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acial disparities are a broad issue for the US healthcare system.
The importance of racial disparity as a policy issue is apparent
Healthy People 2010, which has the elimination of racial
disparities in health and healthcare by 2010 as an overarch-
Similarly, a goal of the US Department of Health and Human
Service’s Initiative on Racial and Ethnic Disparities is elimination of
racial disparities in health and healthcare.
The Centers for Medicare &
Medicaid Services (CMS) is challenged to identify ways to eliminate or
at least reduce these disparities in the Medicare program for both man-
populations, where the existence of
racial disparities in health, receipt of healthcare, and health outcomes is
well documented and recognized. Despite the consistency of the evidence
for the existence of racial disparities in the Medicare program, the best
strategy for eliminating this variation has not been established.
Although often treated as a completely separate issue, geographic
variation in patterns of healthcare is also well recognized and document-
Like racial disparity, the causes of geographic variation are poorly
understood and are attributed to factors such as physician supply, local
practice patterns, and patient culture.
A challenge to studying both race and geography in healthcare is the
strong geographic concentration of our racial/ethnic populations.
example, the black population is geographically concentrated in the
South Central and southeastern United States (
Figure). As others have
the disproportionate use of lower quality providers (be they
physicians, hospitals, or managed care plans) by black elders compared
with white elders complicates studies of racial disparity. As a result,
researchers have 2 related challenges: first, an analytic issue and second,
a policy issue. As an analytic issue, techniques such as multiple regression
are frequently used to “remove” or “adjust for” the effects of potentially
confounding factors such as geography
ypical multivariable approach-
es to studying race after adjusting for geography may underestimate the
magnitude of the disparity by controlling for elements (ie, geography)
that are causally related to the dispari-
ty. Second, clustering of racial or eth-
nic groups into poor
or health plans might point to inter-
ventions aimed at helping elders make
better choices—such as choosing a
VOL. 13, NO. 1 ■ THE AMERICAN JOURNAL OF MANAGED CARE ■ 51
Efforts to Reduce Racial Disparities in Medicare Managed Care
Must Consider the Disproportionate Effects of Geography
Beth A. Virnig, PhD, MPH; Sarah Hudson Scholle, DrPH, MPH;
Ann F. Chou, PhD, MPH; and Sarah Shih, MPH
Objective: To examine the impact of geographic
variation on racial differences in 7 of 15 Health Plan
Employer Data and Information Set (HEDIS) measures
hat assess the quality of the Medicare managed
are program (also known as Medicare+Choice).
ross-sectional analysis using the
004 individual-level HEDIS for Medicare managed
are plans and 2003 Medicare enrollment and
demographic (ie, denominator) data for more than
5.1 million Medicare+Choice enrollees.
Methods: Individual-level HEDIS data were linked
with Medicare enrollment data. Hierarchical general-
ized linear models were used to assess statistical
significance of region and race. Direct standardization
was used to estimate the rate of meeting each HEDIS
standard while controlling for differences in age
Results: Quality of care for white Medicare+Choice
enrollees was strongly correlated with the racial
composition of the geographic area. Except for
cholesterol management after an acute cardiac event,
between-region racial variation was consistently
greater than within-region racial variation.
Conclusion: Removing within-region racial variation
while ignoring geographic differences will not
equalize the experiences of black and white elders.
Rather, both racial and geographic components
of healthcare quality must be addressed if the
Medicare managed care program is to provide care
of equal quality to all elders regardless of race.
(Am J Manag Care. 2007;13:51-56)
For author information and disclosures,
see end of text.
In this issue
Take-away Points / p56
Full text and PDF
higher quality hospital. However, if the inequality has a
strong regional component, the policy options will be quite
different and may need to focus on changing regional care
With these challenges in mind, the objective of this
study was to examine the impact of geographic variation
on racial differences in 7 Health Plan Employer Data and
Information Set (HEDIS) measures that assess the quali-
ty of the Medicare managed care program (also known as
Data sources for this study are (1) the individual-level
HEDIS data submitted by Medicare managed care plans for
reporting year 2004 (based on 2003 experience) as a condition
for continuing their M+C contract; (2) CMS denominator
(ie, enrollment) files for 2003; and (3) US Census data.
HEDIS data were merged with CMS denominator files
using the approach previously described.
HEDIS records, each containing the Health Insurance Claim
(HIC) number, a unique identifier assigned by Medicare,
were merged with the Medicare denominator file to obtain
information on age, race, sex, and ZIP code of residence.
Individuals were excluded from this analysis if they did not
have a valid HIC, if their race was not
classified as black or white, or if they
were younger than age 65 years in 2003.
Plans were excluded from this analysis if
their submitted records failed to achieve
at least a 90% match rate on the HIC.
Overall, 148 of 162 M+C plans sub-
mitted individual-level data that were
linkable with Medicare demographic
information (91.4% of plans). These
plans represent the experience of 81.4%
of the more than 5.1 million 2003 M+C
enrollees. Of 8 regions, 6 had plans that
were excluded because of lack of identi-
fiers. Finally, plans were excluded from
specific measures if the audited summary
reporting for the measure was not similar
to the plan summary calculated from the
individual records. No region had more
than 3 plans excluded.
Table 1 shows the characteristics of
Medicare managed care plans that sub-
mitted individual-level HEDIS data and
the number of persons included in the sample for each HEDIS
quality indicator. The number of plans and subjects included
in these analyses varied from measure to measure because of
differing reporting requirements.
The analysis focused on 7 of the 15 HEDIS 2004 measures
related to quality and outcomes. We confirmed that the pat-
terns we reported held for the remaining 8 measures (analysis
available on request).
Race was obtained directly from the 2003 Medicare
denominator files. The categories included in this analysis
were white and black. All but 1 plan had at least some black
members, with a median of 5.5% black and a maximum of
68% black. We imputed household income indirectly, based
on figures from the US Census on the median disposable
household income by ZIP code for households with persons
age 65 years and older. This income estimate was grouped
into 4 categories: (1)
<$15 000; (2) $15 000 to <$30 000; (3)
$30 000 to
≥$45 000; and (4) >$45 000.
Area of residence was grouped into the 8-level Census
division designation: New England, East North Central,
Middle Atlantic, South Central, South Atlantic, W
Central, Pacific, and Mountain.
Health plan size was classified as fewer than 10
10 000 to 49 999 members, and 50 000 or more members.
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■ Figure. Geographic Distribution of the Black Population by County in
the United States, 1999
CY indicates calendar year
All analyses were conducted using SAS version 9.13 (SAS
Institute Inc, Cary, NC). We used a hierarchical generalized
linear model to account for the nesting of enrollees within
health plans. For each measure, we estimated the within-
region quality of care separately for white and black enrollees.
e used multiple regression mo
dels to assess the impact of
factors on quality of care: demographic variables (age, sex, and
area income), plan size, percentage of the region that was
black, race, geography
, and a race/geography interaction.
Adjusted rates were calculated for each measure.
The study was approved by the Chesapeake Research
Review, Inc, institutional review board (institutional review
board for the National Committee on Quality Assurance),
protocol number CRRI 0204002.
The racial composition of the Medicare managed care pop
ulation varied considerably across census divisions, ranging
from 2% black in New England to 13.8% black in the South
Central division (
Table 2). Likewise, there was considerable
variation across geography in mean level of HEDIS measures
for white populations. We used quality for whites as an over-
all measure of local background quality, which allowed us to
separate quality differences that can be attributed to race
alone, geography alone, and a combination of race and geog
raphy. For example, the spread in performance across geogra-
phy ranged from 6.4% for glycosylated hemoglobin (A1C)
testing to more than 20% for diabetic eye exams and follow-
up after mental health hospitalizations. For all measures, divi-
sions with higher percentages of blacks had significantly lower
HEDIS quality scores (all
P < .05).
illustrates the cumulative effects of race and geog
raphy on the experience of black M+C enrollees compared
with white enrollees. W
ith few exceptions, the geographic dis
advantage is greater than the within-area racial disparity
example, the geographic disparity for controlling high blood
pressure was 11.9%, whereas racial disparities within a geo
graphic area ranged from 3.1% to 10.7%. The only consistent
Geography and Racial Disparities
VOL. 13, NO. 1 ■ THE AMERICAN JOURNAL OF MANAGED CARE ■ 53
■ Table 1. Description of Quality Measures
A1C indicates glycosylated hemoglobin; AMI, acute myocardial infarction; CABG, coronary artery bypass graft; PTCA, percutaneous
transluminal coronary angioplasty.
Quality No. of No. of No. of
Category Indicator Eligibility Plans Whites Blacks
Breast cancer screening Received a mammogram Women age 65-69 y 146 181 180 18 615
in past 2 y
Comprehensive diabetes care A1C screening and Persons age 65-75 y 148 83 166 12 889
eye exam diagnosed with diabetes (screening) (screening)
nd 82 397 and 12 855
β Blocker after heart attack Received prescription for Persons age 65 y or older 141 13 763 1302
β blocker within 7 days discharged alive after AMI
Cholesterol management Screening Persons age 65-75 y 145 24 384 2055
discharged alive after AMI,
CABG, or PTCA
Controlling high blood pressure Maintained blood pressure Persons age 46-85 y 141 46 284 7236
of 140/90 mm Hg diagnosed with hypertension
Follow-up after hospitalization Received follow-up with Persons age 65 y or older 139 7421 883
for mental illness mental health practitioner hospitalized for mental
30 days after hospital illness
exception to the pattern of across-geography variation in
quality for whites exceeding the within-region racial varia-
tion is cholesterol management; for 4 of 8 regions, the
white/black variation was higher within the region than
between regions. Other specific exceptions to this pattern
β-blocker use in the Mid-Atlantic region and A1C test-
ing in the West North Central region. We found significant
geography/race interactions for all measures except control-
ling high bloo
d pressure. This pattern is consistent with the
information in Table 3, which shows considerable variation
in the magnitude of the white/black difference in measures
The relative impact of eliminating the impact of geograph
ic and racial disparity can be illustrated by considering the
effect of eliminating within–census division racial disparity
but not equalizing performance across census divisions. As
can be seen in
, in such a case, the national level of
β-blocker use after heart attack would move from 85.1% to
87.1% for blacks and remain unchanged at 93.4% for whites.
, if racial variation within areas were allowed to
remain but regional variation were removed and all regions
were to achieve the levels experienced by the highest per-
forming area (in this case, New England), performance would
improve to 96.3% for blacks and 97.9% for whites. The
remaining racial variation would be similar to that seen with
a fully regression-adjusted, race-based approach (about 1.4%).
Notice that removing area variation would result in improve
ments in performance for both blacks and whites, but
would particularly benefit blacks (an absolute increase of
12.8% vs 4.5%).
DISCUSSION AND CONCLUSION
Our results are consistent with prior literature showing
considerable racial and geographic variability in quality of
care in the Medicare managed care sector. We observed small
but significant levels of racial disparity within all regions of
the country. The estimates we present are somewhat conser-
vative because we did adjust for differences in area income.
Because of the correlations among race, geography, and pover-
, some of the effect of area variation was removed by our
regression models. Despite these adjustors, both racial and
geographic disparities remained.
The presence of a strong correlation between the racial
composition of an area and the level of quality should not be
seen as evidence of a causal relationship—variation in quality
is complex and due to multiple factors. With all trends, excep-
tions also exist. The Mountain states have a very small per
centage of black beneficiaries, but are among the lowest in
quality as measured by HEDIS. Likewise, for breast cancer
screening, the measure with both the highest overall perfor-
mance and the greatest consistency across divisions, blacks
sometimes have minimally higher-quality scores than whites.
Despite the fact that the correlation should not be interpret
ed as indicating a causal relationship between racial composi
tion and quality, it may point to a strategy to reduce
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■ Table 2. Variation in HEDIS Measures Across Census Divisions for Whites*
Percentage Between Highest
Measure NE MTN PAC WNC SA ENC MA SC Region, %
Black 2.0 2.8 4.9 6.0 11.3 12.6 12.6 13.8 11.8
β-Blocker use 97.9 93.0 96.1 92.7 91.8 90.7 94.6 87.9 10.0
A1C testing 91.6 88.2 91.4 90.95 87.1 87.0 86.0 85.3 6.4
Eye examination 75.6 57.6 78.6 71.3 60.4 61.6 61.7 52.5 26.1
30-day mental illness follow-up 72.2 57.3 57.1 63.9 53.4 59.8 63.8 46.3 25.9
Cholesterol management 85.3 81.6 84.1 78.1 84.3 81.1 84.8 76.2 8.6
Breast cancer screening 84.3 74.6 78.4 75.8 77.3 73.5 72.9 69.9 14.4
Controlling blood pressure 65.4 53.7 58.5 62.3 65.6 61.7 64.8 61.0 11.9
*Adjusted for age, sex, and income.
ifference between the highest-performing and the lowest-performing division.
EDIS indicates Health Plan Employer Data and Information Set; NE, New England; MTN, Mountain; PAC, Pacific; WNC, West North Central;
A, South Atlantic; ENC, East North Central; MA, Middle Atlantic; SC, South Central; A1C, glycosylated hemoglobin.
race-based inequality that will ultimately improve the experi-
ence of both black and white beneficiaries.
We were unable to assess whether the racial disparities
were different in plans that did not submit data or submitted
data without identifiers that could be linked with CMS
enrollment and demographic data. Likewise, because the
HEDIS data do not contain information about the treating
physician, we cannot comment on the role individual physi-
cians play in racial disparity. However, the effects that we
describes are broad and consistent, suggesting that they are
the result of more than the behavior of a single physician.
Likewise, all regions have 9 or more plans, so the experience
of a single plan would not explain a regional effect.
CMS has an ongoing commitment to eliminating racial
disparity that is both commendable and just. It would be
rational for such a policy to focus on within-area interven-
tions. However, this approach would not necessarily remove
or lessen overall racial disparity because it fails to address the
component of racial disparity that is tied to geographic dispar-
ity. A purely within-area approach is unlikely to equalize the
average experience of black and white beneficiaries. The
importance of considering both racial and regional variation
is illustrated in Table 4, which highlights the disproportionate
impact of geographic disparity on black populations and the
need to recognize geographic variation as an important con-
tributor to existing racial disparity.
The obstacles associated with efforts targeted to a particu-
lar racial group are numerous and include problems with iden-
tifying eligible individuals. As a result, a geography-centered
approach to quality improvement may be a strong step toward
Geography and Racial Disparities
VOL. 13, NO. 1 ■ THE AMERICAN JOURNAL OF MANAGED CARE ■ 55
■ Table 3. Quality Differences Between White and Black Enrollees by Census Division and HEDIS Measure*
Measure NE MTN PAC WNC SA ENC MA SC
% Black 2.0 2.8 4.9 6.0 11.3 12.6 12.6 13.8
β-Blocker use 1.6 4.1 0.7 1.5 7.3 6.6 13.3 5.4
A1C testing −2.0 1.4 2.2 10.7 0.5 1.0 5.4 1.0
Eye examination 5.5 0.1 1.1 9.3 −2.5 −0.5 5.8 −3.4
30-day mental illness follow-up −0.6 0.2 6.3 10.6 9.9 25.8 18.2 7.9
Cholesterol management 4.0 7.5 0.2 16.2 7.2 14.3 11.6 9.4
Breast cancer screening −2.8 4.1 −0.4 3.2 −0.2 −2.5 3.5 −0.7
Controlling blood pressure 8.0 8.9 3.1 6.7 5.6 3.8 10.7 7.1
*Calculated as white rate minus black rate; a negative number indicates higher quality for black Medicare+Choice enrollees.
EDIS indicates Health Plan Employer Data and Information Se
t; NE, New England; MTN, Mountain; PAC, Pacific; WNC, West North Central;
SA, South Atlantic; ENC, East North Central; MA, Middle Atlantic; SC, South Central; A1C, glycosylated hemoglobin.
■ Table 4. Impact of Equalizing Race, Geography, or Both on Racial Variation in β-Blocker Use
the Level of the the Level of the
Equalize Best-performing Region; Best-performing Region;
No Change, Within-region Retain That Remove That
No Geographic Racial Region’s Racial Region’s Racial
Adjustment Variation Variation Variation
e Black White Black White Black
β-Blocker use 93.4% 85.1% 93.4% 87.1% 97.9% 96.3% 97.9% 97.9%
CY indicates calendar year
achieving the goal of lessening racial disparity in Medicare
managed care. We believe that this approach is particularly
well suited for improving performance in areas that have poor-
er quality for both black and white populations.
Portions of this work were presented at AcademyHealth’s 2005 Annual
Research Meeting, Boston, Mass, June 26-28, 2005. The authors would like to
thank Drs Trent Haywood, MD, Ignatious Bau, JD, and Alan Zaslavsky, PhD,
for their helpful comments and suggestions, and Russell Mardon, PhD, and
Rich Mierzejewski, MS, for their assistance with analysis.
Author Affiliations: From the Division of Health Policy and Man-
agement, University of Minnesota School of Public Health, Minneapolis,
Minn (BAV); the National Committee on Quality Assurance, Washing-
ton, DC (SHS, SS); and the Department of Health Administration &
Policy, College of Public Health, University of Oklahoma, Oklahoma City,
Funding Source: This work was supported by a grant from The California
Endowment (Targeted Capacity Expansion [TCE] grant 20032907).
Address correspondence to: Beth A. Virnig, PhD, MPH, Associate
Professor, Division of Health Policy and Management, University of
Minnesota School of Public Health, 420 Delaware St SE, MMC 729 A365-
Mayo, Minneapolis, MN 55455. E-mail: firstname.lastname@example.org.
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nalysis of quality of care measures for more than 5.1 million Medicare+Choice
nrollees in 2003 revealed consistent patterns of racial and geographic disparity.
The geographic disparity was greatest in areas with the highest concentration
of black elders suggesting that:
■ Efforts to reduce racial disparity cannot ignore geographic inequality.
strategy that focuses on equalizing geographic disparity may dispropor-
tionately benefit black elders and would be a strong step toward the reduction
of racial disparity.