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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement 3 Defining Categorization Needs for Race and Ethnicity Data The collection of data in the Office of Management and Budget (OMB) race and Hispanic ethnicity categories is improving across a variety of health care entities, but all entities do not yet collect or report data using these categories. Moreover, disparities within the broad groups represented by these categories support the case for collection of granular ethnicity data beyond the OMB categories. Given variations in locally relevant populations, no single national set of additional ethnicity categories is best for all entities that collect these data. Collection of data in the OMB race and Hispanic ethnicity categories, supplemented by more granular ethnicity data, is recommended, with tailoring of the latter through locally relevant categories chosen from a standardized national set. In most cases, rolling up the data on granular ethnicities to the OMB categories will be possible, but it will be necessary to exercise care as there are certain ethnicities that do not correspond with any one race. However, when questions about race and granular ethnicity are both answered, rollup is not necessary. Collecting and maintaining demographic data in medical records and enrollment files allows for analyses stratified by race and ethnicity to identify needed improvements in health care, and for identification of individuals or population groups that might be the focus of interventions designed to address health care needs. The resultant analyses can be used, for example, to plan specific features of interventions (e.g., the use of culturally relevant content in outreach communications about preventive services) and to compare the quality of care being provided by various entities serving similar populations. The primary reason for standardizing categories for the variables of race and ethnicity is to enable consistent comparison or aggregation of the data across multiple entities (e.g., state-level analyses of providers under Medicaid or a health plan’s analysis of disparities in multiple states where it is operating). At the same time, standardized categories must enable persons to self-identify with the categories and increase the utility of the data to the entity collecting them. Both federal and state agencies (e.g., the Social Security Administration and state Medicaid programs) classify individuals by their race or ethnicity to obtain useful information for health and health care purposes (Mays et al., 2003). Other entities, such as health plans, health professionals, hospitals, community health centers, nursing homes, funeral directors, public health departments, and the public, play roles in categorizing, collecting, reporting, and using these data for quality improvement purposes. Coordinating efforts of these stakeholders to ensure accurate collection and reporting of uniformly categorized race and ethnicity data could lead to more powerful
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement TABLE 3-1 OMB Race and Hispanic Ethnicity Categories According to a One- and Two-Question Format Responses for Hispanic ethnicity in two-question format Hispanic or Latino Not Hispanic or Latino Responses for race in two-question format American Indian or Alaska Native Asian Black or African American Native Hawaiian or Other Pacific Islander (NHOPI) White Responses to a single question combining race and Hispanic ethnicity (one-question format) American Indian or Alaska Native Asian Black or African American Hispanic or Latino Native Hawaiian or Other Pacific Islander (NHOPI) White SOURCE: OMB, 1997b. analyses of aggregated data (Sequist and Schneider, 2006). While progress has been made in the past few years to incorporate the existing national standard set of categories promulgated by OMB (see Table 3-1) into the collection and presentation of data, many data collection efforts still do not fully employ these basic standard categories. All health and health care entities are not required to collect data on race and ethnicity, but if they do, the OMB categories are the minimum that a federal agency or recipient of federal funds must include in its categorization and reporting. The OMB standards have acknowledged imperfections, though. The categories are often, as shown by the literature review in Chapter 2, too broad for effectively identifying and targeting disparities in health and health care. Additionally, a substantial portion of Hispanics do not relate to the race options, leading to many Hispanics being reported in Census data as “Some other race” because they do not choose any of the five OMB race categories (del Pinal et al., 2007; NRC, 2006; OMB, 1997a). While OMB allows two formats for the race and Hispanic ethnicity questions—one combining both race and Hispanic ethnicity in a single question and the other asking about them in two separate questions, with the Hispanic ethnicity question being asked first (Table 3-1)—OMB explicitly prefers the latter two-question format (OMB, 1997b). As discussed later in the chapter, the format used may have implications for Hispanic response rates (Baker et al., 2006; Laws and Heckscher, 2002; Taylor-Clark, 2009). This chapter examines approaches to categorizing race and ethnicity by (1) reviewing the current state of standardized collection of race and ethnicity data, with a focus on the sufficiency of the OMB categories and their uptake in various areas of health care data collection; (2) examining the utility of the continued use of the current OMB categories; and (3) considering how the OMB race and Hispanic ethnicity categories can be combined with locally tailored, more detailed ethnicity categories selected from a national standard set, with standardized coding and rollup procedures, to capture important variations among ethnic groups. The chapter concludes by exploring approaches to eliciting responses on race, Hispanic ethnicity, and granular ethnicity, and reviewing models for data collection. CURRENT STATE OF STANDARDIZED COLLECTION OF RACE AND ETHNICITY DATA As previously noted, a variety of entities, many of which fall under the purview of the Department of Health and Human Services’ (HHS’) 1997 inclusion policy, collect race and ethnicity data for a variety of purposes. The HHS inclusion policy mandates the collection of at least OMB race and Hispanic ethnicity data in specific circumstances, such as in administrative records, surveys, research projects, and contract proposals associated with direct
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement federal service programs. While the policy does not state which specific categories should be collected in addition to the OMB categories, it encourages the collection and reporting of subgroup data (HHS Data Council, 1999). Exploring the current state of data categorization provides insight into the challenges faced by health- and health care-related entities in categorizing and collecting the data. Table 3-2 shows the categories used by various federally funded health surveys, state birth records, and cancer registries. Many of these data sources are national-level collection systems designed—among other purposes—to make comparisons across time, providers, and geographic areas (Madans, 2009). These surveys collect race and Hispanic ethnicity data in the six categories specified by OMB and a usually common set of 9 to 12 additional ethnicity categories. For example, the National Health Interview Survey (NHIS), National Survey on Drug Use and Health (NSDUH), and Medical Expenditure Panel Survey (MEPS) all include the OMB categories plus Mexican, Cuban, Puerto Rican, Asian Indian, Chinese, Filipino, Japanese, Korean, and Vietnamese categories, among others. These categories generally correspond to the 15 response check-off boxes included in Census 2000, Census 2010, and intercensal American Community Survey (ACS) questions on race and ethnicity (see Table 3-2). Despite HHS’ inclusion policy, some HHS agencies have not collected even the minimum OMB categories (e.g., Medicare enrollment files). In general, HHS-funded or -sponsored surveys collect the minimum OMB categories—and often additional categories—but all categories are not necessarily reported or analyzed because of small sample sizes. As specific stratifying variables are applied to survey data, for example, the pool of applicable respondents gets smaller (e.g., receipt of diabetes care services by age and race), which may make the number of cases of small racial or ethnic groups too small for analysis. In contrast to surveys, most national administrative datasets are case-rich, meaning they may contain enough data to allow for analyses of even small ethnic groups. For example, the Medicare databases contain a large number of cases and thereby could play an important role in stratifying data by race and ethnicity. Race and Ethnicity Categorization in Medicare Data Medicare, a large source of quality improvement data, has limited race and ethnicity data in the enrollment files for its 44.8 million beneficiaries. Because of the history of how race and ethnicity data have been captured (Reilly, 2009), the available race and ethnicity data are often of low accuracy and quality (Bilheimer and Sisk, 2008; Bonito et al., 2008; Eicheldinger and Bonito, 2008; Ford and Kelly, 2005; U.S. House Committee on Ways and Means Subcommittee on Health, 2008). Analyses of Medicare administrative enrollment data found that while the validity of individual data on race and ethnicity was high for Whites and Blacks (the sensitivity was 97 and 96 percent, respectively), only 52 percent of Asian, 33 percent of Hispanic or Latino, and 33 percent of American Indian or Alaska Native beneficiaries were correctly identified (McBean, 2006). Medicare has historically relied on the race and ethnicity data individuals provided when they applied for a Social Security number (SSN). Before 1980, the SSN application form limited respondents to choosing Black, White, and Other. Since most people age 65 and older today received a SSN prior to 1980, their racial and ethnic identifiers were limited to these responses unless the individual changed enrollment to a specific health plan. The current SSN application combines race and ethnicity into a single question and includes only five of the six OMB categories.1 Consequently, Medicare data have been of limited use in studying differences in patterns of care for populations identified by the OMB categories (Bilheimer and Sisk, 2008; Bonito et al., 2008; Eicheldinger and Bonito, 2008; Ford and Kelly, 2005; U.S. House Committee on Ways and Means Subcommittee on Health, 2008). The limitations of the Medicare data for race and Hispanic ethnicity have been acknowledged by Centers for Medicare and Medicaid Services (CMS) officials, and CMS is actively working to improve its coding of race and ethnicity data by working with the Social Security Administration (SSA) to ensure the capture of data according to the OMB minimum standards (Reilly, 2009). CMS has also explored a variety of indirect estimation techniques 1 The OMB-approved SSA Application for a Social Security Card instructs applicants to “Check one only”: Asian, Asian-American or Pacific Islander; Hispanic; Black (Not Hispanic); American Indian or Alaska Native; or White (Not Hispanic). These five categories do not correspond to the 1997 OMB standards, which split Asians and Pacific Islanders into separate categories, nor do the instructions to “Check one only” allow multirace individuals to “Mark one or more.”
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement TABLE 3-2 Race and Ethnicity Categories Collected by Various Data Sources Categories Collected Census 2010 and ACS (2009) NHIS (2008) NIS (2007) NSDUH (2008) MEPS (2006) NAMCS (2008) NHAMCS (2009) Application for a SSN (update unknown) CMS Nursing Home Minimum Data Set (updated 2000) Standard Certificate of Birth and Death (updated 2003) SEER (updated 2008) Responses to combined race and Hispanic ethnicity format Asian, Asian-American or Pacific Islander X Asian/Pacific Islander X Hispanic X X Black (Not Hispanic) X X North American Indian or Alaskan Native X American Indian/Alaskan Native X White (Not Hispanic) X X Responses for Hispanic ethnicity question in two-question format Yes/Hispanic or Latino X X X X X X No/Not Hispanic or Latino/Not of Spanish, Hispanic, Latino origin X X X X X X X X X Puerto Rican X X X X X X X Cuban/Cuban American X X X X X X X Dominican (Republic) X X X X Mexican X X X X Mexican American X X X Mexican/Mexican American/Mexicano/Chicano X X X Central or South American X X X X Central American X South American X Spanish (from Spain) X Spanish X Spanish-Caribbean X Other Latin American X X Other Hispanic/Latino/Spanish, Specify X X X Other Hispanic/Latino, Specify X X Other Spanish/Hispanic, Specify X X Refused X X X X Don’t know/Unknown X X X X X
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement Categories Collected Census 2010 NHIS NIS NSDUH MEPS NAMCS NHAMCS Application for a SSN Minimum Data Set Certificate of Birth and Death SEER Responses to race question in two-question format White X X X X X X X X X Black/African American X X X X X X X Black X Black, African Am., or Negro X American Indian X X Alaska Native X X American Indian or Alaska Native X X X X American Indian or Alaska Native – Print name of enrolled or principal tribe X X American Indian, Aleutian, Alaskan Native, or Eskimo X Native Hawaiian X X X X X X Native Hawaiian or Other Pacific Islander X X X Samoan X X X X Guamanian or Chamorro X X X X Micronesian X Polynesian X Tahitian X Tongan X Melanesian X Fiji Islander X New Guinean X Other Pacific Islander, Specify X X X X X X Asian X X X X X Asian Indian X X X X X Asian Indian, Pakistani X Chinese X X X X X X Filipino X X X X X X Japanese X X X X X X Korean X X X X X X Vietnamese X X X X X X Other Asian: Specify X X X X X X Laotian X Hmong X Kampuchean (including Khmer and Cambodian) X Thai X Some other race, Specify X X X X X X X Refused X X X Don’t know/Unknown X X X X
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement to improve analyses of race and ethnicity differentials among individuals currently in the Medicare data system (Bonito et al., 2008; Wei et al., 2006).2 Under the Medicare Improvements for Patients and Providers Act of 2008,3 CMS is required to address quality reporting by race and ethnicity. A report by CMS detailing its proposed actions is due to Congress in January 2010. Race and Ethnicity Categorization in State-Administered Programs Much, but not all, of the collection of standardized data at the state level is done under federally funded programs, including Medicaid and the Children’s Health Insurance Program (CHIP). Other state data collection systems, such as hospital discharge data systems and cancer registries, aim to use race and ethnicity data categories that are consistent with nationally collected denominator data (Friedman et al., 2000; Laws and Heckscher, 2002). States face difficulties, though, in consistently collecting accurate and reliable data that are uniformly classified. Medicaid and CHIP The Children’s Health Insurance Program Reauthorization Act of 2009,4 signed into law in February 2009, stipulates the development, by January 2011, of quality measures designed to identify and eliminate racial and ethnic disparities in child health and health care. This legislation has the potential to improve measurement of disparities for children in federally funded programs as it specifies that “data required for such measures is [sic] collected and reported in a standard format that permits comparison of quality and data at a State, plan, and provider level.” A national standard set of race and ethnicity categories is necessary to stratify and compare these quality metrics across the nation. Although states are mandated to submit Medicaid claims data electronically to CMS, there are anomalies in the submitted data (CMS, 2009). For example, in 2003, race and Hispanic ethnicity were listed as “unknown’’ for more than 20 percent of enrollees in New York, Rhode Island, and Vermont (McAlpine et al., 2007). A 2004 survey noted that while the majority of states were collecting self-reported race and Hispanic ethnicity from their Medicaid and CHIP beneficiaries, most commonly during the enrollment process (Llanos and Palmer, 2006), few states were collecting the six OMB minimum categories (Palmer, 2004). Many states were including Hispanic as an option in the race question instead of asking a separate question about ethnicity (McAlpine et al., 2007); as noted earlier, OMB permits this format but explicitly prefers the two-question format. The subcommittee’s research indicates that some progress has been made in the past six years on the collection of Medicaid data using the OMB standards. The subcommittee examined state Medicaid and CHIP application forms and found improved standardization, most notably in collecting the Asian and Native Hawaiian or Other Pacific Islander (NHOPI) categories (Table 3-3). Vital Statistics Data Failure to use standard categories and nonreporting or misreporting of data complicate efforts to calculate national and state birth, mortality, and morbidity rates by the OMB race and Hispanic ethnicity categories or for more detailed categories. The National Vital Statistics System (NVSS), hospital discharge data, and state registries provide data needed to calculate these rates, but the data may not be collected and reported according to the OMB categories or may be of poor quality. While the standard birth, death, and fetal death certificates now include 2 A 2009 white paper by the U.S. Senate Finance Committee presented proposals to improve patient care and health delivery. One proposal included a comprehensive database required of CMS to expand existing data sources, data sharing, and matching across federal and state claims and payment data, including HHS; SSA; the Departments of Veterans Affairs (VA), Defense (DOD), and Justice (DOJ); and the Federal Employees Health Benefit Program (FEHBP) (U.S. Senate Finance Committee, 2009). The results of this and other proposals to revise payment systems and policies in the Medicare program remain to be seen. 3 Medicare Improvements for Patients and Providers Act of 2008, Public Law 110-275 § 118, 110th Cong., 2d sess. (July 15, 2008). 4 Children’s Health Insurance Program Reauthorization Act of 2009, Public Law 111-3, 111th Cong., 1st sess. (February 4, 2009).
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement TABLE 3-3 Race and Hispanic Ethnicity Categories Used by State Medicaid and CHIP Programs OMB Race and Hispanic Ethnicity Categories 2004: State Medicaid Programs Using (out of 21)a 2009: State Medicaid Programs Using (out of 33)b 2009: State CHIP Programs Using (out of 38)c White 20 32 37 American Indian or Alaska Native 20 31 37 Black or African American 19 32 37 Hispanic or Latino 19d 32e 35f Asian 16 32 37 Native Hawaiian or Other Pacific Islander 14 30 36 Other 9 5 8 a SOURCE: Palmer, 2004. b 37 state applications were available online. Four states provided space to write-in a free-text response, so they are not included in the denominator. Of the remaining 33 states, all applications except one solicited race and ethnicity information with specific category choices. c 45 state applications were available online. Seven states provided space to write-in a free-text response, so the categories collected by these states are not included. Of the remaining 38 states, all applications except one solicited race and ethnicity information with specific category choices. d Seven of the 19 states also collected data on Not Hispanic or Latino, indicating differences in using the one-question versus two-question format. e 18 of the 32 states also collected data on Not Hispanic or Latino, indicating about an equivalent number of states using the one-versus two-question format. f 18 of the 35 states also collected data on Not Hispanic or Latino. the OMB categories plus 13 other categories,5 not all jurisdictions have adopted these standard certificates. As of April 1, 2009, 32 jurisdictions (56percent) had adopted the 2003 standard birth and death certificates, and 22 jurisdictions (39 percent) had adopted the 2003 standard fetal death report. The percentage of these vital events covered by the states that have adopted the 2003 standard certificates is higher, however, because they are states with larger populations.6 Death certificates provide the numerator for calculating death rates, while Census data provide the denominator. A deceased individual’s race and ethnicity are often identified by the funeral director relying on his or her own observation, which is often inaccurate, particularly for racial and ethnic groups with a large number of multiracial individuals (Arias et al., 2008; Durch and Madans, 2001). For example, an individual who may self-identify as White and American Indian or Alaska Native may be categorized as only White by a funeral director, resulting in undercounting of deaths in the American Indian or Alaska Native population. Misclassification on death certificates produces a substantial net underestimate of mortality rates for Hispanic, Asian, American Indian or Alaska Native, and NHOPI populations (Arias et al., 2008; Durch and Madans, 2001). An assessment of the quality of death rates found them to be understated by 11 percent for both Asians and Pacific Islanders and about 21 percent for American Indians and Alaska Natives (Rosenberg et al., 1999). Hospital Discharge Data Hospital discharge records sometimes lack race and ethnicity information (Gold et al., 2008; Schoenman et al., 2005) because hospitals either are not required to collect and report this information or choose not to do so (Romano et al., 2003). As of May 2009, at least 39 states included some race and ethnicity data in their discharge 5 The categories collected on the standard death certificate are included in Table 3-2. 6 Personal communication, J. Madans, National Center for Health Statistics, April 17, 2009.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement BOX 3-1 Race and Ethnicity Categories in the HCUP Databases The Healthcare Cost and Utilization Project (HCUP), a family of health care databases sponsored by the Agency for Healthcare Research and Quality (AHRQ), relies on the voluntary participation of 40 states to submit hospital discharge data. HCUP databases contain clinical and nonclinical information, including patient demographics, diagnoses, procedures, discharge status, and charges for all patients, regardless of payer (e.g., persons covered by Medicare, Medicaid, and private insurance, as well as no insurance). One HCUP data element contains source-specific information about the race and ethnicity of the patient: “race” retains information on the race of the patient as provided by the data source, and “Hispanic” retains information on Hispanic ethnicity as provided by the data source. Only 31 of the 40 participating states provide race and ethnicity data to HCUP. Some states report on all the OMB standard categories (e.g., Arizona, Missouri), some states (e.g., Hawaii, Massachusetts, New Jersey) collect more detailed ethnicity data, and some states do not report on the minimum OMB categories (e.g., Arkansas, North Carolina, Utah). HCUP recodes the data into the race and Hispanic ethnicity categories by which it analyzes and stratifies data: White, Black, Hispanic, Asian or Pacific Islander, Native American, and Other. These categories are similar to but do not in totality mirror the OMB standards. SOURCES: AHRQ, 2006; Fraser and Andrews, 2009. data reporting requirements. These data fields, however, are often added without additional resources to support complete and consistent reporting. Consequently, collection and coding practices vary, and data quality may be poor.7 Forty states voluntarily participate in the HCUP databases, but only 31 of these provide HCUP with race and ethnicity data. Of these 31 states, several do not report data using the minimum OMB race and Hispanic ethnicity categories, and others report the data in different categories that HCUP must recode to allow multistate and national-state comparisons (see Box 3-1) (AHRQ, 2006). Cancer Registries State cancer registries collect, classify, consolidate, and link information on new cancer cases from hospital reports, medical records, pathology reports, hospital discharge data, and death certificates (CDC, 2009). Cancer registries operate in 45 states, the District of Columbia, Puerto Rico, and the U.S. Pacific Islands, providing surveillance capabilities for identifying patterns, trends, and variation in disease burden and care among racial and ethnic groups. Difficulties may arise, however, in coding race and ethnicity from such disparate sources including, for example, the hand-written observations of physicians (Izquierdo and Schoenbach, 2000). The National Cancer Institute’s Surveillance, Epidemiology and End Results (SEER) Program coding manual includes two of the OMB categories directly (e.g., White and Black) and more granular ethnicity categories that correspond to the other OMB standard categories (e.g., instead of a broad Hispanic ethnicity category, SEER asks more specifically whether a person is Puerto Rican or Cuban) (see Table 3-2 for the categories coded by SEER); altogether there are 34 categories. Because SEER stratifies the data whenever possible by more discrete groups, registries are instructed to categorize a patient’s ancestry by one of the 34 categories (Johnson and Adamo, 2008). SEER supplements and improves the data it receives from states by, for example, linking with the Indian Health Service to improve American Indian or Alaska Native data (see Box 3-2). SEER also uses an indirect estimation 7 Personal communication, D. Love, National Association of Health Data Organizations, June 5, 2009.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement BOX 3-2 The Use of Data Linkages to Improve Data Coverage and Quality in Cancer Registries The American Indian or Alaska Native population makes up just over one percent of the U.S. population and is dispersed throughout the country. This complicates the collection and aggregation of data on cancer incidence, an especially important task because unique circumstances of culture, locale, history, and health care produce unusual patterns of cancer occurrence among American Indian or Alaska Native populations (Cobb et al., 2008). Alaska Natives, for example, have rates of lung, colon, and breast cancer five times higher than those of Southwestern Indians. Studies have demonstrated that many American Indian or Alaska Natives are misclassified as another race in cancer registry data, and dividing these numerators with population denominators from the Bureau of the Census has the effect of underestimating cancer rates for American Indian or Alaska Natives. To address this problem, SEER cancer registries (which cover 26 percent of the total U.S. population and 42 percent of the American Indian or Alaska Native population) have been linked with Indian Health Service (IHS) beneficiary records using LinkPlus, a probabilistic linkage software program developed by the Centers for Disease Control and Prevention (CDC), to identify records representing the same individual in the IHS and registry databases (Espey et al., 2008). algorithm based on Spanish surnames and birthplace to improve Hispanic classification, and an algorithm based on surnames and birthplace to improve data on Asian and NHOPI ethnic groups (Edwards, 2009). Review of the State of Standardization This review of categories currently used in various data collection activities highlights that there are substantial efforts nationally, by a number of states, and by various health care organizations to collect race and Hispanic ethnicity data according to the OMB standards. However, not all of these efforts have yet achieved that level of categorization, and national surveys, nationally standardized birth and death certificates, and cancer registries have found it useful to use more fine-grained categorizations beyond the basic OMB categories. Efforts to standardize categorization and collection will eliminate some of the problems with comparability among data collected by disparate systems. CONTINUED USE OF THE OMB CATEGORIES The OMB race and Hispanic ethnicity categories were deemed to represent the country’s broad population groups most necessary or useful for a variety of reporting and analytic purposes not specific to health care. The 1997 Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity were developed over a 4-year period during which an interagency taskforce weighed public input, expert testimony, and other evidence to consider whether and how to modify OMB’s 1977 standards (OMB, 1977, 1997b). OMB has no plans to change its current standards (Wallman, 2009). Chapter 2 documented important variations in health and health care that may be masked when data are analyzed using only the OMB race and Hispanic ethnicity categories. Notwithstanding this limitation, a large body of studies has revealed disparities in health and health care among the groups represented by those categories. Thus, use of the OMB categories yields important data for quality improvement analyses and reporting efforts (AHRQ, 2008; Cohen, 2008; Flores and Tomany-Korman, 2008; IOM, 2008; Kaiser Family Foundation, 2009). Additionally, because OMB-level reporting is required by various federal agencies and recipients of federal funds, the
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement OMB categories serve as a denominator for many comparisons related to health and health care. Thus, the OMB categories are useful for high-level analysis, reporting, and policy intervention (e.g., in the National Healthcare Disparities Report), as well as more local uses. If all entities were to collect race and ethnicity data using the OMB categories, the process of combining or comparing data across reporting entities (e.g., hospitals in states contributing to HCUP or health plans’ Healthcare Effectiveness Data and Information Set [HEDIS] data stratified by race and ethnicity) would be greatly facilitated. While the OMB categories do not define more specific subgroups and do not address how to include all difficult-to-categorize groups, they provide a useful common minimum platform for analyzing disparities in health care. Past Activities to Improve the Collection of Data in the OMB Categories One assumption underlying self-identified race and ethnicity data collection is that the categories and designations are recognized and accepted by the populations questioned (CDC, 1993; Lin and Kelsey, 2000). Improving the likelihood that respondents can identify with the races and ethnicities offered as response options is therefore essential to the quality of the data collected. Challenges in capturing accurate and reliable OMB-level data include the lack of detailed categories to which individuals can relate and the format of the questions used to elicit Hispanic ethnicity. Categorizing Diverse Populations A wide range of cultures, languages, and health-related behaviors are encompassed by each of the six OMB race and Hispanic ethnicity categories. For example, the Asian category blurs ancestry distinctions and vast cultural and geographic diversity (Holup et al., 2007). As a result, the Asian race identification may not resonate with all individuals of Pakistani, Vietnamese, or Filipino descent, for example, who might prefer to self-identify according to their ancestry (see Box 3-3) (Laws and Heckscher, 2002). Similarly, the Black or African American, White, American Indian or Alaska Native, and NHOPI populations consist of heterogeneous groups and persons within these groups may not identify with the broader race categories (Bailey, 2001; Mays et al., 2003). The Census Bureau has recognized that check-off boxes that represent more detailed categories in addition to the broad OMB categories resonate better with respondents. The Census includes several ancestry options on the Hispanic origin question and several Asian and NHOPI ancestries on the race question (see Figure 3-1). Additionally, the inclusion of space to write in a free-text response permits individuals who do not identify with any of the provided check-off boxes to self-identify. In Census 2000, about 15.4 million respondents were classified in the “Some other race” alone category, which was added to the OMB categories; this represents 5.5 percent of the total U.S. population.8 More than 97 percent of those who chose this category were Hispanic (Rothenberg, 2006), and the remaining write-in responses included a range of answers, such as German and Guyanese. As Table 3-4 illustrates, 42.2 percent of the 35.2 million Hispanic respondents identified with the response category “Some other race.” High rates of reporting “Some other race” on the Census may indicate that Dominicans, for example, are uncomfortable with saying “I am Black,” or “I am White,” and instead prefer to identify with a separate, distinct group (Bailey, 2001).9 Hispanics (discussed below) dwarf the other ethnicities in the “Some other race” category by virtue of their numbers, but individuals of other ethnicities, such as Cape Verdeans and Guyanese, also often do not self-identify with any of the OMB race and Hispanic ethnicity categories (Hernandez-Ramdwar, 1997; Laws and Heckscher, 2002; Model and Fisher, 2008). Consequently, these individuals, as well as many people of Filipino descent, among others, may not respond to the race question or may check “Some other race” if the option is available. The sub- 8 The 2005 Omnibus Appropriations Bill, at the urging of Congressman José E. Serrano (D-NY), directed that any collection of Census data on race identification must include “Some other race” as a response category. In previous censuses, the Census Bureau had sought and received OMB approval to include “Some other race” as a response category (U.S. Census Bureau, 2002b). 9 Dominicans (58 percent) were the group most likely to self-identify as “Some other race” in Census 2000 (NRC, 2006; Tafoya, 2004).
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement BOX 3-3 The Challenge of Categorizing Filipino Respondents The Philippines consist of over 7,000 islands set in the western Pacific Ocean. The OMB standards define persons of Filipino descent as Asian. To evaluate Asian subgroup responses to race and ethnicity inquiries, Holup and colleagues (2007), asked a subset of adults participating in the Hemochromatosis and Iron Overload Screening Study to complete both the OMB-minimum and the expanded race and ethnicity measure used in the National Health Interview Survey (NHIS). The expanded measure used in the NHIS includes response categories for Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, and Other Asian. While 89 percent of single-heritage Filipinos marked Asian in the OMB-minimum categorization, the remaining 11 percent marked primarily NHOPI. Filipinos have also been known to categorize themselves as Spanish (Mays et al., 2003), Pacific Islander, Asian American, or, if multiracial, White (Yu and Liu, 1992). Holup and colleagues note that while OMB’s decision to separate the Asian and Pacific Islander category in the 1997 OMB revisions was a positive step, specification or provision of definitions when using the minimum OMB categories is “prudent.” FIGURE 3-1 Reproduction of questions on race and Hispanic origin from Census 2000.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement FIGURE 3-3 CDC ethnicities rolled up to the OMB minimum categories for race and Hispanic ethnicity with subcommittee annotations. as White, Black, or some combination of races, or may see themselves as falling into no category beyond Brazilian. As a result, a rollup scheme that assumes all respondents who self-identify as Brazilian are White could wrongly assign a race to a number of individuals. Figure 3-3 highlights some problems with current CDC rollup procedures. For example, Brazilians may not be considered Hispanic because they speak Portuguese rather than Spanish. Additionally, several national origins correspond to two or more major racial populations. For instance, the population of Madagascar is of mixed African, Malayo-Indonesian, and Arab ancestry. This means that rolling up Madagascan to Asian, as recommended by
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement the CDC rollup scheme, would misclassify Africans of Madagascan descent as Asian. Rollup schemes are further complicated by misclassifications introduced by the use of geographic boundaries. While the CDC rollup scheme considers Afghanistan to be Middle Eastern and consequently categorizes Afghanis as White, the Census ancestry list classifies Afghanistan as an Asian country. Additionally, the WCRS coding manual notes that descriptions of religious affiliation should be “used with caution” when determining corresponding races.19 The above discussion highlights some of the difficulties inherent in rolling up some ethnicities because (1) ethnicities can include two or more major racial populations, (2) the geographic boundaries used to distinguish major groups in different classification schemes are arbitrary, and (3) many individuals may not associate with a specific race for cultural or other reasons. Thus, an individual’s race cannot always be presumed based on his or her ethnicity. For this reason, the rollup assignment of a self-reported ethnicity to an OMB category should not be placed in an individual’s health record or supersede a person’s direct self-report. Analysts should understand that making an assignment using a 90 percent (or any other percent) threshold or an assignment based solely on geography incurs a higher probability that the rollup assignment misclassifies individuals based upon how they would self-identify their race. The rates of misclassification, even for granular ethnicities meeting a 90 percent threshold, underscores the fact that rollup schemes only provide probabilistic assignments useful for analysis at the group or population level. Granular Ethnicities with an Indeterminate Race or Hispanic Ethnicity Classification Various methods are used to distinguish ethnic groups that cannot be rolled up to a specific race category. For example, in Census 2010, the Census Bureau will use OMB’s geographic definitions when it reclassifies ethnic responses in the race question to an OMB race category (e.g., all entries reflecting a sub-Saharan African nation will be counted as “Black”). In Census 2000, the Census Bureau applied a 90 percent rule to reclassify write-in responses on the race question according to the OMB race categories (del Pinal et al., 2007).20 Single-ancestry responses were cross-tabulated by race responses, and if 90 percent or more of respondents in a specific ancestry group selected a particular race, that race was assigned to respondents who gave that ethnic response in the race question. To determine whether groups included on the CDC, Census, Massachusetts, and WCRS category lists can be rolled up to a specific OMB race category with some degree of certainty, the subcommittee evaluated 2000 Public Use Microdata Samples (PUMS) data and used the methodology of the Census Bureau’s 90 percent rule. The subcommittee cross-tabulated write-in responses on ancestry with the “alone or in combination with one or more other races” variable for each OMB race group. If fewer than 90 percent of respondents of a specific ancestry group selected an OMB race either alone or in combination with another race, the ancestry group was identified as being problematic for rolling up. The subcommittee did not have sufficient data on some granular ethnicity groups to apply the 90 percent rule to each ancestry subgroup (see Appendix F). The subcommittee finds some granular ethnicities could not be rolled up to an OMB race category with greater than 90 percent certainty. The difficult-to-categorize granular ethnicity groups are included in Appendix F. The subcommittee suggests that those ethnicities that do not meet the 90 percent threshold be classified as “no determinate OMB race classification.” This classification differs from the “Some other race” category because “Some other race” is a response option used by individuals who do not identify with a specific OMB race category. The “no determinate OMB race classification” would be used to identify entire ethnic groups that cannot be assumed to comprise one specific racial group. None of the granular ethnicities associated with the 19 The Census list of categories does not include religiously affiliated ancestries (e.g., Ashkenazi Jewish) because of the Bureau’s constitutionally rooted decision not to identify or count religious populations. For health care purposes, religion may be coded as a separate variable from race and ethnicity. For example, the HL7 EHR System Functional Model states that systems shall provide the ability to capture, present, maintain, and make available for clinical decisions patient preferences such as language, religion, spiritual practices, and culture (Fischetti et al., 2007). 20 Write-in responses to the questions on race and Hispanic ethnicity were allocated to an OMB race or Hispanic ethnicity category using the 90 Percent Rule only in the Census’ Modified Race-Age-Sex (MARS) file. The MARS file is used by other agencies seeking denominators consistent with numerators collected in systems in which “Some other race” is not an option. Otherwise, write-in responses to “Some other race” are reported as they were received in all data released and published by the Bureau.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement Hispanic ethnicity category can be assigned to an OMB race category with greater than 90 percent certainty. Granular ethnicities that cannot easily be rolled up to the OMB Hispanic ethnicity category include individuals identifying a granular ethnicity associated with the non-Spanish-speaking territories in South America (Guyana, Suriname, Brazil, and Belize); additionally, these granular ethnicities should be considered “no determinate OMB race classification” because they do not meet the 90 percent rule. Appendix F highlights some additional difficultto-categorize granular ethnicity groups, including persons of Moroccan, Brazilian, Cape Verdean, Dominican, Guyanese, and South African descent. Rollup Schemes For interventions aimed at quality improvement and reduction of disparities at the local level, mapping granular ethnicities to the OMB race categories may be unnecessary. Locally tailored quality improvement activities may target subgroups without needing to relate those subgroups to a single OMB race category. Collecting race, Hispanic ethnicity, and granular ethnicity data separately allows reporting of the OMB categories when necessary without requiring rollup of the granular ethnicities, provided that individuals respond to all the questions asked. Nonetheless, the subcommittee recognizes that some circumstances will require the use of a rollup scheme to link granular ethnicities to broader categories to allow comparison or data aggregation. The Massachusetts Superset was developed to guide health plans toward a uniform set of ethnicities; this set avoids rolling up granular ethnicities to races and instead aggregates granular ethnicities into broader groups of ethnicities. Such an ethnicity rollup scheme is useful when the sample of a granular ethnicity group is too small for analysis and needs to be aggregated with others. The subcommittee merged several ethnicity lists into a template of granular ethnicity categories. These categories are mapped to the OMB race and Hispanic ethnicity categories (see Appendix E). National agreement needs to be reached on a rollup scheme, recognizing that all ethnicities do not necessarily map to an OMB race category, so that some respondents will have “no determinate OMB classification.” The locus of responsibility for the development of a national standard set of ethnicity categories and a national rollup scheme is addressed in Chapter 6. Recommendation 3-2: Any entity collecting data from individuals for purposes related to health and health care should collect granular ethnicity data in addition to data in the OMB race and Hispanic ethnicity categories and should select the granular ethnicity categories to be used from a national standard set. When respondents do not self-identify as one of the OMB race categories or do not respond to the Hispanic ethnicity question, a national scheme should be used to roll up the granular ethnicity categories to the applicable broad OMB race and Hispanic ethnicity categories to the extent feasible. ELICITING RESPONSES ON RACE, HISPANIC ETHNICITY, AND GRANULAR ETHNICITY The ways in which entities inquire about an individual’s race and ethnicity vary based on the setting in which the questions are asked. For example, paper survey forms use minimal words in questions and category descriptions to solicit race and ethnicity information from respondents. In contrast, surveys administered via an in-person interview can solicit more detailed information and explain the types of responses desired. Table 3-6 highlights ways in which race and ethnicity data are captured and illustrates how the questions may be tailored to specific contexts in health care. Eliciting accurate and reliable race, Hispanic ethnicity, and granular ethnicity data depends on the ways in which the questions are asked, the instructions provided to respondents (e.g., “Select one or more”), and the format of the questions (i.e., one-question versus two-question format). As previously noted, this latter concern is especially relevant to accurately classifying individuals who self-identify as Hispanic. Ensuring that as many respondents as possible answer questions regarding their race and ethnicity will improve data quality. Pilot projects and further study can help determine the best ways to elicit accurate data that are useful for health care quality improvement and will guide current and future data collection systems.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement Recommendation 3-3: To determine the utility for health and health care purposes, HHS should pursue studies on different ways of framing the questions and related response categories for collecting race and ethnicity data at the level of the OMB categories, focusing on completeness and accuracy of response among all groups. Issues addressed should include use of the one- or two-question format for race and Hispanic ethnicity, whether all individuals understand and identify with the OMB race and Hispanic ethnicity categories, and the increasing size of populations identifying with “Some other race.” The results of such studies, together with parallel studies by the Census Bureau and other agencies, may reveal the need for an OMB review across all agencies to determine the best format for improving response among all groups. MODELS FOR DATA COLLECTION Figure 3-4 shows models for the collection of data on race, Hispanic ethnicity, and granular ethnicity, taking into account that the capacity of information systems may limit the number of questions that can be asked. This report emphasizes the importance of collecting granular ethnicity data in addition to the OMB race and Hispanic ethnicity questions. Using the approach preferred by OMB of asking two separate questions about Hispanic ethnicity and race and then asking additionally about granular ethnicity requires collecting three separate variables, regardless of whether through paper-based or electronic collection modes (Model A). For organizations constrained to two data fields, one collection field would be used to collect responses to the OMB combined race and Hispanic ethnicity question, followed by a second collection field for granular ethnicity data (Model B). A distinction needs to be made between limits on collection and storage of coded response information in HIT systems; some organizations are limited in storage capacity by their legacy HIT systems, but could recode responses from multiple inputs to occupy fewer fields in HIT systems. For example, if an individual self-identified as non-Hispanic, White, and Russian on a paper form, the organization could store this information using one code in its HIT system. Doing so would, of course, introduce a very large number of possible combinations for which the organization would need to have codes.21 Ultimately, to achieve compatibility across data systems, it may be necessary for organizations to upgrade their data collection and HIT systems to ensure the ability to collect, report, and use data as recommended in this report. SUMMARY This chapter has explained the subcommittee’s rationale for recommending continued use of the OMB race and Hispanic ethnicity categories, supplemented by locally relevant granular ethnicity categories. The health and health care needs of all racial and ethnic groups can be best addressed through comprehensive strategies that recognize the importance of documenting and addressing variations among and within the locally relevant groups, and that further provide procedures for aggregating data to provide regional or national profiles. To collect OMB race and ethnicity data, entities should use either the one-question or two-question format, depending on their system’s field capacity. In accordance with OMB guidance, when the two-question format is used, the Hispanic ethnicity question should be first, and a “Select one or more” instruction should be included; OMB has indicated a preference for the two-question format. The recording of specific multiracial combinations (e.g., American Indian or Alaska Native and Black) is preferred by the subcommittee over assigning a single “multiracial” category to all persons of mixed race. A “Some other race” response category should be included for questions on race for respondents who do not identify with any of the OMB race categories. The minimum OMB categories to be collected are, then: 21 All possible combinations of just the six OMB categories results in 64 combinations. Introducing granular ethnicities would drastically increase the possible combinations.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement TABLE 3-6 Examples of Instructions, Phrasing, and Terminology to Capture Race and Ethnicity Data Source of Questions Hispanic Ethnicity Question Race Question Granular Ethnicity Question OMB’s preferred format Separate questions shall be used wherever feasible. Ethnicity shall be collected first Respondents shall be offered the option of selecting one or more racial designations. Recommended forms for the instruction are “Mark one or more” and “Select one or more” Response options: Hispanic or Latino, Not Hispanic or Latino Response options: American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander (NHOPI), White Census 2000 long form (paper form) Is Person 1 of Hispanic, Latino, or Spanish origin? What is Person 1’s race? Mark one or more boxes What is this person’s ancestry or ethnic origin? Response options: Not of Hispanic, Latino, or Spanish origin; Mexican, Mexican Am., Chicano; Puerto Rican; Cuban; Another Hispanic, Latino, or Spanish origin Response options: Five OMB race options plus six additional Asian origins, three additional NHOPI origins, and an option for “Some other race” Response option: write-in response HRET Toolkit (in-person interview) Do you consider yourself Hispanic/Latino? Which category best describes your race? “I would like you to describe your race or ethnic background. You can use specific terms such as Korean, Mexican, Haitian, Somali.” Response options: Yes; No; Declined; Unavailable/Unknown Response options: American Indian or Alaska Native; Asian; Black or African American; NHOPI; White; Multiracial; Declined; Unavailable/Unknown Response option: free-text response National Health Interview Survey (NHIS) (in-person interview) Do you consider yourself to be Hispanic or Latino? What race or races do you consider yourself to be? Please select one or more of these categories Response options: Yes; No; Refused; Don’t know Response options: White; Black/African American; Indian (American); Alaska Native; Guamanian; Samoan; Other Pacific Islander; Asian Indian; Chinese; Filipino; Japanese; Korean; Vietnamese; Other Asian; Some other race; Refused; Don’t know Please give me the number of the group that represents your Hispanic origin or ancestry. You may choose up to five, if applicable Response options: Puerto Rico; Cuban/Cuban American; Dominican (Republic); Mexican; Mexican American; Central or South American; Other Latin American; Other Hispanic/Latino/Spanish; Refused; Don’t know (If more than one race entered, which of these groups would you say best represents your race?)
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement Source of Questions Hispanic Ethnicity Question Race Question Granular Ethnicity Question National Ambulatory Medical Care Survey (paper form) Ethnicity Race, mark one or more Response options: Hispanic or Latino; Not Hispanic or Latino Response options: White; Black/African American; Asian; NHOPI; American Indian or Alaska Native Application for a Social Security Card (paper form) Race/ethnic description (check one only) Response options: Asian, Asian-American or Pacific Islander; Hispanic; Black (Not Hispanic); North American Indian or Alaskan Native; White (Not Hispanic) U.S. Standard Certificate of Death (paper form) Decedent of Hispanic origin? Decedent’s race (check one or more boxes to indicate what the decedent considered himself or herself to be) Response options: No, not Spanish/Hispanic/Latino; Mexican, Mexican American, Chicano; Puerto Rican; Cuban; Other Spanish/Hispanic/Latino (specify) Response options: Five OMB race options plus six additional Asian origins, three additional NHOPI origins, and other (specify) Hispanic or Latino (in the two-question format, this is a separate question, having the choice of Hispanic or Latino and Not Hispanic or Latino) Black or African American White Asian American Indian or Alaska Native Native Hawaiian or Other Pacific Islander (NHOPI) Some other race The categories used for the collection of granular ethnicity should be locally relevant and selected from a national standard list. Each set of categories should include an “Other, please specify:__” option to allow individuals to self-identify if their category is not on the prespecified list. Similarly, state or national surveys might limit the number of listed categories, but should also present the “Other, please specify:__” response option. An open-ended approach with no pre-specific granular ethnicity response categories is acceptable in lieu of a specified list, but requires subsequent coding of responses according to the national standard set. The granular ethnicity question, whether presented as a closed- or open-ended question, should be separate from the question(s) involving the OMB categories. Organizations may also want to use codes for tracking the current response status of individuals from whom they have attempted to collect race and ethnicity data, indicating unavailable (no response), declined (refused to answer), or unknown (respondent does not know) for those who fail to select a category.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement FIGURE 3-4 Models for data collection instruments to collect race, Hispanic ethnicity, and granular ethnicity data.
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