0.2

0.4

0.6

0.8

Pharmacy bargaining power for Lipitor

Graph 1.

Variation in Pharmacy Bargaining Power for Lipitor.

1

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Frequency

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40

20

0 0

0.2

0.4

0.6

0.8

1

Pharmacy bargaining power for Lisinopril

Graph 2.

Variation in Pharmacy Bargaining Power for Lisinopril.

Table 1.

Variations in Reimbursements (by Percentile). 10%

25%

50%

75%

90%

Mean

SD

Lipitor PDP reimbursement ($) 105.11 114.06 117.88 121.89 124.29 115.35 12.14 Lisinopril PDP reimbursement ($) 3 4.05 6.44 8.72 10.44 6.80 3.86

Table 1 summarizes the variation in PDP reimbursement rates for Lipitor and Lisinopril. Reimbursements for Lipitor 20 mg (30 tablets) range from $62.40 to $154.80 while reimbursements for Lisinopril 10 mg (30 tablets) ranges from $1.05 to $18. As we can see, the variations in PDP reimbursement rates are sizable. Table 2 presents the summary statistics for the exogenous variables. In our sample, the Herfindahl ownership index ranges from 0.048 to 1, with the mean equaling 0.43. In comparison, the mean pharmacy ownership Herfindahl index from our overall sample zip code areas is 0.44 and the mean from the non-sample zip code areas is 0.51, which indicates that our study sample areas are representative of our overall sample frame as well as the whole country in terms of pharmacy ownership concentration level. There are on average 2.77 pharmacies and 44.6 pharmacy employees per

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Table 2.

Summary Statistics for Exogenous Variables.

Pharmacy owner concentration index Number of pharmacies per 10,000 population pharmacy employees per 10,000 population Per capita income ($10,000) Percent of elderly Percent of rural population

Mean

SD

0.43 3.22 44.60 1.95 15.72 39.87

0.35 5.28 31.12 0.54 6.07 38.27

10,000 people in our sample areas. The average zip code level per capita income is $19,479; the percentage of elderly population ranges from 4 to 47%, with the mean at 15.7%; and the average percentage of rural population is 39.87%. We first explored the correlation between all the independent variables and found two high correlations. The pharmacy ownership concentration Herfindahl index is highly correlated with the rural population percentage in the area (correlation coefficient ¼ 0.67), indicating that pharmacy ownership is more concentrated in rural areas. Also, the number of pharmacies per 10,000 people is highly correlated with number of pharmacy employees per 10,000 people (correlation coefficient ¼ 0.71). This suggests that the average pharmacy sizes are fairly consistent across areas. Tables 3 and 4 present the estimates for brand-name drug Lipitor (Table 3) and generic drug Lisinopril (Table 4) from multivariate analyses. Model 1 is our primary model, which includes all seven independent variables: three market structure measures, three area socioeconomic characteristic measures, and the ‘‘most favorable contract’’ identifier. To assess the implications of the high correlation between some of the independent variables, we dropped the number of pharmacy employees per 10,000 population in Models 2 and 4 and dropped percent rural population in Models 3 and 4. Our estimates stayed consistent across model specifications. Estimates for Lipitor (brand name) are reported in Table 3. Pharmacy ownership concentration index (Herfindahl index) is a consistently significant predictor of pharmacy bargaining power (estimates between 0.084 and 0.097 in Models 1–4). A positive coefficient estimate means that the factor is positively related to pharmacy bargaining power. Our estimates suggest that the independent pharmacies in areas with higher pharmacy ownership concentration, meaning less competition, have higher bargaining power with PDPs.

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Table 3. Relationships between Pharmacy Bargaining Power and Exogenous Variables for Lipitor with Different Model Specifications. Number of Observations: 385

Model 1

Model 2

Model 3

Model 4

Intercept

0.586

0.585

0.580

0.579

Pharmacy ownership concentration index Number of pharmacies per 10000 population Per capita income ($10,000) Percent of elderly population Pharmacy employees per 10,000 population Percent of rural population Most favorable contract Unadjusted R2

0.097 0.01 0.153 0.084 0.465 0.021 0.225 0.231

0.096 0.085 0.084 0.01 0.01 0.01 0.152 0.150 0.149 0.091 0.071 0.079 0.440 0.021 0.225 0.225 0.225 0.231

0.231

0.231

Notes: Dependent variable, pharmacy bargaining power; mean, 0.429; SD, 0.285. We have also tested models with ‘‘number of pharmacies per 100 employers’’ for both Lipitor and Lisinopril. Results are not presented because the estimates are not statistically significant and it does not affect other estimates by including or excluding it. Significantly different from 0 at .90 confidence level. Significantly different from 0 at .95 confidence level. Significantly different from 0 at .99 confidence level.

Table 4. Relationships between Pharmacy Bargaining Power and Exogenous Variables for Lisinopril with Different Model Specifications. Number of Observations: 362

Model 1

Model 2

Model 3

Model 4

Intercept

0.475

0.481

0.486

0.492

Pharmacy ownership concentration index 0.004 0.003 0.024 0.029 Number of pharmacies per 10,000 population 0.001 0.0007 0.002 0.002 0.056 0.064 Per capita income ($10,000) 0.049 0.058 Percent of elderly population 0.353 0.302 0.383 0.331 0.331 Pharmacy employees per 10,000 population 0.337 Percent of rural population 0.048 0.044 Most favorable contract 0.283 0.283 0.283 0.283 Unadjusted R2

0.252

0.245

0.250

Note: Dependent variable, pharmacy bargaining power; mean, 0. 583; SD, 0.298. Significantly different from 0 at .90 confidence level. Significantly different from 0 at .95 confidence level. Significantly different from 0 at .99 confidence level.

0.243


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The number of pharmacies per 10,000 population was also consistently statistically significant and negative across model specifications indicating that higher pharmacy density of pharmacy leads to less independent pharmacy bargaining power. The number of pharmacy employees per 10,000 populations in the area was not significantly related to pharmacy bargaining power with respect to brand-name drug reimbursements. As for area socioeconomic characteristic measures, per capita income is also a strong predictor for pharmacy bargaining power. Independent pharmacies in areas with higher per capita income actually have less bargaining power. Estimates for income are also consistent across different model specifications, ranging from 0.149 to 0.153. Percentage of rural population and elderly population are not significant predictors for pharmacy bargaining power in our sample. We also included the ‘‘most favorable contract’’ dummy in all our models; the estimates are consistent and statistically significant at 0.225. Pharmacy bargaining power is calculated with acquisition cost, cash price, and reimbursement, and the first two values are constant for the same pharmacy, and therefore, this estimate indicates that for the same pharmacy, the reimbursement rate from their most favorable contract is 22.5% higher than the reimbursement rate from their least favorable contract. This result may reflect the variation in PDPs’ bargaining power or goals. For example, one PDP was established by the NCPA and they likely have some different goals than other PDPs. Table 4 reports the estimates for generic drug Lisinopril in contrast with the Lipitor estimates; few statistically significant relationships were found. Pharmacy bargaining power for Lisinopril still varied across PDP contracts within a pharmacy, but only area per capita income and pharmacy employees per 10,000 people had weak impact on pharmacy bargaining power.

DISCUSSION Our study shows that after the implementation of Medicare Part D, there is still significant variation in PDP reimbursement to independent pharmacies and pharmacy bargaining power with PDPs. Pharmacy bargaining power is negatively related to the level of competition in the area. The greater pharmacy ownership concentration (e.g., less competition) in an area, the more bargaining power independent pharmacies have with PDPs. Pharmacy bargaining power is also negatively related to the income level in the area:

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The higher income level, the less bargaining power the pharmacy in that area has. These relationships are stronger and more significant for brandname drugs than for generic drugs. We found the relationship between market characteristics and pharmacy bargaining power is stronger for brand-name drugs than for generic drugs. There are a few reasons that could explain this. First, there are multiple manufacturers for generic drugs; therefore, the variation in acquisition costs for the same generic drug is greater than the acquisition costs for the same brand-name drug across different pharmacies. Secondly, because brandname drugs are usually much more expensive than generic drugs, insurers and pharmacies are more likely to bargain ‘‘harder’’ for brand-name drug reimbursement rates or formulas than for generic drugs. This conjecture is also supported by the fact that the variation in Lisinopril reimbursement rates is greater than the variation in Lipitor reimbursement rates. It also is likely that the MAC reimbursement methodology yields more variation in rates than the AWP-based formulas. Another interesting finding from the study is that there is a strong correlation between reimbursements from the most favorable and the least favorable contracts for each pharmacy (correlation coefficient ¼ 0.83 for Lipitor reimbursements and 0.57 for Lisinopril). In other words, even with the significant difference between the most and the least favorable contract reimbursements for each pharmacy (shown by the estimates for the ‘‘most favorable contract’’ dummy variable), pharmacies that are getting higher reimbursement rates from their most favorable contract are also likely to be getting higher reimbursement rates from their least favorable contract than the other pharmacies, which from another aspect supports the finding that pharmacies are getting different contracts terms and the variations are at least partially affected by market structure. Besides the pharmacy ownership concentration level, we also found that independent pharmacies in higher income areas have less bargaining power and get lower reimbursements than independent pharmacies in lower income areas. We suspect that this is because the independent pharmacies in higher income areas are able to extract revenue or get higher profit margins on the non-pharmaceutical products they sell, which allow them to accept lower reimbursements for prescription drugs. Another interesting finding is that after controlling for number of pharmacies per capita, independent pharmacies in areas with higher pharmacy employee/population ratios have less bargaining power on generic drug reimbursements but not on brand-name drugs. These areas are likely to be areas with larger chain pharmacies. And this correlation


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could be explained as an artifact of high volume chain pharmacies offering the $4 or $10 generic programs. In those areas, independent pharmacies face stronger competition on generic drugs because of the $4/10 generic drug programs and may match their competitors’ generic prices. As most third parties pay pharmacies the lower of their reimbursement rate formula and the pharmacy’s cash price, lower generic cash prices in an area should yield lower generic reimbursements. In our mail survey, many independent pharmacy owners/managers commented that pharmacies have no bargaining power with PDPs and the prevalence of take-it-or-leave-it contracts as evidence of this fact. Those comments are consistent with previous reports and observations that independent pharmacies have substantially less bargaining power with PDPs as compared with chain pharmacies due to the difference in size of business. However, our results show significant variation exist in PDP reimbursement rates to pharmacies and that independent pharmacies in areas with less competition have more bargaining power against PDPs and are more likely to acquire higher reimbursements. As contract offers tend to be non-negotiable, these variations reflect differences in initial contract offerings from the insurer that reflects market characteristics. Such observation perfectly fits a (Rubinstein (1982) subgame perfect equilibrium model. Rubinstein’s mode suggests that because the bargaining process is costly and bargainers are risk averse, bargainers have a distaste for the bargaining process. To avoid the costs associated with the bargaining process, the initial contract offered takes the relative bargaining power innate in each interaction into account and offers contract terms reflecting an estimate of the minimum the other party will accept. The other party accepts the offer and bargaining ceases. In our case, the evidence suggests that PDPs offer customized contracts to pharmacies based on pharmacy local area market structure. Our findings using information acquired through mail surveys are consistent with the findings in earlier studies before the Part D era (Brooks et al., 1999, 2002). There are a few limitations in our study. First, the survey yielded a low response rate. This was not unexpected due to the sensitive information we asked in our mail survey. However, even with the small sample size, our analyses yielded estimates that are statistically significant for the key variables, which in itself is an indication of the robustness of our results. A second limitation is the potential reporting problem with the prices and reimbursement rates in the survey as mentioned in the section ‘‘Data Source.’’ As for policy, especially with respect to the debate over the collective bargaining law, our results demonstrate that pharmacy bargaining power

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increases as the pharmacy ownership in an area becomes more concentrated. In other words, pharmacies in high competition areas are more likely to be getting lower reimbursement rates from PDPs and would face more challenges in terms of survival than pharmacies in areas where competition levels are lower. This suggests that independent pharmacies at the most risk of closing due to low reimbursements from PDPs are most likely pharmacies in areas with many competing pharmacies. This in turn implies that the closures would have a limited effect on the access of Medicare beneficiaries to pharmacies. With that being said, it also appears that pharmacy competition appears lowest in rural areas. Rural independent pharmacies may serve small populations and therefore having higher average costs and need higher reimbursements per prescription to remain open. It is an open question whether the higher reimbursements rural pharmacies receive are enough to sustain the survival of these rural independent pharmacies. For independent pharmacies in areas with more competitors, the anti-trust law prohibiting independent pharmacies from collective bargaining may force some independents to either close or merge with a chain. Considering that independent pharmacies are thought to provide unique and innovative services (Briesacher & Corey, 1997; Doucette & Jambulingam, 1999), the existing anti-trust law may have the unintended consequences of losing the innovative services provided by independent pharmacies and increasing the concentration in the retail pharmacy market.

REFERENCES Anthony, C. J. (1998). The effects of PBMs on the marketplace: A pharmacy perspective. Journal of Pharmaceutical Marketing and Management, 12(2/3), 177–180. Briesacher, B., & Corey, R. (1997). Patient satisfaction with pharmaceutical services at independent and chain pharmacies. American Journal of Health-System Pharmacy, 54(5), 531–536. Brooks, J. M., Dor, A., & Wong, H. S. (1997). Hospital-insurer bargaining: An empirical investigation of appendectomy pricing. Journal of Health Economics, 16(4), 417–434. Brooks, J. M., Dor, A., & Wong, H. S. (1998). The impact of physician payments on hospitalinsurer bargaining in the US. In: D. Chinitz & M. Cohen (Eds), Governments and health systems. New York: Wiley. Brooks, J. M., Doucette, W. R., & Sorofman, B. (1999). Factors affecting bargaining outcomes between pharmacies and insurers. Health Services Research, 34(1 Pt. 2), 439–451. Brooks, J. M., Doucette, W. R., & Sorofman, B. A. (2002). Third party bargaining and contract terms: A link over time? Journal of the American Pharmaceutical Association, 42(3), 420–427.


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Carroll, N. V. (2008). Estimating the impact of Medicare part D on the profitability of independent community pharmacies. Journal of Managed Care Pharmacy, 14(8), 768–779. Carroll, N. V., Miederhoff, P. A., & Waters, L. W. (1996). Profitability, third-party reimbursement, and access to community pharmacies. Clinical therapeutics, 18(4), 703–715, discussion 702. Cave, D. (2006). Drug prices still vary widely across city. New York Times, October 5. Cherveny, T. (2006). Renville pharmacy to join drugstore chain. West Central Tribune, November 29. Congressional Budget Office. (2007). H.R. 971 Community Pharmacy Fairness Act of 2007. Crouch, C. C. (2004). The struggle for rural pharmacies: Will Medicare’s new privately insured prescription drug coverage jeopardize valuable pharmacy services for rural seniors? The Journal of Contemporary Health Law and Policy, 21(1), 99–136. Doucette, W. R., Brooks, J. M., Sorofman, B. A., & Hetrick, A. S. (2001). Bargaining between community pharmacies and third-party payers: Influences on bargaining outcome. Journal of Managed Care Pharmacy, 7(1), 43–49. Doucette, W. R., & Jambulingam, T. (1999). Pharmacy entrepreneurial orientation: Antecedents and its effect on the provision of innovative pharmacy services. Journal of Social & Administrative Pharmacy, 16, 26–37. Fraher, E. P., Slifkin, R. T., Smith, L., Randolph, R., Rudolf, M., & Holmes, G. M. (2005). How might the Medicare prescription drug, improvement, and modernization act of 2003 affect the financial viability of rural pharmacies? An analysis of pre-implementation prescription volume and payment sources in rural and urban areas. The Journal of Rural Health, 21(2), 114–121. Freeman, V., Richardson, I., & Slifkin, R. (2009). Medicare beneficiaries’ access to pharmacy services in small rural towns: Implications of contracting patterns of sole community pharmacies with Part D plans. Chapel Hill, NC: North Carolina Rural Health Research and Policy Analysis Center, The University of North Carolina at Chapel Hill. Fridy, K., DeHart, R. M., & Monk-Tutor, M. R. (2002). Negotiating with third party payers: One community pharmacy’s experience. Journal of the American Pharmaceutical Association, 42(5), 780–788. Gellad, W. F., Choudhry, N. K., Friedberg, M. W., Brookhart, M. A., Haas, J. S., & Shrank, W. H. (2009). Variation in drug prices at pharmacies: Are prices higher in poorer areas? Health Services Research, 44(2 Pt. 1), 606–617. Harsanyi, J. C. (1956). Approaches to the bargaining problem before and after the theory of games: A critical discussion of Zeuthen’s, Hicks’ and Nash’s theories. Econometrica, 24, 144–157. IMS Health. (2008). U.S. sales and prescription information. Top-line Industry Data 2008. Available at www.imshealth.com. Accessed on May 12, 2009. Lopes, G. (2007). Part D eyed in druggist drop off. Washington Times, February 2. Medicine & Health News. (2005). Plans, PBMs, pharmacy jockey for part D positioning: Are pharmacies raising rates for health plans? Medicine & Health, 59(20), 1–2. Nash, J. F. (1950). The bargaining problem. Econometrica, 28, 155–162. Nash, J. F. (1953). Two-person cooperative games. Econometrica, 31, 129–140. National Association of Retail Druggists. (1994). Evaluating third-party prescription programs (3rd ed.). Alexandria, VA: NARD. NCPA-Pfizer Digest. (2007). National community pharmacists association. Alexandria, VA.

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Radford, A., Mason, M., Richardson, I., Rutledge, S., Poley, S., Mueller, K., & Slifkin, R. (2009). Continuing effects of Medicare Part D on rural independent pharmacies who are the sole retail provider in their community. Research in Social and Administrative Pharmacy, 5(1), 17–30. Raisch, D. W., Larson, L. N., & Bootman, J. L. (1989). Pharmacy participation in third-party contracts: Decision making through economic and financial analysis. Journal of Pharmaceutical Marketing & Management, 3(4), 37–49. Reid, S. (2007). Drugstore’s closing brings a dose of sadness. Fayetteville Observer, January 20. Rep. Anthony Weiner [D-NY]. (2007). H.R. 971: Community Pharmacy Fairness Act of 2007. Available at www.govtrack.us/congress/bill.xpd?bill ¼ h110-971 Rep. Anthony Weiner [D-NY]. (2009). H.R. 1204: Community Pharmacy Fairness Act of 2009. Available at http://www.govtrack.us/congress/bill.xpd?bill ¼ h111-1204 Rineheart, E. (2006). Small pharmacies struggled low reimbursement pinch profits, according to industry survey. Grand Forks Herald, August 19. Rubinstein, A. (1982). Perfect equilibrium in a bargaining model. Econometrica, 50, 97–109. Spooner, J. J. (2008). A bleak future for independent community pharmacy under Medicare Part D. Journal of Managed Care Pharmacy, 14(9), 878–881. Steil, M. (2008). Small town pharmacies struggle. Minnesota Public Radio, March 25. Available at http://minnesota.publicradio.org/display/web/2008/03/24/pharmacy/. Accessed on May 12, 2009. Svejnar, J. (1985). Bargaining power, fear of disagreement, and wage settlements: Theory and evidence from U.S. industry. Econometrica, 54(5), 1055–1078. The Council of The City of New York. (2004). Prescription drug prices: All over the map. A Staff Report to The Committee on Oversight and Investigations. Ukens, C., & Rosendahl, I. (1994). Stingy reimbursement is top headache for everyone. Drug Topics, 140, 87–88. Zeuthen, F. (1930). Problems of monopoly and economic welfare (1st ed.). London: G. Routledge.

PATENTS, INNOVATION, AND THE WELFARE EFFECTS OF MEDICARE PART D Adam Gailey, Darius Lakdawalla and Neeraj Sood ABSTRACT Purpose – To evaluate the efficiency consequences of the Medicare Part D program. Methods – We develop and empirically calibrate a simple theoretical model to examine the static and the dynamic welfare effects of Medicare Part D. Findings – We show that Medicare Part D can simultaneously reduce static deadweight loss from monopoly pricing of drugs and improve incentives for innovation. We estimate that even after excluding the insurance value of the program, the welfare gain of Medicare Part D roughly equals its social costs. The program generates $5.11 billion of annual static deadweight loss reduction and at least $3.0 billion of annual value from extra innovation. Implications – Medicare Part D and other public prescription drug programs can be welfare-improving, even for risk-neutral and purely self-interested consumers. Furthermore, negotiation for lower branded drug prices may further increase the social return to the program. Pharmaceutical Markets and Insurance Worldwide Advances in Health Economics and Health Services Research, Volume 22, 317–344 Copyright r 2010 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 0731-2199/doi:10.1108/S0731-2199(2010)0000022017

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Originality – This study demonstrates that pure efficiency motives, which do not even surface in the policy debate over Medicare Part D, can nearly justify the program on their own merits.

1. INTRODUCTION Patents encourage innovation by awarding inefficient monopoly power to inventors. This leads to the familiar trade-off between inducing innovation and ensuring the efficient utilization of invented goods. Public prescription drug insurance provides a way out of this dilemma, because it helps decouple the price consumers pay from the price innovators receive. By subsidizing coinsurance for drugs, public insurance encourages utilization, but without necessarily compromising innovators’ prices, profits, and incentives for research. As such, public insurance can simultaneously promote static and dynamic efficiency, which are often at odds (Lakdawalla & Sood, 2009). The rationale for public drug insurance is typically thought to rely on the social value of insuring against financial risk and on the value of care provided to the less affluent. However, its static and dynamic efficiency effects imply that public drug insurance is valuable to risk-neutral, selfinterested consumers. We demonstrate that this point has a considerable impact on the welfare analysis of Medicare Part D. Even though Part D is not a particularly generous insurance plan, we find that it generates an annual value of $5.1 billion in static deadweight loss reduction and $3.0 billion in additional innovation. The total value of $8.1 billion is approximately 95% of Part D’s social cost. Part D nearly breaks even ignoring the insurance and altruism aspects of the program typically thought to generate its entire social value. We begin with some relevant background material and then present our analysis in three parts. Section 3 presents a simple theoretical model that demonstrates the welfare effects of Medicare Part D, as well as the approach to calculating them. Section 4 uses the theoretical model to quantify the welfare effects of the program. Section 5 analyzes the welfare effects of some potential policy changes.

2. THE MEDICARE PART D PROGRAM Medicare Part D subsidizes the costs of prescription drugs for Medicare beneficiaries and was introduced by the passage of the Medicare

Patents, Innovation, and the Welfare Effects of Medicare Part D

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Prescription Drug, Improvement, and Modernization Act of 2003 (MMA). Beneficiaries can obtain the Medicare Drug benefit through two types of private plans: beneficiaries can join a prescription drug plan (PDP) for drug coverage only, or they can join a Medicare advantage (MA) plan that covers prescription drugs (MA-PD). Beneficiaries are required to make premium payments to obtain Part D coverage. However, premiums are highly subsidized. Medicare Part D covers roughly 75% of the costs (Kaiser Family Foundation, 2007, 2008a). Medicare Part D establishes a standard drug benefit that Part D plans may offer. The standard benefit is defined in terms of the benefit structure and not in terms of the drugs that must be covered. In 2007, this standard benefit requires payment of a $265 deductible. The beneficiary then pays 25% of the cost of a covered Part D prescription drug up to an initial coverage limit of $2,400. Once the initial coverage limit is reached, the beneficiary is subject to another deductible, commonly known as the ‘‘Donut Hole,’’ in which they must pay the full cost of drugs. When total out-of-pocket expenses on formulary drugs for the year, including the deductible and initial coinsurance, reach $3,850, the beneficiary then reaches catastrophic coverage, in which he or she pays a 5% coinsurance. In practice, Part D plans might deviate from this standard benefit, but they must offer coverage that is equivalent to or better than the standard benefit in actuarial terms. The law also stipulates that employers sponsoring prescription drug coverage for retirees can receive a federal subsidy if the coverage is at least actuarially equivalent to the standard Medicare drug benefit. Employers would receive a 28% subsidy to their portion of the individual retiree’s drug costs between $250 and $5,000. Finally, Medicare Part D also provides more generous insurance and additional subsidies to low-income beneficiaries. Currently, dual-eligible (eligible for both Medicare and Medicaid) beneficiaries constitute the majority of the beneficiaries receiving low income subsidies as they are automatically enrolled in Part D plans (Kaiser Family Foundation, 2008a, 2008b). One of the most controversial features of the MMA was it did not allow Medicare to negotiate prices directly with pharmaceutical companies. Many critics regard this as poor stewardship of tax dollars, while those in favor argue that price negotiation could dampen innovation incentives by lowering pharmaceutical profits. While this study does not resolve this controversy, the absence of price negotiation is relevant insofar as it exerts upward pressure on manufacturer prices, which in turn affects dynamic incentives to innovate.

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3. THE WELFARE EFFECTS OF STAND-ALONE PUBLIC DRUG INSURANCE In earlier theoretical work, we analyzed more generally the welfare effects of public prescription drug insurance (Lakdawalla & Sood, 2009). In this section, we provide a simpler and more concrete analysis applying these results to the case of Part D, which has static and dynamic effects on welfare. Public prescription drug insurance has the potential to lower deadweight loss and raise monopoly profits simultaneously. It can achieve this outcome by partially decoupling the consumer’s price from the revenue earned by the monopolist. 3.1. Static Implications The provision of drug insurance can reduce deadweight loss, because copayments below the monopoly price increase utilization by consumers. Define D(p) as the demand function, P(Q) as inverse demand, MC as the constant marginal cost of production, and pm as the equilibrium monopoly price. The social surplus generated by competitive provision of the good is given by Z DðMCÞ PðQÞdQ MC DðMCÞ (1) SS c 0

In the absence of insurance, deadweight loss in the branded pharmaceutical market is social surplus under competition, minus social surplus under monopoly: Z Dðpm Þ PðQÞdQ MC Dðpm Þ (2) DWL SS c 0

Now suppose that the government offers prescription drug insurance. Specifically, suppose the government covers the share (1s) of the market price and leaves the consumer with the coinsurance rate s. If the government continues to pay the monopoly price for pharmaceuticals, the actuarial cost of the insurance is pm ð1 sÞDðspm Þ. From a purely static point of view, this cost is simply a transfer from the government to the pharmaceutical industry; later, we incorporate the deadweight cost of public funds, which is so far absent. The welfare effects emerge from the change in quantity induced by this policy.

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Of course, Part D could impact the monopoly price charged for pharmaceuticals. The monopolists may exploit the subsidy to consumers and choose to raise prices. In earlier theoretical work, we characterize the class of benefit designs that ensure efficiency gains from public prescription drug insurance even if the monopolist raises prices in response to drug coverage (Lakdawalla & Sood, 2009). Another possibility is that the government or private plans participating in the benefit may use their newfound monopsony power to negotiate prices downward (Duggan & Morton, 2008). For now, we analyze the welfare effects of the program, given some arbitrary post-Part D monopoly price, p0m . In Section 4, we calibrate the welfare effects of Part D under alternate assumptions about the price charged by manufacturers. Deadweight loss is a function of what the public program pays monopolists and offers consumers in terms of coinsurance. This relationship can be expressed as follows: DWLðp0m ; sÞ

Z

DðMCÞ

PðQÞdQ MC DðMCÞ

0

Z

Dðsp0m Þ

PðQÞdQ MC

0

(3)

Dðsp0m Þ

Ultimately, what matters for deadweight loss is simply sp0m , the price faced by consumers. The welfare effect of lowering the consumer price – either by lowering the coinsurance or by lowering the price paid to monopolists – is given by the following:

dDWL jp0m ;s ¼ D0 ðsp0m Þ sp0m MC 0 dspm

(4)

From a static point of view, lowering the price paid by consumers always lowers deadweight loss, as long as sp0m 4MC, or that consumers continue to face a price that is at least as large as marginal cost. Empirically, this assumption seems to hold for Medicare Part D. Marginal cost is typically estimated to be 20% of the branded drug price (Caves, Whinston, & Hurwitz, 1991; Grabowski & Vernon, 1992; Berndt, Cockburn, & Griliches, 1996). In contrast, the average coinsurance rate under Medicare Part D is currently about 62%, well above marginal cost.1

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3.2. Dynamic Implications The original intent of Medicare Part D was to provide drug insurance without affecting prices paid to innovators. Earlier, we showed that drug insurance improves static welfare by lowering deadweight loss. We now show that this original aspect of Part D induces more innovation and increases dynamic social surplus. Let I denote industry investment in research, and let g(I) denote the probability of discovery with g0 ðIÞ40 and g00 ðIÞo0. In other words, R&D investment raises the probability of new drug discovery, but in a concave fashion. Suppose the innovator enjoys a patent monopoly for T periods after the discovery and will make zero profits thereafter. If the firm discounts the future at the rate r, it invests in research to maximize the present value of expected profits: Z T rt e pðs; pm Þdt I (5) PðIÞ ¼ gðIÞ 0

By the envelope theorem, stand-alone drug insurance raises the expected profits of innovators, because (dp/ds)o0, and will also induce more innovation. The privately optimal level of innovation is given by the following: g0 ðIÞ ¼ hR

1

T 0

1 i m P ðs; pm ; TÞ ert pðs; pm Þdt

(6)

The marginal product of research is the reciprocal of monopoly profits, Pm and, by extension, of patent length (Nordhaus, 1969). Therefore, because reductions in coinsurance raise profits, they must also stimulate innovation. Define I pat ðPm Þ as the level of investment induced by monopoly profits m P . Expected social surplus can be written as follows: SðT; pm ; sÞ gðI pat ðPm ÞÞ

Z

1

ert SSc dt 0

Z

T

ert DWLðspm Þdt I pat ðPm Þ (7)

0

The marginal value of introducing stand-alone drug insurance is given by the following: Z 1

Z T m 0 rt rt e SS c dt e DWLðpm Þdt 1 Ss js¼1 ¼ I p Ps g ðIÞ 0 0 (8) Z T ert DWL0 ðpm Þpm dt o0

þg 0


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The term in curly braces is strictly greater than unity, because total social surplus from the innovation must be strictly larger than the innovator’s profits.2 Therefore, the first term is negative. This measures the value of drug insurance as a stimulant to innovation. The second term is negative, because pm 4MC, implying that deadweight loss will rise with a higher price. This is the value of insurance in mitigating deadweight loss. Notice the important presumption that the profits of innovators do not exceed social surplus. Clearly, this condition always holds in a completely private market, even one afflicted by moral hazard in insurance provision (Lakdawalla & Sood, 2006). Intuitively, consumers would never voluntarily pay more than their consumer surplus for a drug in a spot market, and they would never pay more for an insurance policy than the expected value of its covered treatments. Public subsidies for employerprovided health insurance make it theoretically possible that profits could exceed social surplus. However, given the estimated rate of surplus appropriation by innovators (see Section 4.3.4), this would require extremely large transfers. It is even less likely among the elderly population, where prescription drug insurance was relatively uncommon.3 Another more controversial question concerns whether there is currently too much or too little innovation, or equivalently, how much innovators ought to be able to appropriate. In the standard model, innovators ought to appropriate the full value of social surplus, which is impossible in the absence of price discrimination. Some economists have pointed out that patent races, public subsidies, and other imperfections can alter this result, sometimes substantially. Others have emphasized the extremely low rates of social-surplus appropriation by innovators.4 Resolving this controversial question lies beyond the scope of this chapter, but we can interpret our analytical results, regardless of whether innovation is too high or too low. If, as in the standard case, innovation is too low, Part D has a direct welfare benefit, without any auxiliary provisions. This is the analysis presented above. If in fact there is too much innovation, the direct effects of Part D on innovation reduce social welfare. However, this adds additional value to price negotiation or similar measures to limit, or even reduce, the profits of innovators. In this case, a Part D program coupled with price reductions that hold innovator profits constant would be strictly welfare-improving. The rest of the analysis is presented from the point of view of the standard model, that there is too little innovation; the possibility of ‘‘over-innovation’’ is discussed further in the sensitivity analyses of Section 4.

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3.3. Public Financing and Deadweight Cost In the analysis above, we abstracted from the costs of public financing. When prescription drug insurance is wholly or partly financed by the public sector, the last component of the welfare calculation must be the deadweight cost from public, rather than private, financing.

4. CALIBRATING THE WELFARE EFFECTS In this section, we calculate – in a ‘‘back-of-the-envelope’’ fashion – the welfare impacts of Medicare Part D. We consider both the static and dynamic benefits of increased drug consumption and the associated increase in pharmaceutical innovation induced by Medicare Part D. We also estimate the social costs of financing this benefit due to deadweight loss from increased taxation. We exclude dual eligibles from the analysis, because they already receive generous public prescription drug insurance from Medicaid. The introduction of Medicare Part D does not substantially change the generosity of insurance for dual eligibles; it merely transfers insurance from Medicaid to Medicare Part D. The net benefits of Part D can be calculated according to the following equation: NB ¼ Enrollees ðPC static benefitÞ þ ðDynamic benefitÞ ðDeadweight costÞ

ð9Þ

In words, the net benefit of the program is equal to the number of enrollees multiplied by the per capita static benefits to those enrollees, plus the dynamic benefit of the program, minus the deadweight costs of financing. The first term embodies the utilization effects on enrollees, the second the innovation effects, and the third the social cost of funding premiums and employer subsidies. A more detailed exposition of this calibration is provided below. 4.1. Part D Enrollees and Non-Enrollees We need to estimate both the number of enrollees and the number of nonenrollees eligible for the employer subsidy payment. The latter group affects the financial cost of the program, if not the static welfare benefit. We used data from the 2005 Medical Expenditure Panel Survey (MEPS) and the


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formularies from the top five Medicare Part D insurance plans to estimate these quantities. 4.1.1. Methods The Medicare Modernization Act classifies an individual’s private prescription drug coverage as ‘‘creditable’’ or ‘‘non-creditable.’’ Broadly speaking, ‘‘creditable coverage’’ is private insurance that is actuarially at least as generous as Medicare Part D. Individuals without creditable coverage are required to enroll in Part D within 63 days of being eligible or face a late enrollment penalty if and when they enroll. Those with creditable coverage are not subject to this penalty provision. Moreover, if a firm provides a Part D-eligible employee with creditable coverage, that firm is entitled to a 28% subsidy on their portion of the individual retiree’s drug costs between $250 and $5,000. In our calculations, we assume that there are no costs of switching to Part D. Therefore, everyone without creditable coverage (i.e., less generous than Part D) switches to the program. We also assume that people with creditable coverage refuse to switch.5 Evidently, therefore, we need to identify individuals with and without ‘‘creditable coverage.’’ To do so, we use actual prescription drug expenditures observed in the MEPS6 and calculate the average coinsurance rate generated by the individual’s current plan (or lack thereof) and the lowest cost option among the top five Medicare Part D plans. Those whose average coinsurance rate is lower under Part D are classified as having no creditable coverage and vice versa. Appendix presents a formal proof that the average coinsurance rate is a sufficient predictor of value for an insurance plan, even one with a non-linear benefit design. 4.1.2. Estimates Using the MEPS sampling weights we estimate that, excluding dual eligibles, 36 million beneficiaries would be eligible for Medicare Part D. Next, for each MEPS respondent, we then calculate the average coinsurance for prescription drugs under two scenarios: (1) their current coverage; or (2) enrollment in most generous among the top five Part D plans, described earlier. Based on this analysis, we identify individuals with and without creditable coverage. We estimate that 61% of eligible respondents, or roughly 22 million beneficiaries, have no creditable coverage. We assume all these individuals enroll in Part D. The remaining 14 million already have more generous insurance compared to the standard Part D benefit and are assumed not to enroll in Part D. Based on information about the source of coverage in MEPS, we estimate that roughly 57% of those with creditable insurance have it

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Table 1.

Prescription Drug Coverage Sources among Medicare Beneficiaries. HHS Estimates

MEPS Estimates

Population (million) Percent Population (million) Percent Coverage type Enrolled/no creditable coverage Creditable employer coverage Others with creditable coverage

21 10 5

59 28 13

22 8 6

61 21 18

Total

36

100

36

100

through an employer or union, which would then receive the employer subsidy instituted by Medicare Part D. The remaining 43% of those with creditable insurance have it from other sources such as the Veterans Administration, Indian Health Service, and state pharmaceutical assistance programs. The employer subsidy is not paid in these cases. Our estimates are presented in Table 1. As a validity check, we compared our enrollment estimates to the actual enrollment rates reported by the Department of Health and Human Services (HHS), also shown in Table 1. The HHS estimates show that, as of January 2007, and excluding dual eligibles, 36 million beneficiaries were eligible for Medicare Part D. Of these, 21 million were estimated to have no creditable coverage before Part D.7 The remaining 15 million had creditable coverage from employer/union or from other sources such as Veterans Administration, Indian Health Service, and state pharmaceutical assistance programs (Kaiser Family Foundation, 2007). These numbers are quite similar to the estimates we derived from analysis of the MEPS. 4.2. Static Benefits The next step is to compute the static benefit enjoyed by enrollees in Part D. For both static and dynamic benefits, prices and expenditures are for branded drugs only, due to the lack of monopoly power in generic drugs. Using a linear approximation to demand, the benefit associated with a particular change in price and quantity for branded drugs is simply the reduction in the size of deadweight loss ‘‘triangle,’’ or 1 1 pnd mc qnd qc abs ðpd mcÞðqd qc Þ 2 2


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In this expression, pnd is the price of drugs without Part D, mc is the marginal cost of producing drugs, pd is the price of drugs under Part D, qc is the quantity of drugs consumed under perfect competition, qnd is the quantity of drugs without Part D, and qd is the quantity of drugs consumed with Part D. Overall, this yields the new level of the deadweight loss triangle subtracted from the original level of deadweight loss. This benefit can be equivalently written using the anticipated percentage changes in price and quantity, along with the elasticity of demand. Appendix derives the formal conditions under which the anticipated percentage change in coinsurance rate and elasticity of demand are sufficient predictors of the change in utilization, even with a non-linear benefit design. Assuming that the price paid to the manufacturer does not change, some simple algebra yields the equivalent formulation of the static benefit for an enrolling consumer:8 1 SB ¼ ðsND MCÞðExpendituresc ExpendituresND Þ 2 1 abs ðsD MCÞ 2 ðsND sD Þ e þ 1 ðExpendituresND Þ ðExpendiutresc Þ ð10Þ sND The terms sD and sND are the average share of price paid by the consumer with and without Part D, respectively. ExpendituresND measures expenditures under the status quo; Expendituresc measures them under perfect competition. The term e is the elasticity of demand. To calculate the static welfare impact of Medicare Part D, we need empirical estimates of (1) the percentage change in branded drug price to the consumer induced by Medicare Part D, (2) the elasticity of demand for prescription drugs, (3) the expenditures on branded prescription drugs, (4) the marginal cost of production, and (5) expenditures under perfect competition. 4.2.1. Percentage Change in Price We calculate the percentage price change that enrolling consumers would enjoy if they took up the program by computing – for each elderly consumer in MEPS – the difference in average coinsurance between (1) status quo insurance and (2) the most generous of the top five Part D insurance plans. We calculate this percentage change in price for each MEPS respondent estimated not to have creditable coverage. Respondents with creditable coverage are assumed not to enroll in Part D and thus experience no change

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in price. Based on these calculations, the average percentage change in coinsurance for branded drugs for those without creditable coverage due to the standard Medicare Part D benefit was estimated to be 25%. 4.2.2. Price Elasticity of Demand Long-run generic prices (assumed to be equal to marginal cost) are approximately 20% of the prices charged for the corresponding on-patent drug (Caves et al., 1991; Grabowski & Vernon, 1992; Berndt et al., 1996). Thus, we assume that the monopoly mark-up over marginal cost is 400%. The standard theory of monopoly would then imply a price elasticity of uninsured demand around 1.25 or 1/(10.2). However, the above elasticity is not the relevant one for insured patients who face copayments, rather than manufacturer prices (Chandra, Gruber, & McKnight, 2007). Thus, for the insured consumers, we use elasticity estimates that rely on changes in patient cost sharing among the insured elderly population. For example, Chandra et al. (2007) estimate the price elasticity of prescription drugs among the elderly by studying a policy change that raised patient cost sharing for retired public employees in California. Their estimates of price elasticity range from 0.5 to 1.5; these elasticities do not vary significantly by age, income, or health status. We take the midpoint of their range and assume that the price elasticity is 1.0 among the insured elderly population. Based on these estimates of the elasticity of demand and percentage change in price, the average percentage change in number of prescriptions for those without creditable coverage was estimated to be 29%. 4.2.3. Out-of-Pocket Costs The out-of-pocket costs of purchasing branded drugs are available directly from MEPS. For those without creditable coverage, expenditures are estimated to be $1,190. 4.2.4. Results Based on these estimates, we estimate the aggregate static benefit of Medicare Part D to be $5.11 billion or $236 per eligible beneficiary. There is wide variation in the per capita benefit enjoyed by beneficiaries depending on insurance coverage, or lack thereof, before the introduction of Medicare Part D. As private insurance coverage is highly correlated with income, the poor enjoy greater benefits than the rich. For example, we estimate the per capita benefits to be $270 for those with incomes less than $15,000 per year, $207 for those with income between 15,000 and 50,000, and $222 for those with incomes greater than $50,000. Similarly, beneficiaries in poor


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self-reported health and those with higher prescription drug costs also enjoy greater benefits. Those reporting their general health to be ‘‘poor’’ enjoy per capita benefits of $394, those reporting ‘‘good’’ health enjoy per capita benefits of $247, and those reporting ‘‘excellent’’ health only receive $157 in per capita benefits.

4.3. Dynamic Benefits As Medicare Part D likely increases pharmaceutical company profits, it has the dynamic benefit of inducing additional innovation. We can estimate the value of this induced innovation just as we estimated the static value of the program.9 First, we maintain the assumption (inherent in the original MMA legislation) that Part D continues to forbid price negotiation and that pharmaceutical firms will continue to receive the monopoly prices set before Medicare Part D (pm). However, firms do experience an increase in demand for their products due to the reduction in price for consumers after the introduction of Medicare Part D. 4.3.1. Step 1: Change in Pharmaceutical Revenues For a given consumer, the percentage change in total drug expenditures is equal to the percentage increase in the quantity of drugs consumed, ((sNDsD)/sND)e, which is calculated as above. Assuming manufacturer prices have so far been unaffected by Part D, the percentage change in quantity is equal to the percentage change in revenues for innovators. The average percentage change in price for all eligible beneficiaries was estimated to be 25%. This estimate combined with the elasticity estimates, also discussed earlier, implies an increase in drug expenditures of $9 billion, or 6% of branded revenues. Duggan and Morton (2008) find an increase of 33% for branded pharmaceutical revenues for drugs with 100% Medicare market share. Translated using MEPS estimates of the share of branded revenues from Medicare Part D recipients (19.7%), this would be a 6.5% increase in the revenue of branded drugs overall. Our estimate is thus quite close to, if slightly smaller than, the Duggan and Morton estimate. Lichtenberg and Sun (2007) find an increase in expenditures from Medicare part D of $9 billion (5.5% of branded drug revenues) using a differences in differences approach utilizing data from a retail pharmacy chain. Yin et al. (2008) also find smaller effects on utilization than in our baseline, again using data from a pharmacy chain, they find a reduction in price of 17% (as compared with

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25% in our estimation). Our estimation may be higher than these two estimates, because we assign enrollees to their best plan, thereby giving them the lowest possible price. 4.3.2. Step 2: Creation of New Chemical Entities The increase in pharmaceutical revenue will induce more R&D and innovation. The number of new drug introductions induced by Part D will depend on the elasticity of new drug introductions with respect to pharmaceutical revenues. Acemoglu and Linn (2004) estimate that the elasticity of non-generic drug approvals with respect to revenues is roughly 3.5. We use this elasticity and the estimate of change in pharmaceutical revenues to calculate the percentage change in the number of new chemical entities (NCEs). This estimate is consistent with Blume-Kohout and Sood (2009) who estimate the effects of Medicare Part D on pharmaceutical R&D. The baseline rate of NCE introduction is assumed to be 32 NCEs per year. This is the average number of NCEs introduced per year during the period 1995–2004 as reported in the FDA Orange Book. Applying the estimated percentage change to this baseline level yields the absolute number of new drugs projected from the passage of Part D. 4.3.3. Step 3: Innovator’s Private Value of New Chemical Entities The next step is to compute the annual private value of these additional drugs to their innovators. In general, it is quite difficult to compute the expected value of the marginal drug directly, because it is hard to identify the marginal drug and just as hard to identify expected value. However, it is easier to calculate the actual marginal cost of bringing an additional drug to market. Theory suggests that this marginal cost ought to be equal to the expected marginal private value of an additional drug; Grabowski, Vernon, and DiMasi (2002) provide empirical evidence that the theory is consistent with the data in the pharmaceutical industry. DiMasi, Hansen, and Grabowski (2003) estimate that the marginal research and development cost of bringing an NCE to market is $939 million in 2005 dollars. To annualize this cost, we use a standard empirical estimate of the annual cost of capital in the pharmaceutical industry, of 12% per year, calculated from annual estimated costs of capital in the pharmaceutical industry (http:// pages.stern.nyu.edu/Badamodar/). This estimated cost is similar to estimates of private rates of return on R&D investments in the pharmaceutical industry (Grabowski et al., 2002). Therefore, the annualized marginal cost of bringing an NCE to market is expected to be (0.12) ($939


331

million) ¼ $113 million. This then yields our estimate of the annualized marginal expected private benefit.

4.3.4. Step 4: Marginal Social Value of New Chemical Entities The last step is to infer the marginal social value, which theory predicts will exceed the private value to the innovator (although see the caveat in note 2). To estimate the social return, we must estimate the fraction of social surplus captured by the innovator. Several estimates are available from the literature. Based on data from 1948 to 2001, for example, Nordhaus (2004) estimates that innovators capture just 2.2% of the total present value of social returns to innovation. In a pharmaceutical context, Philipson and Jena (2006) use data from over 200 published studies of healthcare innovations to estimate the distribution of surplus appropriation by innovators. They find that the median producer share of social surplus is 17%, the first quartile is roughly 10% and the third quartile is roughly 25%. To be conservative, we assume that innovators are able to capture as much as a quarter of the social surplus from pharmaceutical innovation. This parameter yields an estimated social rate of return on pharmaceutical R&D investments of 48% per year, four times the estimated private return. This suggests that the annual social value of the marginal drug is equal to ($113 million)/(0.25) ¼ $452 million. This approach presumes that innovators should capture the full social value of their inventions. As discussed in Section 3, there exists a great deal of controversy about this issue. The impact of this assumption on our results is discussed in our sensitivity analysis, later in this section.

4.3.5. Gross Static and Dynamic Benefit of Part D Using the methods described in Step 1, we estimate that Medicare Part D would increase pharmaceutical sales by $9 billion per year. Given baseline branded pharmaceutical sales of $162 billion in the United States in 2005 (as estimated in MEPS), this corresponds to a 6% increase. The 3.5 innovation elasticity from Acemoglu and Linn (2004) then implies that the number of new drugs per year would increase by roughly 21%, or 6.6 NCEs. Earlier, we calculated an annual social value of $452 million for the marginal additional drug, which yields a gross dynamic benefit of $3.0 billion annually. Combining the dynamic with the static benefit yields a gross risk-neutral welfare benefit for Medicare Part D of $8.1 billion annually.

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4.4. Deadweight Costs of Financing Medicare Part D It remains to compare the aggregate benefits of Part D with its social cost. The program itself is just a costless transfer. However, because it is publicly financed, there are deadweight costs associated with its financing. The actuarial cost of the Medicare Part D insurance for a beneficiary who enrolls in the standard Part D plan is simply as follows:

ðsND sD Þ e s Total_Expð1 sD Þ þ ð1 sD ÞTotal_Exp sND

(11)

where s is the subsidy provided by Medicare Part D and is estimated to be 75%. The first term in the square brackets is the actuarial cost of the benefit under the initial demand for pharmaceuticals and is simply the total cost of prescription drugs times the plan share of costs. The second term is the actuarial cost of the additional demand induced by Medicare Part D and is equal to the change in total drug costs times the plan share of costs. As discussed earlier, the percentage change in total drug costs, ((sNDsD)/ sND)e, is simply the percentage change in price to consumers induced by Part D times the elasticity of demand. All the above quantities can be easily estimated from available data. Using data from the MEPS, we estimate average costs of covered drugs (both branded and generic) for MEPS respondents without creditable coverage who are likely to enroll in Medicare Part D to be $1,520. As discussed earlier, we can also calculate the price change consumers would enjoy if they took up the program; price elasticities of demand for pharmaceuticals (taken from the literature) then imply the associated increase in drug costs. Similarly, the costs of providing the employer subsidy (28% of costs between $250 and $5,000) can be estimated easily using data on prescription drug expenditures for those with creditable employer/union-provided insurance. The results show that for those receiving the premium subsidy, Medicare costs are $1,109 per enrollee and for those receiving the employer subsidy, Medicare costs are $546 per enrollee. The true social cost of the program is the deadweight cost associated with paying the actuarial cost out of public funds. While there is debate in the public finance literature on the magnitude of deadweight loss, we use a conventional estimate – that each additional dollar spent on Medicare Part D generates 30 cents of deadweight costs due to increased taxation (Jorgenson & Yun, 2001).


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Based on these estimates, we estimate the deadweight costs of financing Medicare Part D to be $8.5 billion per year, 95% of which is covered by the risk-neutral benefits of the program. This analysis reveals that conventional estimates of demand, dynamic benefit, and deadweight loss yield the surprising result that Part D insurance is nearly ‘‘break-even’’ for a society of risk-neutral and self-interested consumers. The baseline estimates for static and dynamic benefits, as well as deadweight costs, are summarized in Table 2. Table 2.

Baseline Estimates of Benefits and Costs of Medicare Part D.

Description

Value

Plan participation No creditable coverage (millions of people) Creditable employer coverage (millions of people) Other creditable coverage (millions of people) Total (millions of people)

21.7 7.7 6.4 35.8

Static benefit Change in price for enrollees (%) Change in quantity for enrollees (%) Branded OOP expenditures – Enrollees pre-Part D (dollars) Branded drug expenditures – Enrollees pre-Part D (dollars) Static benefit per enrollee (dollars) Total static benefit ($ billions)

25 29 958 1,190 236 5.11

Dynamic benefit Change in branded drug revenues ($ billions) Change in branded drug revenues (%) Change in number of new drugs (%) Number of new drugs Private return per new drug ($ millions) Social return per new drug ($ millions) Total dynamic benefit ($ billions)

9.42 5.81 21 6.58 113 451 2.97

Social cost Average covered drug expenditures for enrollees pre-Part D ($) Change in quantity for enrollees (%) Actuarial cost of Part D benefit ($ billions) Costs of employer subsidy ($ billions) Total costs of Part D insurance and employer subsidy ($ billions) Deadweight costs of Part D insurance and employer subsidy ($ billions)

1,520 61 24.06 4.19 28.25 8.48

Ratio of social benefit to cost of Medicare Part D

0.95

Notes: OOP, out-of-pocket. Baseline estimate of the welfare benefits and costs of Medicare Part D using 2005 MEPS data.

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4.5. Sensitivity Analysis

Ratio of Benefits to Costs

We perform two sets of sensitivity analyses. Fig. 1 shows the effect of changing the assumption on the elasticity of innovation. This assumption only has an effect on the dynamic benefit, not on static benefit or social cost. Increasing the elasticity of innovation proportionately increases the number of new drugs for a given change in revenues and through this mechanism the dynamic benefit. The estimates show that even if we assume a very conservative elasticity of innovation of 1, the benefits of Part D are sizeable and would cover 70% of the social costs of the program. However, note that if the optimal reward for innovation is lower than social surplus, increases in manufacturer revenues may even be welfare-reducing from a dynamic point of view. In this case, a program that holds manufacturer revenues fixed – for instance, through government price negotiation – while reducing consumer copays, would be optimal (Lakdawalla & Sood, 2009). In Table 3, we perform a sensitivity analysis on the elasticity of demand for drugs for the insured. We estimate the social benefits and costs of the program using estimates of the demand elasticity for the insured from 0.2

1.20 1.00 0.80 0.60 0.40 0.20 0.00 1

1.5

2

2.5

3

3.54

4

4.5

5

Elasticity of Innovation

Fig. 1. Sensitivity to Elasticity of Innovation. Notes: Estimation of the sensitivity of baseline estimates of the welfare effects of Medicare Part D to changes in the elasticity of innovation using 2005 MEPS data. The elasticity of innovation has no effect on present consumption of drugs and thus has no effect on the static benefit or social cost of the program. The elasticity of innovation changes the number of new drugs associated with a particular change in branded drug revenues and through this the dynamic benefit. The value 3.54 (from Acemoglu & Linn, 2004) represents our baseline scenario.

335


Table 3.

Sensitivity to Demand Elasticity.

Demand Elasticity Plan participation No creditable coverage (millions of people) Static benefit Change in price for enrollees (%) Change in quantity for enrollees (%) Branded OOP expenditures – Enrollees pre-Part D (dollars) Branded drug expenditures – Enrollees pre-Part D (dollars) Static benefit per enrollee (dollars) Total static benefit ($ billions) Dynamic benefit Change in branded drug revenues ($ billions) Change in branded drug revenues (%) Change in number of new drugs (%) Number of new drugs Private return per new drug ($ millions) Social return per new drug ($ millions) Total dynamic benefit ($ billions) Social cost Average covered drug expenditures for enrollees pre-Part D ($) Change in quantity for enrollees (%) Actuarial cost of Part D benefit ($ billions) Costs of employer subsidy ($ billions) Total costs of Part D insurance and employer subsidy ($ billions) Deadweight costs of Part D insurance and employer subsidy ($ billions) Ratio of social benefit to cost of Medicare Part D

0.20

0.50

0.75

1.00

1.25

1.50

22

22

22

22

22

22

25 21 958

25 24 958

25 26 958

25 29 958

25 31 958

25 33 958

1,190

1,190

1,190

1,190

1,190

1,190

224 4.86

228 4.95

232 5.03

236 5.11

240 5.19

243 5.27

5.51

6.98

8.20

9.42

10.64

11.85

3.40 12 3.85 113

4.31 15 4.88 113

5.06 18 5.73 113

5.81 21 6.58 113

6.56 23 7.44 113

7.32 26 8.29 113

451 1.74

451 2.20

451 2.58

451 2.97

451 3.35

451 3.74

1,520

1,520

1,520

1,520

1,520

1,520

32 19.66

43 21.31

52 22.69

61 24.06

70 25.44

79 26.81

4.19 23.85

4.19 25.50

4.19 26.88

4.19 28.25

4.19 29.63

4.19 31.00

7.16

7.65

8.06

8.48

8.89

9.30

0.92

0.93

0.94

0.95

0.96

0.97

Notes: OOP, out-of-pocket. Estimation of the sensitivity of baseline estimates of the welfare effects of Medicare Part D to changes in the elasticity of demand for the insured using 2005 MEPS data. The elasticity of demand affects the change in drugs consumed for the price change given in Medicare Part D. This change in consumption alters the static benefit, dynamic benefit, and social cost. The value 1.00 represents the baseline scenario.

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to 1.5, a slightly wider range than estimated by Goldman, Joyce, and Zheng (2007) and Chandra et al. (2007). Reducing the elasticity of demand reduces the effect of Medicare Part D’s change on prices on the quantity of drugs consumed. For example, reducing the elasticity of demand from 1.25 to 0.2 reduces the percentage change in drugs from 31% to 21%. This change is smaller than one might expect because only the elasticity of insured users is altered, the elasticity for the uninsured remains at 1.25. This in turn reduces the static benefit, dynamic benefit (through lower revenues) and program costs. The overall benefit of the program is relatively stable under the alternative parameter estimates, with changes in program costs largely off-setting changes in static and dynamic benefits.

5. POLICY SIMULATIONS The discussion above takes a very conservative view of the welfare benefits associated with Part D. It is useful to consider some commonly proposed Part D reforms from this perspective, which emphasizes just static deadweight loss and dynamic incentives to innovate. We consider reductions in consumer cost-sharing requirements, as well as changes in the prices paid to producers. The latter might occur if the government were to alter its stance on price negotiation by the Part D program.

5.1. Changes in Consumer Prices In Table 4, we consider a publicly financed reduction in copayment or coinsurance for Part D beneficiaries. We assume that the government mandates lower copayment or coinsurance rates and finances 100% of the marginal cost associated with this reduction. In this simulation, we reduce or increase the present formularies copays (or coinsurance depending on the formulary) by particular percentages. Thus, in the simulation where we change reduce copays by 50%, we would take a $5 copayment and make it $2.50. Similarly, if the coinsurance rate had previously been 25%, we make it 12.5% under this scenario. Then, using these adjusted formularies, we assign to each person their optimal plan. Overall, reducing consumer prices yields higher static and dynamic benefits. Consider reducing copays by 50%, as compared with the baseline estimate, which in this case is a change of 0%. This reduction in consumer price entices an additional 3 million people (25 million

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Table 4.

Simulation of Changes in Copayment or Coinsurance.

Percentage Change in Copayment or Coinsurance Plan participation No Creditable coverage (millions of people) Creditable employer coverage (millions of people) Other creditable coverage (millions of people) Total (millions of people) Static benefit Change in price for enrollees (%) Change in quantity for enrollees (%) Branded OOP expenditures – Enrollees pre-Part D (dollars) Branded drug expenditures – Enrollees pre-Part D (dollars) Static benefit per enrollee (dollars) Total static benefit ($ billions) Dynamic benefit Change in branded drug revenues ($ billions) Change in branded drug revenues (%) Change in number of new drugs (%) Number of new drugs Private return per new drug ($ millions) Social return per new drug ($ millions) Total dynamic benefit ($ billions)

75% 0.50% 0.25%

0%

25%

50%

75%

26

25

23

22

20

19

18

5

6

7

8

8

9

10

5

6

6

6

7

7

8

36

36

36

36

36

36

36

51 55

40 44

32 36

25 29

19 23

12 16

6 9

878

900

927

958

974

994

1,005

1,219

1,213

1,191

1,190

1,166

1,162

1,156

245 6.33

245 6.04

245 5.64

236 5.11

226 4.63

209 4.05

186 3.42

16.73

13.71

11.34

9.42

8.09

6.69

5.38

10.32

8.46

7.00

5.81

5.00

4.13

3.32

37

30

25

21

18

15

12

11.70 113

9.59 113

7.93 113

6.58 113

5.66 113

4.68 113

3.76 113

451

451

451

451

451

451

451

5.27

4.32

3.58

2.97

2.55

2.11

1.70

1,517

1,510

1,520

1,504

1,507

1,505

68

64

61

58

55

53

33.68

28.26

24.06

20.33

17.34

14.80

3.47

3.92

4.19

4.46

4.72

4.84

Social cost Average covered drug expenditures 1,516 for enrollees pre-Part D ($) Change in quantity for enrollees 74 (%) Actuarial cost of Part D benefit 39.56 ($ billions) Costs of employer subsidy 3.21 ($ billions)

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Table 4. (Continued ) Percentage Change in Copayment or Coinsurance

75% 0.50% 0.25%

Total costs of Part D insurance 42.77 and employer subsidy ($ billions) 12.83 Deadweight costs of Part D insurance and employer subsidy ($ billions) Ratio of social benefit to cost of Medicare Part D

0.90

0%

25%

50%

75%

37.15

32.18

28.25

24.79

22.06

19.63

11.15

9.66

8.48

7.44

6.62

5.89

0.93

0.95

0.95

0.97

0.93

0.87

Notes: OOP, out-of-pocket. Estimation of the effect of changing copays on the welfare effects of Medicare Part D using 2005 MEPS data. Reducing the copays for prescription drugs makes Medicare Part D more attractive to more people, increasing enrollment. It also reduces the prices people pay, increasing the quantity of drugs demanded. These two effects lead to changes in the static benefit, dynamic benefit, and social cost of the program. The value 0% represents the baseline scenario.

vs. 22 million) to enroll in Medicare Part D. Reducing copayment or coinsurance also results in a much larger average price change, of 40%, and a corresponding increase in the average change in quantity of 44%. Through increases in quantity and plan participation, reducing the copayment by 50% increases the static benefit from $5.1 to 6.0 billion. The same mechanism also increases the change in pharmaceutical revenues (from $9.4 billion to $13.7 billion), resulting in a change in the dynamic benefit of $1.35 billion ($4.3 billion–$3.0 billion). However, this reduction in price is not free. The social cost of the program (from increased usage and lower prices) increases from a baseline of $8.5 to $11.2 billion under this scenario. The benefit cost ratio was 95% under the baseline scenario and is only 93% when copays are reduced by 50%. Reductions in consumer prices of this form generally decrease the net social return of the program as the increase in social costs of financing the program more than outweigh the increase in static and dynamic benefits. The reason is that reducing copays also increases the use of generics that do not add to the static or dynamic benefits of the program.

5.2. Changing Producer Prices Finally, we simulate the effect of changing producer prices for branded drugs, and through this consumer prices (Table 5). As prices increase,

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Table 5.

Changes to Producer Branded Drug Prices.

Percent of Original Price Plan participation No creditable coverage (millions of people) Creditable employer coverage (millions of people) Other creditable coverage (millions of people) Total (millions of people) Static benefit Change in price for enrollees (%) Change in quantity for enrollees (%) Branded OOP expenditures – Enrollees pre-Part D (dollars) Branded drug expenditures – Enrollees pre-Part D (dollars) Static benefit per enrollee (dollars) Total static benefit ($ billions) Dynamic benefit Change in branded drug revenues ($ billions) Change in branded drug revenues (%) Change in number of new drugs (%) Number of new drugs Private return per new drug ($ millions) Social return per new drug ($ millions) Total dynamic benefit ($ billions) Social cost Average covered drug expenditures for enrollees pre-Part D ($) Change in quantity for enrollees (%) Actuarial cost of Part D benefit ($ billions) Costs of employer subsidy ($ billions) Total costs of Part D insurance and employer subsidy ($ billions) Deadweight costs of Part D insurance and employer subsidy ($ billions) Net social benefit of Medicare Part D

150%

125%

100%

75%

50%

18 10 8 36

20 9 7 36

22 8 6 36

24 6 6 36

26 5 5 36

11 14 930

18 21 950

25 29 958

34 38 952

44 48 927

1,071

1,134

1,190

1,269

1,351

159 2.92

198 3.94

236 5.11

271 6.45

284 7.45

8.58 5.29 19 6.00 113 451 2.70

8.95 5.52 20 6.26 113 451 2.82

9.42 5.81 21 6.58 113 451 2.97

11.13 6.87 24 7.78 113 451 3.51

14.64 9.04 32 10.24 113 451 4.62

1,876

1,713

1,520

1,309

1,061

55 19.58 5.00 24.57

57 21.83 4.67 26.50

61 24.06 4.19 28.25

65 26.41 3.49 29.91

70 27.45 2.17 29.62

7.37

7.95

8.48

8.97

8.89

0.76

0.85

0.95

1.11

1.36

Notes: OOP, out-of-pocket. Simulation of the effect of changing producer prices on the welfare effects of Medicare Part D using 2005 MEPS data. Under this scenario, changing producer prices changes consumer prices, enrollment, and drug revenues and thus the static and dynamic benefits, but this also changes the social cost of the program. The value 100% represents the baseline scenario.

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Medicare Part D becomes less attractive. A 25% increase in the price paid to manufacturers of drugs in Medicare Part D is associated with a reduction of 2 million enrollees (from 22 million to 20 million). Increasing prices paid to manufacturers also means that consumers pay higher prices when they are in the donut hole or when they face coinsurance. A 25% increase in price to the manufacturer implies that Medicare Part D only reduces prices for consumers by 18%, consequently increasing utilization by only 21%. This reduction in the quantity also changes the dynamic benefit. While there is a decrease in the quantity demanded, the price paid for these drugs increases, resulting in a small reduction in the dynamic benefit of $0.15 billion. Similarly, program costs remain largely unchanged (a change of $0.22 billion) as the increase in price is offset by reduced enrollment in the program. The net effect of increasing the producer price by 25% is a change in the benefit cost ratio from 95% to 85%. Similarly, were producer prices on brand-name drugs to decrease by 25%, the benefit-cost ratio would increase from 95% to 111%. This suggests that some level of price negotiation on behalf of Part D may be welfareenhancing.

6. CONCLUSION The static benefits of Part D could be considerable. While controversy exists surrounding the relative efficiency of dynamic investments in pharmaceutical innovation, it remains possible that the dynamic benefits are equally important. Regardless, our exercise demonstrates that the net social value of Part D extends well beyond the usual calculus of altruism and insurance that is typically associated with Medicare and other public health insurance schemes. In the design of the benefit, a great deal of attention was paid to traditional ‘‘insurance’’ issues of adverse selection and moral hazard, but less effort was devoted to understanding the risk-neutral efficiency effects on utilization and innovation. Our analysis suggests that these might be quite important. In particular, any evaluation of alternate benefit design or price negotiations with manufacturers should take into account its effect on deadweight loss from monopoly and innovation. At a minimum, the economic case for Part D appears quite a bit broader and more complex than it may initially appear.


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NOTES 1. This number was calculated by the authors using the Medical Expenditure Panel Survey (MEPS) data described later. Appendix presents a formal proof that the anticipated percentage change in coinsurance rate (along with the elasticity of demand) is a sufficient predictor of the change in utilization, even with a non-linear benefit design. 2. In theory, distortions like subsidies for health insurance could result in profits being higher than social surplus. However, we later document empirical evidence confirming social surplus is larger than profits. 3. As of 2003, 60% of the aged (65þ) population had no drug insurance or insurance that was less generous on average than the standard Part D benefit (based on authors’ calculations from the MEPS). 4. For contrasting views in the context of pharmaceuticals, see Garber, Jones, and Romer (2006), compared with Philipson and Jena (2006). In a broader context, see Shapiro (2007), compared with Nordhaus (2004). 5. This assumption has less transparent foundations. Clearly, people with very generous coverage fail to switch, but the effects for people with marginally more generous coverage are theoretically unclear. On the one hand, the subsidy of the Part D premium suggests that some may switch to Part D. On the other hand, employers could pass along the subsidy they receive for privately insuring a worker who is still employed, which may fully counteract the value of the premium subsidy. We adopt the simple assumption of no-switching, because it generates results that reasonably match actual enrollment data. 6. Notice that we calculate the effective coinsurance rate from current spending. This is equivalent to calculating the first-order welfare effect of switching to Part D. Individuals with a positive first-order welfare effect will benefit and vice versa. 7. Of the 21 million beneficiaries, 17 million enrolled in Part D and the remaining 4 million continued to have no creditable coverage. We assume that, over the long run, these remaining 4 million respondents will switch into the more generous coverage afforded by the Part D program. If not, this incomplete take-up rate would lower the welfare benefits of the program. 8. The baseline simulation assumes that prices do not change. The policy simulations section presented later considers the welfare effects of changing manufacturer prices. 9. We are proceeding under the standard assumption that there is too little innovation, because innovators cannot capture full social surplus. In our sensitivity analysis, we discuss how to interpret our results if in fact there is over-innovation in the status quo.

ACKNOWLEDGMENTS We are grateful to the National Institute on Aging for funding (1R01AG021940). For many helpful comments and suggestions, we thank seminar participants at the 2007 NBER Summer Institute Health Care session.

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REFERENCES Acemoglu, D., & Linn, J. (2004). Market size in innovation: Theory and evidence from the pharmaceutical industry. Quarterly Journal of Economics, 119(3), 1049–1090. Berndt, E. R., Cockburn, I. M., & Griliches, Z. (1996). Pharmaceutical innovations and market dynamics: Tracing effects on price indexes for antidepressant drugs. Brookings Papers on Economic Activity, pp. 133–88. Blume-Kohout, M., & Sood, N. (2009). Medicare Part D and Pharmaceutical R&D. Working Paper. RAND Corporation, Santa Monica, CA. Caves, R. E., Whinston, M. D., & Hurwitz, M. A. (1991). Patent expiration, entry, and competition in the U.S. pharmaceutical industry. Brookings Papers on Economic Activity, pp. 1–48. Chandra, A., Gruber, J., & McKnight, R. (2007). Patient cost-sharing, hospitalization offsets, and the design of optimal health insurance for the elderly. National Bureau of Economic Research Working Paper no. 12972, Cambridge, MA. DiMasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003). The price of innovation: New estimates of drug development costs. Journal of Health Economics, 22(2), 151–185. Duggan, M., & Morton, F. S. (2008). The effect of medicare part D on pharmaceutical prices and utilization. National Bureau of Economic Research Working Paper no. 13917. Cambridge, MA. Garber, A., Jones, C., & Romer, P. (2006). Insurance and incentives for medical innovation. Forum for Health Economics and Policy, 9(2), Article 4. Available at http:// www.bepress.com/cgi/viewcontent.cgi?article=1006&context=fhep Goldman, D. P., Joyce, G. F., & Zheng, Y. (2007). Prescription drug cost sharing. Journal of American Medical Association, 298(1), 61–69. Grabowski, H., & Vernon, J. M. (1992). Brand loyalty, entry and price competition in pharmaceuticals after the 1984 drug act. Journal of Law and Economics, 35(2), 331–350. Grabowski, H., Vernon, J., & DiMasi, J. A. (2002). Returns on research and development for 1990s new drug introductions. PharmacoEconomics, 20, 11–29. Jorgenson, D., & Yun, K.-Y. (2001). Investment, Volume 3, lifting the burden: tax reform, the cost of capital, and U. S. economic growth. Cambridge, MA: MIT Press. Kaiser Family Foundation. (2007). The medicare prescription drug benefit. Kaiser Family Foundation Report 7044-06, Menlo Park, CA. Kaiser Family Foundation. (2008a). Fact sheet: The medicare prescription drug benefit. Henry J. Kaiser Family Foundation Publication 7044-08, Menlo Park, CA. Kaiser Family Foundation. (2008b). The Medicare Part D coverage gap: Costs and consequences in 2007. Kaiser Family Foundation, Menlo Park, CA. Lakdawalla, D., & Sood, N. (2006). Health insurance as a two-part pricing contract. National Bureau of Economic Research Working Paper no. 12681, Cambridge, MA. Lakdawalla, D. N., & Sood, N. (2009). Innovation and the welfare effects of public drug insurance. Journal of Public Economics, 93, 541–548. Lichtenberg, F. R., & Sun, S. X. (2007). The impact of Medicare Part D on prescription drug use by the elderly. Health Affairs, 26(6), 1735–1744. Nordhaus, W. D. (1969). Invention, growth, and welfare: A theoretical treatment of technological change. Cambridge, MA: MIT Press. Nordhaus, W. D. (2004). Schumpeterian profits in the American economy: Theory and measurement. National Bureau of Economic Research Working Paper no. 10433, Cambridge, MA.


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Philipson, T. J., & Jena, A. B. (2006). Surplus appropriation from R&D and health care technology assessment procedures. National Bureau of Economic Research Working Paper no. 12016, Cambridge, MA. Shapiro, C. (2007) Patent reform: Aligning reward and contribution. NBER Working Papers. National Bureau of Economic Research, Inc. Yin, W., Basu, A., Zhang, J. X., Rabbani, A., Meltzer, D. O., & Alexander, G. C. (2008). The effect of the Medicare Part D prescription benefit on drug utilization and expenditures. Annals of Internal Medicine, 148(3), 169–177.

APPENDIX Consider a health insurance plan that has a deductible of X dollars and offers a coinsurance of c for expenditures above the deductible. Consider an uninsured consumer with total expenditure E1, where E1 W X. Assume that the elasticity of demand is e. The objective is to predict total expenditures, E2, for this consumer when he or she enrolls in the health insurance plan with this non-linear benefit structure. First, note that the change in expenditures is the same as the change in quantity, because we assume prices paid to the manufacturer do not change. The consumer price does change, and it does so in a non-linear fashion, because it depends on the level of expenditures. Following the standard theory of demand, total expenditure when the consumer enrolls in this health insurance plan is given by the following: E 2 ¼ X þ ðE 1 XÞ þ ðE 1 XÞð1 cÞe The above equation shows that for the first X dollars in expenditures, there is no change in demand as the consumer is below his or her deductible and therefore does not experience any change in price. For the next E1 X dollars, the consumer faces a percentage price reduction of 1 c. Therefore, the percentage change in demand or expenditures for this region is simply the percentage change in price times the elasticity of demand. In this chapter, we argue that the percentage change in coinsurance and the elasticity of demand are sufficient predictors of the percentage change in utilization. In other words, we argue that the percentage change in total expenditures in this scenario can also be calculated by simply multiplying the percentage change in coinsurance times the elasticity of demand. Calculated this way, expenditures when the consumer enrolls in the health plan are given by the following: 1 c 0 E2 ¼ E1 þ E1e 1

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where c is the average coinsurance when enrolled in the health plan. c ¼

X þ cðE 1 XÞ E1

After substituting for c in the above equation, some simple algebra yields the following: E 02 ¼ X þ ðE 1 XÞ þ ðE 1 XÞð1 cÞe Hence, we get that E 02 ¼ E 2 . A corollary of the above result is that the percentage change in coinsurance is a sufficient predictor of whether the consumer would benefit from insurance. A negative percentage change in average coinsurance or decrease in coinsurance implies an increase in use and thus an increase in static welfare.

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