Appendix D
The Role of Racial and Ethnic Data Collection in Eliminating Disparities in Health Care

Allen Fremont and Nicole Lurie*

Racial and ethnic disparities in health have been extensively documented. While the causes are both numerous and diverse, disparities in health care have been shown to play a substantial role. A recent Institute of Medicine report (IOM, 2003) exhaustively catalogued disparities in care and concluded that important differences were present even among groups that were similarly insured. Many observers now conclude that eliminating racial and ethnic disparities in health and health care is a central issue in overall efforts to improve quality (IOM, 2001; Bierman et al., 2002). Consistent with this view, Healthy People 2010 specified elimination of such disparities as one of its two overarching goals (U.S. Department of Health and Human Services, 2000b).

Making progress toward the goal of eliminating disparities will require widespread, reliable, and consistent data about the racial and ethnic characteristics of the U.S. population. This information is needed to identify the nature and extent of disparities, to target quality improvement efforts, and to monitor progress. Tracking the racial and ethnic composition and changing health care needs of different populations is vital if our health care system, which includes both public health and the delivery of personal health care services, is to fulfill its essential functions. Measurement, reporting, and benchmarking are critical to improving care.

*  

Allen Fremont, M.D., Ph.D., and Nicole Lurie, M.D., M.S.P.H, are a medical sociologist and primary care physician, and senior natural scientist and Paul O’Neill Alcoa Professor, respectively, at the RAND Corporation, in Arlington, VA.



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Eliminating Health Disparities: Measurement and Data Needs Appendix D The Role of Racial and Ethnic Data Collection in Eliminating Disparities in Health Care Allen Fremont and Nicole Lurie* Racial and ethnic disparities in health have been extensively documented. While the causes are both numerous and diverse, disparities in health care have been shown to play a substantial role. A recent Institute of Medicine report (IOM, 2003) exhaustively catalogued disparities in care and concluded that important differences were present even among groups that were similarly insured. Many observers now conclude that eliminating racial and ethnic disparities in health and health care is a central issue in overall efforts to improve quality (IOM, 2001; Bierman et al., 2002). Consistent with this view, Healthy People 2010 specified elimination of such disparities as one of its two overarching goals (U.S. Department of Health and Human Services, 2000b). Making progress toward the goal of eliminating disparities will require widespread, reliable, and consistent data about the racial and ethnic characteristics of the U.S. population. This information is needed to identify the nature and extent of disparities, to target quality improvement efforts, and to monitor progress. Tracking the racial and ethnic composition and changing health care needs of different populations is vital if our health care system, which includes both public health and the delivery of personal health care services, is to fulfill its essential functions. Measurement, reporting, and benchmarking are critical to improving care. *   Allen Fremont, M.D., Ph.D., and Nicole Lurie, M.D., M.S.P.H, are a medical sociologist and primary care physician, and senior natural scientist and Paul O’Neill Alcoa Professor, respectively, at the RAND Corporation, in Arlington, VA.

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Eliminating Health Disparities: Measurement and Data Needs Despite widespread public perception that the federal government and the private sector collect vast amounts of data, the availability of racial and ethnic data in the health care system itself is quite limited. A variety of government sources include data on race and ethnicity, but the utility of these data is constrained by ongoing problems with reliability, completeness, and lack of comparability across data sources. With only a few exceptions, private insurers and health plans do not maintain data on the race or ethnicity of their enrollees. In this paper, we provide a framework for describing the role of racial and ethnic data in supporting essential functions of the health system. We first illustrate the value of racial ethnic data collection by describing ways such information can be used to reduce disparities, particularly with respect to the quality of care. We describe how data on primary language and socioeconomic status can complement racial and ethnic information. We then assess current sources of racial and ethnic information and the challenges inherent in collecting it. We conclude with a series of recommendations for enhancing the availability and use of data in the public and private sectors. Throughout our discussion, we emphasize the federal role in data collection. However, since only about half of the minority population in the United States receives care in a public-sector system (e.g., Medicare, Medicaid, Department of Veterans Affairs, Department of Defense), the importance of private-sector and other government efforts should not be overlooked. Nevertheless, successful fulfillment of the federal role will be essential to facilitate state, local, and private-sector initiatives in public health, service delivery, and research. THE CHALLENGE OF IDENTIFYING HEALTH DISPARITIES The United States is becoming increasingly diverse. White Americans currently constitute nearly 70 percent of the population. However, by 2050, persons of color will make up nearly half of the population. In some states, such as California, this transition has already occurred, and the proportion of California’s population that is Hispanic is expected to grow dramatically in the next decade. Asian/Pacific Islanders still constitute only a small proportion of the U.S. population, but they currently have the largest rate of population growth (U.S. Bureau of the Census, 1990). These demographic shifts have profound implications for health and health care in this country because minority populations experience a disproportionate burden of health problems. Overall, African Americans continue to have some of the worst health outcomes. However, discussion of health disparities among racial and ethnic minorities must move well beyond comparisons of African Americans

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Eliminating Health Disparities: Measurement and Data Needs and whites. Indeed, there is considerable variation in health status among all of the major racial and ethnic groups including whites, African Americans, Hispanics, Asian/Pacific Islanders, and Native Americans/Alaska Natives. For example, while rates of diabetes are disproportionately high among African Americans, American Indians, and Hispanics, the prevalence of diabetes among Asians is less than that for whites (National Center for Health Statistics, 2001). There also can be considerable variation within racial and ethnic subgroups. For example, although Hispanics experience lower overall mortality rates than whites, Puerto Ricans have higher infant mortality rates than whites (National Center for Health Statistics, 2000). Some racial and ethnic subgroups have increased burdens of specific diseases. For instance, Vietnamese American women have cervical cancer mortality rates many times higher than those for other Asian and white women (IOM, 1999). At present, the sources of such disparities remain unclear, but a wide range of explanatory factors have been suggested, including sociocultural, socioeconomic, behavioral, and biological risk factors, and environmental living conditions (Robert and House, 2000; Fremont and Bird, 2000; Williams, 1999). For example, minority populations as a whole tend to have lower socioeconomic status (SES) than other groups, and low SES is associated with poorer health, independent of race or ethnicity (Gornick, 2002). It is also generally agreed that differences in access to care, including preventive services, and racial and ethnic differences in the quality of care obtained contribute to observed disparities in health. In some minority groups and subgroups the prevalence of various conditions is especially high. Thus, the benefits of improved care for these groups may be substantially more than for others. The challenge of understanding variations in health between and among racial and ethnic groups is further heightened as more Americans are of mixed racial and ethnic backgrounds. Although only a small proportion of respondents identified themselves as belonging to more than one racial and ethnic group on the latest census, the number of individuals in this group is expected to increase. The Office of Management and Budget (OMB, 1977) has issued guidance and developed a way to bridge the changes that should help examinations of changes over time. THE ROLE OF RACIAL AND ETHNIC DATA IN SUPPORTING THE ESSENTIAL FUNCTIONS OF THE HEALTH CARE SYSTEM The health system serves many important functions, but for the purposes of this paper we focus on three, with a particular emphasis on the last: ensuring the health of the population, ensuring equitable access to care, and ensuring quality of care. Admittedly, the system does not perform

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Eliminating Health Disparities: Measurement and Data Needs optimally in any of these areas, but it performs especially poorly in each of these areas for minority racial and ethnic populations. Data on race and ethnicity are therefore essential for improving performance for each of these functions. Documenting the extent and types of problems and identifying populations at particular risk is a crucial first step to improving performance. When available, these data convey critical information to both providers and policymakers. Ensuring the Health of the Population Identifying Problems The ability to provide consistent and reliable epidemiological data on the incidence and prevalence of various health conditions and related risk factors among different racial and ethnic populations is essential to ensuring the health of the population. It also supports the rationale for allocating health care resources and developing appropriate public health interventions. For example, examination of data on race and ethnicity revealed that rates of HIV infection were rising more rapidly among African Americans and Hispanics than in any other racial and ethnic group (Shapiro et al., 1999). Targeting Risk Factors Risks at the individual or community level such as smoking, unsafe sexual practices, or environmental exposures are irrefutable contributors to poor health outcomes. Racial and ethnic data in federally conducted health surveys such as the CDC Behavioral Risk Factor Surveillance System (BRFSS) and others enable public health officials to better characterize the distribution of such risk factors among different racial and ethnic groups and identify emerging problems. Access to Care Access to care is a prerequisite for entering and staying in the health care system. Available racial and ethnic data have been used to document important differences in access between racial and ethnic groups. For example, Hispanics are substantially more likely to be uninsured than whites, and African Americans are more likely to have public insurance than whites (Collins, Hall, and Neuhaus, 1999; Hoffman and Pohl, 2000). Among blacks and whites, rates of insurance were relatively constant during the 2 decades between 1977 and 1996, but during the same period the proportion of Hispanics who were uninsured increased substantially (Weinick, Zuvekas, and Cohen, 2000).

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Eliminating Health Disparities: Measurement and Data Needs Even when minority individuals have health insurance, they are more likely to experience barriers to care and are less likely to utilize certain types of services. For example, minority patients are less likely to report having a regular source of care (Collins, Tenney, and Hughes, 2002; Doty and Ives, 2002). Conversely, they are more likely to use the emergency room or to be hospitalized for ambulatory care-sensitive conditions such as congestive heart failure (IOM, 2003). Such utilization may reflect poorer care. Finally, because minority patients overall tend to have greater actual need for services, apparently equivalent care between racial and ethnic groups may signify underutilization by minority patients if case-mix issues are not taken into account (Lurie, 2002). Quality of Care The IOM report Crossing the Quality Chasm highlighted considerable gaps between current standards of care and the quality of care that patients actually receive. These gaps were particularly pronounced for racial and ethnic minorities. Many observers now conclude that eliminating racial and ethnic disparities is a core issue in improving quality of care (IOM, 2001). Numerous studies have documented racial and ethnic disparities in care (see IOM, 2003, for an exhaustive review). For example, using RAND/ UCLA appropriateness criteria, Laouri and colleagues (1997) showed that African Americans were only half as likely to undergo a needed coronary artery bypass graft and one-fifth as likely to undergo a percutanerous transluminal coronary angioplasty. Similarly, Ayanian and colleagues (1999) have shown that African Americans with end stage renal disease were considerably less likely to receive a referral for a renal transplant than comparable whites, even when patient preference was taken into account. Several recent studies have also shown racial and ethnic disparities in performance on Health Employer Data and Information Set (HEDIS) process measures, which are widely used in managed-care settings (Schneider, Zaslavsky, and Epstein, 2002; Fremont et al., 2002; Virnig et al., 2002). All three studies showed that black patients were substantially less likely than whites to receive indicated care such as an annual hemoglobin A1c test in diabetics. Virnig and colleagues (2002) also documented disparities between other racial and ethnic groups. Racial and SES disparities were also observed for several intermediate outcome measures including control of lipids and hemoglobin A1c in diabetics and blood pressure in hypertensive enrollees (Fremont et al., 2002). An increasing number of studies have assessed disparities in quality of care by using surveys or qualitative methods to elicit patient reports about their care. Such studies have documented significant differences in how patients from different racial and ethnic groups experience the care they

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Eliminating Health Disparities: Measurement and Data Needs receive and the kinds of barriers they encounter in accessing it. For example, in a recent Commonwealth Fund study, Asian Americans were least likely to report that their doctors understand their backgrounds and values (48 percent) compared to Hispanics (61 percent), African Americans (57 percent) and whites (58 percent) (Collins, Tenney, and Hughes, 2002). Asians were also least likely to report a great deal of confidence in their doctor. Hispanics, regardless of language skills, were more likely than other patients to report having difficulty communicating with and understanding their doctor (33 percent Hispanics and 16 percent whites) (Doty and Ives, 2002). African Americans were nearly twice as likely as whites to report being treated with disrespect during a recent visit (Collins, Tenney, and Hughes, 2002). These sorts of findings have stimulated public and private provider efforts to ensure culturally competent care and to provide language-appropriate services for their patients (IOM, 2002). USING DATA ON RACE AND ETHNICITY TO REDUCE DISPARITIES IN HEALTH AND HEALTH CARE The discussion above has focused on ways in which available racial and ethnic data can support essential functions of the health system by identifying populations at risk for particular conditions or with special needs, and documenting disparities in access to and quality of specific types of care. However, simply collecting racial and ethnic data and describing disparities in health and health care does not automatically lead to reductions in disparities. This information needs to be used in ways that stimulate development and implementation of efforts to effectively eliminate disparities. Thus, in this section we highlight some potential uses of racial and ethnic data to promote improved health and health care in minority populations. Refining Public Health Initiatives and Enhancing Access to Care Knowing which racial and ethnic population groups are most at risk can help more effectively target public health efforts. For example, documentation of high rates of HIV among African Americans and Hispanics has helped stimulate the development of federal programs that target minority groups at high risk, particularly those in low-income communities (Shapiro et al., 1999). In many instances public health efforts, such as educational campaigns, may not work equally well across different racial and ethnic groups. For instance, an apparently successful mass media campaign to educate the public about the importance of placing infants on their side or back to reduce risk of sudden infant death syndrome was subsequently shown to be far less effective among black mothers than white mothers (Malloy, 1998) largely because the educational messages were not

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Eliminating Health Disparities: Measurement and Data Needs appropriately focused on black women. Such evaluations require racial and ethnic data and are essential to refining public health efforts. At the very least they can reinforce the need to tailor public health efforts to meet the needs of different racial and ethnic groups. Racial and ethnic data can also be used to facilitate programs designed to improve access to care. For example, African Americans and Hispanics with cancer typically face substantial barriers to obtaining and completing treatment. Their rates of participation in clinical trials involving state-of-the-art treatment protocols are especially low (National Cancer Institute, 2003). Such data have prompted the development of “Patient Navigator” programs in which culturally and language-appropriate individuals with special training are matched with patients at risk to educate them about screening and prevention measures or guide them through the treatment process once they are diagnosed. Although further evaluation is needed, such programs show promise for reducing disparities in access to cancer care and outcomes (U.S. House of Representatives, 2002). Improving the Quality of Care An important national strategy for improving quality of care has been the promotion of accountability for quality (IOM, 2001; Berwick, 1998). Measurement and reporting are essential components of this strategy. For example, widely used quality monitoring programs such as the National Committee for Quality Assurance (NCQA) Health Employer Data and Information Set (HEDIS) have been shown to improve performance on key quality measures among participating health plans (NCQA, 1999). Currently, however, most health plan quality improvement efforts, including the NCQA program, are not focused on reducing racial and ethnic disparities in care (Fiscella et al., 2000). Several studies have shown that managed care alone is an insufficient mechanism to eliminate disparities in care. Thus, routine reporting of widely used quality measures separately by race and ethnicity provides an excellent opportunity to identify disparities within health plans and to apply quality improvement principles to reduce them. Performance measures are typically reported as averages across all eligible patients but are not broken down by racial and ethnic group. Consequently, important disparities within and between plans are not recognized or addressed. A number of recent studies have shown that it is feasible to obtain and use information on enrollee race and ethnicity with selected HEDIS measures to detect racial and ethnic and socioeconomic disparities (Schneider, Zaslavsky, and Epstein, 2002; Virnig et al., 2002; Fremont et al., 2002; Nerenz et al., 2002). Since the publication of the IOM’s report To Err Is Human (2000), reducing medical errors and improving patient safety have emerged as ma-

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Eliminating Health Disparities: Measurement and Data Needs jor areas of focus for policymakers, providers, and researchers. Though research in this area is still new, early studies show differences in the extent and nature of medical errors and problems with safety experienced among patients from different racial and ethnic groups (Burstin, 1993). Thus, just as with quality of care in general, efforts to identify and eliminate problems with patient safety can be enhanced by taking into account possible racial and ethnic differences of patients at risk. Stimulating Value Purchasing As reflected in the forthcoming National Quality Report, many policy makers believe that encouraging consumers and employers to base purchasing decisions on quality of care will ultimately lead to better quality and lower costs. Experts believe that information about quality is most useful to consumers when the information pertains to care received by people like themselves. Thus, reporting measures of care for specific racial and ethnic subgroups can strengthen the ability of consumers—both individuals and employers who purchase care on their behalf—to rationally choose health care providers. In addition, individuals and advocacy groups can use racial and ethnic data in negotiations with providers, health departments, and elected officials to hold them accountable for results and to develop additional programs and policies to address disparities. In this vein, such data may ultimately have uses in domains outside the personal delivery system. For example, in some communities, examining data on diabetes prevalence and outcomes for blacks and Hispanics has revealed the need for more community-based opportunities for safe exercise. Advocacy groups have used this information in working with local officials to build walking paths and recreational facilities. Similarly, employers and other purchasers can use racial and ethnic data to ensure that they are getting good value for their premiums. For example, after learning of the IOM report on disparities, the benefits specialist at a large national employer became concerned that her workforce, which is largely minority, may not be receiving care of appropriate quality (or quantity). She now systematically queries plans during renewal negotiations and monitors actions they are taking to reduce disparities. Understanding the Underlying Causes of Disparities and What to Do about Them The routine collection of data on patient race and ethnicity can also help researchers disentangle factors underlying health care disparities (IOM, 2003). Recent research efforts such as the AHRQ-sponsored Excellence

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Eliminating Health Disparities: Measurement and Data Needs Centers to Eliminate Ethnic/Racial Disparities (EXCEED) are beginning to clarify causal factors and effective interventions. However, the development of such knowledge is likely to proceed slowly without the availability of additional racial and ethnic data with which to engage a wider group of researchers. (Also see the discussion of language preference and socioeconomic factors below.) Since causal factors and effective interventions may vary across settings, the availability of such data will also be crucial to enabling quality improvement teams to identify specific factors underlying disparities and appropriate interventions in their respective organizations. Provider organizations can enhance their own efforts by sharing best practices for reducing disparities. As previously discussed, access to care and an array of community-level factors also have important influences on health. Expanding the availability of racial and ethnic data is a clear prerequisite both to a better understanding of how such factors affect different groups and to the development and evaluations of interventions to address them. Ensuring Compliance with Civil Rights Law Routine monitoring of access, use of services, and key processes and outcomes of care by race and ethnicity is essential to ensuring compliance with civil rights laws and detecting evidence of discrimination. Whether these practices are intentional or not, whether they are at the level of an individual practitioner or due to system-level problems, they can produce harmful outcomes (IOM, 2003). Title VI of the Civil Rights Act of 1964 (U.S. Office for Civil Rights, 2000) and related statutes were intended to ensure that patients from different racial and ethnic subgroups have equal access to quality care. However, enforcement of these basic rights by the Office for Civil Rights and other entities is made far more difficult without standardized, readily available data on race and ethnicity to monitor the care that different subgroups receive (Smith, 1999). LANGUAGE PREFERENCE AND SOCIOECONOMIC FACTORS Categorizing individuals in racial and ethnic categories has helped to identify health disparities. However, many anthropologists and other social scientists view these categories as relatively crude and inaccurate tools for understanding differences between groups (IOM, 2002). Indeed, substantial variation in important characteristics between and within racial and ethnic groups may have as much or more effect on health and the quality of care than race or ethnicity per se. Two such characteristics are language preference and SES.

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Eliminating Health Disparities: Measurement and Data Needs Language Preference Many individuals experience language barriers ranging from no English proficiency to limited proficiency in speaking, reading, or comprehending English (IOM, 2003). For example, it is estimated that more than one in four Asian/Pacific Islanders and Hispanics live in households where no adolescent or adult speaks English “very well” (U.S. Bureau of the Census, 1990). A recent Commonwealth Fund report documents the over-whelming barriers to care faced by non-English speaking Hispanics (Collins, Tenney, and Hughes, 2002). Language barriers often vary considerably by country of origin within racial and ethnic groups. For instance, whereas less than 2 percent of Hawaiians and 15 percent of Japanese live in households where English is not spoken well, 26–42 percent of Thais, Chinese, Koreans, and Vietnamese, and more than half of Laotians, Cambodians, and Hmong live in such households (U.S. Bureau of the Census, 1990; IOM, 2003). A number of federal regulations encourage the use of interpreters in the health care setting (U.S. DHHS, 2000a; Office for Civil Rights, 2000; Perot and Youdelman, 2001). However, language barriers continue to pose significant problems for both patients and providers. In one recent survey, 43 percent of the Hispanics living in households where Spanish was the primary language reported having difficulty communicating with and understanding their doctor (Doty and Ives, 2002). Only half of the patients who said they needed an interpreter when visiting a doctor said they always or usually got one. In many instances (43 percent), when an interpreter was available, he or she was a member of the patient’s family; rarely (1 percent) was the interpreter professionally trained (Collins, Tenney, Hughes, 2002). Many providers are acutely aware of how language barriers and other cultural differences constrain their ability to provide effective care (IOM, 2002). In a survey of Los Angeles providers who participated in care programs sponsored by the county health authority, more than 70 percent felt that language and culture are important to the care of their patients and more than half believed that their patients did not adhere to medical treatments as a result of linguistic or cultural barriers (Cho and Solis, 2001). In sum, routine collection of information on patients’ primary language or language preference is also an essential step in identifying patient subgroups where language barriers may be present and in developing culturally competent care (Betancourt, Green, and Carillo, 2002; IOM, 2002). Collecting information on a patient’s primary language is legal and authorized under Title VI of the Civil Rights Act of 1964, though few federal statutes require it (Perot and Youdelman, 2001). Some health plans already routinely collect such data. For example, Kaiser Permanente in northern California has made cultural competency a priority for several years. As part of this effort, those using Kaiser data systems for patient care

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Eliminating Health Disparities: Measurement and Data Needs (such as making an appointment) cannot do so unless the language preference field has been filled in: the system thus prompts the user to indicate the patient’s language preference and the need for translator services. Information sheets in the patient’s native language describing his/her condition or treatment can often also be provided. Socioeconomic Status SES is a multidimensional concept that reflects an individual’s access to material and social resources and assets including income, wealth, and educational credentials, as well as an individual’s prestige or status in society as reflected in access to consumption of goods, services, and knowledge (Krieger, Williams, and Moss, 1997). SES is measured in a variety of ways. Income and/or education measures are the most common in the United States; measures such as occupational class may be used in Europe. Regardless of how SES is measured, numerous studies have demonstrated a consistent socioeconomic gradient in which health status, and often the quality of care received, decreases with declining SES. The gradient generally persists even when individual risk factors, including being in a minority racial or ethnic group, are taken into account (Robert and House, 2000; Kaplan, Everson, and Lynch, 2000; Gornick, 2000). Although race or ethnicity and SES may exert some independent effects, the two are often interrelated; hence, information on SES can help highlight the underlying sources of disparities in health status and care between and among different racial and ethnic groups (IOM, 2002; Wong et al., 2002). For example, education and income levels vary substantially among minority racial and ethnic groups. Among adults, Asian Americans/ Pacific Islanders were most likely to have at least a high school education (83 percent), followed by African Americans (72 percent), and Hispanics (53 percent) (Bennet and Martin, 1995). Asian Americans/Pacific Islanders also had the highest median income ($55,500); that of African Americans and Hispanics was substantially lower ($33,400 and $30,400, respectively) (U.S. Bureau of the Census, 2001). However, although Asian Americans/ Pacific Islanders tend to have the highest education and income levels overall, SES varies considerably between Asian subgroups. Although there is wide consensus that SES plays a major role in health disparities and should be taken into account whenever possible, exploring the links between SES and health disparities is a relatively new field of research. Many key issues remain unresolved—in particular the best ways to measure SES (Gornick, 2000). Consider education, one of the most widely used measures. It is popular because it is easy to measure and collect, it applies to persons who are not active in the labor force (e.g., unemployed, retired), and it is relatively stable over an adult’s life span, regardless of

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Eliminating Health Disparities: Measurement and Data Needs ened providers’ concerns that routine collection of racial and ethnic data may violate civil rights laws. Indeed, a widespread misconception held by many providers and health plans is that collecting racial and ethnic data violates federal and state civil rights law (Nerenz et al., 2002; IOM, 2003; Bierman et al., 2002). A recent review of federal laws revealed no laws that prohibit the collection of racial and ethnic data (including language preference). Rather, there are numerous laws and program-specific statutes already in effect or scheduled to go into effect within the next several years that encourage or require the collection of racial and ethnic data (Perot and Youdelman, 2001). Similarly, reviews of state laws have shown that only four states have any sort of restrictions on collecting such racial or ethnic data, and these varied as to whether the restriction applied before, during, or after enrollment. At least one state required health plans to maintain information on enrollee’s race and ethnicity (Perez and Satcher, 2001; Bierman et al., 2002). Previous reviews have not explicitly addressed the legality of collecting data other than race and ethnicity that relate to an individual’s socioeconomic status. However, many believe that the collection of such data for the purposes of monitoring and improving quality is consistent with civil rights laws designed to protect patients from differential treatment or discrimination based on personal characteristics. Some plans and providers report concerns that the routine collection of racial and ethnic data and the reporting of various performance measures by race and ethnicity place them at substantially increased risk of class action lawsuits were disparities in their plans to become apparent. While this is a significant concern, it is not clear that the threat of litigation is greater when such data are collected than when they are not. Two examples illustrate the ambivalence of health plans about the risks of collecting data on race and ethnicity. One health plan executive, in explaining why his plan was “a little gun shy,” described a case in which an individual had voluntarily provided racial data at the time of application for an individually underwritten policy. Although the insurer is adamant that the individual policy was denied on the basis of multiple preexisting conditions, the individual sued the plan on the basis of racial discrimination. By contrast, an executive of a different health plan expressed his firm conviction that the act of examining quality for different racial and ethnic groups was evidence of his company’s taking action to prevent discrimination, and would “probably immunize” the company against a successful civil rights lawsuit. Although some potential legal exposure could be avoided by limiting data collection until after a coverage decision has been made, there is probably no way to fully guard against legal action.

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Eliminating Health Disparities: Measurement and Data Needs Burden of Collecting Data In addition to concerns about patient privacy and civil rights, the entities that collect and analyze information about racial, ethnic, and SES characteristics of persons they serve may face significant costs (Nerenz et al., 2002). Adding data elements to large administrative data sets maintained by health plans can be expensive in terms of time and resources spent reconfiguring files and forms. In addition, depending on how data are obtained (e.g., at time of enrollment or visit, surveys of existing members), plans may need to devote substantial resources to actually filling in the data fields. Once data are collected, there can be additional costs associated with analyzing and reporting measures of care and performance stratified by race, ethnicity, and SES. For example, NCQA HEDIS measures that require chart abstraction can be expensive for plans. To keep costs to a minimum the NCQA requires plans to obtain data only for a sample of approximately 400 enrollees. However, these sample sizes are not sufficient to conduct meaningful analyses for minority subgroups. Consequently, plans would need to oversample these groups (or sample larger overall populations) in order to have sufficient power to detect racial, ethnic, or SES differences. Small sample size is less of a problem for HEDIS measures that can be calculated solely from administrative data and that focus on care for common conditions such as hemoglobin A1c checks in diabetics (Fremont et al., 2002). Health plans have already invested substantial time and resources to reconfigure their data systems to meet HIPAA requirements. Unfortunately, although race and ethnicity are included as optional “situational” data fields for some types of patients and standardized forms, they are listed as “not used” or are not allowed on others. Consequently, routine collection of standardized racial and ethnic data for many types of enrollees is hindered rather than facilitated under current HIPAA rules (see the Bocchino paper in Appendix G for a detailed discussion). Finally, in addition to the risk of litigation because of perceived misuse of racial or ethnic data (discussed above), some plans fear suffering business losses if minority populations they serve view the collection of such data as an effort to ration care. Such populations may also be reluctant to enroll in plans if publicly released “report cards” suggest poorer health and outcomes among minority or low-income groups, although the differences may reflect case mix and the effects of poverty rather than lower-quality care. Without either statistical adjustments for case mix differences, which are extremely difficult to present in report card format, or education of consumers, these report cards could unfairly hurt plans’ efforts to increase market share in minority and low-income populations (IOM, 2003; Bierman et al., 2002).

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Eliminating Health Disparities: Measurement and Data Needs EMERGING LEADERSHIP Although considerable challenges to collecting racial and ethnic data remain, we believe there are reasons for optimism. One such reason is the emergence of innovative initiatives in the private sector. For example, Aetna, a large national insurer, has undertaken an extensive minority health initiative. There appear to be two related reasons for the company’s decision. First, after examining U.S. demographic trends, Aetna has concluded that an increasing proportion of its members will be minorities and, therefore, both wants to and has to measure and ensure high-quality care for these members. Second, Aetna’s leadership has articulated a moral imperative to act on the IOM report (2003), and to move toward ensuring that care for its members is not different on the basis of race or ethnicity. Aetna has adopted a multipronged approach. First, it has begun collecting data on the race and ethnicity of its members (at or after the time of enrollment, in all states in which it is legal to do so) with the goal of using quality of care algorithms to measure quality for different racial and ethnic groups. Second, it is strengthening efforts to ensure that minority providers are part of its provider networks. Finally, the company is altering its marketing strategies, using a more diverse workforce, and stressing its commitment to cultural competence. Although much of this effort is relatively recent, the insurer’s staff report little, if any, consumer resistance to voluntarily providing racial and ethnic information. In fact, they have received significant positive feedback and expressions of appreciation from consumers. When asked about potential concerns about respondent burden in providing racial and ethnic data, staff stressed that such information was obtained during routine assessments of health status and needs, and that the major burden was “having to change all of our forms” and the software programs to read them. Several other plans have reported collaboration with their state Medicaid agencies to examine racial and ethnic data for their Medicaid enrollees. The federal Medicaid Managed Care regulations encourage data sharing, and these plans report that use of these data has already led to quality improvements, particularly in the areas of diabetes and depression care. CONCLUSIONS Improving and broadening the collection of racial and ethnic data are critical to fulfilling core goals of the U.S. health system. While such efforts will pose significant challenges and entail real costs, ignoring this important need may ultimately be higher than definitively solving the problem. There is growing consensus that racial and ethnic disparities in health care reflect serious problems in the overall quality of care that may affect

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Eliminating Health Disparities: Measurement and Data Needs any patient. In this respect, failure to collect racial and ethnic data will hamper much-needed efforts to improve the quality of care, and exacerbate the erosion of trust in the consistency and quality of U.S. health care (IOM, 2003). Without accurate information on the racial and ethnic characteristics of populations served, public health efforts and associated resources are likely to be poorly targeted and may miss large segments of the populations most in need. Poorly managed chronic conditions or undiagnosed disease can result in more severe disease, worse outcomes, and higher health costs. Since minority populations make up an increasing proportion of the workforce, their unmet health needs can substantially reduce workers’ quality of life and productivity, which in turn can affect the economy at all levels. Minority populations constitute an increasingly large segment of the health care market; thus lack of data about their care will undermine efforts to support consumer choice and stimulate market forces. The inability to routinely monitor the health needs and quality of care received by minority populations violates existing federal statutes and contradicts fundamental values of equity and fairness in this country. DHHS has already been sued for not requiring providers to collect and report uniform data on race and ethnicity. Without further initiatives to collect these data, additional lawsuits directed at government agencies and private health plans are likely. Collection of reliable data on race and ethnicity is feasible, and there are good examples of its current collection and use in both the public and private sectors. The federal government can take a leadership role by ensuring the universal, ongoing collection of data in the Medicare program, and can stimulate its use in Medicaid programs and the private sector. Although private-sector data collection will likely remain voluntary, both insurers and employers have already demonstrated leadership in making the case for such data, and will hopefully set new industry standards. We anticipate that their efforts will be further encouraged by federal action in the Medicare program and increased consumer demand for such information. The ongoing debate about the uses of and appropriate measures and methods for collecting SES and language preference data is encouraging. As has been the case in the area of racial and ethnic data collection, voluntary pilot efforts will likely inform the feasibility and usefulness of such data collection. RECOMMENDATIONS In the long run, the completeness and accuracy of data on race, eth-

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Eliminating Health Disparities: Measurement and Data Needs nicity, language preference, and SES will improve only if this information is collected for meaningful and actionable purposes. To that end, we offer recommendations in the following areas. Uniform Standards and Training Standards must be developed to ensure uniform collection of data at federal, state, and local levels. In addition, there is a critical need for guidance about how these data should be collected in different settings (e.g., when, how, by whom) and training for frontline personnel in how best to do this. Recommendation A1: Create a centralized body that can provide guidance and oversight regarding standards for and collection of data. This body should propose a set of incentives for the collection of such data, as well as penalties for failing to collect data in ways that meet a minimal standard. Recommendation A2: Provide an adequate budget to ensure the availability of such training and data collection at state and local levels. Public Health The collection and use of data are critical to the functioning of the public health infrastructure, and provide a basis for ensuring that public health systems are accountable to different populations and communities. Recommendation B3: Ensure sampling of all major racial and ethnic groups for all major epidemiologic and health status data collection efforts funded by the federal government, including those that provide important subnational data. These include, but are not limited to the Behavioral Risk Factor Surveillance System (BRFSS), National Health Interview Survey (NHIS), Medicare Expenditure Panel Survey (MEPS), Healthcare Cost and Utilization Project (HCUP), National Health and Nutrition Examination Survey (NHANES), Consumer Assessment of Health Plans Survey (CAHPS), Medicare, Medicaid, and federal employees. This will require a commitment to oversampling smaller populations in each of these surveys, and an appropriate budget for doing so. Recommendation B4: Ensure the collection of data on race and ethnicity for all vital records.

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Eliminating Health Disparities: Measurement and Data Needs Health Care Delivery System Data collection in the health care system itself provides the basis for assessing disparities in care and for benchmarking progress. While these recommendations apply to Medicare, Medicaid, and the private sector, they may also be relevant to the VA and Department of Defense health care systems. Medicare Recommendation C5: Update and ensure the accuracy of data on race and ethnicity for current and future Medicare beneficiaries. This can be accomplished in a variety of ways, including geocoding and merging other self-reported racial and ethnic data, such as from CAHPS, into the EBD. Continue progress on identification of Native Americans. Extend assignment of Native American status beyond those living on reservations to urban dwellers. Eliminate dependency on the Social Security Administration for racial and ethnic data, or create a permanent fix to the problem of incomplete and inaccurate information. Current estimates of how much CMS spends on this range from a few million to several hundred million dollars annually. It is thus likely that redeployment of these funds would be sufficient to implement a permanent solution. In addition, steps need to be taken now to address the fact that data on race and ethnicity have not been collected in the social security card application process since 1988. Tie elimination of disparities to Medicare performance. The Medicare program should monitor quality of care for different racial and ethnic groups by plan, by hospital, and by state. Quality improvement organizations may be one vehicle to do this. In addition, CMS should use its purchasing power in working with Medicare+Choice plans to develop and implement data collection plans and initiatives to eliminate disparities. CMS should also consider whether the collection and reporting of accurate information about race and ethnicity should be a condition of hospital and nursing home participation in the Medicare program. Medicaid Recommendation C6: Extend the requirements for reporting performance for racial and ethnic groups in SCHIP to the Medicaid program. Make racial and ethnic data collection based on self-report (when feasible) mandatory for all new Medicaid beneficiaries, and update data on

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Eliminating Health Disparities: Measurement and Data Needs current beneficiaries as eligibility redeterminations are made. Require states to report these data to CMS and enforce this requirement. Provide Medicaid programs with sufficient financial support to accomplish this, and consider using financial incentives to meet this goal (such as tying a portion of the federal contribution to data completeness and accuracy). Private Sector The capacity of health plans and insurers, health systems, employers, providers, and consumers to use data on race and ethnicity to improve health and health care depends on availability of such data. The IOM report Unequal Treatment (2003) has provided further evidence for a business case for such data. Recommendation D7: The Department of Health and Human Services, standard-setting organizations, providers and employers should take steps to modify HIPAA standards to facilitate the reporting of racial and ethnic data. This could be accomplished if DHHS and others clearly defined the business case for reporting these data. Change the accompanying guide that describes the racial and ethnic data elements for ANSI X12N 837 from a listing of “not used” to “situational” in order to facilitate reporting of racial and ethnic data. Recommendation D8: Develop educational programs to stimulate consumerism through information provided to employers and individuals about how they can use such data. Recommendation D9: The Department of Health and Human Services, the American Association of Health Plans, employer groups, and others should conduct education and outreach to health plans, purchasers, and employers regarding the legality of collecting data on race and ethnicity. Research In addition to research on disparities, expand research on data, such as types of data, relationships between variables such as race or ethnicity, SES, and education, and methods for oversampling smaller populations. Recommendation E10: The Department of Health and Human Services should provide funding to expand research on ways to collect accurate data, including geocoding.

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Eliminating Health Disparities: Measurement and Data Needs Recommendation E11: The Department of Health and Human Services should provide funding to expand on best ways to make self-report acceptable, training to collect data, and techniques for oversampling. Recommendation E12: The Department of Health and Human Services and the Department of Justice should fund research to identify mechanisms that balance personal protection with protections for plans and others that use data for appropriate purposes as well as mechanisms to protect against misuse of data or detect possible redlining. REFERENCES Abrahamse, A.F., P.A. Morrison, and N.M. Bolton 1994 Surname analysis for estimating local concentration of Hispanic and Asians. Population Research and Policy Review 13:383-398. Arday, S.L., D.R. Arday, S. Monroe, and J. Zhang 2000 HCFA’s racial and ethnic data: Current accuracy and recent improvements. Health Care Financial Review 21:107-116. Ayanian, J.Z., P.D. Cleary, J.S. Weissman, and A.M. Epstein 1999 The effect of racial differences on patients’ preferences in access to renal transplantation. New England Journal of Medicine 341:1661-1669. Bennet, C.E., and B. Martin 1995 The Asian and Pacific Islander population. In Population Profile of the United States: 1995. Washington, DC: U.S. Government Printing Office. Berwick, D.M. 1998 Crossing the boundary: Changing mental models in the service of improvement. International Journal of Quality Health Care 10:435-441. Betancourt, J.R., A.R. Green, and J.E. Carrillo 2002 Cultural Competence in Health Care: Emerging Frameworks and Practical Approaches. New York: The Commonwealth Fund. Bierman, A.S., N. Lurie, K.S. Collins, and J.M. Eisenberg 2002 Addressing racial and ethnic barriers to effective health care: The need for better data. Health Affairs (Millwood) 21:91-102. Burstin, H.R. 1993 Do the poor sue more? A case-control study of malpractice claims and socioeconomic status. Journal of the American Medical Association. 270:1697-1701. Cho, J., and M. Solis 2001 Healthy Families Culture and Linguistics Resources Survey: A Physician Perspective on Their Diverse Member Population. Los Angeles: LA Care Health Plan. Collins, K.S., A. Hall, and C. Neuhaus 1999 U.S. Minority Health: A Chartbook. New York: The Commonwealth Fund. Collins, K.S., K. Tenney, and D.L. Hughes 2002 Quality of Health Care for African Americans: Findings from The Commonwealth Fund 2001 Health Care Quality Survey. New York: The Commonwealth Fund. Doty, M.M., and B.L. Ives 2002 Quality of Health Care for Hispanic Populations: Findings from The Commonwealth Fund 2001 Health Care Quality Survey. New York: The Commonwealth Fund.

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Eliminating Health Disparities: Measurement and Data Needs Fiscella, K., P. Franks, M.R. Gold, and C.M. Clancy 2000 Inequality in quality: Addressing socioeconomic, racial, and ethnic disparities in health care. Journal of the American Medical Association 283:2579-2584. Fremont, A.M., and C.E. Bird 2000 Social and psychological factors, physiological processes, and physical health. In Handbook of Medical Sociology, C.E. Bird, P.C. Conrad, and A.M. Fremont, eds. Englewood Cliffs, NJ: Prentice Hall. Fremont, A.M., S. Wickstrom, C.E. Bird et al. 2002 Socioeconomic, Racial/Ethnic, and Gender Differences in Quality and Outcomes of Care as it Relates to Cardiovascular Disease. Rockville, MD: Agency for Healthcare Research and Quality. Gornick, M.E. 2002 Measuring the effects of socioeconomic status on health care. Pp. 45-74 in Guidance for the National Healthcare Disparities Report, E.K. Swift, ed. Washington, DC: National Academy Press. 2000 Disparities in Medicare services: Potential causes, plausible explanations, and recommendations. Health Care Financial Review 21:23-43. Hoffman, C., and M. Pohl 2000 Health Insurance Coverage in America: 1999 Data Update. Washington, DC: The Kaiser Commission on Medicaid and the Uninsured. Institute of Medicine 2003 Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. B.D. Smedley, A.Y. Stith, and A.R. Nelson, editors. Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care. Board on Health Sciences Policy. Washington, DC: The National Academies Press. 2002 Speaking of Health: Assessing Health Communication Strategies for Diverse Populations. Committee on Communication for Behavior Change in the 21st Century: Improving the Health of Diverse Populations, Board on Neuroscience and Behavioral Health. Washington, DC: The National Academies Press. 2001 Crossing the Quality Chasm: A New Health System for the 21st Century. Committee on Quality of Health Care in America. Washington, DC: National Academy Press. 2000 To Err Is Human: Building a Safer Health System. Linda T. Kohn, Janet M. Corrigan, and Molla S. Donaldson, Editors. Committee on Quality of Health Care in America. Washington, DC: National Academy Press. 1999 The Unequal Burden of Cancer: An Assessment of NIH Programs and Research forMinorities and the Medically Underserved. M.A. Hayes and B.D. Smedley, eds. Washington, DC: National Academy Press. Kaplan, G.A., S.A. Everson, and J.W Lynch 2000 The contribution of social and behavioral research to an understanding of the distribution of disease: A multilevel approach . In Promoting Health: Intervention Strategies from Social and Behavioral Research, B.D. Smedley and S.L. Syme, eds. Washington, DC: National Academy Press. Krieger, N., D.R. Williams, and N.E. Moss 1997 Measuring social class in U.S. public health research: Concepts, methodologies, and guidelines. Annual Review of Public Health 18:341-378. Laouri M., R.L. Kravitz, W.J. French et al. 1997 Underuse of coronary revascularization procedures: Application of a clinical method. Journal of American College of Cardiologists 29:891-897.

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Eliminating Health Disparities: Measurement and Data Needs Lauderdale, D.S., and J. Goldberg 1996 The expanded racial and ethnic codes in the Medicare data files: Their completeness of coverage and accuracy. American Journal of Public Health 86:712-716. Lauderdale, D.S., and B. Kestenbaum 2000 Asian American ethnic identification by surname. Population Research and Policy Review 19:283-300. Lurie, N. 2002 Measuring disparities in access to care. Pp. 99-147 in Institute of Medicine, Guidance for the National Healthcare Disparities Report, E.K. Swift, ed. Washington, DC: The National Academies Press. Malloy, M.H. 1998 Effectively delivering the message on infant sleep position. Journal of the American Medical Association 280:373-374. National Cancer Institute 2003 Plans and Priorities for Cancer Research. Bethesda, MD: National Cancer Institute. National Center for Health Statistics 2001 Health, United States, 2000, with Socioeconomic Status and Health Chartbook. Hyattsville, MD: National Center for Health Statistics. 2000 Health, United States, 1999, with Socioeconomic Status and Health Chartbook. Hyattsville, MD: National Center for Health Statistics. National Committee for Quality Assurance 1999 HEDIS 2000: Narrative—What’s in it and Why it Matters. Washington, DC: National Committee for Quality Assurance. Nerenz, D.R., V.L. Bonham, R. Green-Weir, C. Joseph, and M. Gunter 2002 Eliminating racial/ethnic disparities in health care: Can health plans generate reports? Health Affairs (Millwood) 21:259-263. Office of Management and Budget 1977 Racial and Ethnic Standards for Federal Statistics and Administrative Reporting. (Statistical Directive 15). Washington, DC: Office of Management and Budget. Perez, T.E., and D. Satcher 2001 Letter to Dr. Bruce Zimmerman, President, American Diabetes Association, from T.E. Perez, Director, Office for Civil Rights, U.S. DHHS, and D. Satcher, Assistant Secretary for Health and Surgeon General, U.S. DHHS, January 19, 2001. Perot, R.T., and M. Youdelman 2001 Racial, Ethnic, and Primary Language Data Collection in the Health Care System: An Assessment of Federal Policies and Practices. New York: The Commonwealth Fund. Robert, S.A., and R. House 2000 Socioeconomic inequalities in health: An enduring social problem. In Handbook of Medical Sociology, C.E. Bird, P.C. Conrad, and A.M. Fremont, eds. Englewood Cliffs, NJ: Prentice Hall. Schneider, E.C., A.M. Zaslavsky, and A.M. Epstein 2002 Racial disparities in the quality of care for enrollees in medicare managed care. Journal of the American Medical Association 287:1288-1294. Shapiro, M.F., S.C. Morton, D.F. McCaffrey et al. 1999 Variations in the care of HIV-infected adults in the United States: Results from the HIV Cost and Services Utilization Study. Journal of the American Medical Association 281:2305-2315. Smith, D.B. 1999 Health Care Divided: Race and Healing a Nation. Ann Arbor: University of Michigan Press.

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Eliminating Health Disparities: Measurement and Data Needs U.S. Bureau of the Census 1991 Current Population. Washington DC: U.S. Bureau of the Census. 1990 1990 Census of Population: Asians and Pacific Islanders in the United States. Washington, DC: U.S. Bureau of the Census. U.S. Department of Health and Human Services 2000a Healthy People 2010: Understanding and Improving Health. Washington, DC: U.S. Government Printing Office. 2000b Healthy People 2010. Washington, DC: U.S. Department of Health and Human Services. U.S. Office for Civil Rights 2001 HHS Directory of Health and Human Services Data Resources. Washington, DC: U.S. Office for Civil Rights. 2000 Title VI of the Civil Rights Act of 1964: Policy Guidance on the Prohibition Against National Origin Discrimination as It Affects Persons with Limited English Proficiency. Washington, DC: U.S. Office for Civil Rights. U.S. House of Representatives 2002 H.R. 5187: Patient Navigator, Outreach, and Chronic Disease Prevention Act of 2002. 107th Congress, 2d session. Virnig, B.A., N. Lurie, Z. Huang, D. Musgrave, A.M. McBean, and B. Dowd 2002 Racial variation in quality of care among Medicare+Choice enrollees. Health Affairs (Millwood) 21(6):224-230. Weinick, R.M., S.H. Zuvekas, and J.W. Cohen 2000 Racial and ethnic differences in access to and use of health care services, 1977 to 1996. Medical Care Research Review 57:36-54. Wilhelm, S.M. 1987 Economic demise of blacks in America: A prelude to genocide? Journal of Black Studies 17:201-254. Williams, D.R. 1999 Race, socioeconomic status, and health. The added effects of racism and discrimination. Annals of the New York Academy of Sciences 896:173-188. Wong, M.D., M.F. Shapiro, W.J. Boscardin, and S.L. Ettner 2002 Contribution of major diseases to disparities in mortality. New England Journal of Medicine 347:1585-1592.