International Review of Research in Mental Retardation, Volume 33: Developmental Epidemiology of Mental Retardation and Developmental Disabilities

International Review of RESEARCH IN MENTAL RETARDATION Developmental Epidemiology of Mental Retardation and Development...

Author: Richard C. Urbano | Robert M. Hodapp | Laraine Masters Glidden

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International Review of RESEARCH IN MENTAL RETARDATION Developmental Epidemiology of Mental Retardation and Developmental Disabilities VOLUME 33

International Review of

RESEARCH IN MENTAL RETARDATION EDITED BY

LARAINE MASTERS GLIDDEN

DEPARTMENT OF PSYCHOLOGY ST. MARY’S COLLEGE OF MARYLAND ST. MARY’S CITY, MARYLAND

Board of Associate Editors Philip Davidson UNIVERSITY OF ROCHESTER SCHOOL OF MEDICINE AND DENTISTRY

Elisabeth Dykens VANDERBILT UNIVERSITY

Michael Guralnick UNIVERSITY OF WASHINGTON

Richard Hastings UNIVERSITY OF WALES, BANGOR

Linda Hickson COLUMBIA UNIVERSITY

Connie Kasari UNIVERSITY OF CALIFORNIA, LOS ANGELES

William McIlvane E.K. SHRIVER CENTER

Glynis Murphy LANCASTER UNIVERSITY

Ted Nettelbeck ADELAIDE UNIVERSITY

Marsha M. Seltzer UNIVERSITY OF WISCONSIN-MADISON

Jan Wallander SOCIOMETRICS CORPORATION

Developmental Epidemiology of Mental Retardation and Developmental Disabilities A Volume in

International Review of

RESEARCH IN MENTAL RETARDATION VOLUME 33 EDITED BY

Richard C. Urbano VANDERBILT KENNEDY CENTER VANDERBILT UNIVERSITY NASHVILLE, TENNESSEE

Robert M. Hodapp JOHN F. KENNEDY CENTER FOR RESEARCH ON HUMAN DEVELOPMENT AND PEABODY COLLEGE VANDERBILT UNIVERSITY NASHVILLE, TENNESSEE

AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier

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Contents

Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ix xi xiii

Section I. Introduction Developmental Epidemiology of Mental Retardation/Developmental Disabilities: An Emerging Discipline Robert M. Hodapp and Richard C. Urbano I. II. III. IV. V. VI.

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Epidemiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Developmental Epidemiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Issues Specific to MR/DD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advances and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 4 8 11 16 19 20

Section II. Methodological Issues and Perspectives Record Linkage: A Research Strategy for Developmental Epidemiology Richard C. Urbano I. II. III. IV. V. VI.

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Past and Present Linked Datasets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biases and Prejudices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Embarking on a Linked‐Data Research Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Workflow of Linkage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

v

27 29 31 34 38 51 51

contents

vi Second‐Order Linkage and Family Datasets Shihfen Tu, Craig A. Mason, and Quansheng Song I. II. III. IV. V. VI.

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Is a Second‐Order Linkage and What Are Its Benefits? . . . . . . . . . . . . . . . . . . . . Overview of Data Linkage Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . How to Conduct a Second‐Order Data Linkage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Challenges of Building a Second‐Order Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53 53 56 58 70 76 77

Incorporating Geographical Analysis into the Study of Mental Retardation and Developmental Disabilities Russell S. Kirby I. II. III. IV. V.

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Medical Geography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mental Retardation and Developmental Disabilities Research . . . . . . . . . . . . . . . . . . . . Research Agenda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

79 80 84 85 88 88

Statistical Issues in Developmental Epidemiology and Developmental Disabilities Research: Confounding Variables, Small Sample Size, and Numerous Outcome Variables Jennifer Urbano Blackford I. II. III. IV. V.

Statistical Challenges in Developmental Disabilities Research. . . . . . . . . . . . . . . . . . . . . Confounding Variables: How to Match on More Than Two Variables . . . . . . . . . . . Small Sample Sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Numerous Outcome Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Closing Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

93 94 103 111 118 118

Economic Perspectives on Service Choice and Optimal Policy: Understanding the Effects of Family Heterogeneity on MR/DD Outcomes Stephanie A. So I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. What Is Economics?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

121 121

contents III. IV. V. VI.

vii

The Relationship Between Economics and Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Economics of MR/DD Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Econometric Models and Estimation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

128 133 138 144 146

Section III. Populations Public Health Impact: Metropolitan Atlanta Developmental Disabilities Surveillance Program Rachel Nonkin Avchen, Tanya Karapurkar Bhasin, Kim Van Naarden Braun, and Marshalyn Yeargin‐Allsopp I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Background of Developmental Disabilities Surveillance in Metropolitan Atlanta. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Prevalence Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Findings from Epidemiology Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Public Health Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

149 151 157 164 164 187 187

Using GIS to Investigate the Role of Recreation and Leisure Activities in the Prevention of Emotional and Behavioral Disorders Tina L. Stanton‐Chapman and Derek A. Chapman I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Neighborhood Influences: Effects on Childhood Emotional/Behavioral Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. The Importance of Neighborhood‐Related Leisure Activities . . . . . . . . . . . . . . . . . . . IV. Geographical Information Systems: Basic Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Using GIS to Study Emotional and Behavioral Disorders: Promises and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

191 194 196 198 200 207 207

The Developmental Epidemiology of Mental Retardation and Developmental Disabilities Dennis P. Hogan, Michael E. Msall, and Julia A. Rivera Drew I. Epidemiological Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Models for Understanding Mental Retardation and Developmental Disabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

213 216

contents

viii

III. Data and Measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

226 229 241 243

Evolution of Symptoms and Syndromes of Psychopathology in Young People with Mental Retardation Stewart L. Einfeld, Bruce J. Tonge, Kylie Gray, and John Taffe I. II. III. IV. V. VI.

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Australian Child to Adult Development (ACAD) Study . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

247 248 250 251 252 262 263

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contents of Previous Volumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

267 279

Contributors

Numbers in parentheses indicate the pages on which the authors’ contributions begin.

Rachel Nonkin Avchen (149), Centers for Disease Control and Prevention, National Center on Birth Defects and Developmental Disabilities, Atlanta, Georgia 30333 Tanya Karapurkar Bhasin (149), Centers for Disease Control and Prevention, National Center on Birth Defects and Developmental Disabilities, Atlanta, Georgia 30333 Jennifer Urbano Blackford (93), Vanderbilt Kennedy Center, Department of Psychiatry, Vanderbilt University, Nashville, Tennessee 37203 Kim Van Naarden Braun (149), Centers for Disease Control and Prevention, National Center on Birth Defects and Developmental Disabilities, Atlanta, Georgia 30333 Derek A. Chapman (191), Department of Epidemiology, Virginia Commonwealth University, Richmond, Virginia 23298 Julia A. Rivera Drew (213), Department of Sociology, Brown University, Providence, Rhode Island 02912 Stewart L. Einfeld (247), School of Psychiatry, University of New South Wales, NSW 2031, Australia Kylie Gray (247), Monash University Centre for Developmental Psychiatry, Victoria 3168, Australia Robert M. Hodapp (3), Vanderbilt Kennedy Center, Department of Special Education, Vanderbilt University, Nashville, Tennessee 37203

ix

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contributors

Dennis P. Hogan (213), Population Studies and Training Center, Brown University, Providence, Rhode Island 02912 Russell S. Kirby (79), Department of Maternal and Child Health, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama 35294 Craig A. Mason (53), College of Education and Human Development, The University of Maine, Orono, Maine; and Maine’s University Center for Excellence in Developmental Disabilities, The University of Maine, Orono, Maine 04469 Michael E. Msall (213), University of Chicago Pritzker School of Medicine, Kennedy Mental Retardation Center and Institute of Molecular Pediatrics, Comer Children’s and LaRabida Children’s Hospitals, Chicago, Illinois 60637 Stephanie A. So (121), Vanderbilt Kennedy Center, Department of Economics, Vanderbilt University, Nashville, Tennessee; and Vanderbilt Kennedy Center, Department of Pediatrics, Vanderbilt University, Nashville, Tennessee 37235 Quansheng Song (53), Maine’s University Center for Excellence in Developmental Disabilities, The University of Maine, Orono, Maine 04469 Tina L. Stanton‐Chapman (191), Department of Curriculum, Instruction, and Special Education, Curry School of Education, University of Virginia, Charlottesville, Virginia 22904 John TaVe (247), Monash University Centre for Developmental Psychiatry, Victoria 3168, Australia Bruce J. Tonge, (247), Monash University Centre for Developmental Psychiatry, Victoria 3168, Australia Shihfen Tu (53), College of Education and Human Development, The University of Maine, Orono, Maine; and Maine’s University Center for Excellence in Developmental Disabilities, The University of Maine, Orono, Maine 04469 Richard C. Urbano (3, 27), Vanderbilt Kennedy Center, Department of Pediatrics, Vanderbilt University, Nashville, Tennessee 37203 Marshalyn Yeargin‐Allsopp (149), Centers for Disease Control and Prevention, National Center on Birth Defects and Developmental Disabilities, Atlanta, Georgia 30333

Foreword

This volume, Developmental Epidemiology of Mental Retardation and Developmental Disabilities, guest-edited by Richard C. Urbano and Robert M. Hodapp, continues an important, albeit recent, tradition for the International Review of Research in Mental Retardation series. Since I began my editorship in 1997, six volumes have been organized around five diVerent themes: autism, language, personality/motivation (two volumes), neurotoxicity, and now the current volume on developmental epidemiology. As did the guest editors of the other theme-oriented volumes, Rick Urbano and Bob Hodapp have worked diligently and masterfully, successfully attracting an impressive array of experts to contribute to Volume 33. As they have written in their preface, the field is ready for this volume because developmental epidemiology is cutting edge, using technical tools that did not exist even a few years ago. Nonetheless, the utilitarian purpose of developmental epidemiology is ancient. Research scientists and clinicians have been interested in prevention of mental retardation for as long as we have been able to identify individuals who have it. However, we now have sophisticated methods for measuring, storing, and accessing data about variation at earlier points in time and relating that variation to mental or physical health outcomes at later times. These methods, discussed in the current volume, have resulted in a surge of interest in developmental epidemiology. The media helped to publicize its use in its frenzy to understand the reason for the increase in autism diagnoses. Thus, this volume is timely. Rick Urbano and Bob Hodapp, who proposed and guided it, have done a service to their colleagues. I take this opportunity to thank them personally for their prescience and persistence.

xi

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foreword

I am grateful as one of their served colleagues, but also as the series editor who found them to be conscientious, devoted, and tenacious guest editors. LARAINE MASTERS GLIDDEN SERIES EDITOR

Preface

Since it was begun by Norman Ellis in 1966, the International Review of Research in Mental Retardation has included over 30 volumes produced by three series editors and several editors of individual thematic volumes. Until now, however, no single volume has been devoted exclusively to epidemiology or to developmental epidemiology. Moreover, from among over 250 chapters over four decades, only a few, sporadic chapters of the International Review have been devoted exclusively to epidemiology (Boussy & Scott, 1993; Fryers, 1993). Beyond its uniqueness, the present volume is also timely in ways that are important to the field of Mental Retardation/Developmental Disabilities (MR/DD). As virtually every chapter illustrates, until recently a fully fledged field of developmental epidemiology of MR/DD was not possible. The technological and theoretical advances were simply lacking, making less feasible specific studies. As a result, one needs to describe the many technical–theoretical advances as well as the best research examples in this area. The current volume is therefore divided into three discrete sections. In Section I an introductory chapter describes the ‘‘emerging’’ nature of the field of Developmental Epidemiology of MR/DD. In Section II, consisting of the volume’s next five chapters, contributors review the various methodological, statistical, and theoretical advances that make possible studies in the developmental epidemiology of mental retardation. This section is then followed by the four chapters of Section III. Although employing the latest methods and procedures, contributors of Section III’s articles are more focused on their epidemiological findings per se. As befits a perspective that may be less familiar to most International Review readers, we begin with an introduction. In ‘‘Developmental Epidemiology of Mental Retardation and Developmental Disabilities,’’ Richard Urbano and Robert Hodapp reflect upon the exact meaning of the term ‘‘developmental epidemiology of mental retardation.’’ By way of deconstructing that term’s xiii

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three main components (epidemiology, developmental epidemiology, mental retardation), they highlight the many provocative twists involved in applying a developmental epidemiological perspective to persons with mental retardation. In addition, by comparing developmental epidemiological work as applied to two populations—those with MR/DD versus those with child psychiatric conditions—they illustrate the types of findings that seem imminent in the disabilities field. A general overview, a state-of-the-art description of the present, a peek into the future—this chapter illustrates a subfield at the cusp of providing critical information about both individuals with disabilities and their families. In ‘‘Record Linkage: A Research Strategy for Developmental Epidemiology,’’ Richard Urbano then tackles the entire issue of linking together various administrative records. Simply stated, it is one thing to have access to records of all of the births, deaths, hospitalizations, or other events in a town, state, or country; it is another—far more informative—thing to link these records together. But how, exactly, does one go about linking a person’s birth records to that same person’s hospitalization or other records? More generally, how does research using ‘‘somebody else’s data’’ compare to smaller scale studies in which the researcher collects his or her own data? Even once one has acquired such large, population-based datasets, what are the steps in the process of going from several very large, raw datasets to cleaned, linked sets that can be analyzed using SPSS, SAS, STATA, and other statistical packages? Building upon Urbano’s chapter, Shihfen Tu, Craig Mason, and Quansheng Song discuss ‘‘Second-Order Linkage and Family Datasets.’’ Second-Order Linkage also uses large-scale administrative databases, but this time the linkage goes beyond the individual. For example, using the mother’s social security number and other identifiers, one might be able to construct from a state’s Birth Records (and Marriage Records) various families of children born to the same mother. By performing such secondorder linkage, researchers are now able to use large-scale administrative records to examine questions involving family genetics, family SES, and parental education levels, health, birth defects, and other issues. Tu, Mason, and Song describe common problems and oVer innovative solutions for the many complicated questions involved in second-order, family linkage work. Russell Kirby follows with his chapter, ‘‘Incorporating Geographical Analysis into the Study of Mental Retardation and Developmental Disabilities.’’ In line with the epidemiologist’s focus on ‘‘person, place, and time,’’ Kirby explores issues relating to place—the ways in which where one lives aVects one’s health, well-being, and chances in life. Although a burgeoning subfield within developmental epidemiology, ‘‘geographic information systems’’ (GIS) have yet to be systematically applied in the

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study of disabilities. Yet given the ties of disabilities to certain communities (Fujiura & Yamaki, 2000) and the recent interest in social factors related to health and health care among populations with developmental disabilities (Graham, 2005), the time seems ripe to explore the many advantages of spatially-based statistical analyses of large populations. Jennifer Blackford follows with her chapter on ‘‘Modern Statistical Methods Applied to Developmental Epidemiology.’’ Although most of us are familiar with analyses using standard statistical tests, Blackford relies on the latest statistical and computing advances to address three troublesome issues. The first concerns matching: How does one match on more than a few variables at one time? Noting the usual advice to ‘‘just pick two’’ matching variables, Blackford introduces propensity scores as a technique to match on more than a few variables simultaneously. The second issue concerns the ways in which permutation testing can help oVset the limitations involved in using small samples. This ‘‘small-sample problem’’ is ubiquitous in disability studies, particularly when examining rarely occurring conditions. Third, Blackford extends beyond Bonferroni corrections to address the issues involved in multiple (often correlated) outcome variables. Although specifically addressed to developmental epidemiologists examining persons with disabilities, this chapter will be helpful to all researchers examining behavioral issues in groups with MR/DD. The final chapter in Section II is by Stephanie So, ‘‘Economic Perspectives on Service Choice and Optimal Policy: Understanding the EVects of Family Heterogeneity on MR/DD Outcomes.’’ Although economics is, to most of us, a less well-understood discipline. So illustrates the ways in which economic principles can apply to MR/DD. Her best example involves what might be called ‘‘family centeredness.’’ Across many disability fields (most prominently, early childhood special education), numerous researchers, service providers, and policymakers debate what constitutes a family-centered practice. Few such professionals rely on economic analyses. By analyzing with large-scale epidemiologic data variables such as family resource allocation, competing family values, opportunity costs, and individual family trade-oVs, we may be able to determine which practices might be best, for which families or types of families. In performing such analyses, so implicitly asks whether many of our ideas about family-centered practices might be missing the mark. As the first chapter within the third, ‘‘findings’’ section, it is a pleasure to introduce Rachel Avchen, Tanya Bhasin, Kim Van Naarden Braun, and Marshalyn Yeargin-Allsopp’s ‘‘Public Health Impact: Metropolitan Atlanta Developmental Disabilities Surveillance Program.’’ Quite simply, the CDC’s MADDS study is among the best available, with findings for over a decade

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on mental retardation and developmental disabilities (Yeargin-Allsopp, Murphy, Oakley, & Sikes, 1992). More importantly for our purposes, Avchen, Bhasin, Braun, and Yeargin-Allsopp illustrate the care and attention that this group has brought to basic epidemiological issues such as case definition and case finding. Their results are among the best in the field, telling us both how often various conditions occur and which child, parent, or other factors predispose children to such conditions. Examining ‘‘Using GIS to Investigate the Role of Recreation and Leisure Activities in the Prevention of Emotional and Behavioral Disorders,’’ Tina Stanton-Chapman and Derek Chapman next illustrate how developmental epidemiological principles can be extended in another way. From Bronfenbrenner (1979) on, developmentally oriented researchers have been intrigued by the ecologies of childhood, the ways in which children live within families, which exist within neighborhoods, towns, states, and countries. Yet how such nested circles aVect children with disabilities has only begun to be examined. Returning to concerns expressed by Kirby in his chapter on geographical analysis, Stanton-Chapman and Chapman apply the perspective of GIS to the ways that parks and recreational programs can prevent emotional–behavioral disorders. Stanton-Chapman and Chapman have been instrumental in bringing geographical analyses into developmental epidemiological studies within the MR/DD field. Dennis Hogan, Michael Msall, and Julia Drew, in their chapter ‘‘The Developmental Epidemiology of Mental Retardation and Developmental Disabilities,’’ come at the developmental epidemiology of MR/DD from yet another perspective. Relying on 1997–2003 data from the National Health Interview Survey, these authors use large-scale interview data to determine issues related to prevalence, family situations, and indicators of both health care and health-care service delivery. From a diVerent perspective, their findings corroborate several of the social predictors of disability found by the CDC and other surveillance data. As the last chapter in Section III, Stewart Einfeld, Bruce Tonge, Kylie Gray, and John TaVe examine the ‘‘Evolution of Symptoms and Syndromes of Psychopathology in Young People with Mental Retardation.’’ Having worked for several decades using large-scale data from the Australian Child to Adult Longitudinal Study, Einfeld, Tonge, and their colleagues are among the leaders in the burgeoning field of dual diagnosis. Along with others, Einfeld and Tonge (1992) have developed maladaptive behavior instruments that have been specifically designed for persons with disabilities (see Dykens, 2000 for a review). In addition, these authors have produced one of the best large-scale, longitudinal studies that inform us about the amount, types, and changes over time in maladaptive behavior/psychopathology among children with MR/DD.

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In closing, we hope that this volume imparts a sense of developmental epidemiology’s methodological, statistical, and theoretical advances, as well as the best of those studies using such techniques. For 40 years, only sporadic attention has been paid to the developmental epidemiology of MR/DD. Our hope—and prediction—is that such topics will grow exponentially in importance in the decades to come. RICHARD C. URBANO ROBERT M. HODAPP ACKNOWLEDGMENTS

We would like to thank Marisa Sellinger and Kara Newman for their help in producing this volume, and Dr. Laraine Glidden for her support and encouragement. This volume was supported in part by NICHD grant numbers R03 HD050468 and P30 HD15052 to Vanderbilt University.

References

Boussy, C. A., & Scott, K. G. (1993). Use of data-base linkage methodology in epidemiologic studies of mental retardation. International Review of Research in Mental Retardation, 19, 135–161. Bronfenbrenner, U. (1979). The ecology of human development. Cambridge, MA: Harvard University Press. Dykens, E. M. (2000). Psychopathology in children with intellectual disability. Journal of Child Psychology and Psychiatry, 41, 407–417. Einfeld, S. L., & Tonge, B. J. (1992). Manual for the developmental behavioural checklist: Primary carer version. Sydney, Australia: School of Psychiatry, University of New South Wales. Fryers, T. (1993). Epidemiological thinking in mental retardation: Issues in taxonomy and population frequency. International Review of Research in Mental Retardation, 19, 97–133. Fujiura, G. T., & Yamaki, K. (2000). Trends in demography of childhood poverty and disability. Exceptional Children, 66, 187–199. Graham, H. (2005). Intellectual disabilities and socioeconomic inequalities in health: An overview of research. Journal of Applied Research in Intellectual Disabilities, 18, 101–111. Yeargin-Allsopp, M., Murphy, C. C., Oakley, G. P., & Sikes, R. K. (1992). A multiple-source method for studying the prevalence of developmental disabilities in children: The Metropolitan Atlanta developmental disabilities study. Pediatrics, 89, 624–629.

Section I Introduction

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Developmental Epidemiology of Mental Retardation/Developmental Disabilities: An Emerging Discipline* ROBERT M. HODAPP VANDERBILT KENNEDY CENTER, DEPARTMENT OF SPECIAL EDUCATION VANDERBILT UNIVERSITY, NASHVILLE, TENNESSEE

RICHARD C. URBANO VANDERBILT KENNEDY CENTER, DEPARTMENT OF PEDIATRICS VANDERBILT UNIVERSITY, NASHVILLE, TENNESSEE

I.

INTRODUCTION

Whether one thinks in terms of airplanes taking oV or flowers blossoming forth, the field of developmental epidemiology of mental retardation/ developmental disabilities (MR/DD) is poised on the edge of new advances, new approaches, and new findings. In many respects, the field seems poised to do things that could hardly be imagined a few years ago. There is an excitement, level of interest, and vitality that could only be imaged a few short years ago. In part, this vitality arises from joining more established research traditions to the latest technical advances. As the chapters in this special issue illustrate, several interesting epidemiological studies already exist, and some groups have successfully employed developmental epidemiological methods for a decade or more (Boussy & Scott, 1993). Recently, however, more sophisticated statistical techniques have been developed for developmental epidemiological studies in developmental disabilities (Blackford, this issue), and new computer advances—in both software development and hardware storage and speed—allow for studies that link across and within various types of records (Urbano, this issue). As a result, increasing numbers of *Authors’ Note: This research was supported in part by NICHD grant numbers R03 HD050468 and P30 HD15052 to Vanderbilt University. INTERNATIONAL REVIEW OF RESEARCH IN MENTAL RETARDATION, Vol. 33 0074-7750/07 $35.00

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Copyright 2007, Elsevier Inc. All rights reserved. DOI: 10.1016/S0074-7750(06)33001-7

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Robert M. Hodapp and Richard C. Urbano

studies examine populations of entire countries or regions (Einfeld, Tonge, Gray, & Taffe, this issue) and utilize various large‐scale national and international surveys (Hogan, Msall, & Drew, this issue). Economic and other sophisticated models and theories are also at the ready (So, this issue). Still, despite these preliminaries, one must consider the field of developmental epidemiology—at least as applied to individuals with MR/DD—as only beginning to emerge. Compare developmental epidemiological work in two fields, developmental disabilities and child psychiatry. In studies of the developmental epidemiology of child psychiatric disorders, Costello and Angold (2006) have examined over time the onset, development, and contributors of child psychiatric problems. Tremblay (2004), for example, has examined the trajectory of children’s physical aggression over development, showing that highly aggressive children already were significantly more likely to show diYcult temperaments by 5 months of age (Tremblay et al., 2004). Similarly, various studies have used diverse methodologies to determine whether the prevalence rates of conduct disorder, depression, and autism were rising over time. The general conclusion is that conduct disorder does appear to be rising over successive birth cohorts, whereas neither child‐adolescent depression nor autism are rising, at least not beyond what might be expected given changes in either definition or case‐finding techniques (Costello, Foley, & Angold, 2006). Throughout these studies, there is a sophistication and maturity not yet seen among most studies using developmental epidemiology to study developmental disabilities. The process of getting to there from here—to a more sophisticated, mature research enterprise from the beginnings of a field—is the topic of this chapter. We begin with a backward parsing of the term itself, first describing the basic principles of epidemiology more generally, then how the addition of the term ‘‘developmental’’ complicates and makes more interesting the basic research enterprise. We then tackle issues involved in applying developmental epidemiology to persons with mental retardation, before ending with several advances and new directions. Our purpose throughout is to illustrate to researchers within and outside of developmental disabilities just what might be gained by this new flight into the developmental epidemiological sky. II.

EPIDEMIOLOGY

Although various definitions of the field could be presented, we present below two definitions of the field of epidemiology: Epidemiology is the study of the distribution and determinants of health‐related states or events in specified populations, and the application of this study to control health problems (Last, 1995; Yeargin‐Allsopp & Boyle, 2002, p. 113).

DEVELOPMENTAL EPIDEMIOLOGY OF

MR/DD

5

An emerging concept of epidemiology presents this discipline as the study of health and disease as a full spectrum across the human life span with a population approach, including etiological factors, phenomenology, comorbidities, and the uses and outcomes of clinical care (Mezzich & Ustin, 2005, p. 656).

In many ways, these two definitions might be characterized as more (Yeargin‐Allsopp & Boyle, 2002) and less traditional (Mezzich & Ustin, 2005). Still, despite their diVerences, they nevertheless illustrate several of the main tenets of epidemiology. Four of these tenets are described as follows. A.

A Focus on Populations

Epidemiology examines outcomes from a population perspective. In many epidemiological studies, geographic populations are the unit of interest, whether these involve populations of a country, state, city, or neighborhood. But as epidemiology is concerned with the occurrence of illness in populations, the concept of population can be interpreted as any group at risk: females, children, or persons with Down syndrome. Because the concept of risk within a population is essential both in defining and in interpreting epidemiological studies and results, enormous attention is paid to the gender, ethnic, racial, familial, socioeconomic, urban‐suburban‐rural, and other characteristics of the sample under study. Contrast this population‐based strategy to the approach usually adopted in psychological studies. In most such studies, researchers examine small numbers (20–40) of children or adults. Such small numbers of subjects, while acceptable for answering many types of ‘‘main eVects’’ questions, are usually inadequate to examine various interaction eVects. Such studies also generally involve samples of convenience, individuals who, on the basis of advertisements or word of mouth, volunteer to participate in any given study. Of particular concern to epidemiologists (with their population‐based focus) is whether these small samples truly represent the larger population in terms of their subject or family characteristics. If not, then epidemiologists cannot later infer from this sample the actual risk of occurrence of illness or other health‐related events in the larger population. B.

A Focus on Health‐Related Outcomes

Many epidemiological studies observe the occurrence of illnesses, deaths, hospitalizations, or other health‐related outcomes. Within this focus on health‐related outcomes, researchers expend large amounts of energy and thought to providing exact definitions of a case. Recently, for example, researchers have worked hard to define low birthweight in newborns, as well as

6


to establish exact diagnostic criteria for disease states such as pneumonia, AIDS, or cancer, or for conditions such as intellectual disabilities or autism. Two further issues–complications arise when discussing epidemiology’s focus. Historically, epidemiology’s focus on illness and disease states ensured that its concerns were more or less concrete: How many cases of, for instance, pneumonia occur in a particular population, or within a specific age, gender, or geographic location? In many ways, one of the allures of epidemiology is that, in contrast to the more subjective measures used in much psychological research, epidemiological studies focused on generally easy‐to‐measure, concrete outcomes. In recent years, however, such distinctions may be fading. As noted in Mezzich and Ustin’s (2005) definition, epidemiology has increasingly been concerned with ‘‘the study of health and disease as a full spectrum.’’ Such a full spectrum, health‐and‐disease focus allows epidemiologists to include as outcomes subjective ratings of one’s health, well‐being, and feelings of depression. In one sense, such opening up of epidemiological studies is useful—the field may have been guilty of an overly rigid focus on diseases in past years. At the same time, however, now coming to the fore are all of the tricky measurement issues inherent in any diYcult‐to‐measure construct. A second issue concerns outcomes that, while they may relate to health and disease, are not exactly health outcomes. For example, many large‐scale epidemiological studies examine such things as divorce or employment. Strictly speaking, these outcomes might better come under the purview of demography or economics. But in recent years, some epidemiologists have called for a closer alliance between epidemiology, with its health–disease focus, and demography, with its focus on other characteristics of a population (Susser & Bresnahan, 2001). Although by no means all epidemiologists would consider, for example, divorce and its predictors or sequelae as a proper topic of study within epidemiology, many would. C.

A Focus on Causes or Probable Causes of Such Health‐Related Outcomes

Epidemiological studies are designed to describe, explain, and predict the occurrences of the outcomes; the ultimate goal is to connect outcomes with their predictors. The epidemiologist, however, is not focused specifically on the outcome of a particular individual. Instead, epidemiologists think about outcomes in population terms: how to reduce risk across the population so that the proportion of cases in a population diminishes. Thus, predictors may include individual risk behaviors (e.g., riding in a car without seatbelts) or more broadly based social risk factors [e.g., low socioeconomic status (SES), lack of access to health care]. In all cases, epidemiology has as its


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goal connections between outcomes and predictors that provide clues as to possible cause(s) of the final outcome. Such clues sometimes relate to already‐specified disease mechanisms, sometimes to less well understood societal processes. A good example might involve one’s SES. Many conditions and diseases occur more often in low SES individuals. Within this sample of low‐income people, an epidemiologist would test hypotheses to identify which specific characteristics might indicate a direct pathway to the outcome. The epidemiologist might examine diet, environment, low education, or lack of health insurance for each’s relationship to the cause or progression of a disease. In this regard, it seems useful to distinguish between risk indicators and risk mechanisms (Rutter, Pickles, Murray, & Eaves, 2001). Risk indicators involve variables—such as low SES—that are simply correlated with bad outcomes. It is necessary (but not suYcient) to identify such risk indicators. The truly interesting studies take the next step, moving from an indicator to the exact risk mechanisms that are operative. In relation to diVerent outcomes, low SES might serve as a proxy for poor nutrition, a lack of health care, or households in which children do not have books to be read to them or in which parents work two jobs, not allowing enough time to interact with their children.

D.

A Focus on Intervention, Public Health, and Public Policy

Epidemiology is not solely an academic enterprise. Throughout, the field’s focus on determining amounts and correlates of health–disease outcomes has as its goal the prevention, amelioration, or treatment of those diseases. One might recall the U.S. Safety Council’s ‘‘Back to Sleep’’ advertisement program to get parents to sleep their infants on their backs, thereby reducing sudden infant death syndrome (SIDS). Other, population‐wide advertisements, inducements, and information‐sharing campaigns have all been tried, either to help eliminate diseases or to encourage practices that foster better health (e.g., exercise, nutritious eating). Most such campaigns arise from results derived from epidemiological studies. Again, the potential connections of epidemiology to demography must be noted. Just as studies of heart disease, diabetes, and obesity have led to prescriptions about not smoking, diet, and exercise, so too might public policy campaigns arise from epidemiological studies that examine what have traditionally been considered as more ‘‘social ills.’’ Thus, studies of such phenomena as divorce, school dropout, or unemployment might lead to policies designed to aVect individual behaviors or local, state, or national policy. Although not, strictly speaking, within the purview of epidemiology,

8


such phenomena can be treated similarly to the more health‐related outcomes of interest in traditional epidemiological studies. III.

DEVELOPMENTAL EPIDEMIOLOGY

As a subspecialization of the broader field of epidemiology, developmental epidemiology focuses more intensively on changes in humans, time (both individual and historical), and the changes into and out of problem conditions. In many ways, developmental epidemiology makes more dynamic the original epidemiological focus on predictors and correlates of disease states. Before describing developmental epidemiology itself, it is important to consider the field’s ties to developmental psychopathology. As defined during the 1980s by Dante Cicchetti (1984, 1993) and Sroufe and Rutter (1984), developmental psychopathology goes beyond psychiatry, child psychiatry, or child clinical psychology. In a well‐known definition, Sroufe and Rutter (1984) defined developmental psychopathology as ‘‘the study of the origins and course of individual patterns of behavioral maladaptation . . .’’ (p. 18). In this sense, it is not enough to examine cross‐sectionally diVerent‐ aged children with a specific condition, or to compare children with and without a Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM‐IV) diagnosis. Instead, interest centers on the entry into and out of pathology, as well as on the predictors of such pathology at diVerent ages, levels of functioning, and groups or genders. Like the addition of ‘‘developmental’’ to the field of epidemiology, then, a focus of developmental psychopathology added a dynamic, changing component to child psychopathology. What, then, are the main tenets of a developmental epidemiological approach? We propose the following. A.

Appreciating Developmental Issues

In ways similar to the distinction between developmental psychopathology and either child clinical psychology or child psychiatry, the diVerences between developmental epidemiology and epidemiology somewhat consist of diVering emphases. For example, most epidemiologists would consider themselves interested in examining the number and type of children’s mental or physical health problems, and might search for predictors of such problems. Such studies might even occur at diVerent points during the childhood years. In contrast, developmental epidemiologists highlight the notion of constant, ongoing change throughout childhood (and even over the adult years). In this sense, developmental epidemiologists are adding a


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second layer of development. Thus, ‘‘developmental epidemiology can be seen as concerned with the interaction between two developmental processes: of the organism (the child) and of the disease’’ (Costello & Angold, 2006, p. 49). One good example of such simultaneous developments might involve childhood depression. It is now fairly well established that postpubertal girls are at high risks for depression, whereas depression is less often seen in pre‐ or postpubertal boys or in prepubertal girls. But what, exactly, makes postpubertal girls more prone to depression? To what extent are adolescence’s many hormonal, physical, or social changes involved, and what might the eVects be of unsupportive families or other diYcult environments? Certain of these environments have already been implicated in the onset of depressive episodes. Simmons and Blyth (1987) found that children (both sexes) are most prone to symptoms of depression during their major growth spurt in adolescence (called the period of ‘‘peak height velocity’’ or PHV), particularly when faced with stressful events. Within American schools, most children transition from middle school to high school between eighth and ninth grades, which just happens to be the period of PHV for most girls (but few boys). Although one should not ascribe all cases of depression in adolescent girls to changing schools during this critical period, such changes of school (known to be a particularly stressful time for many children) may be a contributing or enhancing factor. The point is that developmental epidemiology takes seriously the child’s own development. By doing so, one gains a greater appreciation for the many physical, mental, hormonal, social, educational, and other normative changes that children experience as they develop. These normal developmental changes provide the larger context within which the types, amounts, and predictors of various problems and conditions can be examined. B.

Identifying the Developmental Nature of Risk Factors

By fully employing the perspective of developmental epidemiology, one also begins to appreciate the changing nature of environmental influences at diVerent ages and periods of development. Essentially, a particular aspect of the environment may have a specific eVect at one period during development and diVerent (or no) eVects at other developmental periods. Consider the eVect of maternal depression on child behaviors. When mothers are depressed during the child’s first year of life, motor development seems most aVected. Later, during the child’s second year, language but not motor development seems most aVected (Hay, Pawlby, Angold, Harold, & Sharpe, 2003). Since presumably maternal behaviors during depressive episodes are similar at the two age‐periods, a mother’s depression and her

10


behaviors to the infant while depressed vary in their specific eVects on diVerent‐aged children. Developmental epidemiologists highlight several distinctions involving exposure to one or another ‘‘toxic event’’ and later health or mental health outcomes. They distinguish between the age of first exposure to some environmental event, the time since that exposure, the length of exposure, and the amount (dose) of exposure. When examining mental health outcomes of child abuse, experiencing a depressed mother, or growing up in extreme poverty, such diVerent event characteristics seem more or less important for diVerent outcomes. Just as children themselves are developing and changing over the childhood years, so too do the eVects of identical events diVer based on their onset age, length of exposure, intervening years, or dosage. C.

Appreciating the Bidirectionality of Influence on Child, Parent, and Family Outcomes

Historically, developmental psychologists have been ‘‘systems thinkers,’’ conceptualizing from general systems theory (Bertalannfy, 1968) to the phenomenon of human development (SameroV, 1995). Inherent in systems thinking is the idea of the interactions–transactions within and among diVerent levels of the system. In humans, communication occurs across levels both within and outside of the developing child. In this vein, developmental theorists from the late 1960s on have considered parents, siblings, families, schools, and neighborhoods as comprising the many environments amenable to developmental analyses. Bell (1968) emphasized the ways in which infants and caregivers mutually aVected one another; SameroV and Chandler (1975) focused on such interactions over time (i.e., transactions); and Bronfenbrenner (1979) noted the various ecologies of childhood, the fact that children reside within families, which themselves live in neighborhoods, towns, and countries (and that each level of the surrounding environment potentially aVects others). Each perspective embodies for developmentalists the more general notion that various levels of a system interact with one another. In research using developmental epidemiology, then, children comprise one—but not the only—potential outcome of interest. While the child’s entry into, say, depression would constitute one outcome of interest, the eVects of depressed children on their parents and families would constitute other outcomes. One might examine as outcomes maternal or paternal depression, divorce in the couple, or a family’s changing income, employment patterns, or moving. Some of these outcomes might relate to physical or mental health (e.g., health of the mothers, father, or siblings), others to marriage, economic, educational, or other issues. All such other‐than‐child


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outcomes would come under the purview of developmental perspectives that are more interactional, transactional, and ecological. IV.

ISSUES SPECIFIC TO MR/DD

Until now, we have discussed developmental epidemiology with little regard to children with MR/DD. We have simply assumed that both the epidemiological and the developmental terms of developmental epidemiology can simply be transported to children with mental retardation. Unfortunately, the field of MR/DD itself contains a host of tricky issues, many of which complicate developmental epidemiological work. Some of these problems are shared with developmental epidemiological studies within child psychiatry and clinical child psychiatry, whereas others pertain solely to the MR/DD field. We now discuss three such issues. A.

Mental Retardation, Type of Mental Retardation, and the Problem of Caseness

Although all epidemiological studies struggle with the problem of ‘‘caseness,’’ this problem seems particularly salient when considering MR/ DD. Simply put, what constitutes a ‘‘true case’’ of a person with mental retardation? For several decades, the so‐called ‘‘three‐factor definition’’ has been used to define and diagnose individuals with mental retardation, with the three factors involving: significantly subaverage intellectual functioning . . ., concurrent deficits or impairments in present adaptive functioning . . .,

and

the onset is before age 18 years (American Psychiatric Association,

2000, p. 49). The first factor involves deficits in intellectual functioning. Using appropriate, standardized psychometric tests [e.g., the latest Stanford–Binet, Wechsler, Kaufman, or other intelligent quotient (IQ) tests], individuals are considered to fall within the mental retardation range when their IQs are at 70 or below. Due to errors of measurement, most diagnostic manuals allow for some leeway in this ‘‘IQ‐70 criterion,’’ usually up to IQ‐75. Still, significant intellectual impairment constitutes one criterion for a diagnosis of mental retardation. But such intellectual deficits should also involve a ‘‘real‐world’’ component. The second criterion therefore involves deficits in adaptive behavior.

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Deficits in adaptive behavior involve a lessened ability to perform daily activities required for personal and social suYciency. In the newly revised Vineland Adaptive Behavior Scales, Sparrow, Balla, and Cicchetti (2005) examine three domains: communication, or communicating one’s needs to others; daily living skills, or performing such tasks as eating, dressing, grooming, and toileting; and socialization, or following rules and working and playing with others. Although other diagnostic manuals (e.g., American Association on Mental Retardation, 2002) propose diVerent adaptive domains, recent diagnostic manuals include adaptive as well as intellectual impairments for a diagnosis of mental retardation. The third criterion involves onset during the childhood years. Unlike both the intellectual and adaptive criteria, childhood onset has received little criticism. To most professionals, mental retardation should be diVerentiated from problems associated with Alzheimer’s or other degenerative diseases, adult‐ onset traumatic brain damage, or other diseases or conditions that occur during the adult years (Hodapp, Maxwell, Sellinger, & Dykens, in press). In contrast to this three‐factor definition, most epidemiological studies diagnose mental retardation using an ‘‘IQ‐only definition’’ (usually, with IQ 400) data distributions from authors of published research and major test publishers. Data included ability, achievement, and psychometric measures. He examined various characteristics of the distributions and found that only 15% of the distributions were normally distributed. The majority of the distributions (49%) had one heavy tail, indicating skewness. This finding illustrates the ubiquity of nonnormal data even with large sample sizes and should emphasize the need to find appropriate statistics for nonnormally distributed data. In addition to the normal distribution issues inherent in small sample sizes (N < 30), research designs with small sample sizes also have lower statistical power than larger sample sizes, even when the test parameters are respected. Statistical power is a function of sample size and eVect size, the size of the true diVerence. To have a significant finding with a small sample size requires a larger eVect size than that required for a larger sample. Another way to think about this issue is that for a given eVect size, all other things being equal, a significant result is more likely with 40 observations than with 20 observations. With very large eVect sizes, sample size is not an issue. However, most developmental disability research explores eVect sizes that are in the small to medium range, where sample size is a very relevant issue. Statistical analyses seek to maximize the possibility of finding a true statistically significant diVerence, while protecting against erroneous findings. Analytic methods that are especially suited to small sample sizes include nonparametric tests and permutation tests because these methods do not require distributional normality. B.

Traditional Method: Nonparametric Tests

Nonparametric tests, also known as distribution‐free tests, do not make assumptions about the normality of the data distribution, so they are

TABLE II COMMON PARAMETRIC TEST ASSUMPTIONS, IMPORTANCE, Assumption

AND

SOLUTIONS

Description

Importance

Solution

Normality

Data from each population must be normally distributed

Less important, can be violated without a large impact; violations of kurtosis (peakedness of distribution) are worse than violations of skewness (asymmetry of distribution)

Homogeneity of variance

All populations being tested must have the same variance

More important, especially problematic with unequal sample sizes

Independence of observations

Observations must be independent of one another

Nonindependence has serious impact on the significance level and power of t‐test and ANOVA

Use samples of 30 or larger to utilize central limit theorem; use transforms to normalize the data; use nonparametric tests that do not assume normality; use permutation tests that do not assume normality Using the same sample size in each group can help because variance shifts are more likely to occur when the groups are of diVerent sizes; use a correction for unequal variances (e.g., Welch’s); use permutation tests that have no variance assumptions Use a test for dependent measures or a measure that does not have an assumption of independence

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Jennifer Urbano Blackford

appropriate tests when the data distribution is nonnormal or unknown. Nonparametric tests assume that we do not know information about the parameters of the data distribution such as the mean and standard deviation. This is why they are called ‘‘nonparametric.’’ The chi‐square, Mann– Whitney U test, and the sign test are examples of nonparametric tests. Nonparametric tests are alternatives to their parametric test counterparts. For example, the Mann–Whitney U test is the nonparametric counterpart to the t‐test. Nonparametric tests are most commonly used for the analysis of categorical or ordinal data, although they are well suited for small sample size analyses. In first approaching a dataset, it is important to examine the distribution of the data, especially when sample sizes are small. Under certain circumstances, the nonnormal distribution may still allow use of a parametric test (e.g., t‐test), particularly if the sample sizes are equal, samples sizes are fairly large (N > 25–30), and the tests are two‐tailed (Glass, Peckham, & Sanders, 1972; Sawilowsky & Blair, 1992). However, both Type I error and Type II error of parametric tests are impacted when sample sizes are small (N < 30), sample sizes are not equal, or one‐tailed tests are used. Let us briefly review Type I and Type II errors. Type I error is the chance of having a statistically significant diVerence when there is not a true diVerence in the population. When a researcher specifies an alpha level for an analysis, the researcher is explicitly stating what level of Type I error is acceptable for the statistical analysis. The alpha level, also called the nominal alpha, is typically set at 5%. It is expected that the Type I error rate for any given statistical test will be the same as the nominal alpha. When one or more of the assumptions are violated, Type I error is likely to be inflated; that is, there will be more errors than expected and the diVerence between the two groups will appear to be significant when actually it is not. Inflated Type I error can lead to reports of eVects that are not real. Violations of the parametric test assumptions can also increase Type II error. Type II error is the probability of not finding a significant result when there is a true diVerence. As Type II error increases, statistical power decreases. Decreased statistical power makes it possible to miss true diVerences. Thus, both inflated Type I and Type II errors can lead to faulty conclusions about the research findings. But nonparametric tests have more statistical power than parametric tests when sample sizes are small and data distributions are not normal; that is, nonparametric tests are more likely to correctly discover true diVerences between groups (Blair & Higgins, 1981; Sawilowsky & Blair, 1992; Tanizaki, 1997). When data come from a normally distributed population, the Mann– Whitney U test, a nonparametric test for independent groups, was shown to be 95% as powerful as the t‐test (ChernoV & Savage, 1958; Hodges &

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Lehmann, 1956). Nonparametric tests have advantages over parametric test when sample sizes are small or not normally distributed. Nonparametric tests have some limitations. First, while nonparametric tests do not require normality, they do assume other properties of the dataset. The Mann–Whitney U test requires homogeneity of variance and independent observations, just like the t‐test. Failure to meet assumptions of nonparametric tests can impact Type I error, just as with parametric tests (Blair, 1981). The second limitation is that the nonparametric test often answers a question that is diVerent from the question answered by a parametric test. For example, the Mann–Whitney U test tells you the likelihood that an observation randomly selected from one group has a larger value than an observation randomly selected from the other group. Alternately, the t‐test tells you about the mean diVerence between the participants in the two groups. Thus, nonparametric tests are appropriate when the variance between the groups is similar and when the answer provided by the nonparametric test matches the research question. C.

New Method: Permutation Tests

Permutation tests are a powerful, flexible statistical method that can be used where other approaches commonly fail. Permutation tests are known by many names: exact tests, randomization tests, and rerandomization tests. A permutation test is an exact method for determining the p‐value, or statistical significance, of any statistic. A test is exact ‘‘if the actual probability of making a Type I error is exactly a for each and every one of the possibilities that make up the hypothesis’’ (Good, 2000). Permutation tests are not diVerent statistical tests. Standard statistics, like a t‐value, are still used. The diVerence is in the calculation of the p‐value. With traditional statistical methods, the p‐value is based on a standard set of tables which are derived from theoretical distributions that include assumptions (e.g., normality) about the data. When these assumptions are violated, the p‐value from the standard table is likely to be inaccurate. In contrast, the permutation test approach calculates an exact p‐value for the statistic (e.g., t‐statistic) given your observed data. This p‐value is accurate even when standard statistical assumptions are violated. Specifically, the p‐values generated from a permutation test are based on an empirical distribution of the test statistic for the observed data that represents all possible arrangements of the data under the null hypothesis. Stated another way, for a permutation test, a test statistic distribution is created from the repeated random rearrangement of the data observations. This test statistic’s distribution is an empirical distribution that represents all possible statistic values under the condition that the null hypothesis (i.e., no group

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Jennifer Urbano Blackford TABLE III PERMUTATION TEST METHOD Permutation test step

1. Determine your research question 2. State your null hypothesis 3. Choose a test statistic

4. Compute the test statistic for the observed data 5. Rearrange the observations

6. Compute the test statistic for the new arrangement 7. Continue to rearrange the observations and compute the test statistic many times, saving the test statistic values each time

8. Calculate an exact p‐value by counting the number of values from the rearranged samples that equal or exceed the original statistic and dividing by the number of rearranged samples

WITH

EXAMPLE Example

Test for group diVerences in sociability for children with or without mental retardation There is not a diVerence between the two groups on sociability I select the t‐test as my statistic because I want to test for group diVerences in the mean sociability scores The t‐value for the observed data is 1.65 Group assignment is randomly shuZed across the 20 children; children with mental retardation may now be assigned to without mental retardation group The t‐value for the first rearrangement is .98 The t‐values over 20 rearrangements are: .98, .23, .30, 1.6, .22, .86, .15, .34, .56, .75, .32, .08, .90, .76, .30, 1.8, .20, .45, and .85. These data comprise the empirical distribution. In practice you would do a large number of rearrangements—typically 1,000–10,000 One of the 20 t‐values is greater than the original t‐value of 1.65. Thus, the exact p‐value is .05 or 1/20

diVerences) is true. The actual observed statistic value is then compared to the empirical test distribution to determine statistical significance. Statistical significance is still calculated in the traditional way. For a two‐tailed test, if the observed test statistic is greater than ±2.5% of scores in the empirical distribution, then the result is statistically significant. The actual computation for the permutation test method follows these general steps: select the test statistic, generate the empirical distribution, and calculate an exact p‐value. There are eight specific steps to performing a permutation test. Each step, with a practical example, is provided in Table III. As shown in the example, each time the group status (with/without mental retardation) is randomly shuZed across all study participants, the observed relationship between group status (with/without mental retardation) and the outcome measure (sociability) is removed. Group status is now


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randomly shuZed across sociability scores, disrupting any naturally occurring relationship between mental retardation and sociability. This random pairing of group status and sociability represents one permutation sample that might exist under the null hypothesis of no group diVerences. Each time the group status is randomly shuZed, the test statistic value (t‐value) is saved. This process is repeated many times. The many random samples created represent all possible permutations of group status and sociability expected under the null hypothesis. The test statistics from each of the random samples are used to create an empirical test distribution, or a distribution of the test statistics one would expect to find under the null hypothesis. This empirical test distribution is used to determine statistical significance of the observed test statistic. The major benefit of using a permutation test approach is that permutation tests can be used with almost any type of data: small or large sample sizes; normal or nonnormal distributions; homogeneous or heterogeneous variance among groups; and continuous, ordinal, or categorical data. With all of these data types, permutation tests can be used to calculate an accurate and exact p‐value, even when standard statistical assumptions are violated. Since permutation tests are not a diVerent type of test, just an exact way to determine p‐value, any kind of statistic can be used: parametric (e.g., t‐test), nonparametric (e.g., Mann–Whitney U test), or novel (e.g., median divided by median absolute deviation). In summary, permutation tests are not new statistics but are simply a new method for determining statistical significance. Permutation tests are almost free of assumptions or the conditions that must be met in order for the results to be valid. Although permutation tests do not require normality of the data distribution or homogeneity of variance, they do have a few assumptions. First, observations across participants must be independent; that is, the data from one participant does not influence the data from another participant. Second, observations in the sample must be exchangeable. Observations are exchangeable if under the null hypothesis, no group diVerences, all possible pairings of study participant with group membership (e.g., sibling with/without Down syndrome) are possible and valid. These assumptions are usually easily met. Permutation tests are not a new method. They were first introduced in the 1930s by Pitman (1937) and later by Fisher (1966), but they did not immediately gain widespread usage. Permutation tests require large numbers of iterations (e.g., 5000) for calculation, making them nearly impossible to perform by hand or with older computers having slow processors and limited memory. The concept of the permutation tests was very appealing, but only recently aVordable high‐speed processors and memory have made the computation of permutation tests practical. Standard desktop computers

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are now powerful enough to perform permutation tests in relatively manageable running times. This increased computer capacity has resulted in a renewed interest in permutation tests. D.

Applications

Permutation tests provide a versatile tool that is applicable in most situations. The flexibility, combined with the conceptual simplicity, makes permutation tests very appealing to both statisticians and nonstatisticians. Advances in computer availability and processing speed makes permutation tests possible and practical. When sample sizes are large, permutation tests and parametric statistical methods will have the same results and parametric tests may be more readily accepted. However, for small sample sizes, permutation tests should be the tool of choice. As with any other statistical analysis, you should select your analytic approach and stay with that choice. Do not perform a permutation test only following a nonsignificant p‐value from a standard parametric test. Instead, decide if the permutation test is most appropriate given your data. If you decide to use the permutation test, it should be your only statistical analysis method. Table IV provides a guide to which tests are appropriate for various sample sizes, distributions, and group variance situations. Although the use of permutation tests is relatively new, software to perform these analyses is available. Common statistical packages may have options within traditional statistical analyses to request an exact test. For example, with SAS (SAS software, 2003) you can request an exact test for a

RECOMMENDED TYPE

OF

TEST

TABLE IV SAMPLE SIZE, DISTRIBUTION,

BY

AND

VARIANCE

Sample size (per cell)

Between groups variance

Very small (50)

Parametric

Parametric

Permutation

Parametric or permutation Permutation

Parametric

Parametric

Permutation Permutation

Permutation Permutation

Nonparametric Permutation

Nonparametric Permutation

Note: A permutation test is always appropriate. Parametric and nonparametric tests are recommended above because the results are likely to be similar to the permutation tests and are likely to be more familiar to reviewers.


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contingency table, correlation, or logistic regression. The R statistical software (R Development Core Team, 2005) currently has a package (‘‘exactRankTests’’) for computing exact rank tests, one sample tests, two sample tests, and two sample paired tests. SPSS (SPSS for Windows, 2005) has an additional package for conducting exact tests called SPSS Exact Tests. There are also several stand‐alone software packages that specialize in exact tests. The StatXact software package (StatXact version 7, 2005) performs a wide variety of permutation tests and also provides procedures (PROCs) that can be used within the SAS software. Resampling Stats (Resampling Stats, 2003) computes permutation tests and has both a stand‐alone version and companion versions for Excel and Matlab. For simple analytic designs (e.g., t‐test), it is possible to write a short custom program to perform a permutation test, only requiring basic programming skills and a standard statistical package. For programming steps, examples and flowcharts for various statistical tests, see Edgington (1995). The application of permutation tests to more complex designs, such as complex multivariate methods and factor analyses, have yet to be developed. There are several limitations to permutation tests. First, researchers need to be able to use statistical software packages in order to perform permutation tests. They cannot be done in Excel or by hand. Second, at this time permutation tests methods are not available for more complex multivariate statistical analyses. Finally, the unfamiliarity of audiences and reviewers with permutation tests may present a challenge. However, there are increasing numbers of chapters and articles describing the benefit of this method. Some peer‐ reviewed research articles have been published using permutation tests. In the near future, permutation tests should achieve widespread acceptance. To learn more about various nonparametric tests see Nonparametric statistics for the behavioral sciences (Siegel & Castellan, 1988). For more advanced reading on permutation tests see Permutation tests: a practical guide to resampling methods for testing hypotheses (Good, 2000) or Randomization tests (Edgington, 1995). IV. A.

NUMEROUS OUTCOME VARIABLES

The Problem with Analyzing Numerous Outcome Variables

Data collection is often the most expensive and time‐consuming part of a research project. Data collection can be especially diYcult for developmental disability research, where participants can be hard to find, recruit, and retain. Because each participant is so valuable, there is often a desire to collect as much data as possible. Obtaining countless data for each subject is

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a useful strategy, but it results in large amounts of data on few participants. This issue is also present in very large epidemiological studies, where there are often a vast number of measures. The analysis of large amounts of data, especially with small sample sizes, is problematic for several reasons. The main concern is the multiple testing problem, which occurs when multiple statistical tests are performed to answer a single research question. When a nominal alpha level is set, the researcher is stating the amount of Type I error that is acceptable for that single test. For example, a nominal alpha of 5% indicates that the researcher will accept a 5% chance that a statistically significant result is not true. When multiple tests are performed, the actual value for Type I error increases arithmetically with each test. For example, the Type I error for performing t‐tests on 5 outcome variables would be 25% (5% 5 tests) and not the expected 5%. With increased numbers of tests, the actual value for Type I error quickly exceeds the specified alpha level. To prevent Type I errors and misinterpretation of the data, it is necessary to control for Type I error. A second issue involves research designs with many related measures (e.g., EEG, imaging) or serial measurement (e.g., time‐series on biological measurements), both of which often result in substantially more outcome variables than participants. MPTs provide a powerful new statistical tool for analyzing numerous outcome variables, while addressing the multiple testing problem and allowing for more outcome variables than participants. This versatile tool extends the permutation tests described earlier to a multiple outcome variable design. B.

Traditional Methods: Independent Tests, Data Reduction, Single‐Step Corrections

There are several traditional tools for analyzing multiple outcome variables: independent variable analysis, data reduction, and single‐step corrections (e.g., Bonferroni). The first approach to analyzing numerous outcome variables is to analyze each of the outcome variables separately. With this method, the significance for each separate test is reported as though it were an independent test of the research question. The main concern with this method is that multiple tests for any single research question are not independent. These multiple tests are often referred to as a ‘‘family’’ of tests. When conducting a ‘‘family’’ of tests, it is important to use the right alpha level. As described earlier, if there are five tests in your family of tests for a specific research question, the Type I error rate is 25% (5% 5 tests) and not the typical alpha of 5%. Having multiple tests that result in an inflated Type I error is a problem often referred to as the ‘‘multiple testing’’ or ‘‘multiple comparison’’ problem.


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A second approach to multiple outcome variable analysis is to reduce the data from many variables to just a few variables, also known as data reduction. Data from many variables can be combined into a single variable, guided either by theory (e.g., creating an average of multiple items to create a single test score) or by empirical data (e.g., factor analysis). Empirical data reduction methods (e.g., factor analysis) often require very large sample sizes. Data reduction eVectively reduces the number of outcome variables to be analyzed. It is useful when the variable of interest is best represented by the combined variable and not the individual variables. For example, on a common measure of childhood behavior, such as the Child Behavior Checklist, most researchers are interested in the aggregate measure of externalizing behavior and not the individual items on the measure. There are two concerns with data reduction approaches. First, important detail may be lost when data are reduced to a single variable. Losing detail is of particular concern when data analyses are exploratory because there is no prior knowledge about which particular variables may be important. Reducing the data to a single variable in exploratory analyses may obscure critical findings. Second, when data reduction is guided by empirical findings, it may be more diYcult to replicate the results because empirical data reduction in another sample would likely result in a diVerent single outcome variable. Analytic results from these diVerent outcome variables are likely to diVer as well due to naturally occurring diVerences between samples, even when selected from the same population. When decisions about data analyses are made using results from prior analyses on the same data, the probability of finding a result that fails to replicate is increased. A third traditional approach for analyzing multiple outcome variables is to first analyze all of the outcome variables separately. Next, a single‐step correction method is applied to the results to control for Type I error. The most common single‐step correction method is the Bonferroni, where the value of alpha is adjusted relative to the number of tests performed. For example, if the nominal alpha level is 5% and there are 10 tests, the Bonferroni‐corrected alpha would be 0.5% (5%/10). This method is eVective in controlling Type I error. However, this approach is conservative; that is, the overall alpha level for all of the tests is usually less than 5%. A conservative Type I error correction results in decreased statistical power. The uses of data reduction or single‐step correction methods are both acceptable approaches for the analysis of many outcome variables. However, neither approach is ideal. Information loss or study‐specific findings are likely results of data reduction methods. Statistical power is reduced with single‐step corrections. A relatively new statistical tool, MPTs, provides an alternative method. This new strategy has greater statistical power and does not require data reduction, while controlling for Type I error.

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Jennifer Urbano Blackford New Method: Multivariate Permutation Tests

MPTs are an extension of permutation tests. Although permutation testing has been around for a while, MPT (Blair & Karniski, 1993; Blair, Higgins, Karniski, & Kromrey, 1994; Pesarin, 2001; Westfall & Young, 1993) and multivariate stepwise permutation tests (Blair & Karniski, 1994; Troendle, 1995; Westfall & Young, 1993) were only introduced a decade ago. MPTs address all of the concerns found in methods that are more traditional; they control Type I error, do not require data reduction, and have strong statistical power. MPTs provide an excellent solution to the problems inherent in analyzing numerous outcome variables because: (1) they can be used with small sample sizes, (2) they can be used with numerous outcome variables, (3) there are no distributional assumptions, (4) the underlying correlation structure of the outcome variables is preserved in the test and does not impact the validity of the results, and (5) they can be used when there are more outcome variables than observations. These characteristics make MPT an extremely powerful and widely applicable statistical method. The MPT creates an empirical test distribution by rearranging observed data, just as with a permutation test. The main diVerence is that with the MPT there are multiple outcome variables, not just a single outcome variable. In order to maintain the correlations among variables, all of the outcome variables are treated as a single unit and rearranged together. There are eleven basic steps to performing an MPT. Table V illustrates each step along with a practical example. For illustrative purposes, consider a scenario in which the researcher has collected measures of EEG activity, resulting in data for 132 voxels per child. The researcher wants to compare the brain activity of 10 children with autism to 10 children with Down syndrome. Since the study is exploratory, there is no theory to guide data reduction. Instead, the researcher wants to compare the two groups at each of the 132 data points. Since there are more data points than participants, traditional statistics are not possible. In addition, if the 132 tests were performed separately, the uncorrected alpha level would be 100%. The Type I error when using a Bonferroni‐corrected alpha would be conservative, that is, less than the nominal alpha. A conservative Type I error reduces the likelihood of finding true between group diVerences. Compared to the permutation tests, there are three additional aspects to the MPT. First, the data are rearranged as a group of variables instead of each variable being rearranged independently. Rearranging all outcome variables together maintains the natural underlying correlational structure of the data. For example, in time‐series data there may be natural correlations between adjacent time points. It is important to maintain these

TABLE V MULTIVARIATE PERMUTATION TEST METHOD Permutation test step 1. Determine your research question 2. State your null hypothesis 3. Choose a test statistic

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4. Compute the test statistic for each of the dependent variables for the observed data 5. Rearrange the observations

6. Compute the test statistic for each of the dependent variables for the new arrangement 7. Continue to rearrange the observations and compute the test statistic many times for all of the dependent variables 8. Select a multivariate statistic to represent all of the dependent variables and save this statistic for the empirical test distribution. An example of a multivariate statistic is tjmaxj, which is the maximum absolute t‐value out of a group of t‐values. Another is tjsumj which is the sum of all the (absolute) t‐values in a group

WITH

EXAMPLE Example

Test for group diVerences between children with autism and children with Down syndrome on all 132 data points, which represent voxels There is no diVerence between the two groups on any of the 132 data points I select the t‐test because I want to test whether the average value of brain activity at each time point is diVerent for children with autism compared to children with Down syndrome I compute the t‐value for each of the 132 variables. For illustrative purposes, I will just show the t‐values for the first 5 variables: 1.65, 2.26, 1.95, .36, and .24 Group assignment is randomly shuZed assignment across children, without changing the relationships among variables; that is, all 132 variables stay the same and only group assignment changes. Children with autism may now be assigned to the children with Down syndrome group The t‐values for the first 5 variables this time are: .65, .98, .12, .39, and .53 The data are rearranged 20 times resulting in t‐values for all of the variables for each rearrangement The tjmaxj multivariate statistic is used to represent the largest t‐value from all of the 72 data points for each rearrangement. The tjmaxj for the first rearrangement was .98. The tjmaxj values for all 20 rearrangements are: .98, .23, .30, 1.6, .22, .86, .15, .34, .56, .75, .32, .08, .90, .76, .30, 1.97, .20, .45, and .85. These data comprise the empirical distribution. In practice you would do a large number of rearrangements—typically 1,000–10,000 (continued)

TABLE V (Continued )

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Permutation test step

Example

9. Sort the observed statistic values from all of the dependent variables and employ either a single‐step, step‐up, or step‐down testing procedure (see chapter for definitions). For a two‐tailed test, use the absolute values 10. For the first test, calculate an exact p‐value by counting the number of values from the rearranged samples that equal or exceed the original statistic and dividing by the number of rearranged samples. If you are using a single‐step procedure, you can calculate exact p‐values for all dependent variables now and your testing is complete. If you are using a step procedure, continue to the next step 11. If the previous test was significant, proceed with testing the next largest test statistic. For the step‐down‐ testing process, each subsequent test is based on a subset of the original empirical distribution. Rearranged data for the variables already tested are removed by deleting the t‐values associated with those variables from the data generated in step 7. Continue with step 8 to test the next value. This process continues until a test statistic is not statistically significant or the last variable is tested

A step‐down testing procedure is used, sorting the absolute t‐values from largest to smallest

None of the 20 t‐values is greater than the largest absolute original t‐value of 2.26, the t‐value corresponding to the second outcome variable. Thus, the exact p‐value is .00 or 0/20

To continue testing the remaining outcome variables, return to the 132 (voxels) 20 (rearrangements) data matrix created in step 7 and delete the t‐value for variable 2 (the variable just tested) in each of the 20 rearrangements. The new matrix is 131 (voxels) 20 (rearrangements). Continue with step 8 and select the tjmaxj for each rearrangement from the 131 remaining outcome variables to create the empirical distribution. Test the next largest t‐value, 1.95, against that distribution and calculate an exact p‐value


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relationships because maintaining such relationships can substantially increase statistical power in the multivariate analysis. The second addition is in the selection of a value for the empirical test distribution. With the MPT, each rearrangement results in multiple test statistics (e.g., one t‐test for each outcome variable). However, the final empirical distribution still needs to be composed of only one value from each rearrangement. It is necessary to select a single test statistic from each rearrangement. The selection of one value is commonly done using a multivariate statistic. One example of a multivariate statistic is tjmaxj, which is the maximum absolute t‐value out of a group of t‐values. Another commonly used multivariate statistic is tjsumj, or the sum of all of the absolute t‐values in a group. The largest absolute value is best when the eVect is expected to be isolated to one or a few outcome variables. An isolated eVect is frequently seen when analyzing data for a time‐ or region‐specific eVect in large datasets. The sum of the absolute t‐values should be used when testing for an overall eVect of the outcome variables. The third addition is that a stepwise method is applied to the process of significance testing for the group of outcome variables. Single‐step methods, like Bonferroni, test the significance of all outcome variables at once using an adjusted alpha. Multistep methods sort all of the variables and then test the significance of one variable at a time. After the first variable is tested, the test criterion is modified for the remaining variables. Then the next variable is tested and the process continues iteratively. The main diVerence between a single‐step and multistep method is the test criteria used for determining significance. The test criterion is constant with a single‐step method but with the multistep method, it changes iteratively. The multistep method has increased statistical power across the group of significance tests because the criterion is modified with each variable tested. Multistep methods can be either step‐up (starting with the least significant variable) or step‐down (starting with most significant variable). Both methods can be applied to MPTs. Research has shown that Type I error rates are similar for both methods (Blair & Karniski, 1994; Blair, Troendle, & Beck, 1996) and power is slightly increases with step‐up methods (Blair & Karniski, 1994; Blair et al., 1996). D.

Applications

MPTs are new statistical methods that provide exact p‐values for analyses of multiple outcome variables. MPTs have no assumptions about the data distribution, variance, or the underlying correlation structure of the data; however, the permutation test assumption of exchangeability still holds. Type I error is controlled for at a family level and statistical power is higher

118


than that for other methods. MPTs can be used when the number of outcome variables is more than the number of observations. In summary, MPTs provide a very flexible and powerful analytic tool for multivariate analyses. This method is especially well suited for exploratory analyses with numerous outcome variables. As with any technique, MPTs have limitations. MPTs are more challenging to perform than the traditional methods. However, there are increasing numbers of software programs that can perform MPTs. The SAS statistical software (SAS software, 2003) has a procedure, PROC MULTTEST, which can perform multivariate stepwise permutation tests for a variety of basic statistical procedures. Other programs include StatExact (StatXact version 7, 2005) and NPC 2.0 (NPC 2.0, 2001). An MPT for correlations program (Yoder, Blackford, Waller, & Kim, 2004) is available at http://kc. vanderbilt.edu/Quant/Programs/Programs.htm. Finally, one could write a custom program to perform MPTs using the steps outlined in Table V. The other limitations for MPTs are the same as the permutation test limitations. MPTs are not available for complex multivariate analytic methods. Audiences and reviewers may be unfamiliar with this statistical technique. For more information on the MPTs, see Resampling‐based multiple testing: Examples and methods for P‐value adjustment (Westfall & Young, 1993) and Multivariate permutation tests: With applications in biostatistics (Pesarin, 2001). V.

CLOSING REMARKS

Today’s researchers in the fields of developmental epidemiology and developmental disabilities are fortunate. Such new strategies, such as propensity scores, permutation testing, and MPTs, enable researchers to overcome statistical challenges that have been frustrating and have plagued studies for years. These new statistical approaches are flexible, practical, and easy to perform using current computer technology and available software. Use this chapter as the key to opening a door to new research avenues and exciting new discoveries. REFERENCES Bergstralh, E., Kosanke, J., & Jacobsen, S. (1996). Software for optimal matching in observational studies. Epidemiology, 7, 331–332.


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Blair, R. C. (1981). A reaction to ‘‘Consequences of failure to meet assumptions underlying the fixed eVects analysis of variance and covariance.’’ Review of Educational Research, 51, 499–507. Blair, R. C., & Higgins, J. J. (1981). A note on the asymptotic relative eYciency of the Wilcoxon rank‐sum test relative to the independent means t test under mixtures of two normal distributions. British Journal of Mathematical and Statistical Psychology, 34, 124–128. Blair, R. C., & Karniski, W. (1993). An alternative method for significance testing of waveform diVerence potentials. Psychophysiology, 30, 518–524. Blair, R. C., & Karniski, W. (1994). Distribution‐free statistical analyses of surface and volumetric maps. In R. W. Thatcher, M. Hallett, T. ZeYro, E. R. John, & M. Huerta (Eds.), Functional neuroimaging: Technical foundations (pp. 19–28). San Diego: Academic Press. Blair, R. C., Higgins, J. J., Karniski, W., & Kromrey, J. D. (1994). A study of multivariate permutation tests which may replace Hotelling’s T2 test in prescribed circumstances. Multivariate Behavioral Research, 29, 141–163. Blair, R. C., Troendle, J., & Beck, R. W. (1996). Control of familywise errors in multiple endpoint assessments via stepwise permutation tests. Statistics in Medicine, 15, 1107–1121. ChernoV, H., & Savage, I. R. (1958). Asymptotic normality and eYciency of certain nonparametric test statistics. The Annals of Mathematical Statistics, 29, 972–994. D’Agostino, R. B., Jr. (1998). Propensity score methods for bias reduction in the comparison of a treatment to a non‐randomized control group. Statistics in Medicine, 17, 2265–2281. Edgington, E. S. (1995). Randomization tests (3rd ed.). New York: Marcel Dekker, Inc. Erickson, J. D. (1978). Down syndrome, paternal age, maternal age and birth order. Annals of Human Genetics, 41, 289–298. Fisher, R. A. (1966). The design of experiments (8th ed.). New York: Hafner. Glass, G., Peckham, P., & Sanders, J. (1972). Consequences of failure to meet assumptions underlying the fixed eVects analysis of variance and covariance. Review of Educational Research, 42, 237–288. Good, P. I. (2000). Permutation tests: A practical guide to resampling methods for testing hypotheses (2nd ed.). New York: Springer. Hayes, W. L. (1988). Statistics. Fort Worth: Holt, Rinehart and Winston, Inc. Hodges, J. L., Jr., & Lehmann, E. L. (1956). The eYciency of some nonparametric competitors of the t‐test. The Annals of Mathematical Statistics, 27, 324–335. Hook, E. B., & Lindsjo, A. (1978). Down syndrome in live births by single year maternal age interval in a Swedish study: Comparison with results from a New York State study. American Journal of Human Genetics, 30, 19–27. Micceri, T. (1989). The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105, 156–166. NPC 2.0 (2001). Ponte San Nicolo. Italy: Methodologica. Penrose, L. S. (1933). The relative eVect of paternal age and maternal age in mongolism. Journal of Genetics, 27, 219. Pesarin, F. (2001). Multivariate permutation tests: With applications in biostatistics. New York: Wiley. Pitman, E. J. G. (1937). Significance test which may be applied to samples from any population I and II. Journal of the Royal Statistical Society Series, 4, 119–130, 225–232. R Development Core Team (2005). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Resampling Stats (2003). Arlington, VA : Resampling Stats, Inc. Rosenbaum, P. R. (1998). Multivariate matching methods. Encyclopedia of Statistical Sciences, 2, 435–438.

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Economic Perspectives on Service Choice and Optimal Policy: Understanding the Effects of Family Heterogeneity on MR/DD Outcomes* STEPHANIE A. SO VANDERBILT KENNEDY CENTER, DEPARTMENT OF ECONOMICS VANDERBILT UNIVERSITY, NASHVILLE, TENNESSEE; AND VANDERBILT KENNEDY CENTER, DEPARTMENT OF PEDIATRICS, VANDERBILT UNIVERSITY NASHVILLE, TENNESSEE

I.

INTRODUCTION

Other chapters in this issue have explored the nature and usefulness of developmental epidemiology in the world of mental retardation and developmental disabilities (MR/DD) research. How, then, does a chapter on economics fit into this schema? The answer is simple: economic research is complementary to other disciplines that investigate outcomes. MR/DD researchers can use economic perspectives and methods to translate the findings of their research into policy. II.

WHAT IS ECONOMICS?

Economics is a social science. The problems that it studies are all driven by the fundamental realization that resources are scarce, but people have competing uses for them. The contribution of economics comes from its studies of how people make decisions to allocate those scarce resources to one use or another. *Author’s Note: Funding for this research by the Vanderbilt Kennedy Center’s Nicholas Hobbs Society and NICHD grant RO3HD 050468 is gratefully acknowledged by the author. The author would like to thank Dr. John P. Conley, Dr. Robert M. Hodapp, and Dr. Richard C. Urbano for comments on previous versions of this chapter. The views expressed here are solely the responsibility of the author and should not be interpreted as reflecting the views of Vanderbilt University or of any other person associated with the Nicholas Hobbs Society or NICHD. INTERNATIONAL REVIEW OF RESEARCH IN MENTAL RETARDATION, Vol. 33 0074-7750/07 $35.00

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Resources may be defined in various ways, depending on the research question of interest. Resources include time, income, land, breathable air, and so on. In studying (1) the behaviors of people as they decide to use these resources and (2) the outcomes and implications of these decisions, economics can be and is an enormous field. Like any social science, economics includes many subfields. Some subfields specialize on the sets of decisions they examine, while other subfields arise because of a common interest in the outcomes associated with many diVerent decisions. Labor economists, for example, study the decisions to work (supply work) and the decisions to hire workers (demand work). They study the outcomes of those decisions such as the employment rates, wage rates, and productivities by industry or company. They study all of the factors that aVect how people make these decisions (the existence of unions, the structure of unemployment benefits, and so on). This installed base of knowledge about why and how people work helps to develop or analyze policies that are aimed at employment outcomes (e.g., the American Disabilities Act, hours restrictions on medical residents). The central issue in labor economics is how people perceive the value for diVerent uses of time. Policies and circumstances aVect the valuation of these alternative uses of time. The notion that time is scarce, particularly for parents, is a theme that we will develop later in this chapter as we examine the economic decisions that aVect the developmental and health outcomes for children with MR/DD. At the same time, researchers who are interested in the employment outcomes and transitions for adults with MR/DD should be aware that there may be much of interest to them in the labor economics literature. Other economic subfields concentrate on issues surrounding given outcomes of interest. Health economics and the economics of education are examples of these. Health economists are fundamentally interested in how decisions to allocate resources aVect health outcomes. Studies within health economics may vary widely, according to the decision maker. Some studies examine the behaviors and outcomes of patients and their families, because these are the agents who demand care in health markets. Other studies model the decisions of providers (physicians, therapists, and so on) who supply care. Some of the most important questions about providers are how they respond to incentives, such as reimbursement policies, treatment norms, more motivated versus less motivated patients, and regulation. Because we have a third‐party payer system in this country, still other studies model the incentives of payers, such as private or government insurers, and the eVects that their decisions will have on health services use and outcomes. In each case, the central question is how the decisions of these actors in the health care market combine to aVect service use and health outcomes.

ECONOMIC PERSPECTIVES

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Many of these studies have parallel and/or related issues to questions about disability services and outcomes. While the economics of disability services has received less attention to date, interest in disability research is growing as economists begin to explore the distinctions and connections between health and disability, as well as their eVects on other economic decisions, outcomes, and policy‐relevant behaviors. A common interest in outcomes also unites the economics of education. These studies ask about all the inputs associated with educational outcomes. The particular research focus for these decisions may vary with: the diVerences (and relatedness) between educational settings such as public and private schools; the choice and eVectiveness of inputs that are selected and provided in schools, homes, neighborhoods, or before birth (as in the nature versus nurture hypothesis); or the eVects of policy. When evaluating the eVects of policy, or any factor, on choices and outcomes, the eVects may be direct and indirect. These eVects may be conceptually meaningful to the researcher in understanding the mechanisms by which outcomes may be improved. For example, Head Start may have a direct eVect on educational outcomes, say test scores, through its special education services. At the same time, there may be substantial indirect eVects of the program on the same outcome of interest. Head Start may also improve educational outcomes by providing nutritious meals: the meals may improve child health, improve the abilities for children to attend while in school, and translate into higher test scores. Empirical evidence on the strengths of these diVerent eVects may be tremendously helpful in thinking about the net benefits and costs of a policy. MR/DD researchers primarily interested in the developmental and educational outcomes of children may already be familiar with many of the studies and research methods developed and used in the economics of education literature. Finally, there are areas of economic research, such as public economics or the theory of risk and insurance, which add more universal insights about how people make decisions. The lessons and implications from these models cut across many types of decisions, outcomes, and policy. On the technical side of economics, there is econometrics, dedicated to the development and improvement of the statistical techniques that economists need to analyze all of these decisions and outcomes in the data. A.

Common Misperceptions About Economics, Economists, and the Importance of Costs

In an ideal world, with limitless resources, every problem could be solved by devoting as many resources as were needed to the best uses available. In this idealized world, every patient could receive the most eVective treatments

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and services and every student could receive the finest inputs to education. In the real world, however, resources are limited and economic arguments invariably intrude on allocation decisions. People perceive economic interests to be behind managed care organizations, government insurance programs, and payers or politicians who require proof that programs are cost eVective. Through guilt by association, economics often is perceived, at best, as an unimaginative and inflexible realm of inquiry or, at worst, as a capricious and arbitrary set of activities somehow related to the counting of costs and benefits. It is true that costs and benefits play important roles in economics. After all, how people perceive costs and benefits, throughout all studies of economics, shape the behaviors of people as they decide to allocate resources. Economists are keenly interested in understanding these abstract issues (conceptualized as the eVects of incentives on choice), as well as in understanding how specific changes in incentives aVect outcomes. It is also true, however, that faced with the (relatively few) economic studies that catch the public eye, the careful attention that economists pay to diVerent kinds of incentives will likely be lost in the reportage. Instead, the public’s attention will be drawn to the highly situation‐specific decision, like how the government is going to spend your tax money or whether the insurance companies are going to add a specific health benefit that you care about. When these studies are interpreted out of academic context and without reference to the scientific method (the assumptions, the data, the research methodology, the limitations to interpretation), it is easy to understand why the public is led to believe that economists are fixated on dollars and cents. B.

True Relationship Between Costs and Economics

As illustrated earlier, there are economic choices (i.e., behavioral decisions that govern resource allocation) in the framework of every overt decision: the decision to work, the decision to take one’s child to the physician, and the decision to track down the details of the economic study reported in the newspaper. Readers of newspaper articles have many things to do with their time. Learning more about economics is costly. Those who have weighed the costs and benefits and choose not to learn any more about economics likely will never do so unless something changes. This may be despite economists’ enduring certainty that they have delivered the most thorough and illuminating of explanations in their academic journals. And so the outcome persists that readers will tend to misinterpret what economics can contribute. Could we improve outcomes in the economic literacy of newspaper readers? Perhaps we could, but only if we change the fundamental trade‐oVs that face them. We could lower the costs of choosing to learn economics by writing


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more interesting and accessible articles in fields outside of academic economics. Alternatively, we might expect readers to choose to learn more about economics if their situation changes so that they perceive a higher benefit to learning economics. Predicting this change in behavior (and subsequent outcomes) could be made in as systematic a way as predicting that an avid sports reader will be more likely to turn to the arts section when her new love interest reveals his passion for the ballet. Here we come to the two abstract and key potential roles for costs in economic studies. Costs are related to economic decisions, generally, as either (1) a measure of the outcomes we observe (e.g., when one decides to go to the doctor, the action results in an expenditure) or (2) as an important incentive that influences the decision and thus the outcome of interest (e.g., the price of the doctor may help to predict how much health care one uses and thus how healthy one is as a result). Particular costs, such as the price of a commodity or the value of an asset, are of no interest unless they are important to the economic model in one of those two ways. Thus, the labor economist may study how work decisions vary between women and men without ever checking on the exact cost of business attire and the health economist may study the likelihood that hospitals will specialize in cardiac surgery without ever seeing the net income statements of the hospitals involved. Most likely, they are focused on understanding the role of far more important incentives that shape the diVerences in the respective economic decisions and outcomes that result. C.

Economics Research Strategy

The newspaper example contains the essence of the economic research strategy. Regardless of the economic decision under study, economists model the behavior of decision makers, or agents and how this behavior results in a choice. Depending on the decision of interest, agents might be consumers (on the demand side) or firms (on the supply side), but each agent faces a choice that results in an outcome. Consumers decide whether and how much of a good or service to buy. Firms decide what and how much to produce. The choice may be observed as a quantity that is purchased or sold or, downstream, as the eVect on or a change in health outcomes. Agents may be countries which are trying to decide the terms of a trade agreement, politicians who are deciding on a voting strategy, parents who are trying to determine which school district to use, or school oYcials who are trying to figure out how to put their resources to best use. In each case, the model specifies the objective that the decision maker wishes to maximize with his decision and the trade‐oVs that are faced in making the decisions. The trade‐oVs arise because the agent faces scarce resources. These scarce resources, and whatever else limits the agent’s decisions, are called

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constraints. Once the problem is framed, decisions and the outcomes of those decisions are examined as they relate to the model’s parameters. In classical labor economics, the worker may want to maximize his personal happiness, so personal welfare is the objective function. Both leisure (not working) and income (being able to purchase goods) make him happy and so he must decide on how to allocate his time between leisure activities and work. Time has been called the scarcest of resources: it is impossible to manufacture any more hours in the day. A wage rate is a parameter of the model that influences how he will evaluate the trade‐oV between devoting an hour to leisure versus work. Thus, as the wage rate changes, we might observe diVerent workers’ decisions about how hard to work. Some workers may face a more severe problem if they have no other means of support. If those workers do not work at all, they will not survive. An economic model for these workers might reflect their circumstances by modeling an additional constraint. Informally, the additional constraint might be specified so that it restricts their choice set by ruling out an option (e.g., they may not choose a life of leisure). Choices always remain. (Otherwise, it is no longer an economics problem.) Even these workers will vary in how much they decide to work after they have satisfied their survival constraint. In other research, So (2002) has argued that health and disability must be modeled more explicitly in economic models of family labor decisions. Rather than adopt the traditional economic model that distinguishes clearly between labor and leisure as the primary two alternatives to time, So showed that caregiving activities devoted to chronic conditions ought to be considered as a third distinct activity. The rewards and costs that characterize caregiving, under more adverse circumstances, may be conceptually very diVerent and empirically distinguishable from the rewards and costs that characterize typical caregiving activities. So’s model makes the economic argument that decisions and outcomes of families who are faced with these challenges may diVer strikingly from the predictions that one might make about the same families under the assumption that they do not face any within household demands to care for someone with a chronic illness or disability (So, 2002). Furthermore, policies predicated on the idea that families balance only typical trade‐oVs may require serious reevaluation when applied to these caregiving families. Like the workers who face an extra starvation constraint, families who have caregiving requirements may not have the same flexibility to devote resources to other activities as typical economic theory might suggest. Even when the decision maker is not an individual, economic models continue to use the structure of agent, objective, and constraints to study decisions and outcomes. For example, health care organizations may wish to maximize profits, but in doing so must choose between diVerent combinations


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of quantity and quality of care provided. In health economics, firms that face one set of constraints may opt to serve many patients while forgoing the most cutting‐edge technologies, whereas firms that face other constraints may serve a select few patients but provide the most luxurious sets of services possible. These firms face multiple constraints as well: on the kinds of services they can oVer as regulated by the government, by the kinds of market conditions that make diVerent choices possible, and, ultimately, by their financial conditions. As economists have learned from building and testing these models, they have found that outcomes emerge, on average, in ways that are consistent with the idea that agents behave as if they are maximizing their objectives given their constraints. If the price of heating oil goes up, the average person will consume less by turning the thermostat down. These behaviors lead to new questions about new outcomes. Will they find new sources of heat so that the temperature will go back up? Will they wear sweaters instead so that the temperature remains low? Temperature may not be the most important outcome for some people, and so it is that more complicated economic models are required when the outcome is produced via more complex mechanisms. The important concern in more complicated models is that the models produce testable results which are then examined in the data. If diVerent families behave diVerently to an increase in price, what does that tell us about the agents’ decision processes, their objectives, their constraints, or their outcomes? In each of these diVerent situations, economic analysis of the outcomes that we observe help us to formulate the correct questions about the circumstances of the agents themselves, just as we would be led to ask questions if we observed a sudden increase in cases of hypothermia in certain parts of town when heating oil prices went up. If we can identify that outcomes change as constraints or characteristics change, then we can learn something about both the decision process and its consequences. The central issue of empirical economic studies is to seek results that strengthen, test, refute, or add to economic theories about why people behave the way that they do. As empirical evidence on how behaviors that shape choices are understood, and as the outcomes that result from those choices are understood, then economics helps us to understand how to use policy to aVect outcomes. Policies are merely special cases of the sets of circumstances and incentives that guide economic behavior. Economics asks, in order to achieve an objective, what policy should we implement (taxes, regulation, incentives to increase competition)? Then, the discipline of the science begins again. How will this change the incentives to reallocate resources? What outcomes will result? In the context of economics as a behavioral science, the empirical methods and results produced by economic studies improve our ability to answer these kinds of questions.

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Stephanie A. So Newspaper Readers and Children with MR/DD Outcomes: The Connection

The main point of this chapter is to explore the idea of economics as a behavioral science as it relates to health policy generally and MR/DD policy specifically. Our argument is the following. Even if economists believe they have provided fundamental insights in their articles, if people do not choose to read the articles, then there may be (in the starkest case) absolutely no benefit transmitted from that academic service to those outcomes. Similarly, services that are oVered to children with MR/DD outcomes, even if they are thought to be eVective by experts, will not change outcomes if children do not use them. We realize that the problem, in reality, is much more complex than this. The rest of the chapter elaborates on some of these issues.

III.

THE RELATIONSHIP BETWEEN ECONOMICS AND POLICY

Let us begin by considering one of the classic examples of the ‘‘law of unintended consequences.’’ The basic idea, again, is that people respond to incentives. It is irresponsible and often self‐defeating to make policy on the assumption that people will not change their behavior in order to maximize their best interests under the new incentives. In other words, in framing the alternatives of diVerent policies, economists must consider the possibility of unexpected dynamic responses to policy shifts, not just the simple first order static eVects that were intended. Beginning in the 1960s, federal regulators mandated increasingly rigorous safety standards on automobile manufacturers. Seatbelts were required to be installed in every car; later the regulations included safety glass, crumple zones, airbags, and many other smaller improvements. On its face, lowering the probability that any particular auto accident results in death or serious injury seems like very sensible public safety policy. One would expect that the cost–benefit analysis would be straightforward: the costs of improvements versus the savings one might recoup from, say, halving the number of deaths per accident. However, this naı¨ve prediction ignores how drivers respond to driving safer cars. Each driver makes choices about how safely to drive. Single people without families are more inclined to drive after a night out at the bar. If an executive is late to a meeting, he might drive a little faster and not come to a complete stop at a light before turning right. On the other hand, if our children are in the car with us, we take far fewer risks. The point is that


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people weigh the costs and benefits of reckless driving depending on their circumstances. One of the major costs of reckless driving is the chance of being injured or killed in an accident. Improvements in car safety lower these potential costs. Thus, when car safety regulations were put into eVect, one would expect to see more reckless driving and an increase in the number of accidents as a result. How does improved automobile safety aVect the outcome of vehicular fatalities? On the one hand, there are more accidents. On the other, each accident has a smaller probability that it results in a fatality. Because the total number of deaths is a product of these two eVects, without looking at the data, it is unclear if mandated increases in auto safety will, in fact, save lives. What this example tells us is that there is a potentially important unintended consequence of the policy. Determining its degree of importance is an empirical question. Peltzman (1975) executed just such a study and found both of these oVsetting eVects present. Fortunately, he found that the safety eVect outweighed the reckless driving eVect so, on net, vehicular fatalities decreased. He also discovered another unintended consequence: the increase in reckless driving resulted in an increase in the number of pedestrian deaths. Thus, how one feels about further increases in car safety might depend on how one gets around. If one walks, takes the bus, or rides a bicycle, one might want to mandate steel spikes that come out of the steering wheel and point directly at the driver’s chest. Doing so might just save the lives of more pedestrians. Another leading example comes from education policy. When Brown v. Board of Education was settled in 1954, the Supreme Court turned down the argument that schools which were segregated but equal in all physical respects were able to oVer the same opportunities to students of all races. The principle of the law was well reasoned and the mandatory busing and other policy responses that followed were all well‐intentioned eVorts to increase educational opportunities and outcomes for disadvantaged children. However, the immediate eVect of these policies was that wealthier parents withdrew their children from public schools and chose private education instead. Since then, public schools, especially in larger cities, seem to have been in a state of continual crisis. Some of the most recent research in the economics of education has argued for the importance of at least two factors that improve educational outcomes for children. The first is the peer group a child has in the school (Gaviria & Raphael, 1997; Robertson & Symons, 1996). Children from any educational and social background do better when they share a classroom with other children who do well in school, do not disrupt class, and set a good example.

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The second is teacher quality (Hanushek, 1997). A motivated and experienced teacher simply does a better job compared to the alternative. Perhaps surprisingly, something that in itself seems to have very little eVect is increased spending per pupil (Gaviria & Raphael, 1997; Hanushek, 1996). One need only compare the spending in New York City or Chicago to that in rural school districts in Montana to see the plausibility of this result. Here we see the crux of the problem: we cannot force parents to send their children to public schools nor force the best teachers to teach in them. Wealthy parents are willing to pay to buy their children a peer group of smart children in order to increase their child’s opportunities. Such schools benefit from providing such a peer group and so oVer scholarships to poor but smart children. On average, the wealthy and the smart will tend to find alternatives to troubled public schools, while the poor and the less able will be left behind. As the quality of the peer group at public schools declines, other parents who are less wealthy will reach a point where they also feel they need to seek an alternative. An unintended consequence of the desegregation policies was to lower the average quality of the peer group in public schools. Teachers also tend to prefer to teach smart, well‐behaved children, so schools (public or private) that oVer classrooms full of such children have their choice of the best teachers available. Thus, teaching talent becomes concentrated at the best schools, which deepens the problem for public schools. Is it still possible that mandatory busing improved school outcomes for some children? Of course, it might be that the fully segregated schools that minority children were forced to attend before the 1950s were even worse than the partly integrated schools that they subsequently attended. However, it is equally plausible that the newly integrated schools did not do as good a job as they did before Brown v. Board of Education. To find out if minority children’s educational outcomes improved and, if so, by how much, is again an empirical question. Here, the correct comparison is that of the desegregated children’s outcomes to the counterfactual of what the outcomes would have been if no such policy had taken place. It is in the disentangling of the direct eVect of the policy and the indirect (sometimes unintended) eVects that econometrics is useful. We will discuss some of these econometrics problems later. What can be seen from the immediate discussion is that agents acting in their own self‐interests may respond to changes in policy in ways that potentially may more than oVset the intended and socially beneficial first order eVects. Are there solutions to this problem? Economic theory helps us to understand the ways that people will respond to policy changes. Econometric analysis takes us a bit further by helping us to understand the magnitude of these eVects.


A.

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Economics, Health Policy, and Health Outcomes

Incentives matter in health policy as much as they do anywhere else. An obvious example is the fundamental dilemma of how to price health care. We would like people to balance the costs and benefits of their health care consumption. If people use too little, they miss days at work, feel miserable, and may ignore conditions that are ultimately very expensive to treat in the later stages. If they consume too much, they bankrupt the health care system. This creates a basic tension to which there really is no satisfactory solution. If we charge people the full price for physician visits or other services, they will underutilize services and ignore conditions that could be treated cheaply if services are delivered early. If we let them use services for free, some people will use them just to talk to someone. Variations on these trade‐oVs exist for any service. A dilemma exists if we would like to insure people against health care costs that exceed their ability to pay, but still give them incentives not to overuse the system. The implementation of co‐payments by insurers is a crude way to try to balance these competing objectives. How to set these co‐payments in a way that optimally balances these objectives is, again, an empirical question. One obvious problem that arises is that one size could never fit all. Poor people should have lower co‐payments than rich people since a given co‐payment has a larger disincentive eVect on the poor as well as a lessening of the insurance dimension by a larger amount. Co‐payments are an example of what economists call a ‘‘second‐best’’ solution. This means that the problem is so constrained that first‐best, socially optimal policies are not available. It would be impractical and perhaps illegal to charge every patient a diVerent co‐payment based on the patient’s income and on how essential the doctor believed the service was. Thus, we have to look for the best alternative policy that does not violate the institutional and economic constraints that policy makers face. Weighing patients’ benefits gets trickier still when economists consider the reality that a patient may have more than one condition. In recent papers, such as those by Dow, Philipson, and Sala‐i‐Martin (1999) and Peltzman (2002), a slightly more subtle application of the car safety story is applied to health care. These papers examine the incentive eVects that arise when risks to mortality are reduced in only one medical area. For example, as technology has reduced the risk of dying from infectious diseases, people may devote more of their resources to other activities. They may travel more freely, eat more indulgently, and increase their risks of diabetes instead. The empirical estimation of these oVsetting behaviors across mortality risks are of great current interest. The preceding examples contain lessons about the diYculties involved in estimating the eVects from a key variable such as changes in policy incentives

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or in response to new technologies. In health economics, two analogous problems are the following: (1) to estimate the eVect of a service on health outcomes and (2) to estimate the eVect of incentives on service use. As the reader will suspect, these research questions are intertwined with many related decisions that take place within particular family settings. To illustrate the last point, consider a final example from the economics of obesity. Chou, Grossman, and SaVer (2004) reported that two‐thirds of the increase in adult obesity between the mid‐1980s and 1999 were explained by the rapid growth in the per capita number of fast food and full‐service restaurants. However, they argue that rapid increases in the supply of services are usually in response to market demand. Anderson, Butcher, and Levine (2003) provide strong evidence that obesity in children may be explained by demand‐side issues. Their findings show that increases in average maternal work hours account for as much as one‐third of the growth in obesity among some children. Finding the right policy on how to treat the epidemic of childhood obesity will require significant social science input about the within‐home decisions that try to balance these competing objectives of child health and family work. B.

Overarching Lesson: The Economics of Services, Policy, and Outcomes

It is not enough to show that services (such as the academic economic literature or packaged salads in fast food restaurants) are available and could improve outcomes. It is also risky to mandate that services be used in a certain way or in a certain combination. As long as people are free to follow their own interests, they will find ways to select alternatives that work best for them, given their circumstances, even if these services do not yield the best possible outcome for them. It is understandable that researchers, who work for years to show the eVectiveness of a service through painstaking randomized controlled trials, and providers, who follow evidence‐based best practices, may become frustrated with the general public when they do not choose to consume these services. One of the main insights from economics is that we cannot force people to behave as policy intends. Rather than rail against the constraints that people face which in turn cause them to choose less than medically optimal services, public policy must take these behaviors into account and, instead, try to change the incentives that people face. No matter how eVective a service is determined to be in a controlled setting, it is no good unless the services ultimately are chosen by the clients—or their families. Economics can help researchers in other fields to learn what they


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need to know about behavior in order to improve policy. In other words, it is critical to approach public health questions from a social science as well as a biological science standpoint.

IV.

ECONOMICS OF MR/DD OUTCOMES

At first, it may seem strange to consider the outcomes of children with MR/DD from this standpoint. We might be able to provide incentives for diabetes patients to eat better, for obese patients to exercise, and for cancer patients to quit smoking. If the client is an adult, we might be able to tailor these interventions to be more eVective according to the incentives that face the type of patient (rich or poor, male or female, old or young) that we are treating (Ensor & Cooper, 2004). Children in general, including children with MR/DD, however, either do not or may not be allowed to make decisions about their own service use and outcomes. In these cases, economic theory clearly dictates that the parents should be studied because they are the ones who make decisions about which services to consume. In this chapter, we consider how parents aVect service use (thus outcomes) in two important ways. They decide on the kinds of inputs that the family will provide within the household in addition to which services and supports the family will purchase from outside. Family choices of service‐mix clearly will aVect the outcomes of children with MR/DD. Economic studies of within‐family decisions began with a series of seminal papers by Gary Becker (Becker, 1991; Becker & Tomes, 1979). Becker was the first to articulate that families have finite endowments of money, time, energy, and other resources. Parents are faced with decisions about how to allocate these resources among all their competing uses: a child with a disability, other children, a spouse, careers, household production, and so on. Economists have studied how these decisions may be made jointly by couples and how the opportunity costs of the alternatives shape choices and outcomes. For example, one reason that it is more common for mothers to stay at home to care for young children is that the market wage of the father is usually higher. Thus, the value of the forgone opportunity required to stay at home, in terms of lost wages, is typically smaller for the mother than the father. The interesting empirical question is how child outcomes are aVected by a stay‐at‐home mother compared to the alternative. From this perspective, if parents decide not to provide a service to a child with MR/DD, we are never able to conclude that these parents are intentionally shortchanging the child or are unaware of the benefits of the service. While these might be explanations, it is plausible that the parents are making

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rational trade‐oVs and have chosen a diVerent way to balance all of their demands. For example, a single parent might be unable to take a child to speech therapy sessions because the money from the second and third jobs is needed at home to support the other four children. How to reconcile this choice must begin with understanding the relationships between family circumstances, service choice, and outcomes. A.

Family Economics Research Strategy: Families, Services, and Outcomes

How, then, can economics be used to improve outcomes for children with MR/DD? We propose to consider two logically separate problems. The first problem is probably more familiar: to find what combination of services most improve outcomes, for given family and child‐specific characteristics. Note that this is diYcult or impossible to address in many traditional randomized control trials. The empirical objective is to determine how any given combination of family circumstances (income level, access to health insurance, marital status, and so on) interact with the eVectiveness of various service combinations. It is necessary to turn to large, population‐based datasets in order to have enough variation and repetition of family structure to get statistically significant answers to these questions. Some services for children with MR/DD may only be productive if parents match those services with household time. For example, behavioral therapy, special help in reading, and some kinds of physical therapy benefit from reinforcement at home. We would expect such services to be more eVective when parents have more time and energy for them. It follows that two parent households, households with a nonworking mother, households with fewer children, without an unhealthy grandparent living within the home, or with a young and vigorous grandparent would benefit more. The common thread here is that such households would have a lower opportunity cost of time. Many services may not require as significant or sustained complementary family time inputs. Examples may include surgery and other types of acute medical services and therapies in clinic delivered by highly specialized providers. We would expect to see that family demographics have very little impact on outcomes in these cases. Services that give extra time to the family, in contrast, may have a greater impact on families with high opportunity costs of time. Day care services might free a parent to work or take care of household chores during the day so that more time is available when the child comes home. In contrast, these services would not have as great a benefit for children who live in a household with adequate parental time.


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There are other kinds of potential complementarities between services and family characteristics. Children with non‐English speaking parents might respond better to speech therapy when they have siblings in the house than when they do not. Educated parents might be more eVective at managing more complex service arrangements or be better able to find combinations of services that are mutually supportive. The point is that we should not expect a given treatment regimen for a child with MR/DD to have the same eVect, independent of that child’s family circumstances. Best practices require taking family circumstances into account when recommending a set of support services. At a formal level, this first problem calls for using family demographic data and child characteristics to predict health (including developmental) outcomes. The second problem might be less familiar: to find out what combination of services are actually chosen, for given family and child‐specific characteristics. At a formal level, this type of study calls for using family demographic data and child characteristics to predict the likelihood that a given service is chosen by a family for their child. As we have said before, simply knowing what we would like a parent to do for his or her child is not enough. In order to make policy that actually helps children with MR/DD, we also need to know how and why a parent chooses a service for the child. This is a pure social science question. Parents have many objectives, including providing for all children, saving for retirement, getting promoted, and many others, in addition to the welfare of their child with MR/DD. These parents, like other parents in some ways and unlike others in potentially identifiable ways, must find the best possible way to allocate their limited resources. Ignoring this balancing act is like attempting to hold back the sea. What is called for is policy ju‐jitsu. We must use the momentum of self‐interest to steer the parent in the right direction. DiVerent types of families face significantly diVerent costs for using or providing diVerent services. Most obviously, the opportunity cost of time will play a role. Given the current delivery system, many services require that a child be accompanied by an adult. This practice can be especially diYcult for a family in which all parents work outside the home. Such families also may have relatively little time to investigate treatment options or to try to get their child accepted for certain services. Thus, parents may not take advantage of what are considered to be highly beneficial programs for their children. Poor or less educated parents also may be less likely to come in for important follow‐up visits. Studies that examine the ways in which diVerent families systematically choose outcomes can be used to understand how to array the services so that families might more easily choose the services that will be best for their children.

136 B.

Stephanie A. So MR/DD Demographic and Population‐Based Data

For both of the analyses suggested in the previous sections, it is necessary to use large‐scale datasets to get meaningful answers. Large‐scale datasets, such as population‐based surveys or administrative data that capture all the activity in a given area, contain enough observations to allow the researcher to exploit the natural variation in the population’s characteristics. These sources of data are particularly important when the research question of interest is to examine the eVects of family characteristics, such as marital status, family size (number of siblings), maternal education, and so forth on child outcomes and service use. In longitudinal data, it may be possible to observe the actual changes in family structure as marriages form and dissolve or children are added. In cross‐sectional data, if samples are large enough, it may be possible to lessen unobserved heterogeneity by comparing similar types of families. In truth, the outcomes of children with MR/DD largely have been overlooked by economists as they have concentrated on the eVects of families and their circumstances on the educational, health, and later employment, wealth, and family outcomes of children generally. Thus, there is very little to report in the existing literature on MR/DD outcomes directly. Nonetheless, there exists a large literature about the roles that family and service characteristics play in determining outcomes (see, for example, the surveys contained in the Handbook for health economics; Cuyler & Newhouse, 2000). Economic studies have estimated the eVects of policy interventions like State Child Health Insurance Plans on early vaccinations and the eVects of numerous other programs, services, technologies, and family inputs, on educational and health outcomes (Joyce & Racine, 2005). For the reasons argued in the first part of the chapter, special attention has been given to the interactions of family characteristics with these policies and services in the production of these outcomes. The models, techniques, and approaches used in these studies may easily be applied to determine if similar relationships between families, services, and outcomes exist for children with MR/DD. For example, questions of the eVectiveness of parents’ use of therapies that aVect the outcomes of children with MR/DD are not structurally diVerent from studies that isolate the eVects of prenatal care on infant birthweight (Conway & Deb, 2005; Evans & Lien, 2005). C.

Preliminary Evidence of Family Effects on MR/DD Outcomes

In MR/DD research, the emergence of family and/or demographic variables as potential risk factors for outcomes or service use, in the epidemiological sense, opens the door between researchers with content expertise in the


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condition or the services and those with content expertise in economic and econometric research in education and health. For example, Urbano and Hodapp (submitted for publication) examine the correlates of divorce in families of children with Down syndrome (DS) compared to families of children with typically developing children. In their study, the presence of a child with DS was not clearly linked with a higher probability of divorce than in families with typically developing children. When divorce occurred in families of children with DS, however, the families tended to be in rural areas and formerly headed by fathers who had attained lower levels of education. Family poverty and low parental education have been linked with both poorer health outcomes and divorce. Urbano and Hodapp’s (submitted for publication) ongoing work involves asking richer questions of the data about family circumstances such as how rural outcomes and family income interact. One nice feature of their study design is that divorce is unlikely to cause DS to emerge in a child. As a result, they are plausibly able to compare the diVerences in risks of divorce in families of children with DS and families of children with typically developing children, by concentrating on the potential interactions of each child’s characteristics with these demographic features of location and education. One might ask how divorce aVects subsequent service use and outcomes compared to service use and outcomes of children of married parents, both across and within these groups. So, Urbano, and Hodapp (submitted for publication) analyzed population‐ based data on the inpatient outcomes of a birth cohort of young children with DS. Two distinctly diVerent subpopulations emerged with respect to health, some with repeated hospital use, and others with no hospital use after birth at all. Those who used many hospital services experienced hospitalizations primarily in their first year of life. Urbano and Hodapp’s (submitted for publication) data suggest that there may be an association between the timing of divorce and the presence of a child with DS. Further research is required to examine the hypothesis that children with DS who are healthy have no higher risk for divorce than typically developing children, but children with DS who are unhealthy, particularly those who live in areas or families that make it diYcult to access care or reap the benefits of care, are at higher risk for divorce. In a somewhat analogous economic study, Reichman, Corman, and Noonan (2003) investigated the eVect of poor infant health on the probability of divorce. This study built on a small literature, using the National Health Interview Survey, that showed that children with poor health had higher risks of parental divorce. However, in previous studies, infant’s health was assumed to be exogenous. Reichman et al.’s (2003) study investigated the possibility that there may be alternative explanations that caused both the infant’s poor health and divorce. For example, if the parents who divorce

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tend not to be interested in investing in long‐run propositions, then it may be that parents who tend to end their marriages are also parents who tend not to use prenatal care. In that case, the benefits to divorce and child health are systematically diVerent across types of parents and there would be a bias in previously estimated eVects of divorce on health. Reichman et al.’s study used instrumental variables to measure infant health in a new dataset, the Fragile Families and Child Wellbeing Study. They found that, even after addressing the potential biases in the data, the finding of poor infant health on increased parental divorce was robust. Poor child health significantly reduced the probability that the parents would stay together for parents of all socioeconomic status, with the largest eVect for parents with the lowest socioeconomic status. If the children with severe health problems from birth are at higher risk for divorce compared to both typically developing children and healthy children with DS, particularly if they are in low income or in rural areas, as these studies might suggest, then children with DS who are born with congenital health problems may suVer from a quadruple dose of poor health, family instability, low income, and whatever negative eVects that DS itself may confer. So many diYculties in early life are especially troubling given the cumulative eVects, in economic terms, of early failures to accumulate human capital (Grossman, 1972). Compared to both typically developing children and children with DS who do not have health problems, the downstream outcomes for these children may show dramatic disparities as they age that may predispose these children to have relatively very poor adult outcomes. In these as in any economic studies, we will need to separate the direct eVects of a policy, service, or family characteristic, from the indirect eVects in the estimation strategy. First, we will want to estimate the direct and indirect eVects of family circumstances on MR/DD outcomes. Second, we will want to estimate the direct and indirect eVects of family circumstances on MR/DD service choice. The combined results potentially could shape the policy debate dramatically. For this reason and others, it is important to be sure that the estimation strategies are sound. We turn next to a brief discussion of econometrics and the most common problems faced in the economic analysis of large datasets. V.

ECONOMETRIC MODELS AND ESTIMATION TECHNIQUES

In trying to understand the eVects of family, economic, and child‐specific variables on outcomes and services, economic studies typically start with a regression framework. To produce consistent and reliably estimated eVects,


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the regression model must be correctly specified and the assumptions of the regression model should be satisfied. However, it is rarely the case that any economic model is estimated as a simple linear regression. Real world choices are rarely that straightforward; in addition, the forms of the data are never ideal. Thus, diYculties arise, both conceptually and empirically, when the research aims to move beyond documenting correlations in the data to try to infer the causal explanations between independent variables and outcomes. Many problems arise from the fact that the data are not generated within an experimental framework. People are not randomly assigned to conditions; instead, they play some role in choosing their own conditions. Economists, sensitized to the possibilities of choice, will often treat income, marital status, education levels, and even family size as choice variables. This conceptual approach raises the possibility that there are underlying variables, not directly observed in the data, that jointly aVect outcomes, services, and observed characteristics. Broadly speaking, designers of estimation strategies in large social science datasets must be vigilant about possibilities that behavior and choices by agents will cause the assumptions in the regression model to be violated. In response to these challenges, econometricians have developed sophisticated techniques to deal with the threats to validity caused by potential model misspecification. In any given model specification, certain assumptions are included about the underlying relationship. Choice of variables and the ways in which they enter the model have an impact on the relationships that it is possible to explore (or miss) and, of course, on the eventual interpretation of the estimates themselves. The functional forms and the measures used impose conditions that may or may not be appropriate. For example, a study that uses average income in the community as a proxy for household income is not able to ask whether a household in a community with greater variation in income (i.e., inequities) will experience diVerent outcomes than a household in a more homogeneous neighborhood. (This specification may be appropriate, however, for certain kinds of research question where only the average income matters.) Similarly, economists are sensitive to the kinds of measures that are used for household income or wealth. Depending on the question, annual earned income, savings, program benefits (such as social security or retirement benefits), insurance status, access to loans in credit markets, and even perceptions of future income may each be important determinants of decisions to purchase services and outcomes. For example, for most typically developing children, sending a child to a public versus a private college is an objective that shapes and is shaped by many earlier family resource decisions. Why would the prospect of being a caregiver of a child with special needs, late in life, do any less?

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Later, we discuss three other problems with model specification that are of concern because they are known to bias estimates or change the properties of the estimators. Familiarity with the ways that these problems are addressed by econometric techniques will help in the design of future large‐scale studies that examine MR/DD outcomes. Many of these statistical issues are complex and a full review will not be attempted here. A.

Omitted Variables

Problems arise with inference from regression estimates when the model does not include the correct set of variables. Including irrelevant variables reduces the eYciency of the estimates. However, excluding relevant variables introduces potentially more serious problems. In the case of an omitted independent variable, simple estimates of eVects will be biased because the expected value of the disturbance term varies with the omitted independent variable and is not constant. The situation is exacerbated when an included independent variable, such as the one whose eVect is of policy relevance, is correlated with the variable that has been left out. In that case, the estimated eVect of the included variable will capture some of the impact of the omitted variable. The size and direction of the bias depend on the nature of the relationship between the omitted and included variables. The most obvious example of this result (and, unfortunately, one that was prevalent in the early literatures) is when researchers asked the eVect of race on outcomes without including measures of socioeconomic status, such as income. In time periods or regions where race and income were strongly correlated, patterns of outcomes (or service use) that might be strongly aVected by income and family circumstances were instead attributed to race. Of course, this greatly alters the policy implications. How marital status aVects family choices sometimes can be interpreted in the same way. The direct eVect of marriage on outcomes may be reduced once family investments of time are included. For example, certain outcomes of children of single parents who receive time through another means, such as a grandparent or a reading clinic, may be similar to outcomes of children in households with two parents. On the other hand, if diluted income is the causal link in the eVects of divorce on outcomes, then providing financial support for services to children in all low‐income families, whether single or two‐parent households, may be well advised. Researchers who use family structure and demographic variables to investigate within‐household decisions must be particularly concerned about omitted variables related to family characteristics. If they are not, they risk attributing eVects to family structure that might lead the field to believe there


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are strong reasons to support one family structure over another. In fact, much of the economic research on families and educational outcomes has evolved through those stages. Whereas there was great early interest in family size, birth order, and marital status, the eVect sizes of many of these structural characteristics have been adjusted downward, as studies have used increasingly more sophisticated econometric techniques (Nechyba, McEwan, & Older‐Aguilar, 1999). This finding has been interpreted as a reflection of the idea that all families try their best, in their own way, to improve child outcomes (Grossman, 1972). As results such as these become available, new studies focus more intensely on the particular incentives or constraints of potential causal pathways. While the results may be less dramatic, the more precisely estimated eVects can be used more meaningfully to inform policy about which services to deliver to whom. In economic studies, the best way to avoid the identification errors associated with omitted variables is to use economic reasoning to determine the best set of variables to include. Variables are preselected by articulating theoretical relationships. A variable that happens to have large explanatory power, but cannot be justified on the basis of theory, is looked on with suspicion: including it when it is a proxy for some relevant omitted variable will not automatically solve the statistical problem of the omitted variable and may introduce new problems. In studies that involve service use and outcomes for a particular service, people with expertise in all content areas should collaborate on determining the set of variables to be examined. How economic studies deal with omitted variables depends on the consequences that they may have, statistically, within the regression framework. For example, sometimes the omitted variable is correlated with an explanatory variable (as in the previous example) and with the outcome. In this case, the simple ordinary least squares estimator is biased even asymptotically. This is because the process of assigning credit to the independent variable for variation in the dependent variable always erroneously attributes some of the variation of the dependent variable. A common approach to this contemporaneously correlated problem among variables is to use the instrumental variables approach. If a new independent variable, called an instrument, can be found that is correlated with the original variable and yet contemporaneously uncorrelated with the disturbance, the estimate will be consistent. Economic studies have grappled with this issue as they have attempted to measure the direct eVect of maternal education on health services use. The concern in these studies is that there is an omitted characteristic about a mother (say, innate ability) that causes her to choose her level of education

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and also the level of services that she demands for her child. Currie and Moretti (2003) use instruments for maternal education (perhaps related to the time period in which she was merely a student) that are unrelated to the level of services that she consumes as a mother. A fairly robust finding from these studies is that the level of maternal education itself continues to play a significant role in child health outcomes. These findings are encouraging for those who support educational interventions to mothers, regardless of previous educational backgrounds. B.

Endogeneity

Endogeneity is another common model misspecification problem and is a special case of the correlation between variables problem described previously. Endogeneity occurs whenever variables are determined simultaneously. For example, in cross‐sectional data, families are observed who experience both poverty and divorce. Divorced families experience lower income as a direct consequence of divorce; on the other hand, greater stress from low income increases the probability of divorce. In statistical terms, a change in the disturbance term changes all of these variables simultaneously, which leads to identification issues. As a consequence, the ordinary least squares estimator is biased, even asymptotically. When the assumptions of the linear regression model are violated in this way, an alternative estimator or estimation strategy is necessary. As an alternative to finding longitudinal data in which to observe income before and after divorce, econometric studies typically employ maximum likelihood estimation or two‐stage least squares. The two‐stage least squares estimator is the most widely used and easily understood because it is a special case of the instrumental variables approach already described. For example, if the research question is how income might predict divorce, then one would like to find an instrument for income that is correlated with income but not with the decision of that family to divorce. A two‐stage least squares approach might use the predicted income of the household, based on all of its other demographic characteristics, as an instrument for actual income. In removing the household’s actual income as the explanatory variable, and using predicted income (for a typical family in that situation, but not that particular family) in its place, the explanatory income variable is no longer tied to the disturbance on that family’s divorce term. As in the ordinary instrumental variables case above, the predicted value for income is a good instrument because it is highly correlated with the variable (actual family income) for which it is acting as an instrument. However, there is no reason to expect that predicted income is itself correlated with the family’s decision to divorce.


C.

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Selection Bias

One of the most serious problems encountered in statistical analysis is selection bias. When groups are not randomly assigned, individuals may self‐ select. If a patient seeks care, then she has revealed herself to be willing to do at least some of the work. Selection bias may be at work whenever it is possible that the fact that one observes people in a group may indicate that they are not representative of the population at large. Exit polls may not reflect the popular sentiment (and vice versa). Average treatment eVects in controlled samples may not be applicable to the population that chooses to use the service. Labor studies of the wages and hours data that are collected from people who work would not be accurate reflections of the hours and wages people would be willing to accept, unless the analysis takes into account the responses of people who would like to work but failed to find employment. Econometric studies often deal with this problem by explicitly modeling the choice of self‐selection. In doing this, the study is designed to use as much of the data as are available. Formally, this means that the first stage of analysis is to model the probability that a person will choose to participate in the group. As an example, the desire to work (known as the work participation choice) is modeled before wages and hours are ever considered. In order to model work participation, data are used on all potential workers, both employed and unemployed. The models diVerentiate between those people who choose to be unemployed and those people who are involuntarily unemployed. Similar studies were proposed earlier in this chapter, with respect to the service choices that people make. Many surveys ask about unmet need for services. Using economics to understand selection bias issues, more knowledge can be gained to understand if people do not use services for insurance reasons, for lack of availability reasons, or from their own readings of their particular circumstances. To restate the statistical lesson of social science selection bias more generally, epidemiologists know how important it is to think about the interpretation of average treatment eVects in out‐of‐sample populations. The same is true in economic studies of health services use for similar, albeit social science reasons. The eVects of service utilization may not translate to out‐of‐sample populations because they may be mitigated by the very characteristics that caused people not to choose the services in the first place. However, newly available population‐based data can help to alleviate concerns about selection bias. With careful study design and statistical interpretation, combining economic and epidemiological research may yield insights not only about the eVectiveness of services, but about how people might be helped to choose the services that would most benefit them.

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A final note concerns the econometric issues when studies select subjects, knowingly, on the basis of the value of a variable of interest. For example, a study may examine the outcomes only of families most at risk. Suppose a study selects only families who live in poverty. In statistical terms, the data come from a truncated distribution because observations from the upper income tail in the distribution of error terms are eliminated systematically. Not only is the expected value of the error term nonzero, it will also vary from observation to observation. The solution for dealing with truncated or censored data usually involves maximum likelihood techniques. VI.

CONCLUSIONS

In order to improve child outcomes, service guidelines and best practices must be based on far more than the individual child’s disabilities, health, preferences, or other characteristics. Even the best purely scientific (albeit not social‐scientific) professional advice, tailor‐made to the child’s own conditions or severity level, may not be suYcient because the child is not the one who makes the service choices. Parents and caregivers do. As a result, parents’ decisions must be understood in the broader context of families’ constraints, needs, and choices between competing alternatives. Instead of viewing each family’s individual circumstances as a reason to make exceptions and work around a fixed set of recommendations, those involved in policy setting and service delivery eventually must accept the reality that diVerent families are best served diVerently. Thus, the goal of research should be to understand the ways in which care should be arranged to provide, consistently, the best sets of services customized to the family’s circumstances. Let us be perfectly clear. Even in the special case where one cares about no one else’s outcomes except the child’s, this is the only practical way to think about delivering services. Economics provides a systematic framework in which to think about these decisions across diverse family conditions. Just as economists are accustomed to studying how diVerent families decide how, when, and where to work, which neighborhoods to move to and how much to pay in taxes, whether to obtain private health insurance or accept some form of governmental support, or how much to accumulate in savings/dissavings now that later will aVect their well‐being in retirement, so too can researchers in MR/DD use economics to understand the interactions between families, disability and health services, and the short‐ and long‐term outcomes of all involved. The core intuition and starting point for this chapter is that services cannot be useful if they are not chosen voluntarily. Even mandates will not


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work perfectly in the face of unintended consequences. Obviously, families try to do the best that they can. The overwhelming evidence from economic research is that families do try to do the best they can for their children, but in diVerent ways. This is exactly what economics predicts. True economic research on outcomes and services research deepens our understanding of why certain outcomes and services are chosen. The results inform how incentives (policy) may improve these decisions. It should be obvious now that economic research goes far beyond counting the costs and benefits, in a strictly descriptive fashion, to include careful discussions of how families in general, and families of children with special needs specifically, aVect their children’s outcomes. Economic studies of actual service choice are important because these expose not only the decisions of the families, but also of the providers. Service delivery systems that do not find the clients who will be most benefited by them are an indication that the provision of services must be improved. When services are not chosen, it is always appropriate to think about how to change the incentives that face families in order to help the families to choose more of the services. In other words, change does not always depend on having to change the intrinsic characteristics of the clients themselves. Alternatively, services that are oversubscribed (as measured by long waiting lists and so on) indicate that rebalancing is needed by the system, whether through greater incentives to existing providers to provide more care, increases in the numbers of providers allowed in the system, changes in prices, or some other mechanism. Either way, understanding how families choose which services will help explain how to provide the most appropriate services to maximize outcomes. Whether studying families, providers, or anyone else involved in the care of children with MR/DD, econometric techniques may be translated easily to empirical studies of outcomes and service use. Statistical problems arise when the data are nonexperimental and when values and variables are related because they are linked by agents’ fundamentally consistent (nonindependent) behaviors, attitudes, barriers, environments, and past decisions. However, many sophisticated techniques have been developed in response to the statistical challenges that these underlying behaviors pose to the analysis. The empirical literature already contains a huge installed base of related evidence and hypothesis tests that researchers may use to estimate the true eVects of families’ incentives on service use and outcomes. As the economic literature has evolved, it has sought more and more content knowledge from people who know about the actual services and decision makers. As the empirical literature in MR/DD outcomes evolves, we hope that its researchers will seek more and more interaction with economists and econometricians.

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Anderson, P. M., Butcher, K. F., & Levine, P. B. (2003). Maternal employment and overweight children. Journal of Health Economics, 22, 477–504. Becker, G. S. (1991). A treatise on the family. Cambridge, MA: Harvard University Press. Becker, G. S., & Tomes, N. (1979). An equilibrium theory of the distribution of income and intergenerational mobility. Journal of Political Economy, 87, 1153–1189. Chou, S. Y., Grossman, M., & SaVer, H. (2004). An economic analysis of adult obesity: Results from the behavioral risk factor surveillance system. Journal of Health Economic, 23, 565–587. Conway, K. S., & Deb, P. (2005). Is prenatal care really ineVective? Or, is the ‘Devil’ in the distribution? Journal of Health Economics, 24, 489–513. Currie, J., & Moretti, E. (2003). Mother’s education and the intergenerational transmission of human capital: Evidence from college openings. Quarterly Journal of Economics, VCXVIII, 1495–1532. Cuyler, A. J., & Newhouse, J. P. (2000). Handbook of health economics. Amsterdam: North‐Holland. Dow, W., Philipson, T., & Sala‐i‐Martin, X. (1999). Longevity complementarities under competing risks. American Economic Review, 89, 1358–1371. Ensor, T., & Cooper, S. (2004). Review article: Overcoming barriers to health services on the demand side. Health Policy and Planning, 19, 69–79. Evans, W. N., & Lien, D. S. (2005). The benefits of prenatal care: Evidence from the PAT bus strike. Journal of Econometrics, 125, 207–239. Gaviria, A., & Raphael, S. (1997). School‐based peer eVects and juvenile behavior. San Diego, CA: University of California. Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80, 223–255. Hanushek, E. A. (1996). School resource and student performance. In G. Burtless (Ed.), Does money matter? The eVect of school resources on student achievement and adult success Washington, DC: Brookings Institution Press. Hanushek, E. A. (1997). Assessing the eVect of school resources on student performance: An update. Educational Evaluation and Policy Analysis, 19, 141–164. Joyce, T., & Racine, A. (2005). Chip shots: Association between the state children’s health insurance programs and immunization rates. Pediatrics, 115, e526–e534. Nechyba, T., McEwan, P., & Older‐Aguilar, D. (1999). The impact of family and community resources on student outcomes: An assessment of the international literature with implications for New Zealand. Wellington, New Zealand: Ministry of Education. Peltzman, S. (1975). The eVects of automobile safety regulation. Journal of Political Economy, 83, 677–725. Peltzman, S. (2002). OVsetting behavior and medical breakthroughs. University of Chicago (Working Paper No. 177). Reichman, N. E., Corman, H., & Noonan, K. (2003). EVects of child health on parents’ relationship status. National Bureau of Economic Research (Working Paper No. 9610). Robertson, D., & Symons, J. (1996). Do peer groups matter? Peer group versus schooling eVects on academic attainment. Discussion Paper No. 311. London School of Economics Centre for Economic Performance, London, England. Retrieved from: http://cep.lse.ac.uk/pubs/ download/DP0311.pdf So, S. A. (2002). Jointly determined labor supply for married couples. A review of the theory on the added worker eVect. Disability Research Institute. So, S. A., Urbano, R. C., & Hodapp, R. M. (submitted for publication). Hospitalizations for infants and young children with Down syndrome: Evidence from person‐records from a statewide administrative database. Urbano, R. C., & Hodapp, R. M. (submitted for publication). Divorce in families of children with Down syndrome: A population‐based study.

Section III Populations

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Public Health Impact: Metropolitan Atlanta Developmental Disabilities Surveillance Program* RACHEL NONKIN AVCHEN, TANYA KARAPURKAR BHASIN, KIM VAN NAARDEN BRAUN, AND MARSHALYN YEARGIN‐ALLSOPP CENTERS FOR DISEASE CONTROL AND PREVENTION, NATIONAL CENTER ON BIRTH DEFECTS AND DEVELOPMENTAL DISABILITIES, ATLANTA, GEORGIA

I.

INTRODUCTION

Sixty years ago, on July 1, 1946, CDC was founded in an eVort to provide service and consultation to combat communicable diseases throughout the United States. At its inception, CDC was an abbreviation for the Communicable Disease Center, and there were fewer than 400 employees, most of whom were engineers and entomologists with expertise in malaria. Today, the acronym CDC represents the Centers for Disease Control and Prevention and the agency, as one of the 13 major operating components of the Department of Health and Human Services, is comprised of over 15,000 employees from a wide spectrum of disciplines. CDC continues to head public health eVorts to prevent and control infectious and chronic diseases, injuries, workplace hazards, disabilities, and environmental health threats through surveillance and epidemiologic research. CDC has consistently been at the forefront of investigating birth defects and developmental disabilities with almost 40 years of experience in surveillance and epidemiologic research in this area. The Metropolitan Atlanta Congenital Defects Program (MACDP) was initiated in 1967 as the first population‐based birth defects surveillance system in the United States, but no comparable surveillance system existed for developmental disabilities. In 1979, a request was made of CDC for data regarding the prevalence of *The findings and conclusions in this chapter are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention. INTERNATIONAL REVIEW OF RESEARCH IN MENTAL RETARDATION, Vol. 33 0074-7750/07 $35.00

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mental retardation and cerebral palsy and a pilot study was planned and implemented. After the success of this small pilot study, which aimed to quantify the prevalence of severe mental retardation in school‐age children in one county of metropolitan Atlanta, the Metropolitan Atlanta Developmental Disabilities Study (MADDS) was launched in 1984. This was the first United States prevalence study of multiple developmental disabilities. The MADDS methodology evolved into the Metropolitan Atlanta Developmental Disabilities Surveillance Program (MADDSP) beginning in 1991. MACDP and MADDSP serve as the gold standard models for monitoring birth defects and developmental disabilities in the metropolitan Atlanta area and across the United States. The public health framework for developmental disabilities activities at CDC has three core components: surveillance, epidemiology, and prevention (Fig. 1). Surveillance represents the ongoing ability for monitoring and routine reporting of prevalence for select developmental disabilities as well as for examining temporal trends in the prevalence of these conditions. Developmental disabilities surveillance activities provide a population‐based case series of children with select conditions for the epidemiologic investigation of risk or protective factors and clues to the causes of conditions. Subsequently, findings from epidemiologic studies can help to facilitate

FIG. 1. Three core components of the public health framework for developmental disabilities activities at CDC.

PUBLIC HEALTH IMPACT

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planning and implementation of health intervention and prevention initiatives by influencing changes in health education, policy, and practice. Surveillance activities can capture changes in health policy practice, illustrating the cyclical influence from one component of the model to the next. The ultimate goals of these activities are to decrease the occurrence and improve both short‐ and long‐term consequences of developmental disabilities. This brief history of developmental disabilities activities within CDC is intended to provide background on the beginnings of MADDS and MADDSP. The next section will detail the methods for developmental disabilities surveillance used at CDC, followed by a detailed compilation of results from surveillance reports and epidemiologic studies on developmental disabilities using MADDS and MADDSP data. The chapter concludes with a discussion of the public health impact of MADDS and MADDSP on the epidemiology and public health practice of developmental disabilities.

II. A.

BACKGROUND OF DEVELOPMENTAL DISABILITIES SURVEILLANCE IN METROPOLITAN ATLANTA

Metropolitan Atlanta Developmental Disabilities Study

MADDS was initiated in 1984 due to growing concern about the lack of prevalence data on developmental disabilities in the United States. It was the first population‐based epidemiologic program to monitor multiple developmental disabilities in school‐aged children conducted in the United States. The study was funded by the Agency for Toxic Substances and Disease Registry (ATSDR) through a cooperative agreement between the CDC and the Georgia Department of Human Resources. The goal of MADDS was to devise methods for ascertaining children with select developmental disabilities so that prevalence of these disabilities could be systematically monitored. The study aimed to establish the prevalence of five developmental disabilities in 10‐year‐old children: cerebral palsy, epilepsy, hearing impairment, mental retardation, and visual impairment. The other goals of this study were to relate the five developmental disabilities to service needs in the metropolitan area and to generate hypotheses about risk factors for these conditions. The latter goal of this study will be discussed in greater detail in Section V of this chapter. The MADDS study area encompassed the five metropolitan Atlanta counties, which included Clayton, Cobb, DeKalb, Fulton, and Gwinnett. The study population was composed of 10‐year‐old children who (1) were born in the study area between 1975 through 1977 and still resided there between 1985 through 1987, (2) were born in the study area between 1975

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through 1977 but did not reside there during the study years (1985–1987), or (3) were not born in the study area but resided there during the study years. Ten‐year‐old children were chosen as the target population for MADDS because developmental disabilities are generally recognized by the time a child is school‐aged, including mild disabilities. Second, from the literature, the prevalence of mental retardation was thought to peak by 10 years of age (Yeargin‐Allsopp, Murphy, Oakley, & Sikes, 1992). 1. CASE DEFINITIONS

Cerebral palsy was defined as a group of nonprogressive disorders occurring in young children in which abnormalities of the brain cause impairment of motor function. The impairment of motor function may result in paresis, involuntary movement, or incoordination. Motor disorders that are transient, disorders that result from progressive disease of the brain, and motor disorders due to spinal cord abnormalities are not included (Murphy, Yeargin‐Allsopp, Decoufle, & Drews, 1993). Records were reviewed for any indication of cerebral palsy including cerebral palsy from physical findings (e.g., spasticity, athetoid movements). MADDS included children with congenital and acquired cases of cerebral palsy and central nervous system disorders such as hydrocephaly and microcephaly. For the purpose of the study, children with myelomeningocele were excluded unless there was evidence of another neurologic process that resulted in physical findings consistent with a diagnosis of cerebral palsy. A child was classified as having cerebral palsy by a qualified professional based on the description of the physical findings that was available in the child’s record. In cases where a determination could not be made a classification of cerebral palsy, not otherwise stated was given. The classification of cerebral palsy subtypes was established based on the Little Club Classification (MacKeith, 1959). Epilepsy was defined as two or more epileptic seizures diagnosed by a physician. Clusters of seizures (two or more) that occurred within a 24‐hour period were considered a single seizure. Children who only had simple febrile seizures were not included as having epilepsy. Children with febrile status epileptics were included as having epilepsy only if they otherwise qualified on the basis of afebrile seizures. Hearing impairment was defined as a bilateral pure‐tone hearing loss that averaged 40 decibels (dB) or worse unaided in the better ear at frequencies of 500, 1000, or 2000 Hz (normal speech range; Roeser, 1988). The diagnosis of a hearing loss was accepted only from a physician or professional in the field of audiology. Mental retardation was defined as an intelligent quotient (IQ) of 70 or less on the most recent psychometric test performed by a psychometrist;


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this included children with sensory problems such as blindness or deafness. It was reported that most children (85%) ascertained by this survey had a psychometric test (Yeargin‐Allsopp et al., 1992). If a child with Down syndrome did not have an IQ score they were considered to have mental retardation. While many facilities use the definition provided by the American Association of Mental Deficiency which includes adaptive functioning, the definition used by MADDS was based on IQ alone. Adaptive functioning was not included for case definition because inconsistencies in the types and uses of adaptive instruments among the diVerent school systems was evident (Yeargin‐Allsopp et al., 1992). Visual impairment (legal blindness) was defined as follows: (1) a measured visual acuity of 20/200 or worse in the better eye with correction, (2) a description of visual acuity that reflected 20/200 or worse (e.g., light perception only), or (3) a statement by a trained person, such as ophthalmologist or optometrist, that the child was ‘‘blind.’’ This definition represents children with legal blindness and not children with milder forms of visual impairment. 2. METHODOLOGY

Children ascertained by MADDS were identified using a multiple source approach by reviewing records from educational, medical, and social service providers for children with developmental disabilities. All records from any participating source were reviewed and pertinent information was abstracted to ensure a complete developmental and health profile for each child. In addition to information obtained from source records, maternal/ child demographic data were obtained from the child’s birth certificate. Linkage with MACDP also allowed MADDS investigators to collect additional information on structural and chromosomal anomalies. Educational sources used for this study consisted of nine public school systems and psychoeducational and state school programs. The Department of Education sources were used due to the mandate by the Federal Education for All Handicapped Children Act (PL 94–142) to identify and educate all children with developmental disabilities (US Congress, 2005). While this act mandates the identification of all children with special educational needs, the investigators of this study acknowledged that children with milder forms of the disabilities, such as mild cerebral palsy, might not receive special educational services through the public school systems. Other sources used to ascertain children for MADDS included medical facilities such as hospitals specialized in treating children with developmental disabilities, pediatric clinics, Georgia Department of Human Resources, and other public and private agencies. Children were identified from one of these facilities using relevant disability codes from the International

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Classification of Diseases, 9th edition‐clinical modification (Puckett, 1993). The records were reviewed for any child with a flagged ICD‐code, and abstractors collected all behavioral, developmental, and pertinent medical information from the child’s source file. MADDS investigators found a majority (95%) of the children ascertained for the study were identified at an educational source. This finding stressed the importance of educational source records for identifying children with developmental disabilities. Yet, information from medical and clinical sources were also critical in many instances to determine final case status, particularly for cerebral palsy and epilepsy. The findings from MADDS further illustrated that surveillance of developmental disabilities in school‐aged children was possible. B.

Metropolitan Atlanta Developmental Disabilities Surveillance Program

Due to the success of MADDS, CDC initiated MADDSP in 1991. MADDSP is an active surveillance program that was established to monitor the occurrence of cerebral palsy, hearing loss (previously termed hearing impairment), mental retardation, and vision impairment in children 3–10 years of age in the five county metropolitan Atlanta area (Clayton, Cobb, DeKalb, Fulton, and Gwinnett). In 1996, autism spectrum disorders were added to MADDSP. While epilepsy was included in MADDS, it was not included in MADDSP because surveillance of epilepsy was deemed too resource‐intensive; it required record review at over 20 electroencephalogram (EEG) laboratories. Therefore, surveillance for epilepsy did not facilitate timely surveillance of the other disabilities. To date, MADDSP estimates the prevalence of the five disorders in the metropolitan Atlanta area and serves as a model program for federally funded state programs monitoring developmental disabilities across the United States. The main objective of MADDSP is to provide regular and systematic monitoring of prevalence for select developmental disabilities according to various demographic characteristics of children and their mothers. MADDSP data were used to measure the progress of Healthy People 2000 prevention of mental retardation objectives, and the surveillance data will continue to be used to evaluate the Healthy People 2010 objectives for the prevention of mental retardation and early identification of children with autism spectrum disorders. MADDSP uses a similar methodology to that employed by MADDS. MADDSP ascertains children who have one or more of the five developmental disabilities. The age cohort was expanded from only 10‐year‐old children to include children 3–10 years of age. This age range was originally


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chosen because the lower age boundary corresponds to the beginning of the age span covered by Part B of the Individuals with Disabilities Education Act (IDEA) (US Congress, 2005), and the upper age boundary is consistent with the age at which most children served under IDEA should have received special education services (Boyle et al., 1996). However, as of the 1996 surveillance year, only 8‐year‐old children are actively monitored by MADDSP. This decision was made in an eVort to ascertain the most complete number of children with one of the five developmental disabilities and to maximize the timeliness of surveillance reports. Further, previous surveillance data indicated that prevalence rates were stable by age 8 for all the disabilities monitored. MADDS demonstrated that a multiple source methodology was critical for obtaining comprehensive information on children with developmental disabilities, and for this reason, MADDSP also reviews records from nine public school systems, psychoeducational programs, medical facilities, pediatric clinics, Georgia Department of Human Resources, and other public and private agencies serving children with developmental disabilities. Since 1991, children identified through MADDSP have also been linked to MACDP and birth certificate data. These linkages provide additional information regarding congenital and structural abnormalities as well as birth and maternal characteristics. MADDSP method relies on the ability to use multiple sources of administrative data to determine the prevalence of the disabilities and to build on surveillance data to identify hypotheses that can be tested using case‐ control methods. Because there was no examination of children for MADDS or MADDSP, having access to records at sources where large numbers of children are evaluated in the community for developmental disabilities is critical for complete ascertainment of children with developmental disabilities (to the extent possible). For MADDS and MADDSP, the data sources mentioned above are divided into two categories: (1) educational sources: public school special educational and psychoeducational sources and (2) health sources: medical facilities, pediatric clinics, Georgia Department of Human Resources, and other public and private agencies serving children with developmental disabilities with educational sources being the major source of cases. Like MADDS, a similar percentage of cases were identified from educational data (Boyle et al., 1996; Yeargin‐Allsopp et al., 1992). Further, approximately 6% of cases were identified uniquely through health sources for MADDSP (Bhasin, Brocksen, Nonkin Avchen, & Van Naarden Braun, 2006). These data underscore the importance of access to educational records for obtaining as complete a count as possible of children with developmental disabilities.

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1. CASE DEFINITIONS

The case definitions for MADDSP were refined where appropriate subsequent to MADDS and were derived from national standards for each of the disabilities. The current definitions are as follows. Autism spectrum disorders are defined as a constellation of behaviors indicating social, communicative, and behavioral impairment or abnormalities. The essential features of autism spectrum disorders are: (1) impaired reciprocal social interactions, (2) delayed or unusual communication styles, and (3) restricted or repetitive behavior patterns. Confirmed autism spectrum disorder cases include children that display behaviors (as described on a comprehensive evaluation by a qualified professional) that are consistent with the diagnostic criteria listed in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM‐IV‐TR) for any of the following conditions: autistic disorder, pervasive developmental disorder‐not otherwise specified (PDD‐NOS, including atypical autism), or Asperger’s disorder; child disintegrative disorder and Rett’s disorder are not currently included as conditions monitored by MADDSP. Cerebral palsy continues to be defined as a group of nonprogressive, but often changing, motor impairment syndromes secondary to lesions or anomalies of the brain arising at any time during brain development, and motor impairment function may result in paresis, involuntary movement, or incoordination. Like MADDS, children with postnatally acquired cerebral palsy are eligible as cases in MADDSP, but children with motor disorders that are transient, result from progressive disease of the brain, or are due to spinal cord abnormalities/injuries are not considered cerebral palsy cases for MADDSP. Confirmed cases of cerebral palsy include children diagnosed as having cerebral palsy from a qualified physician, or children identified by another qualified professional as having this disability on the basis of physical findings noted in source records. MADDSP considers physicians, physical therapists, occupational therapists, nurse practitioners, or physician’s assistants to be a qualified professional. Final case determination is made by medical staV aYliated with MADDSP. Hearing loss remains defined as a measured, bilateral, pure‐tone hearing loss at frequencies of 500, 1000, and 2000 Hz averaging 40 dB or more, unaided, in the better ear. In the absence of a measured, bilateral hearing loss, children meet the case definition if their source records include a description, by a licensed or certified audiologist or qualified physician, of a hearing loss of 40 dB or more in the better ear (e.g., profound sensorineural hearing loss). Severity is defined on the basis of hearing impairment levels as measured in the better ear as follows: (1) moderate, a hearing loss of

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40–64 dB; (2) severe, a hearing loss of 65–84 dB; and (3) profound, a hearing loss of 85 dB. Mental retardation is defined using only an IQ of less than or equal to 70 as was the criterion for MADDS. In the absence of an IQ score and in the context of testing, MADDSP accepts a written statement by a psychometrist that a child’s intellectual functioning falls within the range for severe or profound mental retardation for case ascertainment. The severity of mental retardation is defined according to the following International Classification of Disease, Ninth Edition, Clinical Modification (1993) categories: (1) mild, an IQ ¼ 50–70; (2) moderate, an IQ ¼ 35–49; (3) severe, an IQ ¼ 20–34; and (4) profound, IQ < 20. Vision impairment is defined as a measured visual acuity of 20/70 or worse, with correction, in the better eye. In the absence of a measured visual acuity, a child is considered a case if a source record includes a functional description, by a qualified physician or vision professional, of visual acuity of 20/70 or worse (e.g., light perception only) or a statement by a qualified physician or vision professional that the child has low vision or blindness. Severity of visual impairment is defined using both the WHO categories for low vision (20/70 to better than 20/400) and blindness (worse than 20/400) and the United States categories for low vision (20/70 to better than 20/200) and blindness (worse than 20/200). C.

MADDS Follow‐Up (MADDSFU) Study of Young Adults

The MADDS Follow‐Up Study of Young Adults (MADDS Follow‐Up) was conducted from 1997 to 2000 and followed a subset of children originally identified by MADDS. At the time of the follow‐up study the population was 21–25 years of age. The MADDS Follow‐Up consisted of an extensive in‐person or telephone interview that obtained information about daily functioning, competitive employment, educational attainment, living arrangements, financial and transportation assistance, living arrangements, marital status, and participation in daily leisure activities. The MADDS Follow‐Up provided an unprecedented opportunity to examine the outcomes of young adults with developmental disabilities identified in childhood using various factors and outcomes related to transition into young adulthood. III.

PREVALENCE ESTIMATES

The prevalence estimates derived from MADDS and MADDSP are considered administrative prevalence estimates. Administrative prevalence in these systems reflects ascertainment of cases from data sources that provide

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services to children with developmental disabilities such as educational and clinical sources. Data are collected by actively reviewing such administrative records for pertinent information to inform case status. This surveillance methodology does not rely on formal reporting of conditions to the state health department; in fact, most developmental disabilities are not reportable conditions. Instead, this methodology extends beyond special education data to incorporate necessary information from other medical and service provisions. This methodology provides a closer approximation of the true population prevalence than does reliance on one administrative database alone. Only a small percentage of children, particularly those with milder disabilities, are thought not to be identified by MADDS or MADDSP because these children may not be receiving special education, diagnostic, or treatment services.

A.

MADDS Prevalence Summary

Table I provides a summary of the prevalence findings for cerebral palsy, epilepsy, hearing impairment, mental retardation, and vision impairment overall and by sex and race for 10‐year‐old children who were residing in the five county metropolitan Atlanta area between 1985 and 1987. The numerator consists of children ascertained by MADDS who have one or more of the studied disabilities. The denominator used to calculate the prevalence of these developmental disabilities was from the Georgia intercensal population estimates provided by the Georgia OYce of Planning and Budget (Georgia Department of Human Resources, 1985). The intercensal estimates were reported in 5‐year age groups (0‐ to 4‐year olds, 5‐ to 9‐year olds, 10‐ to 14‐year olds). In order to determine the prevalence of epilepsy and hearing impairment, the number of 10‐year‐old children residing in the five counties of metropolitan Atlanta was established from the percentages of 10‐year‐old children (specific for county, race, and sex) in the 10‐ to 14‐year‐old group from the 1980 census. As indicated in Table I, a majority of the children ascertained by MADDS had mental retardation. With the exception of vision impairment, a higher prevalence of cerebral palsy, epilepsy, hearing impairment, and mental retardation was found among black and male children as compared to white children and females; however, the diVerence in magnitude varied by disability. The greatest disparity between black and white children and males and females was observed among children with mental retardation. The MADDS data were also used to establish the prevalence of other epilepsy syndromes including Lennox–Gastaut and infantile spasms. In a prevalence study conducted by Trevathan, Murphy, and Yeargin‐Allsopp

PREVALENCE

TABLE I CEREBRAL PALSY, EPILEPSY, HEARING IMPAIRMENT, MENTAL RETARDATION, AND VISUAL IMPAIRMENT, IN 10‐YEAR‐OLD CHILDREN IN METROPOLITAN ATLANTA, MADDS, 1985 THROUGH 1987

OF

Cerebral palsy a

PE (95% CI) Total Racec White Black Sex Male Female a

b

Epilepsy a

Hearing impairment b

PE (95% CI)

b

PE (95% CI)

Mental retardation a

b

Vision impairment

PE (95% CI)

PEa (95% CI)b

2.3 (2.0, 2.6)

6.0 (5.5, 6.5)

1.1 (0.9, 1.4)

12.0 (11.3, 12.7)

0.7 (0.5, 0.9)

2.1 (1.7, 2.5) 2.7 (2.1, 3.3)

5.7 (5.1, 6.4) 6.4 (5.6, 7.3)

1.0 (0.8, 1.3) 1.3 (0.9, 1.7)

7.4 (6.7, 8.1) 19.7 (18.3, 21.3)

0.8 (0.6, 1.0) 0.5 (0.3, 0.9)

2.7 (2.3, 3.2) 1.2 (0.9, 1.6)

6.7 (6.0, 7.5) 5.2 (4.6, 6.0)

1.2 (0.9, 1.6) 1.0 (0.7, 1.3)

13.8 (12.7, 14.9 10.1 (9.2,11.1)

0.9 (0.6, 1.2) 0.7 (4.0, 1.1)

Prevalence estimate. 95% confidence interval. c White includes white Hispanic and black includes black Hispanic. b

a

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(1997), a prevalence estimate of 0.3/1000 was found for Lennox–Gastaut. A majority of these children (91%) had mental retardation and 39% had infantile spasms. All of the 17% of children with profound mental retardation identified through MADDS had Lennox–Gastaut. This study also found that children with Lennox–Gastaut were more likely to have at least one of the other developmental disabilities tracked by MADDS. Another study conducted by Trevathan, Murphy, and Yeargin‐Allsopp (1999) assessed the epidemiologic profile of infantile spasms among 10‐year‐ old children in metropolitan Atlanta using the data collected by MADDS. It was found that 0.2/1000 10‐year‐old children had infantile spasms and 83% also had mental retardation. Further, 56% of children with infantile spasms had profound mental retardation while 50% had Lennox–Gastaut. B.

MADDSP Prevalence Summary

Table II indicates the average annual prevalence estimates for cerebral palsy, hearing loss, mental retardation, and vision impairment among 8‐year‐old children in metropolitan Atlanta during 1991–1994. Prevalence estimates for cerebral palsy, hearing loss, mental retardation, and vision impairment in children 3–10 years for study years 1991 through 1994 have been reported elsewhere (Boyle et al., 1996; Mervis, Boyle, & Yeargin‐ Allsopp, 2002; Van Naarden Braun, Decoufle, & Caldwell, 1999; Winter, Autry, Boyle, & Yeargin‐Allsopp, 2002). Table III summarizes the prevalence of those disabilities and autism spectrum disorders for the 1996 and 2000 study years. Overall, race‐ and sex‐specific estimates are provided. Prevalence estimates were calculated using, as the denominator, the number of 8‐year‐old children who resided in the five county metropolitan Atlanta area during the specified study year of interest according to the bridged‐race intercensal population estimates determined by the (National Center for Health Statistics, 2005). During the study period, 1991–1994, MADDSP ascertained 477 children who met the case definition for cerebral palsy, hearing loss, mental retardation, and vision impairment in 1991, 564 in 1992, 597 in 1993, and 644 in 1994. According to the bridged‐race intercensal population estimates there were 126,796 children 8 years of age residing in metropolitan Atlanta. Ninety‐five percent confidence intervals were computed based on the exact binomial method (Fleiss, 1981). Two race categories (white and black) represented the majority of children in these analyses. The average annual prevalence of hearing loss and mental retardation during this period was consistent with the prevalence findings for these disabilities in MADDS. However, a higher prevalence of cerebral palsy was observed during this period than was previously found in MADDS,

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TABLE II AVERAGE ANNUAL PREVALENCE OF CEREBRAL PALSY, HEARING LOSS, MENTAL RETARDATION, AND VISION IMPAIRMENT AMONG 8‐YEAR‐OLD CHILDREN BY RACE AND SEX, METROPOLITAN ATLANTA, MADDSP, 1991–1994

Total Race White, non‐Hispanic Black, non‐Hispanic Sex Male Female

Cerebral palsy

Hearing loss

Mental retardation

Vision impairment

PEa (95% CI)b

PEa (95% CI)b

PEa (95% CI)b

PEa (95% CI)b

3.2 (2.9, 3.5)

1.2 (1.1, 1.5)

12.5 (11.9, 13.1)

1.1 (0.9, 1.3)

3.2 (2.8, 3.6)

1.1 (0.9, 1.4)

8.9 (8.2, 9.6)

1.2 (0.9, 1.5)

3.5 (3.0, 4.1)

1.5 (1.2, 1.9)

19.3 (18.1, 20.5)

1.2 (0.9, 1.6)

3.4 (3.0, 3.9) 2.9 (2.5, 3.4)

1.5 (1.2, 1.8) 1.0 (0.8, 1.3)

14.8 (13.9, 15.7) 10.3 (9.5, 11.1)

1.2 (1.0, 1.5) 1.0 (0.8, 1.3)

a

Prevalence estimate. 95% confidence interval.

b

3.2/1000 children versus 2.3/1000 children, respectively. The prevalence of vision impairment was also slightly higher than was found for children with visual impairment in MADDS. However, this increase is likely due to the change in case definition of vision impairment, which was limited to children with legal blindness (visual acuity of 20/200 or worse) in MADDS and expanded to include children with a visual acuity of 20/70 or worse in MADDSP. The race and sex patterns observed in MADDSP (1991–1994) are similar to those found by MADDS. As found previously by MADDS data, a preponderance of black and male children were aVected with mental retardation as compared to white and female children. A higher prevalence of cerebral palsy was also observed among black and male children; however, the magnitude of diVerence was smaller than that observed for mental retardation. The race and sex diVerence among children with sensory impairment was minimal. Prevalence estimates decreased from 1996 to 2000 for all the developmental disabilities except autism spectrum disorders; the estimates for 2000 were more in line with previous prevalence reports than those from 1996 for cerebral palsy, hearing loss, mental retardation, and vision impairment (Table III). Further, the sex–race pattern for these disabilities was similar to previous MADDS and MADDSP reports, which might suggest that the influence of certain social or biologic mechanisms have not changed over time.

TABLE III PREVALENCE OF AUTISM SPECTRUM DISORDERS, CEREBRAL PALSY, HEARING LOSS, MENTAL RETARDATION, AND VISION IMPAIRMENT AMONG 8‐YEAR‐OLD CHILDREN BY RACE AND SEX, METROPOLITAN ATLANTA, MADDSP, 1996 AND 2000 ASD

Total Racec White, non‐Hispanic Black, non‐Hispanic Sex Male Female a

CP

HL

MR

1996

2000

1996

2000

1996

2000

PEa (95% CI)b

PEa (95% CI)b

PEa (95% CI)b

PEa (95% CI)b

PEa (95% CI)b

PEa (95% CI)b

4.2 (3.6, 5.0)

6.5 (5.8, 7.3)

3.6 (3.0, 4.3)

3.1 (2.6, 3.7)

1.4 (1.1, 1.9)

4.6 (3.7, 5.7)

7.9 (6.7, 9.3)

3.3 (2.5, 4.2)

2.7 (2.0, 3.6)

4.0 (3.1, 5.1)

5.3 (4.3, 6.4)

4.1 (3.1, 5.2)

6.7 (5.6, 8.0) 1.8 (1.2, 2.5)

11.0 (9.7, 12.4) 2.0 (1.5, 2.7)

3.8 (2.9, 4.8) 3.5 (2.7, 4.4)

1996

VI 2000

2000

PEa (95% CI)b

PEa (95% CI)b

PEa (95% CI)b

PEa (95% CI)b

1.2 (0.9, 1.6)

15.5 (14.2, 16.8)

12.0 (11.0, 13.1)

1.4 (1.0, 1.8)

1.2 (0.9,1.6)

1.5 (1.0, 2.2)

0.9 (0.5, 1.4)

9.8 (8.4, 11.3)

6.8 (5.6, 8.1)

1.4 (0.9, 2.0)

1.1 (0.6, 1.6)

4.1 (3.1, 5.2)

1.3 (0.8, 2.0)

1.4 (0.9, 2.0)

22.7 (20.4, 25.2)

16.5 (14.7, 18.4)

1.4 (0.8, 2.1)

1.5 (1.0, 2.1)

3.6 (2.8, 4.5) 2.6 (2.0, 3.4)

1.2 (0.8, 1.9) 1.6 (1.1, 2.3)

1.4 (1.0, 2.0) 1.0 (0.6, 1.5)

19.1 (17.1–21.1) 11.8 (10.2, 13.5)

14.0 (12.5, 15.7) 10.0 (8.7, 11.4)

1.6 (1.0, 2.2) 1.2 (0.7, 1.8)

1.5 (1.0, 2.1) 0.9 (0.5, 1.4)

Prevalence estimate. 95% confidence interval. c White includes white non‐Hispanic only and black includes black non‐Hispanic only. ASD, autism spectrum disorders; CP, cerebral palsy; HL, hearing loss; MR, mental retardation; VI, vision impairment. b

1996


163

In comparison to previous MADDSP study years and the 2000 study year, the greatest magnitude of increase in prevalence was observed among children with mental retardation in 1996. This increase is partly attributed to the higher number of children participating in special education programs for children with intellectual disability in 1996. In 1996, using the special education data, a greater percentage (9.5%) of children were in classes for mild, moderate, severe, and profound intellectual disability compared with the percentage of children in these programs in 2000 (8.6%) (Bhasin et al., 2006). As stated in a recent publication of this population, it is evident that the prevalence data obtained by MADDSP partly reflects the trends observed in special education placement of children with disabilities. In comparison to the 1996 study year, a higher prevalence of autism spectrum disorders was observed in the 2000 study year. At the time of this publication, extensive analyses were being conducted to evaluate plausible diVerences in the estimates. Race/ethnicity, sex, cognitive functioning, previous classifications, previous diagnoses, and special education exceptionality are factors being explored. Final analyses for this comparison between study years are currently in progress and thus, conclusions are not available at this time. Similar to the findings for MADDS and previous MADDSP study years, a greater proportion of black children were found among children with mental retardation and cerebral palsy in the 1996 and 2000 study years. A greater proportion of black children were also observed among children with sensory impairment in the 2000 study year; this race pattern diVered slightly from what was observed in 1996 for this group of children. In comparison to the other disabilities, a greater proportion of white children were aVected with autism spectrum disorders. Study results also show that male children were more likely to be aVected with autism spectrum disorders, cerebral palsy, hearing loss, mental retardation (only in 2000), and vision impairment. The higher proportion of males aVected with these disabilities is well known (Drews, Yeargin‐Allsopp, Murphy, & Decoufle, 1994; Fombonne, 1999, 2003; Gillberg & Wing, 1999; Mervis et al., 2002; Murphy et al., 1993; Van Naarden Braun et al., 1999). The predominance of males with mental retardation and vision impairment can partly be attributed to X‐linked conditions such as Fragile X and ocular albinism (Mervis et al., 2002; Yeargin‐Allsopp, Drews, Decoufle, & Murphy, 1995). The higher male‐to‐female ratio that has been consistently reported for autism spectrum disorders could also be partly due to X‐linked conditions like Fragile X since many children with autism spectrum disorders also have mental retardation. However, other possible reasons for this sex diVerence might be attributed to factors related to recognition and diagnosis (i.e., higher functioning males might come to the attention of a health care professional earlier than higher functioning females).

164

Rachel Nonkin Avchen et al. IV.

FINDINGS FROM EPIDEMIOLOGY STUDIES

Not only do population‐based surveillance programs allow for reporting of prevalence estimates, but they provide a framework for etiologic and descriptive studies. MADDS and MADDSP data have been used for many fruitful epidemiologic analyses and both datasets are linked to external database systems such as birth certificate, census, and MACDP databases. A number of special epidemiologic studies have also been carried out by collecting additional information on cases identified by MADDS and MADDSP, and in some instances MADDS and MADDSP cases were compared to matched controls. Following section reviews prevalence and epidemiologic studies that have been conducted using MADDS, MADDSP, and MADDS Follow‐Up data. Three tables (Tables IV–VI) were created to summarize the research conducted to date. The tables are organized by surveillance system and include a reference for each study and information about developmental disability(s) focus, surveillance period, population, major findings, and conclusions. V.


The studies described throughout this chapter provide information on morbidity and mortality associated with developmental disabilities across the life span. These studies reflect the more than 20‐year history of CDC surveillance and research activities that have contributed to the understanding of developmental disabilities in key areas across the life stages including prenatal, perinatal, and postnatal risk factors; sociodemographic risk factors; and transition of children with developmental disabilities into young adulthood. In addition to the specific policy and public health implications of MADDS, MADDSP, and MADDS Follow‐Up Study data, the methodology developed through these systems is being replicated across the United States as part of the Autism and Developmental Disabilities Monitoring Network (ADDM) and Centers for Autism and Developmental Disabilities Research and Epidemiology (CADDRE), which have the potential for public health impacts far beyond metropolitan Atlanta. A.

Prenatal Risk Factors

Although the cause of a developmental disability is often unknown, studies have identified several prenatal risk factors for adverse developmental outcomes (Yeargin‐Allsopp, Murphy, Cordero, Decoufle, & Hollowell, 1997). Studies conducted by MADDS and MADDSP have examined a number of

SUMMARY References

OF

TABLE IV PUBLICATIONS USING MADDS DATA

DD focus

Surveillance period

CP, HI, MR, VI, EP

1985–1987

10‐year olds

Number of observed deaths was greater than the number of expected deaths regardless of the number of disabilities Observed‐to‐expected mortality ratio was 3:1 In general, the magnitude of mortality ratios was directly related to various measures of DD severity, except for isolated MR and cardiovascular problems

The specific underlying causes of death among deceased cohort members included some that were the putative cause of the developmental disability (e.g., a genetic syndrome) and others that could be considered intercurrent diseases or secondary health conditions (e.g., asthma)

Yeargin‐Allsopp et al. (1992)

CP, HI, MR, VI

1985–1987

10‐year olds

Prevalence of CP was 2.3/1000 children Prevalence of HI was 1.1/1000 children Prevalence of MR was 12.0/1000 children Prevalence of VI was 0.7/1000 children About 95% of the children with one or more of these four disabilities were initially identified through the school systems

A multiple source case ascertainment method based on extant records is much less costly than conducting medical and psychological assessments on populations of children. In addition, this method made it possible to estimate accurately the ‘‘administrative prevalence’’ of the DD tracked by MADDS

Drews et al. (1992)

Blindness

1985–1987

10‐year olds

Overall prevalence of blindness was 6.8/10,000 children Prevalence varied by gender and race and ranged from a low of 1.8/10,000 black girls to a high of 8.8/10,000 black boys Retinopathy of prematurity was most common cause of blindness About 66% of children with blindness also had a comorbid DD tracked by MADDS

The low prevalence of blindness among black girls and the frequent occurrence of blindness with other disabilities are noteworthy

MADDS Decoufle and Autry (2002)

Population

Major findings

Conclusions

165

(continued )

TABLE IV (Continued ) DD focus

Murphy et al. (1993)

CP

1985–1987

Winter et al. (2002)

CP

1985–1987, 1991–1994 (MADDSP)

Murphy, Trevathan, and Yeargin‐Allsopp (1995)

EP

1985–1986

166

References

Surveillance period

Population

Major findings

Conclusions

10‐year olds

Prevalence of CP was 2.3/1000 children Prevalence of CP was higher among boys and black children CP was acquired postnatally in 16% of the children and these children were more likely to be black males Spastic CP was the most common subtype (88%) About 75% of children with CP also had one of the other four DD tracked by MADDS

Multiple source case ascertainment is a useful method to obtain the administrative prevalence of CP among school‐age children. However, this method may be limited in its ability to estimate mild cases of CP and to accurately determine CP subtypes and severity

3‐ to 10‐year olds

There was a modest increase in the overall prevalence of congenital CP from 1.7 to 2.0/1000 1‐year survivors during the period from 1975 to 1991. This trend was primarily attributable to a slight increase in CP in infants of normal birthweight CP rates in moderately low and very low birthweight infants did not show consistent trends

There has been a modest increase in the prevalence of CP in 1‐year survivors born from 1975–1991. This increase was seen only in infant survivors of normal birthweight

10‐year olds

Prevalence of EP was 6.0/1000 children Capture recapture showed prevalence of EP could be as high as 7.7/1000 There was a greater prevalence of EP among boys; the prevalence did not vary appreciably by race 40% of children with EP had their first seizure at 1 year of age Partial seizures, including secondarily generalized seizures, were the most common seizure type (58%) About 3.5% of children with EP also had one of the other DD tracked by MADDS

An accurate estimate of the public health burden of childhood EP and determination of possible risk factors for idiopathic EP both depend on conducting complete community‐based case ascertainment and obtaining detailed clinical data

EP

1985–1987

10‐year olds

Prevalence of Lennox–Gastaut syndrome was 0.26/1000 children 91% of the children with Lennox–Gastaut syndrome also had MR; 17% of the profound MR cases had Lennox–Gastaut syndrome 39% of the children with Lennox–Gastaut syndrome also had infantile spasms Lennox–Gastaut syndrome accounts for 4% of all childhood EP Children with severe Lennox–Gastaut syndrome were more likely to have at least one of the DD tracked by MADDS

Lennox–Gastaut syndrome accounts for only 4% of all childhood EP, yet is a significant contributor to childhood morbidity

Trevathan et al. (1999)

EP

1985–1987

10‐year olds

The cumulative incidence of infantile spasms was 2.9/10,000 live births Half of the children with infantile spasms had cryptogenic IS The age‐specific prevalence of infantile spasms was 2.0/10,000 among 10‐year‐old children 83% of children with infantile spasms had MR; 56% profound MR. Among children with profound MR, 12% had a history of infantile spasms DD outcome did not diVer between those with cryptogenic spasms versus symptomatic infantile spasms 50% of those with infantile spasms had Lennox–Gastaut syndrome

Infantile spasms are rare in the general population. Yet, a significant percentage of all children with profound MR and severe childhood EP syndromes in the general population have a history of infantile spasms

Kuenneth et al. (1996)

EP

1985–1987

10‐year olds

Mothers of children with EP had more previous live births and more adverse reproductive outcomes including spontaneous abortions, very low birthweight, and infants with birth defects than mothers of children without EP Risk of EP was especially strong for a mother of child with birth defects Birth defects that were reported most frequently were CNS defects and Down syndrome

There are a few strong risk factors for childhood EP. Our results suggest that women who gave birth to a child with EP are likely to have a reproductive history characterized by adverse outcomes

167

Trevathan et al. (1997)

(continued)

TABLE IV (Continued ) DD focus

Surveillance period

Population

Drews et al. (1994)

HI

1985–1987

10‐year olds

Prevalence of HI was 1.1/1000 children Etiology could not be determined for 55% of the cases of HI 74% of the cases of HI were diagnosed after age 2 About 25% of the HI cases had a comorbid DD tracked by MADDS

Methods for early identification of children with hearing loss need to be improved

Murphy, Yeargin‐Allsopp, Decoufle, and Drews (1995)

MR

1985–1987

10‐year olds

Prevalence of MR was 12.0/1000 children Prevalence of mild MR was 8.4/1000 children Prevalence of severe MR was 3.6/1000 children Prevalence of MR was higher among blacks and males Children with severe MR were more likely to have one of the other DD tracked by MADDS

The MR prevalence rates reported in this study may reflect social and demographic characteristics unique to metropolitan Atlanta. Caution should be applied when comparing results across MR prevalence studies due to likely diVerences in case definitions, case ascertainment, time periods, age categories, and social and demographic composition of study populations

Drews et al. (1996)

MR

1985–1986

10‐year olds

Maternal smoking during pregnancy was associated with slightly more than 50% increase in the prevalence of idiopathic MR (OR ¼ 1.6) Children whose mothers smoked at least one pack a day during pregnancy had more than a 75% increase in the occurrence of idiopathic MR

Our data suggest that maternal smoking may be a preventable cause of MR

Decoufle and Boyle (1995)

MR

1985–1986

10‐year olds

Maternal education was the strongest predictor for having a child with MR Relative to children of white mothers with 12 years of education, children of black mothers, except those whose mothers had 16 or more years of education, were at increased risk

For isolated MR, maternal educational level was the most important predictor from among seven sociodemographic variables examined. There was a significant race– education interaction that indicated a steeper gradient in risk among white mothers than among black mothers

168

References

Major findings

Conclusions

169

Williams and Decoufle (1999)

MR

1985–1987

10‐year olds

There was not an elevated odds of having children with MR among mothers 30 years that was not attributed to Down syndrome

Educational level measured at time of delivery may not adequately characterize the ultimate educational attainment of teenaged mothers. Codevelopmental retardation among children of older black mothers could have resulted from multiple risk factors operating separately or synergistically

Decoufle et al. (1993)

MR

1985–1987

10‐year olds

48% of women worked while pregnant Risk for having children with MR increased among women with low level white collar occupations, especially service occupations There was a strong positive association between children with MR and maternal employment in the textile and apparel industries

Most comparisons yielded OR that were not indicative of unusual risks, but we did find lower than expected risks among children of teachers and health‐care professionals. We also found a strong, positive association between MR and maternal employment in the textile and apparel industries


MR

1985–1986

10‐year olds

There were elevated OR for mild MR among black children compared to white children after controlling for socioeconomic factors including sex, maternal age and education, birth order, and economic status

Five sociodemographic factors accounted for approximately half of the excess prevalence of mild MR among Black children. Possible reasons for the residual diVerence are discussed

Mervis et al. (1995)

MR

1985–1986

10‐year olds

Low birthweight children as a whole had an increased odds for MR (OR ¼ 2.8) Low birthweight children had a greater odds of severe MR than mild MR Normal birthweight children that were born preterm also had an elevated odds for MR

This was one of the first population‐based study of the association between low birthweight and MR conducted in the United States. Although the data document the magnitude of the association between low birthweight and MR, they do not shed any new light on the etiological mechanisms involved

(continued)

TABLE IV (Continued ) References

DD focus

Surveillance period

Population

Major findings

Conclusions

MR

1985–1986

10‐year olds

Boys were more likely than girls to have MR Older mothers were more likely than younger mothers to have a child with MR accompanied by another neurologic condition Other neurologic conditions were more common with severe MR than with mild MR High birth order, black maternal race, and low maternal education were associated with a higher prevalence of isolated MR

These findings suggest that sociodemographic risk factors for MR vary according to the presence of other neurologic conditions and that subdivisions based on medical or physical criteria may be useful in epidemiologic studies of MR


MR

1985–1987

10‐year olds

The majority of identified cases of MR (78%) did not have a known cause Of the cases where cause was determined, prenatal insults were present in 12%, perinatal causes in 6%, and postnatal causes in 4% of cases

Intensive use of public health prevention strategies can reduce the number of children who receive a MR diagnosis

170

Drews et al. (1995)

CP, cerebral palsy; DD, developmental disabilities; EP, epilepsy; MR, mental retardation; OR, odds ratio; HI, hearing impairment; VI, vision impairment.

SUMMARY References

OF

TABLE V PUBLICATIONS USING MADDSP DATA

DD focus

Surveillance period

Population

CP, HL, MR, VI

1991


Prevalence of MR varied by age, race, and sex and ranged from 5.2/1000 children to 16.6/1000 children Prevalence of CP was 2.4/1000 children Severe MR accounted for one‐third of all cases Prevalence of CP was higher among black children (3.1/1000 children) than among white children (2.0/1000 children) Prevalence of moderate to severe HL was 1.1/1000 children Prevalence of HL was higher among black males than among children in the other race and sex groups Prevalence of VI was 0.8/1000 children

MADDSP data will be used to direct early childhood intervention eVorts to reduce the prevalence of these four DD. MADDSP data also are being used to measure progress toward the year 2000 national objectives for the prevention of serious MR

Centers for Disease Control and Prevention (1996)

CP, HL, MR, VI

1991


4.5% of children in MADDSP had at least one DD attributable to a postnatal cause 3.5% of the cases of MR and 12.4% of the cases of HL were caused by postnatal insults Bacterial meningitis and child battering were the most common postnatal causes of DD The prevalence of two or more DD was more than twofold higher for those with postnatally acquired DD than for DD attributable to other causes

Surveillance for DD should include information about the underlying causes. Knowledge about cause can be used to inform and design public health prevention eVorts, because most postnatally acquired DD is preventable

Ashley‐Koch et al. (2001)

CP, HL, MR, VI

1991–1993


Children with sickle cell disease had increased risk for DD (OE ¼ 3.2, p < .0001), particularly MR (OE ¼ 2.7, p ¼ .0005) and CP (OE ¼ 10.8, p < .0001) This risk was confined to DD associated with stroke (OE ¼ 130, p < .0001)

Intervention to prevent strokes in children with sickle cell disease is crucial to preventing a DD

MADDSP Boyle et al. (1996)

Major findings

Conclusions

171

(continued)

TABLE V (Continued )

172

References

DD focus

Surveillance period

Population

Major findings

Conclusions

Decoufle et al. (2001)

CP, HL, MR, VI

1991–1994


7.2% of children with a birth defect had a serious DD 17.8% of children with DD had a birth defect Birth defects that originated in the nervous system and chromosomal defects resulted in the highest prevalence ratios (PR) for a subsequent DD PR were lowest for isolated birth defects Regardless of the severity of the defect or whether defects of the nervous system, chromosomal defects, or ‘‘other syndromes’’ were counted—PR for any DD monotonically increased with the number of coded birth defects per child or the number of diVerent birth defect categories per child

These data highlight the possible early prenatal origins of some DD and suggest that both the number of coded birth defects present and the number of anatomic systems involved are strongly related to functional outcomes

Van Naarden Braun et al. (2005)

CP, HL, MR, VI

1991–1994


Recurrence risk estimates for MR, CP, HL, and VI ranged from 3% to 7% and were many times greater than the baseline prevalence for each disability The RR for MR was eight times greater than the baseline prevalence Isolated MR was highly concordant between siblings with MR Demographic, SES factors, and birthweight were not significantly associated with recurrence risks for MR

Further research is needed to investigate the roles of genetic and environmental factors on the recurrence of DD, particularly isolated mild MR


BD, MR, SED, SDD, SLD, SLI

1991–1994, 1991–1998, Special Education Database of Metropolitan Atlanta (SEDMA)


Approximately 147 infants screened were positive for a metabolic or endocrine disorder and were at risk for MR if left untreated using the MADDSP and newborn screening program database linkage. Yet, only three children were identified with MR Approximately 216 children would be expected to have MR if their metabolic disorder was left untreated using the SEDMA and newborn screening program database linkage. Yet, nine children were identified with a less severe DD from this linkage

Although children found in MADDSP or SEDMA have a low occurrence of DD attributable to these metabolic or endocrine disorders, our finding of any cases of DD of varying severity attributable to a metabolic or endocrine disorder suggests a need for ongoing population‐based monitoring of the long‐term developmental outcomes of children identified through newborn screening programs

173


CP, HL, MR, VI

1991–1994


Estimated lifetime costs in 2003 in dollars are expected to total $51.2 billion for persons born in 2000 with MR, $11.5 billion for persons with CP, $2.1 billion for persons with HL, and $2.5 billion for persons with VI

The costs associated with DD in the United States highlight the need for strategies to reduce the prevalence of these conditions and prevent development of secondary conditions


ASD

1996


Prevalence of ASD was 3.4/1000 children The male–female ratio was 4:1 68% of children with IQ or developmental test results had cognitive impairment 40% of children with autism were identified only at educational sources

The rate of ASD found in this study was higher than the rates from studies conducted in the United States during the 1980s and early 1990s, but it was consistent with those of more recent studies. Schools were the most important source for information on black children, children of younger mothers, and children of mothers with less than 12 years of education

DeStefano et al. (2004)

ASD, MR

1996


There was no significant association between the MMR vaccine and ASD The overall distribution of ages at time of MMR vaccination among children with ASD was similar to that of matched control children

Similar proportions of case and control children were vaccinated by the recommended age (18 months) or shortly after and before 24 months of age, the age atypical development is usually recognized in children with autism. Vaccination before 36 months was more common among case children than control children, especially among children 3–5 years of age, likely reflecting immunization requirements for enrollment in early intervention programs

Winter et al. (2002)

CP

1985–1987 (MADDS), 1991–1994


There was a modest increase in the overall prevalence of congenital CP from 1.7 to 2.0/1000 1‐year survivors during the period from 1975 to 1991. This trend was primarily attributable to a slight increase in CP in infants of normal birthweight CP rates in moderately low and very low birthweight infants did not show consistent trends

The reason for an increase in CP in heavier birthweight infants is unclear. Additional trends in CP rates may emerge as monitoring of CP continues. With more years of data, we may better understand the impact of recent medical interventions on the risk of CP and other DD

(continued)

TABLE V (Continued ) DD focus

Surveillance period

Population

Scher et al. (2002)

CP

1991–1992


Twins were at an approximately fivefold increased risk of fetal death, sevenfold increased risk of neonatal death, and fourfold increased risk of CP compared with singletons Twins from growth–discordant pairs and twins whose cotwin died were at increased risk of both mortality and CP

This is the largest population‐based study of CP to date. The overall rates of death or CP were higher in twins than singletons, although small twins generally did better than small singletons. Cotwin death was a strong predictor of CP in surviving twins. This risk was the same for same‐ and diVerent‐sex pairs, and observed across the birthweight spectrum

Boyle et al. (2000)

CP

1991–1992


There was no association between exposure to magnesium sulfate and CP risk (OR ¼ 0.9, CI ¼ 0.3, 2.6)

Several ongoing randomized clinical trials of magnesium and CP may shed more definitive light on this relation

Schendel et al. (1996)

CP, MR

1986–1988


There was no association between prenatal magnesium sulfate exposure and infant mortality (adjusted RR ¼ 1.02, CI ¼ 0.8, 1.25) Among Atlanta‐born survivors, those exposed to magnesium sulfate had a lower prevalence of CP and MR than those not exposed

A reduced risk for CP, and possibly MR, among very low birthweight children is associated with prenatal magnesium sulfate exposure. The reduced risk for childhood CP or MR does not appear to be due to selective mortality of magnesium sulfate‐exposed infants


HL

1991–1993


Average annual prevalence of HL was 1.1/1000 children Only 8% of children had their HL diagnosed during their first year of life The mean age of earliest known diagnosis was 2.9 years

This study highlights the public health intervention opportunity for universal newborn hearing screening programs to identify children with HL at younger ages. Interventions to reduce the occurrence of communication disabilities associated with HL are most successful if aVected children are identified early, ideally during the first few months of life

174

References

Major findings

Conclusions

175


HL

1991–1993


Prevalence of HL was 1.1/1000 children The highest rate of HL was among male, black children (1.4/1000 children) 90% of case children with a known type of loss had sensorineural loss The mean age of earliest diagnosis was 2.9 years overall, 3.6 years for moderate HL, and 2.4 years for severe‐profound HL 30% of case children had another neurodevelopmental condition, most frequently MR

MADDSP is currently the only ongoing source of information on the prevalence of serious HL among children in the US that is not derived from a one‐time hearing screen. Further research to examine reasons for racial disparities among rates for children with HL may be fruitful

Van Naarden Braun and Decoufle (1999)

HL

1991–1993


Overall prevalence of bilateral congenital sensorineural HL was 5.3/10,000 children Prevalence of bilateral congenital sensorineural HL was 6.6/10,000 children with birthweight 2500–2999 g Prevalence of bilateral congenital sensorineural HL was 12.7/10,000 children with birthweight 1500–2499 g Prevalence of bilateral congenital sensorineural HL was 51.0/10,000 children with birthweight

International Review of Research in Mental Retardation, Volume 33: Developmental Epidemiology of Mental Retardation and Developmental Disabilities

International Review of Research in Mental Retardation, Volume 24 (International Review of Research in Mental Retardation)

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