Addressing racial and ethnic disparities in health care: using federal data to support local programs to eliminate disparities.
ABSTRACT To reduce racial and ethnic disparities in health care, managers, policy makers, and researchers need valid and reliable data on the race and ethnicity of individuals and populations. The federal government is one of the most important sources of such data. In this paper we review the strengths and weaknesses of federal data that pertain to racial and ethnic disparities in health care. We describe recent developments that are likely to influence how these data can be used in the future and discuss how local programs could make use of these data.
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ABSTRACT: The elimination of racial and ethnic disparities in health has become a national priority in the United States (U.S. Department of Health and Human Services, Healthy people 2010: understanding and improving health, 2nd ed. Washington, DC: U.S. Government Printing Office, 2000). These disparities have many causes and potential solutions. In the landmark Unequal Treatment report, the Institute of Medicine reviewed and highlighted racial and ethnic disparities in health care as an important factor contributing to disparities in health outcomes (Institute of Medicine, Unequal treatment: confronting racial and ethnic disparities in health care. Washington, DC: National Academy Press, 2002). This report concluded with a strong call for action to eliminate racial and ethnic disparities in the US healthcare system. Since 2003, the federal government has issued an annual National Healthcare Disparities Report to monitor racial, ethnic, and socioeconomic disparities in access to care and quality of care. Whereas the initial report released by the federal Department of Health and Human Services generated considerable controversy and debate about the content and interpretation of key findings (Bloche, N Engl J Med 350(15):1568–70, 2004), subsequent reports have become a useful tool for tracking national trends in disparities across a wide array of quality measures. In 2004, this report found that lower quality of care was experienced by African Americans for two-thirds of measures, by Hispanics for one-half of measures, and by American Indians/Alaskan Natives for one-third of measures (Moy et al., Health Aff (Millwood) 24(2):376–87, 2005). In this chapter, five principles are presented to guide policy makers, health care leaders, and healthcare professionals seeking to reduce and ultimately eliminate racial and ethnic disparities in health care. These principles are based on clinical and policy experience, the research literature on healthcare disparities, and findings and recommendations of key reports from the Institute of Medicine (Unequal treatment: confronting racial and ethnic disparities in health care. Washington, DC: National Academy Press, 2002) and the American College of Physicians (Groman and Ginsburg, Ann Intern Med 141(3):226–32, 2004). The principles have been refined with input from an advisory committee of clinical and academic leaders in minority health care convened by the Minority Health Institute. The principles address aspects of access to care and quality of care that are especially important for minority populations in the United States, including African Americans, Latinos, Asian Americans, Native Hawaiians and other Pacific Islanders, and American Indians and Alaska Natives. These principles have become more timely and attainable with passage of the federal Patient Protection and Affordable Care Act (PPACA) that was enacted by Congress and signed by President Obama in March 2010.
Chapter: Disparities and Colorectal Cancer[Show abstract] [Hide abstract]
ABSTRACT: Colorectal cancer (CRC) is the third most common cancer of both men and women in the US. In 2007, an estimated 153,760 men and women received a diagnosis of colon or rectal cancer, and 52,180 individuals died of the disease (Cancer of the Colon and Rectum 2007). Substantial literature documents that both the US incidence of CRC and mortality rates from the disease are not evenly distributed throughout the general population but vary by age, gender, race, ethnicity, educational attainment, income, and geography. Other factors, such as lacking health insurance, have also been associated with increased mortality from CRC.12/2008: pages 161-178;
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ABSTRACT: Assessing health status is a core function of public health on the state level. This report summarizes the data available in each state for health assessment, a necessary step in achieving public health goals. Using the leading indicators from Healthy People 2010, the authors found that while data generally are available across all states, gaps do exist. Across states, data sometimes are not available for a few specific health objectives and for certain age groups.01/2007;
Addressing Racial and Ethnic
Disparities in Health Care: Using
Federal Data to Support Local Programs
to Eliminate Disparities
Thomas D. Sequist and Eric C. Schneider
To reduce racial and ethnic disparities in health care, managers, policy mak-
ers, and researchers need valid and reliable data on the race and ethnicity of
individuals and populations. The federal government is one of the most im-
portant sources of such data. In this paper we review the strengths and weak-
We describe recent developments that are likely to influence how these data
can be used in the future and discuss how local programs could make use of
Key Words. Federal government, health disparities, racial disparities, databases
Institute of Medicine 2002; Virnig et al. 2002; Weech-Maldonado et al. 2003;
Haas et al. 2004; Fremont et al. 2005). The recent National Health Care Dis-
parities Report (NHDR) and other studies summarize these disparities on a
Disease Control and Prevention 2005; Felix-Aaron et al. 2005). Collectively,
Much of what we know about racial and ethnic disparities has been
disparities must occur in the local collaborative efforts of regional health care
organizations, communities, health care institutions, and providers (Nerenz
r Health Research and Educational Trust
2005). Local groups need data to guide interventions that address locally
relevant disparities. Some efforts are already under way. In 2001, Aetna ex-
panded efforts for the collection of race and ethnicity data on members, and
used these data to target initiatives like the African American Preterm Labor
Prevention and Breastfeeding Program (Hassett 2005).
Even as local projects are implemented, the federal government will
remain a major source of data for monitoring racial and ethnic health care
disparities (Lurie, Jung, and Lavizzo-Mourey 2005). In this paper we summa-
rize the key features of selected federal health care databases. We examine
their strengths and weaknesses, as well as trends that may affect their future
use. Finally, we propose a model that would enable local initiatives on health
care disparities to make use of federal health data.
SUMMARY OF FEDERAL SOURCES OF RACE AND
ETHNICITY DATA FOR HEALTH DISPARITIES
A varietyof federal programs collectdata on race andethnicity and health care
(Committeeon National Statistics 2004).Two federal data resources,theSocial
Security Administration (SSA) and the United States decennial census (United
States Census Bureau 2005b), contain limited information on health care proc-
esses or outcomes, but supply the race and ethnicity data to federal databases
used inanalyses of health disparities.The SSAprovides raceand ethnicity data
to other federal agencies including the Centers for Medicare and Medicaid
Services (CMS). The U.S. census offers detailed population estimates and a
detailed set of socioeconomic factors including race and ethnicity. These data
are readily available through the U.S. census website (United States Census
census block), permitting detailed geocoding analyses involving race and eth-
nicity (Fiscella and Franks 2001; Fremont et al. 2005; Krieger et al. 2005).
The federal agencies that offer health-related data can be divided into
those that purchase or deliver health care and those that monitor health care
(Table 1). Of the former, CMS accounts for the majorityof databases (Centers
Address correspondence to Thomas D. Sequist, M.D. M.P.H., Brigham and Women’s Hospital,
Division of General Medicine, 1620 Tremont Street, Boston, MA 02120. Dr. Sequist is also with
the Department of Health Care Policy, Harvard Medical School, Division of General Medicine
and Primary Care, Brigham and Women’s Hospital, Boston, MA. Eric C. Schneider, M.D. M.Sc.,
is with the Department of Health Policy and Management, Harvard School of Public Health,
Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA.
1452 HSR: Health Services Research 41:4, Part I (August 2006)
for Medicare and Medicaid Services 2005b). The latter group includes the
Centers for Disease Control and Prevention, the National Institutes of Health,
andtheAgencyforHealthcareResearch and Quality(AHRQ).These federal
databases vary in the racial and ethnic composition of the populations as-
sessed, the allowance for multiple race designation, whether ethnicity is col-
lected independent of race, how race and ethnicity are designated (individual
self-report versus other mechanisms), and the smallest geographic unit avail-
able for analysis (e.g., census region, state, or smaller units).
The datasets in Table 1 are the most widely used and each is available to
that individuals can be identified. Some datasets are completely deidentified
and freely accessible on the Internet (e.g., some CDC surveys), while others
require formal approval from the collecting organization (e.g., CMS). Com-
prehensive lists of federal datasets containing information on race and eth-
Department of Health and Human Services (United States Department of
Health and Human Services 2005a).
Table 2 summarizes the general types of health-related data available in
and not meant to be mutually exclusive: (1) mortality, (2) preventive services,
(3) management of chronic conditions, (4) quality of care measures, and (5)
patient reported experiences and quality of life. To support analyses of racial
and ethnic disparities, these databases must contain or be linked to other
databases that include information on health status, use of health services, or
health outcomes. Linking databases to one another further enhances the ques-
tions that can be addressed. For example, the individual-level HEDIS data
submitted by health plans participating in the Medicare Managed Care pro-
gram do not contain race and ethnicity information, yet linkage to the CMS
enrollment files has allowed comparisons of the quality of care between racial
HISTORY AND VARIABILITY OF FEDERAL RACE AND
Federal definitions and methods for collecting race and ethnicity data have
evolved between the time the U.S. census first began collecting race infor-
mation in 1790 and the 2000 decennial census that for the first time allowed
individuals to self-report either a single race or multiple racial and ethnic
Addressing Racial and Ethnic Disparities in Health Care1453
Overview of Federal Data Sources That Provide Health-Focused Race and Ethnicity Data
Approximate Sample Size
Federal purchasers or providers
41 ? 106
19 ? 106
4 ? 106
11 ? 106
1.6 ? 106
Federal health monitoring agencies
2.4 ? 106
74 ? 106
19 ? 106
nW, white; B, black; H, Hispanic; AI, American Indian; As, Asian/Pacific Islander. Totals may exceed 100 if ethnicity was collected separately.
Percentages are unweighted. (For reference purposes, the distribution of race/ethnicity in the 2000 U.S. Decennial Census was 72% W, 12% B, 13% H,
1% AI, 4% As).
1454HSR: Health Services Research 41:4, Part I (August 2006)
kMedicareHEDIS data doesnotcontainraceorethnicityinformation,butcanbelinkedtotheMedicare enrollmentdatabasetoobtainthis information.
zGeo, smallest geographic unit (Ce, Census division; S, state; Z,zip code or county); Inst, smallest institutional unit (H, hospital; C,clinic; P, health plan).
§CMS, Centers for Medicare and Medicaid Services; Enrollment, CMS enrollment database (Arday et al. 2000); MCBS, Medicare Current Beneficiary
Survey (Arday et al. 2000); CAHPS, Consumer Assessment of Health Plans Survey (Personal communication, Alan Zaslavsky, Medicare; CAHPS
Administration (Kressin et al. 2003); HRSA BPHC, Health Resources and Services Administration Bureau of Primary Health Care (Bureau of Primary
Health Care 2004); IHS, Indian Health Service (Indian Health Service 1999); CDC, Centers for Disease Control and Prevention; BRFSS, Behavioral
Risk Factor Surveillance System ([Centers for Disease Control and Prevention 2005a], personal communication, Eric Schneider, BRFSS 2001);
NHANES, National Health and Nutrition Examination Survey (NHANES 2005); NHIS, National Health Interview Survey (NCHS 2005); NVSS,
National Vital Statistics System (Kochanek, Murphy, and Anderson 2002); NIH, National Institutes of Health; SEER, Surveillance, Epidemiology, and
End Results (NCI 2005); AHRQ, Agency for Healthcare Research and Quality; HCUP, Health Care Utilization Project (NIS 2002); MEPS, Medical
Expenditure Panel Survey (MEPS 2002).
zData are obtained by linking to the Social Security Administration files.
wSR, self report; MR, medical record; R, registration; FD, funeral director.
nnNot available in public use files, but can be obtained upon special request.
wwThe SEER program collects data from the following 13 states: AK, AZ, CA, CT, GA, HI, IA, KY, LA, NJ, NM, UT, WA.
Addressing Racial and Ethnic Disparities in Health Care1455
backgrounds. The collection of ethnicity data has also evolved, with the first
collection of Hispanic ethnicity in 1970 (Gibson and Jung 2002).
by the Office of Management and Budget (OMB) Directive 15 (Office of
of ‘‘other race,’’ and instead required collection of race in 4 categories: white;
black; Asian, Asian American, or Pacific Islander; and Northern American
Indian or Alaskan Native. It also required that Hispanic ethnicity be collected
as a data element separate from race, with the option to collapse race and
ethnicity into one variable, with Hispanic listed as a race. A subsequent re-
one race per individual (Federal Register 1997b).
Types of Health-Related Data Available for Evaluation of Racial/
Quality of Life
nAvailable through linkage to Medicare administrative database.
wThecategories are illustrativeofthe typesandusualuseofdata available,andare not meanttobe
inclusive of all data in each dataset.
zRefer to Table 1 footnote for listing of abbreviations for each dataset.
1456HSR: Health Services Research 41:4, Part I (August 2006)
These directives affected multiple national databases, including the
Medicare program and all surveys conducted by AHRQ and the CDC. In the
Medicare enrollment database (EDB), race was originally stored as white,
black,other,orunknown based on SSA data derivedatthe time ofapplication
for a new or replacement Social Security card (Lauderdale and Goldberg
1996). After expanding the number of race categories in 1994 to comply with
the original OMB Directive 15, the Medicare EDB contained a substantial
number of patients with ‘‘other’’ and ‘‘unknown’’ race. To remedy this, 2.2
million beneficiaries with a race of ‘‘other,’’ ‘‘unknown,’’ or with a Hispanic
surname or country of birth (from SSA files) were surveyed about their race
and ethnicity in 1997. Approximately 40 percent of individuals responded to
this survey, improving the completeness of the Medicare race and ethnicity
data (Arday et al. 2000).
Despitethe mandate ofOMB Directive 15,federal race and ethnicity data
still vary in several ways (Table 1). First, the relative distribution of the race and
ethnicity of populations differs across databases, particularly if minority popu-
lations were deliberately oversampled. For example, while the U.S. census es-
timates that 12 percent of the population report black race, 23 percent
(unweighted) of the National Health and Nutrition Examination Survey sample
reports black race because of deliberate oversampling. Second, collection of
multiple race data on individuals is not yet routine except for the U.S. Census
and the surveys conducted by the CDC. The majority of other data sources do
not yet include such detail (Table 1). Third, the method for collecting ethnicity
data is inconsistent among databases. The Medicare EDB includes Hispanic
ethnicity as a race category, whereas Medicare surveys such as the Medicare
Current Beneficiary Survey (MCBS) (Centers for Medicare and Medicaid Serv-
ices 2005a) and the Medicare CAHPSs(CAHPS 2005) separate Hispanic eth-
nicity from race (Table 1). Fourth, the mechanism of assessing of an individual’s
race by clerical workers at the time of patient registration (Table 1). This var-
iability in the method of assigning race may affect the interpretation of research
related to race and ethnicity (Kaplan and Bennett 2003).
KEY METHODOLOGICAL ISSUES FOR LOCAL USE OF
FEDERAL HEALTH CARE DATA
There are at least four methodological issues to consider before using federal
data to assess racial or ethnic disparities in health care: (1) the validity of the
Addressing Racial and Ethnic Disparities in Health Care1457
classification of individuals’ race and ethnicity, (2) sample size limitations, (3)
the smallest analyzable geographic orinstitutional unit, and (4) theavailability
of data on other cultural or socioeconomic characteristics of the individuals
that may be important mediators of health disparities.
Researchers have examined the validity of assignment of race and eth-
nicity in the Medicare EDB using self-report within the MCBS as a gold
standard (Arday et al. 2000). The specificity of classification of minorities
within the enrollment database is very high, resulting in negative predictive
values ranging from 96 percent among Hispanics to100 percent among Asian
and Pacific Islanders. The sensitivity of the EDB for identifying blacks is also
high (95 percent), but the sensitivity is much lower for identifying Hispanics
(39 percent), Asian/Pacific Islanders (58 percent), and American Indians (11
percent). The positive predictive values for the Medicare enrollment data
range from96 percent forblackstoaslowas78 percentforAmericanIndians,
suggesting the need to exercise caution when using Medicare datasets to
evaluate care for nonblack minority populations. Similar studies of the Vet-
erans Health Administration (VHA) data compared with patient self-report
found 98 percent agreement for whites, 92 percent for blacks, 76 percent for
Asians, 83 percent for Hispanics, and only 23 percent for Native Americans.
Even after excluding the 36 percent of patients with missing race information
in the VHA, the accuracy of classification of nonblack minority populations is
not optimal (Kressin et al. 2003).
Misclassification of nonblack minority individuals may bias estimates of
health status or mortality. Correcting or adjusting for this can reduce the bias.
the National Center for Health Statistics noted that the proportion of Amer-
ican Indians misidentified in the National Death Index ranged from 1 percent
in Arizona to 30 percent in California. Using corrected estimates, the IHS has
produced adjusted disease-specific mortality rates for American Indians in its
be used to improve the low sensitivity of the Medicare EDB for identifying
surname analysis (Morgan, Wei, and Virnig 2004).
Medicare health plans are uniquely positioned to assess the distribution
of race and ethnicity for their enrolled populations by comparing estimates
from the Medicare EDB to estimates based on self-reported race and ethnicity
from surveys like the Medicare CAHPS. For a given health plan, if the
CAHPS data produce an unbiased estimate of the prevalence of racial and
ethnic groups, and these estimates are comparable to those in the Medicare
1458HSR: Health Services Research 41:4, Part I (August 2006)
EDB, then health plan analysts could rely on the EDB to supplement race and
ethnicity data on Medicare plan members. Otherwise, plans may need to
request race and ethnicity information at the time of enrollment or survey
members. For commercial enrollees there is no Medicare EDB equivalent.
Obtaining adequate sample sizes to reliably estimate the use of services
or health status of minority populations is a second important consideration.
The large numbers of individuals included in federal databases enable sta-
tistically precise estimates of many health measures. Nevertheless, the rela-
tively small numbers of nonblack minority individuals make it difficult to
measure health care delivery and outcomes with precision. Most nonblack
minority groups are clustered within specific geographic regions of the coun-
try. For example, while American Indians comprise less than 1 percent of the
population throughout all counties in Ohio, this proportion ranges from 1
percent to as high as 90 percent in New Mexico (United States 2005a). The
utility of specific federal databases to local health care leaders will depend on
the racial and ethnic composition of the local population. Fortunately, the
prevalence of minority populations at the county level is readily available
through maps created by the U.S. Census website (Figure 1).
Healthcare managers using federal data sources to assess local dispari-
tieswill alsowanttoknowthesmallestanalyzablegeographic ororganizational
unit available, such as the state, city, or individual hospital or clinic (Table 1).
Most federal datasets can provide summary information at the state level and
many can do so at the county or zip code level. Administrative datasets are
more likely than survey datasets to achieve adequate sample sizes within
smaller geographic units. Very few databases provide detailed information at
the facility (hospital or clinic) level. The Dartmouth Atlas based on Medicare
based on local hospital referral regions (Baickeret al. 2004). Additional sources
of facility-level data include the Medicare End Stage Renal Disease program
and the AHRQ Health Care Utilization Project database. While not available
routinely, the Medicare CAHPS, the MCBS, and the Medicare HEDIS pro-
gram may be able to provide information on specific health plans, but whether
this information is actionable will depend on the total enrollment of the health
plan and the prevalence of its minority population.
Measures of socioeconomic position (SEP) such as income or education
attained are useful to understand mediators of racial and ethnic disparities
(Braveman et al. 2001). The primary language spoken by an individual is also
increasingly recognized as an important determinant of care ( Jacobs et al.
Addressing Racial and Ethnic Disparities in Health Care1459
New Mexico and Ohio, 2000 Census
Geographic Distribution of the American Indian Population within
The distribution of minority populations is clustered throughout the United
Mexico compared with Ohio.
1460HSR: Health Services Research 41:4, Part I (August 2006)
variables among federal databases. Surveys conducted by the CDC can be
administered in either English or Spanish, and many federal surveys include
extensive SEP data, but most federal administrative and claims databases
contain varying levels of information on SEP data and no information on
spoken language. While it is possible to use geographic locators such as zip
code within administrative or claims databases to estimate SEP through geo-
coding, these methods are less precise than individual level data (Krieger,
Williams, and Moss 1997).
Federal databases currently lack data on other potentially important
determinants of health care, such as language proficiency, health literacy, and
immigrant status (Kandula, Kersey, and Lurie 2004). For example, disparities
in health care within a specific racial group such as blacks may depend not
Barr-Anderson, and Kington 2003; Read, Emerson, and Tarlov 2005). These
to address disparities on a local level. For the foreseeable future, such data
must be collected locally.
RELEVANT TRENDS AFFECTING THE USE OF FEDERAL
RACE AND ETHNICITY DATA
In the near term, three major trends seem likely to affect the collection of race
and ethnicity data and increase the complexity of their analysis: (1) the in-
creasing diversity of the minority populations themselves, (2) the rising prev-
alence of non–English-speaking individuals, and (3) increasing numbers of
black parent, the proportion with the second parent listed as white increased
from 2 to 9 percent during 1968–1994 (Federal Register 1997a). The first two
demographic trends tend to reduce the homogeneity of analytic categories
and may increase bias due to nonresponse. In 1980, only 11 percent of the
population reported speaking a language other than English at home, and this
increased to 18 percent in the 2000 Census (Shin and Bruno 2003). It is likely
that using survey data to assess health care disparities will underestimate ex-
isting disparities in the non–English-speaking population owing to nonre-
sponse bias resulting from language barriers. To overcome this limitation,
federal data collection efforts should translate survey instruments into addi-
tional languages as is done with the Medicare CAHPS survey. Federal agen-
cies might also incorporate primary language spoken as a unique
Addressing Racial and Ethnic Disparities in Health Care1461
administrative or claims data field to enable health assessment of the non–
Between 1982 and 1994, the proportion of patients reporting multiple
races to the National Health Interview Survey (NHIS) increased from 1.2 to
1.8 percent (Federal Register 1997a), and the 2000 Census recorded 2.4 per-
cent of respondents with multiple races (Greico and Cassidy 2001). The pro-
portion of individuals that self-identify as more than one race also varies
significantly among racial groups. For example, individuals reporting Amer-
multiple race individuals in the NHIS analyses (Federal Register 1997a). By
contrast, black race in combination with white race accounted for 11 percent
of multiple race individuals in the 2000 Census (Greico and Cassidy 2001).
Allowing individuals to select up to six race designations generates 63
approach to combining them is not obvious. For example, the surveys con-
ducted through CDC include a summary question of ‘‘Which group would
you say best represents your race?’’ The National Center for Health Statistics
has constructed statistical models that can be used to assign a single ‘‘most
likely’’ race for multiple race individuals responding to the 2000 census. Re-
sults from these models tend to increase the relative proportions of minority
populations by anywhere from 2.5 percent (blacks) to as much as 12 percent
for the American Indian population (Ingram et al. 2003).
less numerous minority populations such as Asians and American Indians.
Collection of data on nonblack minority populations might also be enhanced
by refining race and ethnicity categories in special geographic locations. For
example, the ‘‘Hispanic’’ category may not suffice in cities such as New York,
where expanded definitions including Puerto Rican or Dominican might be
more appropriate. CMS could play an active role in such collection efforts by
defining required race/ethnicity categories to be used in each region based on
the local race/ethnicity profile obtained from the U.S. Census.
A MODEL OF FEDERAL AND LOCAL COLLABORATION TO
ADDRESS HEALTH DISPARITIES
through collaboration between individual health care organizations and the
1462HSR: Health Services Research 41:4, Part I (August 2006)
federal government. Any model for federal and local collaboration needs to
address the division of responsibility for three data-related activities: collec-
tion, analysis, and reporting. Challenges to bidirectional sharing of standard-
ized race and ethnicity data include time lags in the production of data,
significant political and legal barriers (Kamoie and Hodge 2004), suspicions
about the benevolent intent of such programs, and limited willingness to de-
vote resources to this issue.
The federal government and states or other local agencies already col-
laborate to collect some forms of health care data. Three examples include
collaborations between the Medicare program and the Quality Improvement
Organizations (QIOs) ( Jencks, Huff, and Cuerdon 2003), between the Medi-
care program and the National Committee for Quality Assurance and local
health plans to collect standardized health plan performance measures, and
between the Centers for Disease Control and Prevention and state health
departments to operate the Behavioral Risk Factor Surveillance System (Cen-
ters for Disease Control and Prevention 2005a). In each instance, a local
agency or organization collects data that are transmitted to the national or-
organization for its own purposes. These or similar partnerships could be
tasked with collection of standardized data on racial and ethnic health care
How would analysis and reporting on regional and local disparities be
model involves analysis and reporting of local or regional results by a national
organization. For example, the National Committee for Quality Assurance
(NCQA) currently generates regional and health plan summaries of HEDIS
results. The other model involves reporting by regional, state, or local groups.
For example, the Massachusetts Health Quality Partnership has produced
statewide reports on the quality of physician groups by aggregating and an-
alyzing the data that local health plans report to NCQA. The Medicare Qual-
ity Improvement Organizations, many of which have expertise in data
collection and analysis could also play this role ( Jencks et al. 2003). As CMS
has included the elimination of health disparities as part of the scope of work
for the QIOs, local disparity reports could be a product of the QIOs (Depart-
ment of Health and Human Services 1999).
Currently, the federal government reports on racial and ethnic dispar-
ities in health care via the NHDR. Future releases of the NHDR might rou-
tinely include regional and local reporting on health disparities. Alternatively,
the NHDR might generate local reports in response to requests from state
Addressing Racial and Ethnic Disparities in Health Care1463
related to disparities. Much as census data guide local planners, these reports
would enable health care leaders to better understand local manifestations of
nationally documented health care disparities and enable institutional invest-
ment of resources to address locally relevant disparities (as opposed to dis-
parities that may be relevant in other parts of the United States). Results of
these local reports might also reveal resource limitations. For example, survey
data could reveal local disparities in the availability of primary care or spe-
Some federal data, sampled nationally, may lack sufficient sample sizes
to assess health care disparities among minority populations with particular
medical conditions. Large claims databases are less vulnerable to sample size
limitations than survey data. To address disparities using these surveys, the
sampling schemes would have to be modified to oversample minority pop-
ulations or initiate data collection in geographic areas that are not adequately
represented. For example, there is already precedent within the Medicare
Current Beneficiary Survey for oversampling selected populations (such as
managed care enrollees) in some survey rounds. Likewise, monitoring cancer
care for Native Americans has stimulated the addition of Arizona and Alaska
to the SEER program. Sampling modifications could be guided by results of
racial disparities analyses using larger administrative datasets.
In conclusion,racial and ethnic disparitiescontinue tobean important
national problem. Much of what we know about these disparities has been
derived from federal databases. The federal government has key roles as
standard setter, as data collector for federally sponsored programs, and as a
data clearinghouse. The collection of data on race, ethnicity, and health
must reflect the growing diversity of our population. Coordinating the ef-
forts of states and local insurers with federal efforts to enhance and stand-
ardize race and ethnicity data collection could lead to more powerful
analyses of aggregated data. With modifications, federal datasets can also be
useful to local health care leaders and policy makers as they strive to reduce
racial and ethnic disparities and improve care for all of the citizens of the
This work was supported by a grant from the Robert Wood Johnson
1464HSR: Health Services Research 41:4, Part I (August 2006)
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