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Populations with attributes marking social disadvantage in the United States—such as persons living in poverty and racial and ethnic minorities—often experience worse outcomes than others when diagnosed with cancer. They frequently have later-stage cancer diagnoses, less intensive or appropriate therapies, and shorter survivals than persons with greater social advantages (Haynes and Smedley 1999). In recent years, such observations have prompted nationwide efforts to track and eliminate disparities in health-related experiences of vulnerable populations. To date, much of the public focus on health disparities has targeted racial and ethnic minorities. However,Healthy People 2010, which set national health priorities, warns that persons with disabilities might also receive substandard health care. Noting well-documented disparities in their use of screening and preventive services (Nosek and Howland 1997; Chan et al. 1999; Iezzoni et al. 2000,2001),Healthy People 2010 cautions that “as a potentially underserved group, people with disabilities would be expected to experience disadvantages in health and well-being compared with the general population” (Department of Health and Human Services 2000). In a July 2005 “Call to Action,” the United States Surgeon General states that persons with disabilities sometimes lack equal access to care and encourages the inclusion of persons with disabilities in studies concerning disparities in health care access and outcomes (U.S. Department of Health and Human Services 2005). Exploring whether persons with disabilities do, in fact, experience worse cancer-related outcomes is complicated by the lack of information on disability in data sources typically used to conduct disparities research concerning cancer. Data from the Surveillance, Epidemiology, and End Results (SEER) cancer registries have produced important insights into racial and ethnic disparities (Klabunde et al. 1998; McCarthy et al. 1998; Bach et al. 1999; Ngo-Metzger et al. 2003; Jemal et al. 2004; Zeliadt et al. 2004; Shavers, Brown, Klabunde et al. 2004; Shavers, Brown, Potosky et al. 2004; Steyerberg et al. 2005). SEER data merged with Medicare claims have yielded additional findings about outcomes, treatments, and health care costs for Medicare beneficiaries with cancer (Potosky et al. 1993; Warren et al. 2002). Neither SEER nor Medicare data contain indicators of patients' functional status. However, one potential approach to investigate an important subpopulation of people with disabilities involves focusing on persons receiving Social Security Disability Insurance (SSDI) who eventually become eligible for Medicare. Persons with SSDI are presumably too medically disabled to work (an employability-based definition of disability). To qualify, persons must convince the Social Security Administration (SSA) that they cannot engage in “substantial gainful activity” because of medically proven sensory, physical, cognitive, or emotional impairments (Social Security Administration 2003a). Five months after qualifying for SSDI, they start receiving monthly cash payments, although many find their annual incomes plummet—posing another social disadvantage (Martin and Davies 2003/2004). Twenty-four months after first receiving cash benefits, SSDI beneficiaries become eligible for Medicare. In 2004, Medicare covered 6.4 million SSDI recipients under age 65 among its estimated 41.7 million beneficiaries (Centers for Medicare and Medicaid Services 2003). Prior studies using merged SEER–Medicare data have exclusively considered Medicare beneficiaries age 65 and older. Our study is the first to use these data to examine cancer experiences of Medicare beneficiaries under age 65. We examined stage at diagnosis and survival for disabled Medicare beneficiaries diagnosed with non-small cell lung, colorectal, female breast, or prostate cancers and compared their experiences with those of persons diagnosed in this same age group who do not have SSDI/Medicare. Because in previous studies we found that persons with certain types of disabilities (e.g., physical disabilities) are less likely to receive cancer screening and preventive services (Iezzoni et al. 2000,2001), we hypothesized that disabled Medicare beneficiaries would have delayed cancer diagnosis compared with others diagnosed under age 65.

Internet use in healthcare has grown quickly, but nursing has not explored its use in ambulatory settings. We developed an innovative internet-based coaching intervention conducted by nurses, aimed at improving patient communication with clinicians about 3 chronic conditions: pain, depression and impaired mobility. The intervention utilized a secure patient portal to deliver coaching and other information. The internet-based coaching was well received by patients to help in preparing for upcoming primary care visits.

Context Studies documenting racial/ethnic disparities in health care frequently implicate physicians’ unconscious biases. No study to date has measured physicians’ unconscious racial bias to test whether this predicts physicians’ clinical decisions. Objective To test whether physicians show implicit race bias and whether the magnitude of such bias predicts thrombolysis recommendations for black and white patients with acute coronary syndromes. Design, Setting, and Participants An internet-based tool comprising a clinical vignette of a patient presenting to the emergency department with an acute coronary syndrome, followed by a questionnaire and three Implicit Association Tests (IATs). Study invitations were e-mailed to all internal medicine and emergency medicine residents at four academic medical centers in Atlanta and Boston; 287 completed the study, met inclusion criteria, and were randomized to either a black or white vignette patient. Main Outcome Measures IAT scores (normal continuous variable) measuring physicians’ implicit race preference and perceptions of cooperativeness. Physicians’ attribution of symptoms to coronary artery disease for vignette patients with randomly assigned race, and their decisions about thrombolysis. Assessment of physicians’ explicit racial biases by questionnaire. Results Physicians reported no explicit preference for white versus black patients or differences in perceived cooperativeness. In contrast, IATs revealed implicit preference favoring white Americans (mean IAT score = 0.36, P

Breast-conserving surgery combined with axillary lymph node dissection and radiotherapy or mastectomy are definitive treatments for women with early-stage breast cancer. Little is known about breast cancer treatment for women with disabilities.To compare initial treatment for early-stage breast cancer between women with and without disabilities and to examine the association of treatment differences and survival.Retrospective cohort study.11 Surveillance, Epidemiology, and End Results (SEER) Program tumor registries.100,311 women who received a diagnosis of stage I to IIIA breast cancer at 21 to 64 years of age from 1988 to 1999. Women who qualified for Social Security Disability Insurance (SSDI) and Medicare at breast cancer diagnosis were considered disabled.Receipt of breast-conserving surgery versus mastectomy. For women who had breast-conserving surgery (n = 49 166), the authors examined receipt of radiotherapy and axillary lymph node dissection. Survival was measured from diagnosis until death or until 31 December 2001.Women with SSDI and Medicare coverage had lower rates of breast-conserving surgery than other women (43.2% vs. 49.2%; adjusted relative risk, 0.80 [95% CI, 0.76 to 0.84]). Among women who had breast-conserving surgery, women with SSDI and Medicare coverage were less likely than other women to receive radiotherapy (adjusted relative risk, 0.83 [CI, 0.77 to 0.90]) and axillary lymph node dissection (adjusted relative risk, 0.81 [CI, 0.74 to 0.90]). Women with SSDI and Medicare coverage had lower survival rates than those of other women in all-cause mortality (adjusted hazard ratio, 2.02 [CI, 1.88 to 2.16]) and breast cancer-specific mortality (adjusted hazard ratio, 1.31 [CI, 1.18 to 1.45]). Results were similar after adjustment for treatment differences.Findings are limited to women who qualified for SSDI and Medicare. No data on adjuvant chemotherapy and hormonal therapy were available, and details about the underlying disability were lacking.Women with disabilities had higher breast cancer mortality rates and were less likely to undergo standard therapy after breast-conserving surgery than other women. Differences in treatment did not explain the differences in breast cancer mortality rates.

Persons with major mental illness often have chronic diseases and poor physical health. Therefore, all practicing physicians should learn about communicating effectively with these patients. Few efforts to teach medical students communication skills have specifically targeted patients with major mental illness. Indeed, most of the limited literature on this topic is decades old, predating significant scientific advances in cognitive neuroscience and psychiatric therapeutics and changes in social policies regarding major mental illness. To gather preliminary insight into training needs, we interviewed 13 final-year students from 2 Boston medical schools. Students' observations coalesced around 4 themes: fears and anxieties about interacting with persons with major mental illness; residents “protecting” students from patients with major mental illness; lack of clinical maturity; and barriers to learning during psychiatry rotations. Educational researchers must explore ways to better prepare young physicians to communicate effectively with patients with major mental illness.

Universal design is the notion of considering all potential users when designing places, information, communication, and other items, services, and policies. This book builds upon universal design concepts in its final chapters, which propose solutions to the barriers described in earlier chapters. This chapter starts this process by describing the origins and tenets of the universal design movement. Examples of basic principles of universal design include equitable use, flexibility in use, simple and intuitive use, and low physical effort in use.

Electronic health information technologies and the Internet are playing increasing roles in health care and the ways that patients obtain information about their conditions and options for care. This chapter reviews how these electronic sources are used by health care consumers and makes a case that these information technologies could offer potentially enormous benefits to persons with disabilities. Despite this, persons with disabilities may confront obstacles to accessing information through electronic sources and to sorting through voluminous information to evaluate its accuracy and utility. The chapter reviews guidelines about how clinicians and their patients might safely and effectively communicate via secure Internet portals.

Nearly 20% of Americans live today with some disability, and this number will grow as the “baby boomers” age. Despite this, the U.S. health care system is ill equipped to provide optimal, safe, and efficient care to this population. Significant barriers still block people with disabilities from receiving high quality health care. This book examines these barriers then proposes solutions to make health care accessible and welcoming to people with disabilities, focusing on adults who are blind, deaf, hard of hearing, or who have difficulties using their legs, arms, or hands. The book draws upon stories told by persons with these conditions and reviews of national surveys, governmental policies, and current practices. Some barriers are obvious, such as examining tables that are too high. Other problems arise from faulty communication between patients and health care professionals, including misconceptions among clinicians about the daily lives, preferences, values, and abilities of patients with disabilities. Yet others relate to restrictive health insurance policies. Finding patient-centered solutions to these many impediments will take more than simply building ramps. The first part of the book examines broad social and health insurance policies affecting people with disabilities. The second part discusses current barriers to health care, while the third proposes ways to overcome these hurdles and improve care. Using universal design principles, these solutions recognize that creating safe and accessible health care for people with disabilities will benefit virtually everyone at some point in their lives.

The factors outside the traditional health care system—or straddling the boundary between health care and other sectors—can often have huge implications for persons' health and well being. This chapter briefly explores four such topics: assistive technology, personal care assistance, accessible public transportation, and accessible housing. It looks at controversies about health insurance coverage of these items and services.

This chapter starts by considering the Americans with Disabilities Act and regulatory requirements for ensuring effective communications with persons with disabilities. It then reviews approaches for accommodating communication needs, ranging from simple approaches (such as large point fonts and Braille signage) to an increasingly sophisticated range of communication technologies for persons with a wide variety of accommodation needs. For example, the chapter describes various technologies that assist persons who are blind or who have low vision, as well as communication strategies and technologies for persons who are deaf or hard of hearing. It also describes the training and professional obligations of sign language interpreters.

This chapter addresses the design of health care settings that are accessible to persons with physical and sensory disabilities. It starts by reviewing Americans with Disabilities Act regulations. It then discusses a process for designing accessible space, including the involvement of persons with disabilities as partners. The chapter reviews such items as lighting, acoustics, flooring, air quality, heating and cooling, emergency notification alarms, and signage as well as basic questions about the size and configuration of spaces, medical equipment, and furniture. It offers suggestions about accessible equipment, such as weight scales.

This chapter describes the physical barriers persons with disabilities can encounter when they visit health care settings, including difficulties opening doors, inaccessible restroom facilities, and examining tables that are too high for persons to access. It also examines problems encountered by patients who have blind or low vision as they try to navigate health care settings, including staff being unaware of appropriate ways to assist them with transiting through space (e.g., use of sighted guides). The chapter presents information about relatively low use of screening and preventive services among persons with mobility problems, and relates these findings to inaccessible equipment and other access barriers. Given higher rates of obesity and overweightness among persons with disabilities, particular issues relating to access for persons who are obese are also discussed.

The community environments within which people live have enormous consequences for their health. This chapter reaches beyond health care settings into communities to explore briefly those attributes that make communities safe and welcoming to persons with disabilities, and that allow all residents to participate fully in community life. It argues that demographic imperatives force the need for greater public policy focus on making accessible built environments. With an aging population and growing rates of obesity, along with declining rates of physical fitness, planners should emphasize building environments that support independent living and promote physical activity, healthy eating, and other positive health-related behaviors. Healthy built environments can improve not only physical health but also mental health, over the life span. The chapter briefly reviews examples from other countries of efforts to construct walkable communities and discusses challenges to bringing those changes to the United States.

This chapter starts by describing the importance of interpersonal communication and relationships between patients and clinicians for ensuring high quality health care, drawing upon numerous studies that report better clinical outcomes with better communication. It then presents various challenges to interpersonal communication between patients with disabilities and clinicians, ranging from issues such as attitudes of clinicians to lack of accommodations for persons who are blind, experience low vision, deaf, or hard of hearing. The chapter presents examples of inadequate or damaging interpersonal communication. It raises particular concern about discussions of potentially sensitive topics, such as sexuality and reproductive health, mental health, and substance abuse.

This chapter explores how persons with disabilities find doctors and other health care professionals to provide primary and specialty care. It presents information about health care services used by persons with disabilities. It looks at different approaches for paying for health care services and describes how persons with disabilities must pay considerable costs out-of-pocket. The costs of accommodating disabilities, such as expenses for sign language interpreters, are also described. The chapter addresses a particularly challenging problem, as young adults with disabilities try to transition from the pediatric health care settings of childhood into the adult health care sector.

This introductory chapter provides demographic information about the prevalence of disability among U.S. residents and outlines population trends suggesting rising numbers of persons with disabilities. It also reviews concerns about health care quality in the United States, drawing upon reports of the Institute of Medicine and other sources to describe worrisome quality shortfalls, especially affecting persons with chronic health conditions. It suggests that persons with disabilities confront particular risks of receiving substandard care, along a variety of dimensions of quality and access.

Approximately 15% of Americans lack health insurance, and many have insurance policies with limited benefits, inadequate to meet their needs. This chapter examines the health insurance coverage of persons with disabilities, focusing primarily on working-age adults (the vast majority of elderly adults have Medicare). Since most working-age Americans obtain health insurance coverage through their employment, the chapter explores the consequences of leaving the workforce—or being unemployed—especially when individuals apply for Social Security Disability Insurance and must then await Medicare coverage. It also reviews basic concerns relating to benefits design, particularly policies (such as medical necessity requirements) that might have special implications for persons with disabilities.

Interpersonal interactions during physical examinations and medical procedures convey volumes about respect, empathy, and understanding. Listening and following patients' instructions, such as about transferring them from a wheelchair to examination table, can also determine the safety of both patients with disabilities and clinical staff. This chapter examines etiquette within health care settings, starting with first contacts with office staff at the check-in desk and following patients through waiting rooms and into the examining or testing room. It identifies challenges patients with disabilities face in trying to accommodate their needs for positioning and appropriate communication during clinical interactions. The chapter also discusses telephone communication for persons who are deaf or hard of hearing.

This chapter explores communication between patients with disabilities and clinicians about “technical” matters—the purportedly objective “facts” that guide clinical decision making, such as discussions about symptoms, signs, therapeutic options, and treatment side effects. It discusses concerns that clinicians may have inadequate scientific knowledge about many disabling conditions, and they therefore make decisions based on their personal views of patients' quality of life rather than objective medical evidence. Conflicts can arise when clinicians fail to appreciate the value persons with disabilities place on their lives. The chapter also discusses problems making clinical information, such as recommendations for treatments and warnings about side effects, accessible to persons with impaired vision or hearing. Failure to accommodate patients' communication needs can compromise patients' safety.

This chapter offers suggestions for improving open and effective patient-clinician communication. The first step involves training clinicians to recognize communication barriers. All members of health care organizations, from senior leadership to front-line office assistants, require training to enhance awareness of potential communication barriers and strategies for accommodating communication needs. The chapter reviews various approaches for improving communication, as well as training clinicians to improve their interview and interaction skills with patients with disabilities. Training should include efforts to increase sensitivity to stigmatizing attitudes and other social barriers experiences by persons with disabilities. The chapter also discusses clinicians with disabilities.

Shifting the paradigm of care from traditional roles of clinicians directing patients to a model of collaboration in care has gained currency as a strategy for improving both health care quality and outcomes. This chapter describes self-management and collaborative care programs, which are well developed for certain chronic conditions (such as arthritis and diabetes). It then discusses various considerations in moving toward a self-management and collaborative care approach for persons with disabilities. The core to any such effort is for clinicians to listen to and learn from their patients with disabilities, who are often the experts about their health and needs. The chapter looks at accessibility of self-management and resources and technologies to aid self-management.

Background: Risk adjustment is useful for adjusting health care payments based on patients' health status. Objective: This work seeks to examine how well pharmacy- and diagnosis-based risk adjusters predict child health expenditures in Medicaid populations. Research Design: We used 1994-1995 Medicaid claims files for all children ages 0-18 years who were not covered by managed care in 3 states- Georgia, New Jersey, and Wisconsin. We examined separately 6 risk adjustment methods, 2 pharmacy-based and 4 diagnosis-based. We compared predictive accuracy of the methods for the whole sample and stratified by state and Medicaid enrollment category. Findings: Models with risk adjustment (either diagnosis- or pharmacy-based) had better predictive accuracy than demographic models. The pharmacy and diagnosis-based models had similar predictive accuracy. Risk adjuster performance differed by Medicaid enrollment category and state. Risk-adjusted models generally underpredict expenditures in populations with worse health status (eg, those in the Supplemental Security Income program [SSI]). The pharmacy-based models performed well for children in SSI relative to children in foster care. Conclusions: Both pharmacy- and diagnosis-based risk adjustment improved the prediction of health expenditures compared models without risk adjustment. No single risk adjuster performed best in all situations, suggesting that optimal choices of risk adjusters may differ by purpose and context.

We evaluated racial and ethnic differences in use of medical care between patients with diabetes enrolled in Medicaid and explored whether differences varied by state Medicaid program. Using data from 137,006 patients we created a multivariable Poisson regression model to examine the effect of race on ambulatory care visits, emergency ward visits, and hospitalization rates for patients with diabetes mellitus enrolled in three state Medicaid programs. We found significant differences in service use between groups, which varied depending on state. For example, black patients compared with whites had significantly fewer outpatient visits but more hospitalizations in New Jersey; by contrast, blacks had higher outpatient visit rates and lower hospitalization rates in Georgia. Racial and ethnic differences in health service use among Medicaid enrollees were not consistent across states, suggesting that local factors, including varied Medicaid policies, may affect racial and ethnic differences in use of health care services.

Background: Health-related quality of life (HRQL) is optimally reported from the individual vantage point; consequently, prevalence estimates from the National Health Interview Survey (NHIS) could be misleading, because approximately one third of the information is proxy-provided. Objective: The objective of this study was to discern the degree to which disparities in the crude prevalence of reduced HRQL is explainable by case-mix differences between proxy- and self-reportees. Subjects: We studied the cross-sectional data on 96,091 persons from the 1994–1995 NHIS Supplement on Disability. Research Design: We conducted a study of reduced HRQL expressed as a respondent type indicator (proxy vs. self) adjusting for socioeconomic differences and limitations in function. Measures: We studied reduced HRQL indicated by 1) perception of poor health, 2) perception of disability, 3) 30 or more bed days in the last year, or 4) 20 or more doctor visits in the last year. Results: The crude estimated prevalence of reduced HRQL for proxy reports was approximately half that of self-reports. Although the unadjusted odds ratios (ORs) for proxy- compared with self-reportees ranged from 0.51 (95% confidence interval [CI], 0.47–0.55) to 0.59 (95% CI, 0.55–0.64), case-mix adjustment resulted in a significant directional change for poor health perception, and all adjusted ORs were substantially closer to 1.0 than their unadjusted counterparts. Conclusion: Adjustment for case-mix explained away most of the proxy-/self-report disparity, suggesting that a major component of differences in prevalence between proxy- and self-respondents is case-mix-related. Consequently, compared with excluding proxy reports, inclusion of proxy reports results in relatively less bias when the NHIS-D is applied to obtain prevalence estimates for the indicators studied.

The complexity and mix of rehabilitation patients varies across clinicians and institutions. Comparisons of outcomes across providers must therefore adjust for differences in risk factors across patient populations. Research on risk adjustment has generally focused on acute care hospital outcomes, although techniques for risk adjusting financial outcomes are fairly well developed in rehabilitation, primarily to support Medicare and other prospective payment systems. This article reviews important methodologic issues in risk adjusting rehabilitation outcomes in observational studies of routine clinical practice or for management, such as assessing quality or costs of care. Risk adjusting rehabilitation outcomes is more difficult than risk adjusting other clinical results, such as outcomes of many acute care services. At the outset, characterizing rehabilitation interventions is frequently difficult. Furthermore, outcomes are diverse and depend on myriad factors, including patients' physical and cognitive abilities, underlying medical diseases, sensory and emotional factors, willingness to participate in care, and supportive environments. No risk-adjustment approach can control for every factor affecting outcomes of care. Knowing which risk factors are missing helps guide interpretation of the results and determines how well risk-adjusted outcomes fairly compare providers or treatments.

The movement to measure medical care quality has been accelerating, spurred on by evidence of poor quality of care and trials of interventions to improve care. Appropriate measurement of quality of care is an essential aspect of improving the quality of care, yet some quality measures may be influenced by patients' attributes unrelated to quality of care. Risk adjustment is the term commonly applied to those methods that account for patient-related attributes, making measurement of health care quality as comparable as possible across providers or organizations seeing different mixes of patients. The measurement of quality of care for children poses specific challenges. In addition to these measurement challenges, analysts must ensure that quality comparisons among doctors, groups of doctors, hospitals, or health plans are not adversely affected by the likelihood that different types of patients seek care in different places. Although some techniques designed to adjust performance measures for case mix were developed for both adults and children, other systems are specific to childhood circumstances. The theoretical issues involved in risk-adjusting childhood outcomes measures for newborns in the neonatal intensive care unit were reviewed recently. Here, we go beyond the neonatal intensive care unit setting to consider risk adjustment for pediatric quality measures more broadly. In particular, we 1) review the conceptual background for risk-adjusting quality measures, 2) present policy issues related to adjusting pediatric quality measures, and 3) catalog existing risk-adjustment methodologies for pediatric quality measures. We conclude with an overall assessment of the status of risk adjustment for pediatric quality measures and recommendations for additional research and application.

The health care delivery system aims to improve the functioning of Americans, but little information exists to judge progress toward meeting this goal. Administrative data generated through running and overseeing health care delivery offer considerable information about diagnoses and procedures in coded formats comparable across settings of care. This article explores the issues raised when considering adding coded information about functional status to administrative databases throughout the health care system. The National Committee on Vital and Health Statistics (NCVHS) identified the International Classification of Functioning, Disability and Health (ICF) as the only viable code set for consistently reporting functional status.

More than ten years have passed since the Americans with Disabilities Act (ADA) mandated that all employers provide "reasonable accommodations" for employees with disabilities. This mandate applies to medical schools, but no systematic information is available to assess the accommodations provided to medical school faculty with disabilities. This Issue Brief summarizes anecdotal evidence from several medical schools about the experiences of faculty with disabilities, and the barriers they face in establishing and maintaining their careers. It also recommends practical steps medical schools can take to provide a welcoming and accessible academic medical environment.

Primarily because of immigration, Asian Americans are one of the fastest growing and most ethnically diverse minority groups in the United States. However, little is known about their perspectives on health care quality.To examine factors contributing to quality of care from the perspective of Chinese- and Vietnamese-American patients with limited English language skills.Qualitative study using focus groups and content analysis to determine domains of quality of care.Four community health centers in Massachusetts.A total of 122 Chinese- and Vietnamese-American patients were interviewed in focus groups by bilingual interviewers using a standardized, translated moderator guide.Domains of quality of care mentioned by patients in verbatim transcripts.In addition to dimensions of health care quality commonly expressed by Caucasian, English-speaking patients in the United States, Chinese- and Vietnamese-American patients with limited English proficiency wanted to discuss the use of non-Western medical practices with their providers, but encountered significant barriers. They viewed providers' knowledge, inquiry, and nonjudgmental acceptance of traditional Asian medical beliefs and practices as part of quality care. Patients also considered the quality of interpreter services to be very important. They preferred using professional interpreters rather than family members, and preferred gender-concordant translators. Furthermore, they expressed the need for help in navigating health care systems and obtaining support services.Cultural and linguistically appropriate health care services may lead to improved health care quality for Asian-American patients who have limited English language skills. Important aspects of quality include providers' respect for traditional health beliefs and practices, access to professional interpreters, and assistance in obtaining social services.

Iezzoni LI. The canary in the mine. Arch Phys Med Rehabil 2002;83:1476-8. Many Americans are concerned about their access to health care in the future, especially their ability to pay for needed services. However, a person with a disabling condition requiring ongoing clinical vigilance, supportive care, and other assistive services or technologies faces special difficulties: that person is the "canary in the mine," warning others about fundamental problems within our health care system. Persons with disabilities who have health insurance are often unable to get items and services not covered by their plans. They experience more problems than others with follow-up care, availability of specialists, getting to doctors, and obtaining help during off hours. These problems suggest that people with disabilities fall into the "quality chasm," the metaphor used by the Institute of Medicine to describe the gap between ideal care and current reality. The Crossing the Quality Chasm report suggests 6 aims for fundamental reform, exhorting the health care system to become safe, effective, patient-centered, timely, efficient, and equitable. Each of these aims holds special resonance for persons with disabilities. Despite the compelling need to overhaul the health care system, the American public as yet seems little inclined to fundamental change. Perhaps the impetus must come from subgroups within the population who are particularly at risk from the current system, such as persons with disabilities. As solutions are crafted, people with disabilities, their families, and communities should help design and direct fundamental changes to the health care system. © 2002 by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation

Context Obesity, a leading cause of preventable death and chronic disease, is associated with disability. Little is known about obesity among adults with specific disabilities. Objectives To determine the prevalence of obesity in adults with physical and sensory limitations and serious mental illness. Design, Setting, and Participants The 1994-1995 National Health Interview Survey of 145007 US community-dwelling respondents, 25626 of whom had 1 or more disabilities. Main Outcome Measures Likelihood of being obese, attempting weight loss, and receiving exercise counseling among adults with and without disabilities. Results Among adults with disabilities, 24.9% were obese vs 15.1% of those without disabilities. After adjusting for sociodemographic factors, adults with a disability were more likely to be obese, with an adjusted odds ratio (AOR) of 1.9 (95% confidence interval [CI], 1.8-2.0). The highest risk occurred among adults with some (AOR, 2.4; 95% CI, 2.3-2.5) or severe (AOR, 2.5; 95% CI, 2.3-2.7) lower extremity mobility difficulties. After further adjustment for comorbid conditions, adults with disabilities were as likely to attempt weight loss as those without disabilities, except for adults with severe lower extremity mobility difficulties, who were less likely (AOR, 0.7; 95% CI, 0.5-0.9]), and adults with mental illness, who were more likely (AOR, 1.4; 95% CI, 1.2-1.8). Physician exercise counseling was reported less often among adults with severe lower extremity (AOR, 0.5; 95% CI, 0.4-0.7) and upper extremity (AOR, 0.7; 95% CI, 0.5-1.0) mobility difficulties. Conclusion Obesity appears to be more prevalent in adults with sensory, physical, and mental health conditions. Health care practitioners should address weight control and exercise among adults with disabilities.

BACKGROUND, There have been few studies of the extent to which differences in the pool of patients being managed might account for geographic variations in treatment rates. OBJECTIVE. For two cardiac procedures, cardiac catheterization and revascularization, we evaluate the hypothesis that differences in "the percentage of patients for whom the procedure is appropriate" is a factor explaining variations in use rates among those hospitalized with coronary heart disease (CHD). RESEARCH DESIGN. Based on hospital utilization patterns in Massachusetts in 1990, we created 70 small geographic areas. Using 1992 Massachusetts Peer Review Organization data, areas were ranked from highest to lowest based on (empirical-Bayes-adjusted) hospitalization rates for each procedure. One thousand seven hundred four cases from 43 hospitals were sampled, roughly half each from high and low use areas. Half had a procedure and half were candidates for the same procedure but did not have it. For each procedure, medical records were reviewed to determine whether the procedure was (or, for those not having it, would have been) appropriate, based on criteria developed using a modified Delphi approach. RESULTS. Among those having either procedure, appropriateness rates were similar in high and low rate areas (P = 0.59 for catheterization and P = 0.30 for revascularization). However, among candidates for either procedure who did not have it, appropriateness for performing the procedure was greater in high-rate areas (41.4% vs. 32.1%, P = 0.05 for catheterization; 71.2% vs. 57.2%, P = 0.003, for revascularization). CONCLUSION. Among those hospitalized with CHD, appropriateness rates for two cardiac procedures are higher in areas with higher use rates.

To identify and characterize discrepancies between explicit and implicit medical record review of complications and quality of care.Forty-two acute-care hospitals in California and Connecticut in 1994.In a retrospective chart review of 1,025 Medicare beneficiaries age65, we compared explicit (nurse) and implicit (physician) reviews of complications and quality in individual cases. To understand discrepancies, we calculated the kappa statistic and examined physicians' comments.With Medicare discharge abstracts, we used the Complications Screening Program to identify and then select a stratified random sample of cases flagged for 1 of 15 surgical complications, 5 medical complications, and unflagged controls. Peer Review Organization nurses and physicians performed chart reviews.Agreement about complications was fair (kappa = 0.36) among surgical and was moderate (kappa = 0.59) among medical cases. In discordant cases, physicians said that complications were insignificant, attributable to a related diagnosis, or present on admission. Agreement about quality was poor among surgical and medical cases (kappa = 0.00 and 0.13, respectively). In discordant cases, physicians said that quality problems were unavoidable, small lapses in otherwise satisfactory care, present on admission, or resulted in no adverse outcome.We identified many discrepancies between explicit and implicit review of complications and quality. Physician reviewers may not consider process problems that are ubiquitous in hospitals to represent substandard quality.

Objective. Although Peer Review Organizations (PROs) and researchers rely on physicians to assess quality of care, little is known about what physicians think about when they judge quality. We sought to identify features of individual cases that are associated with physicians’ judgments. Design. Using 1994 Medicare data, we selected hospitalizations for 1134 beneficiaries in 42 acute care hospitals in California and Connecticut. The sample was enriched with 17 surgical and six medical complications identified using diagnosis and procedure codes. PRO physicians confirmed quality problems using a structured implicit chart review instrument and provided written open-ended comments about each case. We coded physicians’ comments for factors presumed to influence judgments about quality. Results. In crude and adjusted comparisons, reviewers questioned quality more frequently in cases with serious or fatal outcomes, technical mishaps and inadequate documentation. Among surgical (but not medical) patients, they were less likely to record poor quality among patients presenting with an acute illness. Conclusion. Factors other than the adequacy of key processes of care are associated with physician-reviewers’ judgments about quality.

Roughly 54 million Americans have some disability; at older ages, women are more likely to be disabled than men. Many people with disabilities today live virtually normal life spans, and therefore routine screening and preventive services are essential to their overall quality of care. We used the 1994-1995 National Health Interview Survey (NHIS), with Disability, Family Resources, and Healthy People 2000 supplements, to examine screening and preventive service use for adult women with disabilities living in the community--about 18.4% of women (estimated 18.28 million). Disability was associated with higher age-adjusted rates of: poverty; living alone; low education; inability to work; obesity; and being frequently depressed or anxious. Disabled women generally reported screening and preventive services at rates comparable to all women. Women with major lower extremity mobility difficulties had much lower adjusted odds of Papanicolaou smears (odds ratio, 0.6; 95% confidence interval, 0.4-0.9), mammograms (odds ratio, 0.7; 95% confidence interval, 0.5-0.9), and smoking queries (odds ratio, 0.6; 95% confidence interval, 0.5-0.8). Various approaches exist to improve access for disabled women to health care services.

This chapter describes the dimensions of risk that are related to a variety of outcomes—age; gender; race and ethnicity; acute clinical stability; severity of principal diagnosis; extent and severity of comorbidities; physical functional status; psychological, cognitive, and psychosocial functioning; socioeconomic status; and health status and quality of life. In any practical situation, it is unlikely that adequate measures for all dimensions of risk will be available. Despite the acknowledged value of randomized controlled trials, much of what one can infer about the clinical effectiveness of surgical interventions and procedures will come from nonexperimental studies. A major problem in such studies is that patient characteristics that are strongly related to treatment outcomes are often different for those receiving the intervention versus those not. It is important to both measure and adjust for these differences in risk factors when assessing effectiveness.

Hospital discharge diagnoses, coded by use of the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), increasingly determine reimbursement and support quality monitoring. Prior studies of coding validity have investigated whether coding guidelines were met, not whether the clinical condition was actually present.To determine whether clinical evidence in medical records confirms selected ICD-9-CM discharge diagnoses coded by hospitals.Retrospective record review of 485 randomly sampled 1994 hospitalizations of elderly Medicare beneficiaries in Califomia and Connecticut.Proportion of patients with specified ICD-9-CM codes representing potential complications who had clinical evidence confirming the coded condition.Clinical evidence supported most postoperative acute myocardial infarction diagnoses, but fewer than 60% of other diagnoses had confirmatory clinical evidence by explicit clinical criteria; 30% of medical and 19% of surgical patients lacked objective confirmatory evidence in the medical record. Across 11 surgical and 2 medical complications, objective clinical criteria or physicians' notes supported the coded diagnosis in90% of patients for 2 complications, 80% to 90% of patients for 4 complications, 70% to80% of patients for 5 complications, and70% for 2 complications. For some complications (postoperative pneumonia, aspiration pneumonia, and hemorrhage or hematoma), a large fraction of patients had only a physician's note reporting the complication.Our findings raise questions about whether the clinical conditions represented by ICD-9-CM codes used by the Complications Screening Program were in fact always present. These findings highlight concerns about the clinical validity of using ICD-9-CM codes for quality monitoring.

Objective The use of administrative data to identify inpatient complications is technically feasible and inexpensive but unproven as a quality measure. Our objective was to validate whether a screening method that uses data from standard hospital discharge abstracts identifies complications of care and potential quality problems. Design This was a case-control study with structured implicit physician reviews. Setting Acute-care hospitals in California and Connecticut in 1994. Patients The study included 1,025 Medicare beneficiaries greater than 265 years of age. Methods Using administrative data, we stratified acute-care hospitals by observed-to-expected complication rates and randomly selected hospitals within each state. We randomly selected cases flagged with 1 of 17 surgical complications and 6 medical complications. We randomly selected controls from unflagged cases. Main outcome measure Peer-review organization physicians' judgments about the presence of the flagged complication and potential quality-of-care problems. Results Physicians confirmed flagged complications in 68.4% of surgical and 27.2% of medical cases. They identified potential quality problems in 29.5% of flagged surgical and 15.7% of medical cases but in only 2.1% of surgical and medical controls. The rate of physician-identified potential quality problems among flagged cases exceeded 25% in 9 surgical screens and 1 medical screen. Reviewers noted several potentially mitigating circumstances that affected their judgments about quality, including factors related to the patients' illness, the complexity of the case, and technical difficulties that clinicians encountered. Conclusions For some types of complications, screening administrative data may offer an efficient approach for identifying potentially problematic cases for physician review. Understanding the basis for physicians' judgments about quality requires more investigation.

Objectives: To ascertain hospital inpatient mortality in England and to determine which factors best explain variation in standardised hospital death ratios. Design: Weighted linear regression analysis of routinely collected data over four years, with hospital standardised mortality ratios as the dependent variable. Setting: England. Subjects:Eight million discharges from NHS hospitals when the primary diagnosis was one of the diagnoses accounting for 80% of inpatient deaths. Main outcome measures: Hospital standardised mortality ratios and predictors of variations in these ratios. Results: The four year crude death rates varied across hospitals from 3.4% to 13.6% (average for England 8.5%), and standardised hospital mortality ratios ranged from 53 to 137 (average for England 100). The percentage of cases that were emergency admissions (60% of total hospital admissions) was the best predictor of this variation in mortality, with the ratio of hospital doctors to beds and general practitioners to head of population the next best predictors. When analyses were restricted to emergency admissions (which covered 93% of all patient deaths analysed) number of doctors per bed was the best predictor. Conclusion: Analysis of hospital episode statistics reveals wide variation in standardised hospital mortality ratios in England. The percentage of total admissions classified as emergencies is the most powerful predictor of variation in mortality. The ratios of doctors to head of population served, both in hospital and in general practice, seem to be critical determinants of standardised hospital death rates; the higher these ratios, the lower the death rates in both cases.

When Flanders and collaborators applied to their own patient population the pneumonia severity index (PSI) that was developed by Fine and colleagues, 1,2 they discovered discrepancies. The PSI predicted about 2.4 times more deaths than were actually observed.3 This finding is not surprising; it confirms something we already knew. Unlike mathematical representations of basic natural phenomena, such as E= mc2, algorithms derived from statistical modeling reflect only associations embedded within the derivation data set—a snapshot in place and time, not fundamental truth. When transporting statistically derived measures, such as the PSI, to another place and time, users should observe the axiom, “Caveat emptor.” Fortunately, as Flanders and colleagues knew, 3 relatively straightforward methods exist to identify problems such as imperfect calibration. Justice, Covinsky, and Berlin suggest ways to address concerns about what they call the “external validity” of prognostic measures derived from statistical models.4 They emphasize two broad concepts: accuracy (whether predictions match outcomes in the original patient sample), which is assessed by examining calibration and discrimination; and generalizability (the extent to which the model provides accurate predictions in different patient samples). Generalizability itself has several components: reproducibility in comparable patient populations and transportability across different dimensions, including place and time.4 Justice, Covinsky, and Berlin offer a systematic approach for evaluating the performance of prognostic models, but as they note, the relative importance of different aspects of external validity depends on one’s purpose.4 The difficulty arises when the prognostic model’s external validity cannot be determined for its planned use or purpose. In most such instances, the missing ingredients are data. For example, Charleson and collaborators developed their comorbidity index to predict 1-year mortality using information on a cohort of 604 patients admitted to the medical service at New York Hospital during 1 month in 1984.5 To validate their comorbidity index, they used information on 685 patients treated between 1962 and 1969 for primary breast cancer at Yale New Haven Hospital. Obviously, 1-year mortality is heavily influenced by which medical therapies are available, and presumably treatments for comorbid conditions had changed during the 15 to 20 years that separate the initial cohort and the validation sample. Exactly why the breast cancer sample was used for validation is unclear, unless it was to provide a severe test of generalizability in quite different patients, but the authors acknowledge that their index required “further evaluation in much larger populations.”5 The Charlson Comorbidity Index remains popular today, but its dependence on 1984 therapeutic outcomes must be considered when applying it to 1999 data. An example relevant to health policy is the diagnosis-related group (DRG) classification system, which since late 1983 has determined Medicare’s prospective payment for most hospitalized patients.6 Periodically, DRG payment weights must be calibrated to hospital costs nationwide. Given the importance of setting accurate payments, this calibration requires timely data. Although its basic diagnostic groupings were first derived clinically, the final DRG classification scheme resulted from statistical modeling of a database containing more than 1.5 million Medicare discharges.7 Annually, for the October 1 start of the federal fiscal year, DRG payment weights and sometimes the fundamental structure of the DRGs themselves are recalibrated using the most recent available Medicare data. Sometimes, major changes are made. For example, in the initial version, some DRGs were created by splitting certain patient groups into those younger than and those older than 69 years; that split was soon abandoned when analyses found that the age break had little statistical value. Other major alterations in the classification system include the creation of new categories for continuous mechanical ventilation and for operating room procedures unrelated to the principal diagnosis. These changes, although justified statistically, have complicated DRG-based comparisons over time. The Medicare data available for this annual recalibration are inevitably 1 or 2 years old. The data, therefore, cannot capture the cost implications of rapidly disseminated, new, diagnostic or therapeutic technologies, such as thrombolytic therapy for acute myocardial infarction, endoscopic surgery, or novel transplant techniques. To some extent, DRG payment weights are always playing catch-up with the way hospital-based medicine is practiced. Another Medicare situation that involves the use of outdated or inadequate date for calibrating prognostic payment models could have significant consequences. The 1997 Balanced Budget Act (P.L. 105-33) mandated substantial changes in Medicare, including the expansion of capitated care. To lessen any financial incentives that would discourage the enrollment of sick and disabled people, the Balanced Budget Act requires Medicare to link capitation payments to health status, or to “risk adjust” the reimbursement, by January 1, 2000.8–10 In this context, to risk adjust means to adjust payment so its reflects the expected cost of care based on the patient’s clinical characteristics, for example, to pay more for a lung cancer patient than for a person without known illness. The major problem is that the data for setting and calibrating these payments are inadequate. Historically, Medicare did not require capitated plans to submit records on hospitalizations or outpatient services, to minimize the administrative burden. The Balanced Budget Act changed that practice by allowing Medicare to require that managed care organizations report diagnosis and procedure codes for individual patients. To meet the start date of January 1, 2000, however, the only diagnostic data available for calibrating payment levels come from bills submitted through fee-for-service Medicare. Because of this and other data concerns, 9,10 Medicare currently plans to risk adjust year 2000 capitated payments using statistical models derived from 1995 and 1996 data from hospitalized, fee-for-service patients.9 Thus, Medicare’s risk-adjusted, capitated payments will reflect 5-year-old data from a care system with a fundamentally different philosophy than its intended target—managed care. Fixing problems in statistical models that are caused by the available data can be relatively straightforward. For example, Flanders and colleagues fixed the PSI so it could be used for their purpose with a simple statistical patch—they recalibrated the PSI using logistic regression. Of course, their recalibrated PSI now is inextricably linked to the content and setting of its own data. Each recasting of a prognostic model is thus essentially unique. Just as the annual updating of the DRGs and their payment weights has created more than 15 versions, the continual recalibrating of prognostic models like the PSI could produce a bewildering array of methods. Subtle variations in the models could hamper efforts of investigators to replicate or compare findings. In 1991, concerned about the proliferation of “black box” medical information systems (methods whose inner workings were kept secret, typically by their proprietary developers), I recommended instituting a formal, national process to evaluate these measures.11 Since payments and reports on provider quality are increasingly based on such statistically derived methods, a national clearinghouse that could rate these systems objectively—for example, by examining their reliability, accuracy, and external validity—seemed warranted. A clearinghouse could also maintain catalogs of different versions of the models, delineating their pedigrees and data specifications. A national clearinghouse is, however, even more unlikely to be established now than it was in the early 1990s. Health plans and large chains of providers increasingly employ their own, homegrown methods to profile their performance, and many such organizations refuse to share details of their methods. Nevertheless, the research community itself should adopt guidelines for published reports employing statistically derived models. First, all publications should clearly state the version of the model employed. For example, many authors of studies using DRGs to control for hospital case mix do not indicate the version of the DRGs, which could be done by stating the year the version was adopted by the Health Care Financing Administration. Second, when appropriate, as in the work by Flanders and colleagues, 3 investigators should examine the discrimination and calibration of the model in their own data set and report those results. If investigators modify these models in any way, for example, by recalibrating them, they should describe all the changes. Finally, investigators who develop statistically derived models should report explicit details about the derivation database and population sample, including any speculation about unique features that could compromise comparisons with other samples. Though buyers of statistically derived models must still beware, these strategies should at least produce a more informed consumer.