The New England Journal of Medicine - Nov 10, 2011 - Vol. 365 No. 19

The

NEW ENGLA ND JOURNAL

of

MEDICINE

Perspective november 10, 2011

Making Good on ACOs’ Promise — The Final Rule for the Medicare Shared Savings Program Donald M. Berwick, M.D.

D

uring my career as a practicing pediatrician, my patients and I benefited from being part of a well-managed system of care, coordinated and financed to support seamlessness and patient-

centeredness. We had an electronic health record — always available and up to the minute — which reminded me when a test or follow-up was due. For children with severe asthma, I worked as part of a team, with a home health nurse to teach skills and anticipate needs, an allergist instantly available as a coach, pharmacists to help plan care and detect errors, and advanced practice nurses to ensure 24/7 access. As a result, my patients stayed out of emergency rooms and hospital beds, remaining at home and in school, where they belonged. Function improved and costs fell. The dedicated professionals in

the U.S. health care system work to deliver the highest-quality health care they can. But as any health care provider will tell you, our system is full of roadblocks, red tape, and frustrations that keep them from practicing the type of medicine that most clinicians envisioned when they chose their noble field. Physicians, nurses, and other health care professionals want the support required to work with engaged patients to make the clinical decisions most appropriate to their circumstances; to collaborate with colleagues to provide a safe, seamless experience; and to be paid for keeping people well. Instead, the status quo — with

inadequate dissemination of usable clinical information, misaligned financial incentives, and in many cases, inertia — is rife with barriers to the coordinated care that patients want, providers want to give, and our unsustainable system so desperately needs. To be sure, exactly this type of medicine is practiced every day in hundreds of places throughout the country. Innovative entrepreneurs and dedicated clinicians have found ways to break down barriers and redesign care to better help their patients and communities. But bringing the best of our system to every community in the country is the health care challenge of our time. Eighteen months after President Barack Obama signed the Affordable Care Act, the Department of Health and Human Services (DHHS) has created a broad array of pathways for health care

n engl j med 365;19  nejm.org  november 10, 2011

The New England Journal of Medicine

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PERSPE C T I V E

Final Rule for Medicare Shared Savings Program

Proposed Rule vs. Final Rule for Accountable Care Organizations (ACOs) in the Medicare Shared Savings Program. Topic

Proposed Rule

Modifications in Final Rule

Transition to risk in Track 1

ACOs could choose from two tracks, each entailing a 3-year agreement. Track 1 would comprise 2 years of one-sided shared savings with a mandatory transition in year 3 to performancebased risk under a two-sided model of shared savings and losses. Track 2 would comprise 3 years all under the two-sided model.

Remove two-sided risk from Track 1. Two tracks would still be offered for ACOs at different levels of readiness, with one providing higher sharing rates for ACOs willing to also share in losses.

Prospective vs. ­retrospective

Retrospective assignment based on utilization of primary care services, with prospective identification of a benchmark population.

A preliminary prospective-assignment method with beneficiaries identified quarterly; final reconciliation after each performance year, made on the basis of patients served by the ACO.

Proposed measures to assess quality

65 measures in 5 domains, including patient ­experience of care, utilization claims–based measures, and measures assessing process and outcomes. Pay for full and accurate reporting first year, pay for performance in subsequent years. Alignment of proposed measures with existing quality programs and private-sector initiatives.

33 measures in 4 domains. (Note: Claims-based measures not finalized to be used for ACO-monitoring purposes.) Longer phase-in of measures over course of agreement: first year, pay for reporting; second and third years, pay for reporting and performance. Finalize as proposed.

Sharing savings

One-sided risk model: sharing beginning at savShare on first dollar for all ACOs in both models once minimum ings of 2%, with some exceptions for small, savings rate has been achieved. physician-only, and rural ACOs. Two-sided risk model: sharing from first dollar.

Sharing beneficiary identi­fication claims data

Claims data shared only for patients seen by ACO The ACO may contact beneficiaries from provided quarterly lists primary care physician during performance to notify them of data sharing and opportunity to decline. year; beneficiaries given opportunity to decline at the point of care.

Eligible entities

The four groups specified by the Affordable Care In addition to groups included in the proposed rule, Federally Act, as well as critical access hospitals paid Qualified Health Centers and Rural Health Clinics are also through Method II, are eligible to form an ACO. eligible to both form and participate in an ACO. In order for ACOs can be established with broad collaborabeneficiaries to be assigned on the basis of utilization of prition beyond these providers. mary care services, these organizations must provide a list of practitioners who directly render primary care services in their facilities so that beneficiaries can be assigned on the basis of utilization of their services.

Start date

Agreement for 3 years with uniform annual start Program established by January 1, 2012; first round of applicadate; performance years based on calendar years. tions are due in early 2012. First ACO agreements start April 1, 2012, and July 1, 2012. ACOs will have agreements with a first performance “year” of 18 or 21 months. ACOs starting April 1, 2012, or July 1, 2012, have option of an interim payment if they report calendar year (CY) 2012 quality measures. ACO must report quality measures for CY 2013 to qualify for first-performance-year shared savings.

Aggregate reports and pre­liminary ­prospective list

Reports will be provided at the beginning of each performance year and include: name, date of birth, sex, and health insurance claim number.

Additional reports will be provided quarterly.

Electronic health record (EHR) use

Aligning ACO requirements with EHR requirements, 50% of primary care physicians must be defined as meaningful users by start of second performance year.

No longer a condition of participation. Retained EHR as quality measure but weighted higher than any other measure for quality-scoring purposes.

Assignment process

One-step assignment process: beneficiaries assigned on the basis of a plurality of allowed charges for primary care services rendered by primary care physicians (internal medicine, general practice, family practice, and geriatric medicine).

Two-step assignment process: Step 1: for beneficiaries who have received at least one primary care service from a physician, use plurality of allowed charges for primary care services rendered by primary care physicians. Step 2: for beneficiaries who have not received any primary care services from a physician, use plurality of ­allowed charges for primary care services rendered by any other ACO professional.

Marketing guidelines

All marketing materials must be approved by the Cen­ters for Medicare and Medicaid Services (CMS).

“File and use” 5 days after submission and after certifying compliance with marketing guidelines; CMS to provide approved language.

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n engl j med 365;19  nejm.org  november 10, 2011

The New England Journal of Medicine

PERSPECTIVE

Final Rule for Medicare Shared Savings Program

providers to begin — or in many cases, accelerate — their careimprovement journey in partnership with the Medicare and Medicaid programs and in synergy with the private sector. Today, the DHHS is taking its next major step by finalizing the rules for the establishment of accountable care organizations (ACOs) under the Medicare Shared Savings Program created by Section 3022 of the health care reform law. ACOs are voluntary groups of physicians, hospitals, and other health care providers that are willing to assume responsibility for the care of a clearly defined population of Medicare beneficiaries attributed to them on the basis of patients’ use of primary care services. If an ACO succeeds in both delivering high-quality care or improving care and reducing the cost of that care below what would otherwise have been expected, it will share in the savings it achieves for Medicare. Under the ACO model, Medicare beneficiaries are still free to seek care from any Medicare provider they wish. Indeed, Medicare beneficiaries should find their care experience enhanced by a program that supports providers in engaging with their patients to deliver on the three-part aim: better care for individuals, better health for populations, and lower cost growth through improvements in care. The DHHS proposed its initial set of guidelines for ACOs on March 31, 2011, and sought widespread comment on both the direction and the details of this important new program for Medicare. We at the Centers for Medicare and Medicaid Services (CMS) received more than 1200 formal comments from throughout the health care community, supple-

mented by feedback at dozens of informal listening sessions. The vast majority of the comments we received were supportive of the vision of the Shared Savings Program and optimistic about the potential for ACOs to be a force for change in our broken health care system. However, numerous suggestions were also offered for improvements to the proposed rule that would lead to a larger, more pluralistic set of ACO participants without compromising patient outcomes or choice. In partic­ ular, commenters asked CMS to reduce barriers to entry by streamlining governance and reporting burdens on potential ACOs; improve the potential financial return for ACOs willing to make the necessary, and often substantial, investments to improve care; and ensure beneficiary protections. In response, CMS is making several significant changes in its final rule to strengthen the ACO program for providers and beneficiaries alike (see table). Major changes include providing better, and more timely, information to ACOs at the outset of the performance year through preliminary prospective alignment of beneficiaries (while retaining a retrospective reconciliation to ensure that ACOs are measured on the basis of the patients they actually care for during the year); retaining a strong monitoring and qualitymeasurement mechanism while streamlining the metrics to focus on what matters most, including reducing the total number of quality measures by about half; allowing start-up ACOs to choose a “savings only” track without financial risk during their initial contract period; sharing savings with successful ACOs on a “first dollar” basis when the ACO achieves meaningful savings for

the Medicare program and improves care or provides highquality care; and creating a pathway for full participation of federally qualified health centers and rural health clinics that provide a primary care safety net for Medicare beneficiaries in underserved areas. Taken together, these changes and numerous others create a more feasible and attractive on-ramp for a diverse set of providers and organizations to participate as ACOs. In addition, the Center for Medicare and Medicaid Innovation is announcing today an advanced payment initiative that will allow small physician practices and rural community hospitals that face particular challenges in forming ACOs to receive up-front access to needed capital. For established organizations with a track record of providing robust coordinated care, the CMS innovation center is offering a pioneer ACO program designed to encourage and support the next wave of innovation from vanguard organizations that are positioned to help realize the full potential of the ACO model. And for organizations and clinicians not yet prepared to make the transition to ACOs, the DHHS is offering a menu of alternative options — including a comprehensive primary care program, bundled payments for care improvement, and a community-based transitional care program — that all seek to provide the incentives and supports necessary to move the mainstream of U.S. health care toward accountable care. Whether provided through ACOs or an alternative innovation opportunity, coordinated care is meant to allow providers to break away from the tyranny of the 15-minute visit, instill a re-

n engl j med 365;19  nejm.org  november 10, 2011

The New England Journal of Medicine

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PERSPE C T I V E

Final Rule for Medicare Shared Savings Program

newed sense of collegiality, and return to the type of medicine that patients and families want. For patients, coordinated care means more “quality time” with their physician and care team (a patient’s advocate in an increasingly complex medical system) and more collaboration in leading a healthy life. And for Medicare, coordinated care represents the

most promising path toward financial sustainability and away from alternatives that shift costs onto patients, providers, and private purchasers. We believe that today’s ACO rule is the next step in our shared commitment to a better, more lasting health care system. We look forward to being a trusted partner in our nation’s journey

toward patient-centered, coordinated care. Disclosure forms provided by the author are available with the full text of this article at NEJM.org. Dr. Berwick is the administrator of the Centers for Medicare and Medicaid Services, Baltimore. This article (10.1056/NEJMp1111671) was published on October 20, 2011, at NEJM.org. Copyright © 2011 Massachusetts Medical Society.

Getting Moving on Patient Safety — Harnessing Electronic Data for Safer Care Ashish K. Jha, M.D., M.P.H., and David C. Classen, M.D.

M

ore than a decade ago, the Institute of Medicine released its famous report To Err Is Human, which set an ambitious agenda for the United States to reduce the number of Americans who were hurt or killed by medical errors and adverse events. In response, a series of new initiatives was launched, including the funding of new research on ways of making care safer and encouragement of programs shielding health care providers from liability if they reported adverse events. Federal agencies set up patientsafety organizations and established ambitious patient-safety goals; accrediting organizations set aggressive patient-safety standards; and providers hired patient-safety officers and implemented numerous patient-safety initiatives. So what are the fruits of these efforts? Recently, we have received some deeply disappointing news: three studies have called into question whether we’ve made any progress at all. Landrigan et al. found that rates of injury due to medical error had remained es-

1756

sentially unchanged between 2000 and 2008 at 10 North Carolina hospitals.1 A report from the Inspector General of the Department of Health and Human Services (DHHS) revealed that Medicare patients experienced substantial harm in U.S. hospitals as recently as 2008.2 Finally, Classen and colleagues found that almost one in three patients are harmed during their hospital stay and that traditional approaches to measuring adverse events, whether using voluntary reporting or patient-safety indicators, substantially underestimate the events’ frequency.3 If the United States has made progress in patient safety, it has been inadequate. The primary reason for insufficient progress is the lack of a robust measurement program: there are still no nationally agreed-on methods for systematically identifying, tracking, and reporting adverse events. Here, the patientsafety movement can learn from the quality-improvement efforts that predate it. In the 1990s, emerging evidence suggested that providers were inconsistent in

their adherence to evidence-based treatments such as the use of aspirin for patients with acute myocardial infarction. Efforts by the Joint Commission for the Accreditation of Healthcare Organizations to systematically measure performance and give feedback to hospitals, coupled with subsequent efforts to publicly report performance on these measures, led to dramatic improvements in compliance.4 In the few areas of patient safety that have seen demonstrable improvement (e.g., catheter-related bloodstream infections), the changes are due, at least in part, to robust measurement programs, such as those run by the Centers for Disease Control and Prevention. In other areas, inadequate measures have hindered progress, and patients continue to suffer from the consequences of unsafe care. Although there is a shortage of good patient-safety metrics, poorquality measures are plentiful. The best known among these are patient-safety indicators, which use billing data to identify potential complications during a hos-

n engl j med 365;19  nejm.org  november 10, 2011

The New England Journal of Medicine

PERSPE C T I V E

Final Rule for Medicare Shared Savings Program

newed sense of collegiality, and return to the type of medicine that patients and families want. For patients, coordinated care means more “quality time” with their physician and care team (a patient’s advocate in an increasingly complex medical system) and more collaboration in leading a healthy life. And for Medicare, coordinated care represents the

most promising path toward financial sustainability and away from alternatives that shift costs onto patients, providers, and private purchasers. We believe that today’s ACO rule is the next step in our shared commitment to a better, more lasting health care system. We look forward to being a trusted partner in our nation’s journey

toward patient-centered, coordinated care. Disclosure forms provided by the author are available with the full text of this article at NEJM.org. Dr. Berwick is the administrator of the Centers for Medicare and Medicaid Services, Baltimore. This article (10.1056/NEJMp1111671) was published on October 20, 2011, at NEJM.org. Copyright © 2011 Massachusetts Medical Society.

Getting Moving on Patient Safety — Harnessing Electronic Data for Safer Care Ashish K. Jha, M.D., M.P.H., and David C. Classen, M.D.

M

ore than a decade ago, the Institute of Medicine released its famous report To Err Is Human, which set an ambitious agenda for the United States to reduce the number of Americans who were hurt or killed by medical errors and adverse events. In response, a series of new initiatives was launched, including the funding of new research on ways of making care safer and encouragement of programs shielding health care providers from liability if they reported adverse events. Federal agencies set up patientsafety organizations and established ambitious patient-safety goals; accrediting organizations set aggressive patient-safety standards; and providers hired patient-safety officers and implemented numerous patient-safety initiatives. So what are the fruits of these efforts? Recently, we have received some deeply disappointing news: three studies have called into question whether we’ve made any progress at all. Landrigan et al. found that rates of injury due to medical error had remained es-

1756

sentially unchanged between 2000 and 2008 at 10 North Carolina hospitals.1 A report from the Inspector General of the Department of Health and Human Services (DHHS) revealed that Medicare patients experienced substantial harm in U.S. hospitals as recently as 2008.2 Finally, Classen and colleagues found that almost one in three patients are harmed during their hospital stay and that traditional approaches to measuring adverse events, whether using voluntary reporting or patient-safety indicators, substantially underestimate the events’ frequency.3 If the United States has made progress in patient safety, it has been inadequate. The primary reason for insufficient progress is the lack of a robust measurement program: there are still no nationally agreed-on methods for systematically identifying, tracking, and reporting adverse events. Here, the patientsafety movement can learn from the quality-improvement efforts that predate it. In the 1990s, emerging evidence suggested that providers were inconsistent in

their adherence to evidence-based treatments such as the use of aspirin for patients with acute myocardial infarction. Efforts by the Joint Commission for the Accreditation of Healthcare Organizations to systematically measure performance and give feedback to hospitals, coupled with subsequent efforts to publicly report performance on these measures, led to dramatic improvements in compliance.4 In the few areas of patient safety that have seen demonstrable improvement (e.g., catheter-related bloodstream infections), the changes are due, at least in part, to robust measurement programs, such as those run by the Centers for Disease Control and Prevention. In other areas, inadequate measures have hindered progress, and patients continue to suffer from the consequences of unsafe care. Although there is a shortage of good patient-safety metrics, poorquality measures are plentiful. The best known among these are patient-safety indicators, which use billing data to identify potential complications during a hos-

n engl j med 365;19  nejm.org  november 10, 2011

The New England Journal of Medicine

PERSPECTIVE

Harnessing Electronic Data for Safer Care

pitalization. They generally have poor sensitivity and specificity, and their utility varies with hospitals’ billing practices.3 And because data on them are collected in a post hoc fashion, they fail to engage clinicians at the time of care delivery — and data are generally unavailable to providers for review until years after the care is delivered. In an attempt to make patient-safety measures more visible, the Centers for Medicare and Medicaid Services (CMS) now makes hospital performance data on certain patient-safety indicators publicly available.5 However, these inadequate measures of safety, even if delivered to hospitals more quickly, are unlikely to engage front-line clinicians in activities that will make care safer. Another approach to safety measurement has been the use of voluntary (or occasionally, mandatory) reporting of adverse events. These efforts sporadically yield important insights — but generally have very low sensitivity (most adverse events are never reported), which makes it difficult for provider organizations to know whether they’re making progress. Finally, some have used the “trigger tools” method, which, though not extensively validated, appears to be sensitive in detecting adverse events.3 However, this approach, used primarily in research, is resource-intensive to implement and has gained little traction among providers as an ongoing approach to monitoring safety. Despite these challenges, we currently have an opportunity to turn the tide. The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 provides, through CMS, financial incentives for physicians and hospitals to become “mean-

ingful users” of electronic health records (EHRs). EHRs can systematically measure patient safety, turning a laborious, manually intensive, and expensive process of sifting through medical records to identify adverse events into an automated one that is efficient, consistent, and affordable. Although the technology is already available, most EHRs today are not built with this capability in mind — and it won’t be easy (or cheap) to retrofit EHR systems later. Without an explicit commitment on the part of EHR vendors to build systems that can systematically track adverse events, most EHRs will fail to do it adequately, if at all. However, the federal government, which creates the meaningful use criteria, could include the ability to effectively measure and report adverse event rates as a “core” requirement of meaningful use. By staking out this ground, CMS can signal to vendors the importance of including such a capability in every EHR sold in the United States. If CMS chooses to use EHR-derived safety measures for public reporting or pay for performance, these metrics will need further validation, a lengthy process, but the agency can expedite the activities needed to ensure that we have validated measures quickly. Even without these additional validation efforts, simply providing better-quality EHR-derived safety data to physicians and hospitals can have a profound effect on patient-safety activities throughout the country. We recognize that EHR vendors face competing demands, and many advocacy groups are clamoring to have particular functions included in meaningful use. But the $30 billion in

taxpayer subsidies for EHR adoption was sold to the U.S. public principally as a way of making health care safer. The current EHR systems, if implemented well, may have a modest effect on safety. Requiring the presence and use of a safety-measurement module for identifying and tracking adverse events would provide a critical signal to providers that monitoring adverse events is essential. Such systems would provide information to hospitals on their performance relative to their peers and their progress toward the goal of causing no harm. Most important, it would allow them to track the effects of their interventions and determine which efforts worked and which ones didn’t. Data from such EHR surveillance systems could have patient identifiers removed and be pooled across many sites, which would increase the depth and breadth of the possible analyses and lead to new insight into delivering safer care. The U.S. health care system is at a crossroads when it comes to ensuring patient safety and earning the trust of the public. Our inadequate progress since To Err Is Human is disheartening, but we have an opportunity to right the ship. By making systematic measurement of adverse events a requisite function of the EHRs that are eligible for financial incentives, the federal government can change the way safety is measured and improved throughout the health care system. Without these data, we are likely to repeat our recent experience: good intentions, a lot of effort, and little demonstrable benefit. According to IOM estimates, as many as a million Americans may have died owing to adverse events in U.S. hospitals

n engl j med 365;19  nejm.org  november 10, 2011

The New England Journal of Medicine

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PERSPE C T I V E

Harnessing Electronic Data for Safer Care

over the past decade. We must do better over the next decade. EHRs can improve the safety and culture of U.S. health care, but only if the federal government, as the nation’s largest health care payer, demonstrates that it is serious about improving patient safety. Disclosure forms provided by the authors are available with the full text of this article at NEJM.org. From the Department of Health Policy and Management, Harvard School of Public

Health, and the VA Boston Healthcare System — both in Boston (A.K.J.); and Computer Sciences Corporation and the University of Utah — both in Salt Lake City (D.C.C.). 1. Landrigan CP, Parry GJ, Bones CB, Hackbarth AD, Goldmann DA, Sharek PJ. Temporal trends in rates of patient harm resulting from medical care. N Engl J Med 2010;363: 2124-34. [Erratum, N Engl J Med 2010;363: 2573.] 2. Levinson D. Adverse events in hospitals: national incidence among Medicare beneficiaries. Washington, DC: Office of the Inspector General, Department of Health and Human Services, 2010.

3. Classen DC, Resar R, Griffin F, et al. ‘Global trigger tool’ shows that adverse events in hospitals may be ten times greater than previously measured. Health Aff (Millwood) 2011;30:581-9. [Erratum, Health Aff (Millwood) 2011;30:1217.] 4. Chassin MR, Loeb JM, Schmaltz SP, ­Wachter RM. Accountability measures — using measurement to promote quality improvement. N Engl J Med 2010;363:683-8. 5. Hospital quality initiatives: outcome measures. Baltimore: Centers for Medicare & Medicaid Services, 2011 (https://www .cms.gov/HospitalQualityInits/ 20_OutcomeMeasures.asp). Copyright © 2011 Massachusetts Medical Society.

Evidence-Based Medicine in the EMR Era Jennifer Frankovich, M.D., Christopher A. Longhurst, M.D., and Scott M. Sutherland, M.D.

M

any physicians take great pride in the practice of evidence-based medicine. Modern medical education emphasizes the value of the randomized, controlled trial, and we learn early on not to rely on anecdotal evidence. But the application of such superior evidence, however admirable the ambition, can be constrained by trials’ strict inclusion and exclusion criteria — or the complete absence of a relevant trial. For those of us practicing pediatric medicine, this reality is all too familiar. In such situations, we are used to relying on evidence at Levels III through V — expert opinion — or resorting to anecdotal evidence. What should we do, though, when there aren’t even meager data available and we don’t have a single anecdote on which to draw? We recently found ourselves in such a situation as we admitted to our service a 13-year-old girl with systemic lupus erythematosus (SLE). Our patient’s presentation was complicated by nephroticrange proteinuria, antiphospholipid antibodies, and pancreatitis. Al-

1758

though anticoagulation is not standard practice for children with SLE even when they’re critically ill, these additional factors put our patient at potential risk for thrombosis, and we considered anticoagulation. However, we were unable to find studies pertaining to anticoagulation in our patient’s situation and were therefore reluctant to pursue that course, given the risk of bleeding. A survey of our pediatric rheumatology colleagues — a review of our collective Level V evidence, so to speak — was equally fruitless and failed to produce a consensus. Without clear evidence to guide us and needing to make a decision swiftly, we turned to a new approach, using the data captured in our institution’s electronic medical record (EMR) and an innovative research data warehouse. The platform, called the Stanford Translational Research Integrated Database Environment (STRIDE), acquires and stores all patient data contained in the EMR at our hospital and provides immediate advanced text searching ca-

pability.1 Through STRIDE, we could rapidly review data on an SLE cohort that included pediatric patients with SLE cared for by clinicians in our division between October 2004 and July 2009. This “electronic cohort” was originally created for use in studying complications associated with pediatric SLE and exists under a protocol approved by our institutional review board. Of the 98 patients in our pediatric lupus cohort, 10 patients developed thrombosis, documented in the EMR, while they were acutely ill. The prevalence was higher among patients who had persistent nephrotic-range proteinuria and pancreatitis (see table). As compared with our patients with lupus who did not have these risk factors, the risk of thrombosis was 14.7 (95% confidence interval [CI], 3.3 to 96) among patients with persistent nephrosis and 11.8 (95% CI, 3.8 to 27) among those with pancreatitis. This automated cohort review was conducted in less than 4 hours by a single clinician. On the basis of this real-time, informatics-

n engl j med 365;19  nejm.org  november 10, 2011

The New England Journal of Medicine

PERSPE C T I V E

Harnessing Electronic Data for Safer Care

over the past decade. We must do better over the next decade. EHRs can improve the safety and culture of U.S. health care, but only if the federal government, as the nation’s largest health care payer, demonstrates that it is serious about improving patient safety. Disclosure forms provided by the authors are available with the full text of this article at NEJM.org. From the Department of Health Policy and Management, Harvard School of Public

Health, and the VA Boston Healthcare System — both in Boston (A.K.J.); and Computer Sciences Corporation and the University of Utah — both in Salt Lake City (D.C.C.). 1. Landrigan CP, Parry GJ, Bones CB, Hackbarth AD, Goldmann DA, Sharek PJ. Temporal trends in rates of patient harm resulting from medical care. N Engl J Med 2010;363: 2124-34. [Erratum, N Engl J Med 2010;363: 2573.] 2. Levinson D. Adverse events in hospitals: national incidence among Medicare beneficiaries. Washington, DC: Office of the Inspector General, Department of Health and Human Services, 2010.

3. Classen DC, Resar R, Griffin F, et al. ‘Global trigger tool’ shows that adverse events in hospitals may be ten times greater than previously measured. Health Aff (Millwood) 2011;30:581-9. [Erratum, Health Aff (Millwood) 2011;30:1217.] 4. Chassin MR, Loeb JM, Schmaltz SP, ­Wachter RM. Accountability measures — using measurement to promote quality improvement. N Engl J Med 2010;363:683-8. 5. Hospital quality initiatives: outcome measures. Baltimore: Centers for Medicare & Medicaid Services, 2011 (https://www .cms.gov/HospitalQualityInits/ 20_OutcomeMeasures.asp). Copyright © 2011 Massachusetts Medical Society.

Evidence-Based Medicine in the EMR Era Jennifer Frankovich, M.D., Christopher A. Longhurst, M.D., and Scott M. Sutherland, M.D.

M

any physicians take great pride in the practice of evidence-based medicine. Modern medical education emphasizes the value of the randomized, controlled trial, and we learn early on not to rely on anecdotal evidence. But the application of such superior evidence, however admirable the ambition, can be constrained by trials’ strict inclusion and exclusion criteria — or the complete absence of a relevant trial. For those of us practicing pediatric medicine, this reality is all too familiar. In such situations, we are used to relying on evidence at Levels III through V — expert opinion — or resorting to anecdotal evidence. What should we do, though, when there aren’t even meager data available and we don’t have a single anecdote on which to draw? We recently found ourselves in such a situation as we admitted to our service a 13-year-old girl with systemic lupus erythematosus (SLE). Our patient’s presentation was complicated by nephroticrange proteinuria, antiphospholipid antibodies, and pancreatitis. Al-

1758

though anticoagulation is not standard practice for children with SLE even when they’re critically ill, these additional factors put our patient at potential risk for thrombosis, and we considered anticoagulation. However, we were unable to find studies pertaining to anticoagulation in our patient’s situation and were therefore reluctant to pursue that course, given the risk of bleeding. A survey of our pediatric rheumatology colleagues — a review of our collective Level V evidence, so to speak — was equally fruitless and failed to produce a consensus. Without clear evidence to guide us and needing to make a decision swiftly, we turned to a new approach, using the data captured in our institution’s electronic medical record (EMR) and an innovative research data warehouse. The platform, called the Stanford Translational Research Integrated Database Environment (STRIDE), acquires and stores all patient data contained in the EMR at our hospital and provides immediate advanced text searching ca-

pability.1 Through STRIDE, we could rapidly review data on an SLE cohort that included pediatric patients with SLE cared for by clinicians in our division between October 2004 and July 2009. This “electronic cohort” was originally created for use in studying complications associated with pediatric SLE and exists under a protocol approved by our institutional review board. Of the 98 patients in our pediatric lupus cohort, 10 patients developed thrombosis, documented in the EMR, while they were acutely ill. The prevalence was higher among patients who had persistent nephrotic-range proteinuria and pancreatitis (see table). As compared with our patients with lupus who did not have these risk factors, the risk of thrombosis was 14.7 (95% confidence interval [CI], 3.3 to 96) among patients with persistent nephrosis and 11.8 (95% CI, 3.8 to 27) among those with pancreatitis. This automated cohort review was conducted in less than 4 hours by a single clinician. On the basis of this real-time, informatics-

n engl j med 365;19  nejm.org  november 10, 2011

The New England Journal of Medicine

PERSPECTIVE

Evidence-Based Medicine in the EMR Era

Results of Electronic Search of Patient Medical Records (for a Cohort of 98 Pediatric Patients with Lupus) Focused on Risk Factors for Thrombosis Relevant to Our 13-Year-Old Patient with Systemic Lupus Erythematosus.* Keywords Used to Conduct ­ xpedited Electronic Search E

Outcome or Risk Factor

Prevalence of Thrombosis

Relative Risk (95% CI)

no./total no (%) Outcome — thrombosis

“Thrombus,” “Thrombosis,” “Blood clot”

10/98 (10)

Not applicable

Present at any time

“Nephrosis,” “Nephrotic,” “Proteinuria”

8/36 (22)

  7.8 (1.7–50)

Present >60 days

“Urine protein”

7/23 (30)

14.7 (3.3–96)

Pancreatitis

“Pancreatitis,” “Lipase”

  5/8 (63)

11.8 (3.8–27)

Antiphospholipid antibodies

“Aspirin”

6/51 (12)

  1.0 (0.3–3.7)

Thrombosis risk factor Heavy proteinuria (>2.5 g per deciliter)

* In all cases, the sentences surrounding the keywords were manually reviewed to determine their relevance to our patient. Pancre­ atitis was defined as an elevated lipase level (twice the upper limit of normal) coexisting with abdominal pain. We used the word “aspirin” as a proxy for antiphospholipid antibodies, since it is standard practice at our institution to give all patients with these antibodies aspirin; if “aspirin” was found in the chart, than antiphospholipid-antibody status was confirmed by investigating the laboratory results.

enabled data analysis, we made the decision to give our patient anticoagulants within 24 hours after admission. Our case is but one example of a situation in which the existing literature is insufficient to guide the clinical care of a patient. But it illustrates a novel process that is likely to become much more standard with the widespread adoption of EMRs and more sophisticated informatics tools. Although many other groups have highlighted the secondary use of EMR data for clinical research,2,3 we have now seen how the same approach can be used to guide real-time clinical decisions. The rapid electronic chart review and analysis were not only feasible, but also more helpful and accurate than physician recollection and pooled colleague opinion. Such real-time availability of data to guide deci-

sion making has already transformed other industries,4 and the growing prevalence of EMRs along with the development of sophisticated tools for real-time analysis of deidentified data sets will no doubt advance the use of this datadriven approach to health care delivery. We look forward to a future in which health information systems help physicians learn from every patient at every visit and close the feedback loop for clinical decision making in real time. Did we make the correct decision for our patient? Thrombosis did not develop, and the patient did not have any sequelae related to her anticoagulation; truthfully, though, we may never really know. We will, however, know that we made the decision on the basis of the best data available — acting, as the fictional detective Nero Wolfe would say, “in the light of experience as guided

by intelligence.”5 In the practice of medicine, one can’t do better than that. Disclosure forms provided by the authors are available with the full text of this article at NEJM.org. From the Division of Rheumatology (J.F.), the Division of Systems Medicine (C.A.L.), and the Division of Nephrology (S.M.S.), Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA. This article (10.1056/NEJMp1108726) was published on November 2, 2011, at NEJM.org. 1. Lowe HJ, Ferris TA, Hernandez PM, Weber SC. STRIDE — an integrated standards-based translational research informatics platform. AMIA Annu Symp Proc 2009;Nov 14:391-5. 2. Prokosch HU, Ganslandt T. Perspectives for medical informatics: reusing the electronic medical record for clinical research. Methods Inf Med 2009;48:38-44. 3. Gunn PW, Hansen ML, Kaelber DC. Underdiagnosis of pediatric hypertension — an example of a new era of clinical research enabled by electronic medical records. AMIA Annu Symp Proc 2007;October 11:966. 4. Halevy A, Norvig P, Pereira F. The Unreasonable Effectiveness of Data. IEEE Intelligent Systems, March/April 2009:8-12. 5. Stout R. In the best families. New York: Viking Press, 1950:71. Copyright © 2011 Massachusetts Medical Society.

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PERSPE C T I V E

Cost-Effectiveness and U.S. Vaccination Policy

The Role of Cost-Effectiveness in U.S. Vaccination Policy Jane J. Kim, Ph.D.

V

accination policy is driven by several factors, including vaccine safety and efficacy, avertable disease burden, acceptability, and societal value. One measure of value is an intervention’s costeffectiveness, defined as the additional cost required per additional unit of health benefit produced as compared with the next-mosteffective alternative. It is important to differentiate cost-effectiveness (value for money) from affordability (financial resources required); indeed, interventions with high value may not always be affordable. Although information on the cost-effectiveness of health interventions is increasingly being used in health policy globally, the extent to which this information influences decisions varies by country. For example, the governments in Britain and Australia explicitly and routinely incorporate findings from costeffectiveness analyses into coverage and reimbursement decisions; in contrast, in the United States, it has been essentially taboo for anyone in the public sector to refer explicitly to cost as a factor in health decisions. One exception is the Advisory Committee on Immunization Practices (ACIP), an independent expert advisory board that formally includes cost-effectiveness among the types of evidence it considers when making vaccinepolicy recommendations to the Centers for Disease Control and Prevention (CDC). The ACIP strives to be transparent and balanced, inviting perspectives from stakeholders ranging from scientists to patient groups, and tries to harmonize its recommendations with those of professional

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organizations, such as the American Academy of Family Physicians and the American Academy of Pediatrics. Historically, ACIP recommendations have influenced coverage decisions by both private and public insurers. Through a separate process, the ACIP also determines what vaccines are to be covered by the federal Vaccines for Children (VFC) program, which covers children who are Medicaid-eligible, uninsured or underinsured, or American Indians or Alaska natives up to the age of 18. With nearly 50% of U.S. children eligible for VFC coverage,1 the ACIP faces dual pressures: it must maximize underserved children’s access to vaccines while selecting vaccines that provide the most bang for the buck. This pressure will increase with the rollout of the Affordable Care Act, which mandates coverage of all ACIPrecommended childhood immunizations. With low cost and high efficacy, many vaccines are estimated to be cost-saving — the up-front expenditure for vaccination is entirely offset by costs averted through disease prevention. However, newly licensed and expensive vaccines, such as those against human papillomavirus (HPV, the virus causally linked to cervical cancer) and meningococcal disease, are being considered for use in ways that raise questions regarding their overall public health value as estimated in cost-effectiveness analyses. In late October, the ACIP is expected to vote on routine HPV vaccination in boys and young men and to discuss meningococcal vaccination in infants, includ-

ing its cost-effectiveness. Since 2007, routine HPV vaccination has been recommended for girls 11 to 12 years of age (and as early as 9 years), with “catch-up” vaccination recommended up to the age of 26, despite evidence of rapidly diminishing marginal returns and decreasing cost-effectiveness after 21 years of age.2 After the Food and Drug Administration (FDA) approved the quadrivalent HPV (HPV4) vaccine for males in 2009, the ACIP voted for “permissive” — but not routine — use of it in boys and men 9 to 26 years of age for prevention of genital warts. Despite this less enthusiastic stance, the ACIP voted in favor of VFC coverage for eligible males 9 to 18 years of age. The committee was persuaded not to recommend routine male HPV vaccination in part by evidence that it may not be costeffective, especially if vaccine uptake in girls and young women is high, given the sexual transmission of HPV infections and expected herd-immunity benefits through female-only vaccination. Recent data on uptake among adolescent girls, however, show less than 50% completion of the three-dose series, suggesting that HPV vaccination of boys may be cost-effective at this time. Furthermore, since the 2009 guidelines were issued, the indications for HPV4 have expanded to include prevention of anal cancers. Routine male HPV vaccination, especially if targeted at an early age, when the vaccines are expected to have highest benefit, would maximize protection for men who have sex with men, a group at high risk for HPV-related cancers that would receive little

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PERSPECTIVE

Cost-Effectiveness and U.S. Vaccination Policy

herd-immunity protection from female-only vaccination. With respect to meningococcal vaccination, in October 2010, the ACIP decided in a narrow vote to recommend a single booster dose of the quadrivalent meningo­ coccal conjugate vaccine (MCV4) at the age of 16 despite evidence that routine adolescent MCV4 vaccination does not provide good value for money, largely because of low disease incidence rates and relatively high vaccine cost. Since then, the FDA has approved the licensure of one meningococcal vaccine for use in infants and is reviewing the licensing application for another. In considering expanding use to infants, the ACIP will need to contend with evidence that MCV4 vaccination at such young ages, which requires at least two doses, is even less cost-effective than adolescent vaccination.3 The cost-effectiveness of vaccines is influenced by several factors, including vaccine efficacy and durability, severity of disease burden, vaccine price, and deliveryprogram costs. The meningococcal and HPV vaccines are among the most expensive vaccines on the market, with costs of $82 and $109 per dose, respectively, in the public sector (private-sector costs are 20 to 30% higher).4 With the relatively high costs of new vaccines, the U.S. immunization program is placing an increasing financial strain on the health system. Today, the schedule of recommended routine child and adolescent vaccines includes more than 30 doses against 16 diseases — more than double the number in 1980. The public-sector cost of fully vaccinating one person as recommended through adulthood (not including annual influenza vaccines) is roughly $1,450 for males and $1,800 for females, of

which the HPV and meningococcal vaccinations alone account for more than 25% at current prices. Cost-effectiveness analysis provides information on whether the health gain associated with each new vaccine is worth the cost, as compared with other options for health spending. For example, the VFC program must weigh the cost of covering expensive vaccines against an alternative use of those dollars, such as outreach to improve uptake of other routine vaccines in the eligible population. Indeed, a recent CDC analysis showed that it would be more cost-effective to spend up to the purchase price of the HPV vaccine on improving vaccine uptake among girls than it would be to extend the program to boys.5 As the use of cost-effectiveness information increases, we should consider some important limitations of current analyses. The tendency to evaluate single diseases or interventions in isolation is restrictive. Individual vaccines may appear cost-effective, but the overall U.S. vaccination program may be unaffordable or provide less value than other bundled preventive health services targeting the same age group. Real-world obstacles should also be integrated into analyses; for example, the lack of organized vaccine-delivery mech­ anisms for older age groups can affect vaccine-uptake rates among adolescents and adults, and shortages in vaccine supply (as experienced with influenza vaccines) can influence cost-effectiveness results. To make cost-effectiveness analysis a more practical tool, analysts should evaluate investments across multiple diseases and interventions and include the influences of nonmonetary constraints. As we confront sobering proposals to cut more than $300 bil-

lion in federal health spending over the next decade, public health decision makers will increasingly have to make explicit choices among health investments while keeping a vigilant eye on total expenditures. Identification of highvalue health interventions through comparative effectiveness analysis has been prioritized by the new Patient-Centered Outcomes Research Institute. Evidence of cost-effectiveness, if provided in a transparent, standardized, and comprehensive manner, can help to highlight important tradeoffs and contribute to policy recommendations for vaccinations and other health interventions. Disclosure forms provided by the author are available with the full text of this article at NEJM.org. From the Center for Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston. This article (10.1056/NEJMp1110539) was published on October 19, 2011, at NEJM.org. Editor’s Note: On October 25, the ACIP voted to recommend that boys 11 to 12 years of age be routinely vaccinated against HPV, indicating that the vaccine series can be started as early as age 9 and that men up to age 21 who have not yet received the vaccine should be vaccinated. 1. Smith PJ, Lindley MC, Rodewald LE. Vaccination coverage among U.S. children aged 19-35 months entitled by the Vaccines for Children program, 2009. Public Health Rep 2011;126:Suppl 2:109-23. 2. Kim JJ, Brisson M, Edmunds WJ, Goldie SJ. Modeling cervical cancer prevention in developed countries. Vaccine 2008;26:Suppl 10:K76-K86. 3. Shepard CW, Ortega-Sanchez IR, Scott RD II, Rosenstein NE. Cost-effectiveness of conjugate meningococcal vaccination strategies in the United States. Pediatrics 2005; 115:1220-32. 4. CDC vaccine price list. Atlanta: Centers for Disease Control and Prevention (http:// www.cdc.gov/vaccines/programs/vfc/ cdc-vac-price-list.htm). 5. Chesson HW, Ekwueme DU, Saraiya M, Dunne EF, Markowitz LE. The cost-effectiveness of male HPV vaccination in the United States. Vaccine 2011;29:8443-50. Copyright © 2011 Massachusetts Medical Society.

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new england journal of medicine The

established in 1812

november 10, 2011

vol. 365  no. 19

German Outbreak of Escherichia coli O104:H4 Associated with Sprouts Udo Buchholz, M.D., M.P.H., Helen Bernard, M.D., Dirk Werber, D.V.M., Merle M. Böhmer, Cornelius Remschmidt, M.D., Hendrik Wilking, D.V.M., Yvonne Deleré, M.D., Matthias an der Heiden, Ph.D., Cornelia Adlhoch, D.V.M., Johannes Dreesman, Ph.D., Joachim Ehlers, D.V.M., Steen Ethelberg, Ph.D., Mirko Faber, M.D., Christina Frank, Ph.D., Gerd Fricke, Ph.D., Matthias Greiner, D.V.M., Ph.D., Michael Höhle, Ph.D., Sofie Ivarsson, M.Sc., Uwe Jark, D.V.M., Markus Kirchner, M.D., M.P.H., Judith Koch, M.D., Gérard Krause, M.D., Ph.D., Petra Luber, Ph.D., Bettina Rosner, Ph.D., M.P.H., Klaus Stark, M.D., Ph.D., and Michael Kühne, D.V.M., Ph.D.

A bs t r ac t Background

A large outbreak of the hemolytic–uremic syndrome caused by Shiga-toxin–producing Escherichia coli O104:H4 occurred in Germany in May 2011. The source of infection was undetermined. Methods

We conducted a matched case–control study and a recipe-based restaurant cohort study, along with environmental, trace-back, and trace-forward investigations, to determine the source of infection. Results

The case–control study included 26 case subjects with the hemolytic–uremic syndrome and 81 control subjects. The outbreak of illness was associated with sprout consumption in univariable analysis (matched odds ratio, 5.8; 95% confidence interval [CI], 1.2 to 29) and with sprout and cucumber consumption in multivariable analysis. Among case subjects, 25% reported having eaten sprouts, and 88% reported having eaten cucumbers. The recipe-based study among 10 groups of visitors to restaurant K included 152 persons, among whom bloody diarrhea or diarrhea confirmed to be associated with Shiga-toxin–producing E. coli developed in 31 (20%). Visitors who were served sprouts were significantly more likely to become ill (relative risk, 14.2; 95% CI, 2.6 to ∞). Sprout consumption explained 100% of cases. Trace-back investigation of sprouts from the distributor that supplied restaurant K led to producer A. All 41 case clusters with known trading connections could be explained by producer A. The outbreak strain could not be identified on seeds from the implicated lot. Conclusions

Our investigations identified sprouts as the most likely outbreak vehicle, underlining the need to take into account food items that may be overlooked during subjects’ recall of consumption.

From the Department of Infectious Disease Epidemiology (U.B., H.B., D.W., M.M.B., C.R., H.W., Y.D., M. Heiden, C.A., M.F., C.F., M. Höhle, J.K., G.K., B.R., K.S.) and the Post­ graduate Training for Applied Epidemiology Program (H.W., C.A.), Robert Koch Insti­ tute; the Federal Office of Consumer Protec­ tion and Food Safety (G.F., P.L.); and the Federal Institute for Risk Assessment (M.G.) — all in Berlin; the Governmental Institute of Public Health of Lower Saxony, Hannover (J.D., M. Kirchner), the Lower Saxony State Office for Consumer Protection and Food Safety, Oldenburg (J.E., U.J., M. Kühne), and the Veterinary University, Hannover (M.G.) — all in Germany; the Swedish Institute for Communicable Disease Control, Solna, Swe­ den (S.I.); and the Department of Epidemi­ ology, Statens Serum Institut, Copenhagen (S.E.). Address reprint requests to Dr. Buch­ holz at the Department of Infectious Dis­ ease Epidemiology, Robert Koch Institute, DGZ-Ring 1, 13086 Berlin, Germany, or at [email protected]. Members of the Task Force EHEC (Entero­ hemorrhagic Escherichia coli) at the Fed­ eral Office of Consumer Protection and Food Safety in Germany, the Danish HUS (Hemolytic–Uremic Syndrome) Investiga­ tion Team, the Governmental Institute of Public Health of Lower Saxony HUS Investi­ gation Team, the Lower Saxony State Of­ fice for Consumer Protection and Food Safety HUS Investigation Team, the Robert Koch Institute HUS Investigation Team, and the Swedish HUS Investigation Team are listed in the Supplementary Appendix, available at NEJM.org. This article (10.1056/NEJMoa1106482) was published on October 26, 2011, at NEJM.org. N Engl J Med 2011;365:1763-70. Copyright © 2011 Massachusetts Medical Society.

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H

uman infection with Shiga-toxin– producing Escherichia coli is a major cause of postdiarrheal hemolytic–uremic syndrome. This life-threatening disorder, which is characterized by acute renal failure, hemolytic anemia, and thrombocytopenia, typically affects children under the age of 5 years. Shiga-toxin–producing E. coli O157 is the serogroup that is most frequently isolated from patients with the hemolytic–uremic syndrome worldwide.1 In May 2011, a large outbreak of the hemolytic– uremic syndrome associated with the rare E. coli serotype O104:H4 occurred in Germany.2-5 The main epidemiologic features were that the peak of the epidemic was reached on May 21 and May 22 4,5 and that the vast majority of case subjects either resided or had traveled in northern Germany. Almost all patients from other European countries or from North America had recently returned from northern Germany.2,6,7 Of the affected case subjects, 90% were adults, and more than two thirds of case subjects with the hemolytic–uremic syndrome were female.4 Early studies in Hamburg suggested that infections were probably community-acquired and were not related to food consumption in a particular restaurant. A first case–control study that was conducted on May 23 and 24 suggested that raw food items, such as tomatoes, cucumbers, or leaf salad,3 were the source of infection. The consumption of sprouts, which was previously implicated in outbreaks of Shiga-toxin–producing E. coli in the United States8 and Japan,9 was mentioned by only 25% of case subjects in exploratory interviews, so consumption of sprouts was not tested analytically. This report describes the investigations that were conducted by the federal agencies under the auspices of the German Ministry of Health and the Ministry of Food, Agriculture, and Consumer Protection, as well as by the respective state agencies, to identify the vehicle of infection of this international outbreak.

Me thods Study Design

Three types of parallel studies were conducted: one case–control study, one recipe-based restaurant cohort study, and combined trace-back and trace-forward investigations. The main results of

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all three studies became available between June 2 and June 9, 2011. Results, even if preliminary, were communicated among the investigation groups and the Task Force EHEC (Enterohemorrhagic Escherichia coli) at the Federal Office of Consumer Protection and Food Safety in Berlin. If the results were judged to have appropriate validity, they were communicated as soon as possible to the public. The case–control and cohort studies were conducted within the framework of the Communicable Diseases Law Reform Act of Germany. Mandatory regulations were observed, and review by an ethics committee was not required. Case–Control Study

From May 29 to June 4, we conducted a case–control study to further specify the type of raw vegetables associated with illness in this outbreak. A case was defined as clinically diagnosed hemolytic–uremic syndrome in an adult who was hospitalized in one of three hospitals in northern Germany, located in the cities of Bremen, Bremerhaven, and Lübeck. Control subjects were individually matched with case subjects on the basis of age group and neighborhood. Case and control subjects were predominantly asked about consumption of fruit and vegetable items, including sprouts, during the 14 days before the onset of illness (for case subjects) or before the interview date (for control subjects) (for details, see the Supplementary Appendix, available with the full text of this article at NEJM.org). Recipe-Based Restaurant Cohort Study

Since the earlier studies had not identified a single source of infection, we conducted a cohort study at restaurant K in Lübeck, Schleswig–Holstein. Preliminary information revealed that several visitor groups with subsequent cases of gastroenteric disease had eaten in restaurant K between May 12 and May 16, 2011, which was defined as the outbreak period in this study. Using the booking notes, we identified cohorts that had eaten in the restaurant during this period and asked all members about the menu items they had consumed. We interviewed the chef of the restaurant about the ingredients and their quantities used to prepare the menu items offered in the restaurant. A case was defined as an illness in a member of any of the cohorts that was associated with bloody diarrhea, self-reported laboratory-confirmed Shiga-toxin–producing E. coli O104 infection, or

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Escherichia coli O104:H4 Associated with Sprouts

the hemolytic–uremic syndrome with an onset of diarrhea within 2 weeks after visiting restaurant K. Non–case subjects were those who remained healthy after visiting restaurant K. We excluded from the analysis all subjects who had diarrhea that was not bloody, who did not have laboratory-confirmed Shiga-toxin–producing E. coli O104 infection, or whose disease onset was later than 14 days after visiting restaurant K. For the analysis, we evaluated only data on ingredients that were used in the dishes that were served to the restaurant guests (i.e., data that were solely based on the information obtained from the chef) (for details, see the Supplementary Appendix). To estimate the total number of cases accrued at restaurant K during the study period, we collected information on the total number of main dishes purchased in this period from billing data. Using the attack rate among persons who were served sprouts from the cohort study, we calculated the number of ill persons as the number of main dishes served containing sprouts times the attack rate among sprout eaters.

Table 1. Vegetables or Fruits Evaluated in a Case–Control Study in the German Outbreak.*

Food Item

Case Subjects Control Subjects Exposed Exposed

Matched Odds Ratio (95% CI)

P Value

no./total no. (%) Sprouts

6/24 (25)

7/80 (9)

4.35 (1.05–18.0)

0.04

Cucumbers

22/25 (88)

52/79 (66)

3.53 (0.96–12.9)

0.06

Apples

22/24 (92)

57/81 (70)

3.91 (0.86–17.7)

0.08

Peppers

16/24 (67)

35/80 (44)

2.66 (0.90–7.9)

0.08

Strawberries

19/26 (73)

43/81 (53)

2.33 (0.90–6.0)

0.08

* P>0.10 for raw onions, tomatoes, leaf salad, asparagus, carrots, and basil.

sprouts from suspect producer A to outbreak clusters, a study that was conducted by the food-safety authorities of the respective counties, compiled by the pertinent federal food-safety authorities, and analyzed by the task force. The task force also ini­ tiated a trace-back investigation from producer A.

R e sult s

Environmental, Trace-Back, and TraceForward Investigations

Case–Control Study

From physicians, patients, county and regional health departments, and foreign national public health institutes, we received information on clusters or apparently sporadic cases of illness that occurred in the context of the overall epidemic among persons who had probable exposure at only one location or venue (see the Supplementary Appendix). Information was continuously reported to national and local food-safety authorities and to the task force. On the basis of findings in the early studies, food-safety authorities initially concentrated their investigations on tomatoes, cucumbers, and leaf salads, as well as on other vegetables eaten raw and salad ingredients, including toppings. Local and state food-safety authorities assessed distribution channels of raw food products connected with clusters or single case subjects with single exposures. In addition, both epidemiologically suspected and other raw food items were sampled and, after specific enrichment procedures, were tested by means of immunoassay for Shiga toxin and polymerase-chain-reaction assay for the Shiga-toxin stx2 prophage gene cluster and for genetic markers of the O104:HA strains. The task force initiated a trace-forward investigation for

We included 26 case subjects (9 male and 17 female) and 81 control subjects in the study. On univariable analysis, the only significant variable was sprouts (Table 1). Other food items, such as raw minced beef and milk and other dairy products, were not significantly associated with illness. The sequential addition and removal of other variables resulted in a multivariable model containing only sprouts (matched odds ratio, 5.8; 95% confidence interval [CI], 1.2 to 29.0) and cucumbers (matched odds ratio, 6.0; 95% CI, 1.1 to 31.0). Before the date on which the public was advised not to consume sprouts (June 10, 2011), only 6 of 24 case subjects (25%) remembered having consumed them (Table 1, and the Supplementary Appendix). After that date, we wished to ascertain the possible degree of false recall among the case subjects. We tried to recontact all case and control subjects who had not reported sprout consumption previously. Of 8 case subjects who could be reached, 3 (38%) remembered having eaten sprouts in the 14 days before the onset of illness. By contrast, all 37 control subjects who had not reported sprout consumption in previous interviews continued to report that they had not eaten sprouts.

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Table 2. Characteristics of 10 Cohorts of Subjects Who Visited Restaurant K between May 12 and May 16, 2011.*

Cohort Subjects No. Size with Data no. 1

37

Male Sex

Median Age (IQR)

%

yr

37

5

49 (45–57)

Subjects with LaboratoryConfirmed STEC Infection

Attack Rate for Subjects Subjects Subjects Subjects Evaluated Fulfilling Fulfilling with for Case Case Case HUS Definition† Definition Definition

Subjects with Diarrhea

Subjects with Bloody Diarrhea

10

9

10

4

no.

no. (%)

no.

%

34 (92)

9

26

2

2

2

50

49 (46–52)

1

1

2

0

1

50

3

31

31

55

57 (45–68)

11

4

5

1

25 (81)

5

20

4

11

10

0

54 (53–55)

2

0

1

0

9 (90)

1

11

5

12

9

0

46 (43–48)

1

1

2

0

9 (100)

6

19

19

37

32 (15–49)

1

1

1

0

7

10

10

50

40 (40–42)

0

0

0

8

17

14

57

68 (65–73)

3

3

4

9

25

25

40

74 (69–75)

9

9

10

13

11

36

24 (21–48)

3

1

177

168

32

53 (42–67)

41

29

Total

2 (100)

1

11

17 (89)

1

6

0

9 (90)

0

0

1

14 (100)

3

21

6

1

25 (100)

9

36

2

1

8 (73)

1

13

33

8

152 (90)

31

20

* HUS denotes hemolytic–uremic syndrome, IQR interquartile range, and STEC Shiga-toxin–producing Escherichia coli. † The case definition was the presence of bloody diarrhea, laboratory-confirmed infection with Shiga-toxin–producing E. coli, or the hemolytic– uremic syndrome with an onset of disease within 2 weeks after visiting restaurant K. According to the case definition, 16 subjects were ex­ cluded either because they had diarrhea that was not bloody or because the date of the onset of diarrhea was more than 14 days after visit­ ing restaurant K or was unknown.

Recipe-Based Restaurant Cohort Study

We identified 10 cohorts with a total of 177 persons who had eaten at restaurant K (Table 2). Of these persons, we interviewed 168 (95%), including 161 who were interviewed directly and 7 for whom information was obtained from a proxy. Among the 152 persons who could be evaluated for the case definition, 31 (20%) had an illness that fulfilled the case definition. Among these subjects, the hemolytic–uremic syndrome developed in 8 (26%) (see the Supplementary Appendix). In univariable analysis of all raw food items, only visitors who had been served sprouts were significantly more likely to become ill (Table 3). The P value for the risk ratio for all other items was greater than 0.15. Of 115 persons who had been served sprouts, 31 (27%; 95% CI, 19 to 36) became case subjects, whereas none of 37 persons who had not been served sprouts reported having gastrointestinal symptoms that fulfilled the case definition. Thus, all 31 case subjects had been served menu items containing sprouts. Nearly half the menu items served in the restaurant contained raw sprouts as a garnish or were served with

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a side salad containing raw sprouts. Side salads contained radicchio, Chinese cabbage, lettuce, cucumber, tomato, and sprouts. No menu item contained cooked sprouts. During this period, the restaurant used only one type of sprout assortment, which was received from a distributor in Schleswig-Holstein and contained four types of sprouts: lentil sprouts, alfalfa sprouts, fenugreek sprouts, and adzuki bean sprouts. During the outbreak period, 884 main dishes containing sprouts were served to the guests of the restaurant. Applying the attack rate of 27% among sprout eaters, we extrapolated that a to­t al of 239 cases of bloody diarrhea (95% CI, 168 to 318) occurred among customers of this restaurant. Environmental, Trace-Back, and TraceForward Investigations

The task force identified 41 clusters or cases with single exposures. One of the clusters was a hotel in lower Saxony in which Swedish citizens were affected (cluster 1) (Fig. 1). A “spicy sprout mixture” that was sampled from the distributor (distributor 1) of the hotel’s restaurant on June 2, 2011,

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Table 3. Relative Risk of Infection Associated with Sprouts and Other Raw Food Items in Univariable Analysis.

Food Item

Total Subjects Evaluated

Subjects Exposed (Percent of Cohort)

Cases among Subjects Exposed (Attack Rate)

no.

Subjects Not Exposed (Percent of Cohort)

Cases among Subjects Not ­Exposed (Attack Rate)

Relative Risk (95% CI)

P Value

no. (%)

Sprouts

152

115 (76)

31 (27)

37 (24)

Tomatoes

152

50 (33)

14 (28)

102 (67)

17 (17)

1.68 (0.77–3.62)

0.18

Cucumbers

152

50 (33)

14 (28)

102 (67)

17 (17)

1.68 (0.77–3.62)

0.18

Chinese cabbage

152

45 (30)

13 (29)

107 (70)

18 (17)

1.72 (0.77–3.71)

0.17

Radicchio

152

45 (30)

13 (29)

107 (70)

18 (17)

1.72 (0.77–3.71)

0.17

Lettuce

152

45 (30)

13 (29)

107 (70)

18 (17)

1.72 (0.77–3.71)

0.17

tested positive for Shiga toxin on a commercial immunoassay. Although confirmatory testing later proved to be negative, immediate trace back led to producer A in Lower Saxony, at which a total of 452 water, seed, sprout, and surface samples tested negative for Shiga-toxin–producing E. coli O104:HA. Producer A was licensed as a horticultural farm and produced 18 different sorts of sprouts at the time. Protective measures consisted of regularly instructing employees on the application of proper hygiene necessary for the production of sprouts and the frequent testing of sprouts for salmonella, according to European Union regulations, as well as for coliforms. All employees of producer A were interviewed, and 5 of 15 had become ill in May 2011 or tested positive for O104:H4. Employees frequently ate sprouts produced at their company. Preferred types were fenugreek, broccoli, and garlic sprouts. Tracing forward from producer A led to four distributors (Fig. 1). Distributor 1 was connected not only to the restaurant in which cluster 1 had occurred but also to restaurant K. Distributor 4 delivered food to a caterer in Frankfurt that was linked to a cafeteria outbreak that occurred early in the epidemic.3 Subsequently, 22 more distributors (for a total of 26) that obtained sprouts from producer A were identified. Distributors were located in 7 of the 16 federal states. Each of the 41 case clusters could be linked with at least one of the identified sprout distributors (Fig. 2).10 Fenugreek or lentil sprouts were suspected as the outbreak vehicle because these types of sprouts were the common ingredients in two different sprout mixtures that were packaged for distribution by producer A and had been supplied to most of the 41 clusters.

0

14.23 (2.55–∞)

0.001

In mid-June, investigations on the origin of sprouts that were consumed by additional case subjects revealed that two case subjects from Lüneburg, Lower Saxony, had eaten a homegrown sprout mix that included fenugreek sprouts. The seeds for these sprouts had been purchased at a retail store that had the same supplier of seeds (supplier X) as producer A (Fig. 1).

Discussion We report evidence from epidemiologic, microbiologic, and food trace-back and trace-forward investigations that incriminates sprouts as the vehicle of infection in this large outbreak of the hemolytic–uremic syndrome associated with Shigatoxin–producing E. coli. Although definitive molecular evidence is lacking, the argument that sprouts were responsible for this outbreak is strong on the basis of the following five factors: both epidemiologic studies implicated sprouts, the restaurant study showed that 100% of cases of illness could be explained by the consumption of sprouts, no other food ingredient consumed at restaurant K was associated with the risk of illness, all 41 clusters or cases of single exposure could be linked to sprout producer A and its distribution channels, and several employees of sprout producer A who frequently consumed sprouts at the company became symptomatically ill early in the outbreak period or tested positive for Shiga-toxin–producing E. coli O104:H4. Information obtained during the outbreak investigation in Germany already hinted at an outbreak source before producer A in the seed and sprout distribution chain. Seeds that were used by producer A and by the two case subjects from

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Cluster 1 Hotel and restaurant, District Lüneburg, Lower Saxony

At least 18 cases

Cluster 2 Restaurant K, Lübeck, Schleswig– Holstein

At least 50 cases

Distributor 2, Lower Saxony

Cluster 3 Restaurant, District Rotenburg/ Wümme, Lower Saxony

At least 4 cases

Distributor 3, North Rhine– Westphalia

Cluster 4 Cafeteria, Bochum, North Rhine–Westphalia

At least 6 cases

Distributor 1, Schleswig–Holstein

Sprout producer A

Cluster 5 Cafeteria, Frankfurt, Hesse

At least 20 cases

Several distributors, Germany

Homegrown sprouts, District Lüneburg, Lower Saxony

At least 2 cases

Distributors, United Kingdom, France

Homegrown sprouts, Bordeaux, France

15 cases

Distributor 4, Hesse

Sprout seeds

Supplier X

Distributor D

Figure 1. Anatomy of the German Outbreak. Shown are the trading connections from supplier X to sprout producer A and through four distributors to five outbreak clusters, as well as to two other distributor groups leading to a cluster of illnesses caused by Shiga-toxin–producing E. coli in Lüneburg and an outbreak in France unrelated to producer A. Known case subjects at restaurant K included those who were part of the cohort study and others who were not.

Lower Saxony who grew their own sprouts originated from supplier X. In June 2011, an outbreak of the hemolytic–uremic syndrome associated with Shiga-toxin–producing E. coli O104:HA occurred in Bordeaux, France.11 The E. coli responsible for the outbreak was genetically related to that in the German outbreak, and there was an epidemio1768

logic association with consumption of homegrown fenugreek sprouts. These findings spurred trace-back investigations by a task force (set up by the European Food Safety Authority (EFSA), which consisted of experts from the European Commission, relevant European Union member states, the European Center for Disease Preven-

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tion and Control, the World Health Organization, the Food and Agriculture Organization of the United Nations, and EFSA staff members. It concluded that a certain lot of fenugreek seeds that was imported in late 2009 from Egypt was the most likely common link for the outbreaks in Germany and France.12 With respect to the point of contamination, it is possible that it occurred at the site where seeds were produced, during transportation, or at the importer. By August 2011, this question had not been resolved. The account of the outbreak was dramatic: 4321 outbreak cases, including 3469 cases of Shigatoxin–producing E. coli and 852 cases of the hemolytic–uremic syndrome, had been reported by July 26, 2011, when the outbreak was declared to be over.13 By that time, 50 patients had died. After epidemiologic and food-safety investigations had concurred in identifying sprouts as the outbreak vehicle, the public had been advised on June 10 to abstain from the consumption of raw sprouts, to eliminate raw sprouts in possession at that time, and to remove any sprouts stemming from producer A.14 In addition, producer A was temporarily closed. Cases of illness still occurred until the end of July 2011, partially as a consequence of secondary transmission,15 but the number of cases dropped substantially. Early in the outbreak investigation, raw food products other than sprouts had been suspected as the vehicle. The three studies that we present here built on these findings and complement one another. The early epidemiologic findings helped food-safety authorities to streamline their investigations, which led them to turn their attention to producer A. Since the case–control study was conducted before sprouts came into focus, the significant association between sprouts and illness is therefore revealing and important. Nevertheless, the findings received strong support only through the restaurant study and the food traceback and trace-forward investigations. The restaurant study provided an idea of why tomatoes, cucumbers, and leaf salad had been suspected early on. The one dish that frequently exposed guests to sprouts was the side salad, which contained tomatoes, cucumbers, three sorts of leaf salads, and sprouts. Sprouts may have been the ingredient that visitors recalled least in such a mixed salad. This hypothesis is also suggested by the results of the repeated interviews in the case– control study. Because the earlier studies had attempted to find a vehicle that explained the n engl j med 365;19

Figure 2. Trading Network Leading to the German Outbreak. The trading network for the contaminated sprouts led from producer A in Lower Saxony (yellow circle) to 26 sprout distributors (black dots) and 41 identified out­ break clusters (red squares), established by combined back and forward tracing.

majority of cases, sprouts were missed. Although international guidelines16 generally recommend otherwise, this experience suggests that food items or ingredients that are deemed to be hard to remember should be included in analytical studies, even if such items are mentioned by less than 50% of those surveyed. Producer A was licensed as a horticultural company. Although hygienic measures were satisfactory and local food-safety authorities had inspected the company routinely under the same conditions as a food-processing company, it became apparent that European legislation has important deficits regarding Shiga-toxin–producing E. coli. Production of food that is vulnerable to contamination with this pathogen, such as sprouts or sprout seeds, should be monitored for this organism so that hygienic measures prevent amplification. In addition, both incoming seeds and outgoing food products should be tested for Shiga-toxin–producing E. coli. In general, focused restaurant studies provide a favorable situation to identify the vehicle, even in large, geographically dispersed outbreaks, because place and time of exposure are known and

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menu cards can be used to spur visitors’ memories. If one or few specific menu items are identified, ingredients can be further analyzed.17 If not, the reason may be that a common ingredient is contained in many menu items, resulting in the necessity to collect detailed ingredient information of the whole or a large part of the menu,18,19 information that needs to be obtained from those preparing the food. In conclusion, we have presented investigative results regarding an outbreak of the hemolytic– uremic syndrome associated with Shiga-toxin– producing E. coli. Under favorable circumstances, the recipe-based restaurant cohort study proved to be a quick method for detecting suspected food

ingredients with high reliability in a complicated setting of exposures. Recommendations regarding sprout use and consumption may need to be strengthened or adjusted as a consequence of this outbreak. Disclosure forms provided by the authors are available with the full text of this article at NEJM.org. We thank the senior and junior owner and particularly the chef of restaurant K in Lübeck for their support and patience; physicians, patients, and colleagues in the hospitals in Bremen (Klinikum Bremen Mitte), Bremerhaven (Klinikum Bremerhaven), and Lübeck (Universitätsklinikum Schleswig-Holstein, Campus Lübeck), as well as county health departments of Bremen, Bremerhaven, Cuxhaven, and Lübeck for their support in conducting the case–control study; and Johan Giesecke, Stephen Palmer, Kåre Mølbak, Henriette de Valk, Roland Salmon, Lyle Petersen, Robert Tauxe, and Hubert Deluyker for their input into the interpretation of study and surveillance data and the management of the outbreak.

References 1. Tarr PI, Gordon CA, Chandler WL.

Shiga-toxin-producing Escherichia coli and haemolytic uraemic syndrome. Lancet 2005; 365:1073-86. 2. Askar M, Faber MS, Frank C, et al. Update on the ongoing outbreak of haemolytic uraemic syndrome due to Shiga toxin-producing Escherichia coli (STEC) serotype O104, Germany, May 2011. Euro Surveill 2011;16:19883. 3. Frank C, Faber MS, Askar M, et al. Large and ongoing outbreak of haemolytic uraemic syndrome, Germany, May 2011. Euro Surveill 2011;16:19878. 4. Frank C, Werber D, Cramer JP, et al. Epidemic profile of Shiga-toxin–producing Escherichia coli O104:H4 outbreak in Germany. N Engl J Med 2011. DOI: 10.1056/ NEJMoa1106483. 5. Wadl M, Rieck T, Nachtnebel M, et al. Enhanced surveillance during a large outbreak of bloody diarrhoea and haemolytic uraemic syndrome caused by Shiga toxin/ verotoxin-producing Escherichia coli in Germany, May to June 2011. Euro Surveill 2011;16:19893. 6. European Centre for Disease Prevention and Control. ECDC rapid risk assessment: outbreak of Shiga toxin-producing E. coli (STEC) in Germany. June 14, 2011 (http:// www.ecdc.europa.eu/en/publications/ Publications/1106_TER_Risk_Assessment _EColi_revised.pdf). 7. World Health Organization. International Health Regulations: EHEC outbreak in Germany (http://www.euro.who.int/en/ what-we-do/health-topics/emergencies/

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international-health-regulations/ehecoutbreak-in-germany). 8. Taormina PJ, Beuchat LR, Slutsker L. Infections associated with eating seed sprouts: an international concern. Emerg Infect Dis 1999;5:626-34. 9. Michino H, Araki K, Minami S, et al. Massive outbreak of Escherichia coli O157:H7 infection in schoolchildren in Sakai City, Japan, associated with consumption of white radish sprouts. Am J Epidemiol 1999;150:787-96. 10. Bundesinstitut für Risikobewertung (Bf R). Bedeutung von Sprossen und Keimlingen sowie Samen zur Sprossenherstellung im EHEC O104:H4 Ausbruchs­ geschehen im Mai und Juni 2011 (Stel­lung­ nahme Nr. 023/2011 des BfR. July 5, 2011) (http://www.bfr.bund.de/cm/343/bedeutung _von_sprossen_und_keimlingen_sowie _samen_zur_sprossenherstellung_im _ehec_o104_h4_ausbruchsgeschehen _im_mai_und_juni_2011.pdf ). 11. Gault G, Weill FX, Mariani-Kurkdjian P, et al. Outbreak of haemolytic uraemic syndrome and bloody diarrhoea due to Escherichia coli O104:H4, south-west France, June 2011. Euro Surveill 2011;16:19905. 12. European Food Safety Authority. Tracing seeds, in particular fenugreek (Trigonella foenum-graecum) seeds, in relation to the Shiga toxin-producing E. coli (STEC) O104:H4 2011 outbreaks in Germany and France. July 5, 2011 (http://www.efsa .europa.eu/en/supporting/doc/176e.pdf). 13. Robert Koch-Institute. Informationen zum EHEC-/HUS-Ausbruchsgeschehen von

Mai bis Juli 2011 in Deutschland — Ende des Ausbruchs. Epidemiol Bull 2011;31:295-6 (http://www.rki.de/cln_160/nn_2030884/ DE/Content/Infekt/EpidBull/Archiv/2011/ 31__11,templateId=raw,property= publicationFile.pdf/31_11.pdf ). 14. Idem. Neue Erkenntnisse zum EHECAusbruch: Gemeinsame Pressemitteilung von BfR, BVL und RKI. June 10, 2011 (http://www.rki.de/cln_169/nn_467482/DE/ Content/Service/Presse/Pressemitteilungen/ 2011/08__2011.html). 15. Hauri A, Gotsch U, Strotmann I, et al. Secondary transmissions during the outbreak of Shiga toxin-producing Escherichia coli O104 in Hesse, Germany, 2011. Euro Surveill 2011;16:19937. 16. Foodborne disease outbreaks: guidelines for investigation and control. Geneva: World Health Organization, 2008. 17. Mody RK, Greene SA, Gaul L, et al. National outbreak of Salmonella serotype saintpaul infections: importance of Texas restaurant investigations in implicating jalapeño peppers. PLoS One 2011;6(2): e16579. 18. Buchholz U, Mermin J, Rios R, et al. An outbreak of food-borne illness associated with methomyl-contaminated salt. JAMA 2002;288:604-10. 19. Wheeler C, Vogt TM, Armstrong GL, et al. An outbreak of hepatitis A associated with green onions. N Engl J Med 2005; 353:890-7. Copyright © 2011 Massachusetts Medical Society.

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Epidemic Profile of Shiga-Toxin–Producing Escherichia coli O104:H4 Outbreak in Germany Christina Frank, Ph.D., Dirk Werber, D.V.M., Jakob P. Cramer, M.D., Mona Askar, M.D., Mirko Faber, M.D., Matthias an der Heiden, Ph.D., Helen Bernard, M.D., Angelika Fruth, Ph.D., Rita Prager, Ph.D., Anke Spode, M.D., Maria Wadl, D.V.M., Alexander Zoufaly, M.D., Sabine Jordan, M.D., Markus J. Kemper, M.D., Per Follin, M.D., Ph.D., Luise Müller, M.Sc., Lisa A. King, M.P.H., Bettina Rosner, Ph.D., Udo Buchholz, M.D., M.P.H., Klaus Stark, M.D., Ph.D., and Gérard Krause, M.D., Ph.D., for the HUS Investigation Team*

ABSTRACT Background

We describe an outbreak of gastroenteritis and the hemolytic–uremic syndrome caused by Shiga-toxin–producing Escherichia coli in Germany in May, June, and July, 2011. The consumption of sprouts was identified as the most likely vehicle of infection. Methods

We analyzed data from reports in Germany of Shiga-toxin–producing E. coli gastroenteritis and the hemolytic–uremic syndrome and clinical information on patients presenting to Hamburg University Medical Center (HUMC). An outbreak case was defined as a reported case of the hemolytic–uremic syndrome or of gastroenteritis in a patient infected by Shiga-toxin–producing E. coli, serogroup O104 or serogroup unknown, with an onset of disease during the period from May 1 through July 4, 2011, in Germany. Results

A total of 3816 cases (including 54 deaths) were reported in Germany, 845 of which (22%) involved the hemolytic–uremic syndrome. The outbreak was centered in northern Germany and peaked around May 21 to 22. Most of the patients in whom the hemolytic–uremic syndrome developed were adults (88%; median age, 42 years), and women were overrepresented (68%). The estimated median incubation period was 8 days, with a median of 5 days from the onset of diarrhea to the development of the hemolytic–uremic syndrome. Among 59 patients prospectively followed at HUMC, the hemolytic–uremic syndrome developed in 12 (20%), with no significant differences according to sex or reported initial symptoms and signs. The outbreak strain was typed as an enteroaggregative Shiga-toxin–producing E. coli O104:H4, producing extended-spectrum beta-lactamase.

From the Departments of Infectious Disease Epidemiology (C.F., D.W., M.F., M.H., H.B., M.W., B.R., U.B., K.S., G.K.) and Infectious Diseases (A.F., R.P.) and the Postgraduate Training for Applied Epidemiology Program (M.A.), Robert Koch Institute, Berlin; the Department of Internal Medicine, University Medical Center Hamburg–Eppendorf (J.P.C., A.Z., S.J., M.J.K.) and the Health Department of the Hamburg Northern District (A.S.) — both in Hamburg, Germany; the Department of Communicable Disease Control and Prevention, Västra Götaland Region, Gothenburg, Sweden (P.F.); the Statens Serum Institut, Copenhagen (L.M.); and the Institut de Veille Sanitaire, Saint-Maurice, France (L.A.K.). Address reprint requests to Dr. Werber at [email protected]. Drs. Frank and Werber contributed equally to this article. *The members of the Hemolytic–Uremic Syndrome (HUS) Investigation Team are listed in the Supplementary Appendix, available at NEJM.org. A preliminary version of this article was published on June 22, 2011, and updated on June 24, 2011. This final version (10.1056/NEJMoa1106483) was published on October 26, 2011, at NEJM.org. N Engl J Med 2011;365:1771-80.

Conclusions

In this outbreak, caused by an unusual E. coli strain, cases of the hemolytic–uremic syndrome occurred predominantly in adults, with a preponderance of cases occurring in women. The hemolytic–uremic syndrome developed in more than 20% of the identified cases. n engl j med 365;19  nejm.org  november 10, 2011

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n May 19, 2011, the Robert Koch Institute, Germany’s national-level public health authority, was informed about a cluster of three cases of the hemolytic–uremic syndrome in children admitted on the same day to the university hospital in the city of Hamburg. On May 20, a team from the Robert Koch Institute arrived in Hamburg to assist with the public health investigation. It quickly became clear that the case numbers were continuing to rise, that there were also cases in adults, and that other areas of Germany, especially northern Germany, were also affected. An investigation of the outbreak involving all levels of public-health and food-safety authorities was initiated to identify the causative agent and the vehicle of infection in order to prevent further cases of disease. Sprouts were eventually identified as the most likely vehicle of infection.1 The hemolytic–uremic syndrome, which was first described in children in the 1950s,2 is characterized by the triad of acute renal failure, hemolytic anemia, and thrombocytopenia. Diarrheaassociated hemolytic–uremic syndrome occurs primarily in children, and a precipitating infection with Shiga-toxin–producing Escherichia coli, mainly of serotype O157:H7, is the primary cause.3 The usual reservoir for these bacteria is ruminants, particularly cattle. Human infection with Shigatoxin–producing E. coli occurs through the inadvertent ingestion of fecal matter — for example, through contaminated food or water or through contact with animals or their farm environment or, secondarily, through contact with infected humans. In contrast, in adults, the hemolytic–uremic syndrome with prodromal diarrhea, indicating an infectious cause, is a rare event. For example, from 1989 through 2006, only 21 of the 322 adults (7%) listed in the Oklahoma registry as having thrombotic thrombocytopenic purpura or the hemolytic– uremic syndrome presented with bloody diarrhea.4 Earlier, we presented descriptive epidemiologic, clinical, and microbiologic information on the unusual outbreak in Germany in a preliminary report (available at NEJM.org). This report updates and finalizes this information.

Methods German Surveillance System

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(Stx) in E. coli isolates or of its encoding gene (stx) in stool enrichment culture or isolates must, by law, be reported by diagnosing laboratories to local health departments. This reporting process allows the identification of Shiga-toxin–producing E. coli infection independently of serogroup (serotyping information is requested but not required). The German case definition of Shiga-toxin–producing E. coli gastroenteritis (without the hemolytic–uremic syndrome) requires, besides laboratory confirmation, the presence of at least one of the following symptoms: diarrhea (three or more loose stools in a 24-hour period), abdominal cramps, or vomiting. In addition, physicians are required to report clinical symptoms compatible with diarrhea-associated hemolytic–uremic syndrome in a patient. The German case definition of the hemolytic– uremic syndrome comprises thrombocytopenia (platelet count of