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Efforts to reduce racial disparities in Medicare managed care must consider the disproportionate effects of geography



To examine the impact of geographic variation on racial differences in 7 of 15 Health Plan Employer Data and Information Set (HEDIS) measures that assess the quality of the Medicare managed care program (also known as Medicare+Choice). Cross-sectional analysis using the 2004 individual-level HEDIS for Medicare managed care plans and 2003 Medicare enrollment and demographic (ie, denominator) data for more than 5.1 million Medicare+Choice enrollees. Individual-level HEDIS data were linked with Medicare enrollment data. Hierarchical generalized 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 and sex. 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. 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.
© Ascend Media
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-
ing goal.
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-
aged care
and fee-for-service
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
. T
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
-quality hospitals
or health plans might point to inter-
ventions aimed at helping elders make
better choices—such as choosing a
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).
tudy Design:
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
and sex.
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
Medicare+Choice [M+C]).
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.
Briefly, individual
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.
Study Measures
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
est North
Central, Pacific, and Mountain.
Health plan size was classified as fewer than 10
000 members,
10 000 to 49 999 members, and 50 000 or more members.
52 www
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Figure. Geographic Distribution of the Black Population by County in
the United States, 1999
CY indicates calendar year
Statistical Analysis
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).
able 3
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
. For
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
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
(exam) (exam)
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
of discharge
Cholesterol management Screening Persons age 65-75 y 145 24 384 2055
discharged alive after AMI,
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
by region.
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
able 4
, 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%).
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
54 www
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Table 2. Variation in HEDIS Measures Across Census Divisions for Whites*
Percentage Between Highest
and Lowest
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
Table 3. Quality Differences Between White and Black Enrollees by Census Division and HEDIS Measure*
% 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
All Regions
All Regions
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,
Okla (AFC).
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:
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56 www
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ake-away Points
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.
... Evidence has also shown that racial/ethnic minority beneficiaries, which constitute a disproportionate share of dual-eligible beneficiaries, are often enrolled in lowerperforming managed care plans relative to non-minority beneficiaries [14]. Consequently, augmented D-SNP care coordination and MOC requirements may have stronger beneficial effects on health outcomes among racial/ethnic minorities who experience significant health disparities. ...
Full-text available
Background To determine if requiring Dual Eligible Special Need Plans (D-SNPs) to receive approval from the National Committee of Quality Assurance and contract with state Medicaid agencies impacts healthcare utilization. Methods We use a Multiple Interrupted Time Series to examine the association of D-SNP regulations with dichotomized measures of emergency room (ER) and hospital utilization. Our treatment group is elderly D-SNP enrollees. Our comparison group is near-elderly (ages 60–64) beneficiaries enrolled in Medicaid Managed Care plans ( N = 360,405). We use segmented regression models to estimate changes in the time-trend and slope of the outcomes associated with D-SNP regulations, during the post-implementation (2012–2015) period, relative to the pre-implementation (2010–2011) period. Models include a treatment-status indicator, a monthly time-trend, indicators and splines for the post-period and the interactions between these variables. We conduct the following sensitivity analyses: (1) Re-estimating models stratified by state (2) Estimating models including interactions of D-SNP implementation variables with comorbidity count to assess for differential D-SNP regulation effects across comorbidity level. (3) Re-estimating the models stratifying by race/ethnicity and (4) Including a transition period (2012–2013) in the model. Results We do not find any statistically significant changes in ER or hospital utilization associated with D-SNP regulation implementation in the broad D-SNP population or among specific racial/ethnic groups; however, we do find a reduction in hospitalizations associated with D-SNP regulations in New Jersey (DD level = − 3.37%; p = 0.02)/(DD slope = − 0.23%; p = 0.01) and among individuals with higher, relative to lower levels of co-morbidity (DDD slope = − 0.06%; p = 0.01). Conclusions These findings suggest that the impact of D-SNP regulations varies by state. Additionally, D-SNP regulations may be particularly effective in reducing hospital utilization among beneficiaries with high levels of co-morbidity.
... The disentangling of geographic and demographic disparities is important for policies designed to reduce disparities in health outcomes. There is increasing recognition that health disparities vary widely among states in the USA, such that the effects of geographic place may be difficult to disentangle from racial, ethnic, social, economic, or cultural determinants of health [9,10,20,22,23,26,32]. To see why this is a problem, consider that the apparent national disparities measured for one racial group relative to whites may reflect disadvantages and cultural differences in the geographic places where they are most heavily concentrated and thus reflect geographic rather than racial disparities. ...
Full-text available
Background We determined whether there were disparities in the likelihood of being diagnosed at a late stage for breast cancer (BC) or colorectal cancer (CRC) in each of 40 states, using the recently available US Cancer Statistics (USCS) database. Methods We extracted 981,457 BC cases and 558,568 CRC cases diagnosed in 2004?2009. Separate multilevel regressions were run for each state and each cancer type. Models included person and area-level covariates and were identically specified across states. The disparities foci were race or ethnicity (white, African-American, Hispanic, Asian, all other), gender, and age (<40, 40?49, 50?64, 65?74, and 75+). Using whites, males, and the oldest age group as reference groups, we noted the statistically significant disparities coefficients (p value ?0.05) and translated the findings via a set of maps of states in the USA. Results National disparity estimates were not consistent with disparities identified in the states. Some states had estimates consistent with the national average, while others did not. Patterns of disparities across states were different for each covariate and mapped separately. Conclusion National disparity estimates may mask what is true at the more local, state level because national estimates can confound the effects of race with place. Cancer control efforts are local and require locally relevant information to assess needs. Findings from the period 2004?2009 establish valuable benchmarks against which to assess changes following national health reform implemented in 2010. The USCS database is a valuable new resource that will facilitate future disparities research.
... * There are 1,781 outcomes for 555 patients in the analyzed data.MENTAL HEALTH AND DIABETES IN SC MEDICAID MANAGED CARE -Lòpez-De Fede et al incorporate measures of deprivation using census ZIP Census Tract Areas (ZCTA) to classify enrollees into low, medium, or high deprivation areas.13 This type of classification can help explain differing proportions of observed differences across racial and quality measures. ...
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To assess differences in services associated with mental health status and prescriptions among Medicaid patients diagnosed with diabetes mellitus. Secondary data analyses of South Carolina (SC) Medicaid enrollees. SC Medicaid enrollees with a diagnosis of diabetes mellitus (N = 555) continuously enrolled in either managed care (MC) or fee for service (FFS) programs between 2006 and 2008. Health Plan Employer Data and Information Set (HEDIS)-based diabetes management service outcomes including: 1) whether the recipient received a nephrology exam; 2) the number of eye exams received; 3) the number of low-density lipoprotein cholesterol services received; and 4) the number of Hemoglobin A1c blood tests conducted. Outcomes were fitted to regression models adjusting for sex, race, health program provider type (MC or FFS), rurality, poverty indexes, clinical risk group status, whether there was a female head of household, and indicators for classes of prescription pharmaceuticals (antipsychotics, antidepressants, and anticonvulsants). There are significant differences in the incidence of diabetes management service-use between enrollees in management plans and between recipients of classes of pharmaceuticals and mental health status. Enrollees in FFS have fewer claims associated with diabetes management services compared to counterparts in MC. Our early findings demonstrate the importance of efforts to collect HEDIS measures data and their potential as a resource for assessing quality of care. More importantly, this study illustrates the association between mental health status and associated pharmaceutical prescriptions.
The Medicare Modernization Act of 2003, implemented in 2006, increased managed care options for seniors. It introduced insurance plans for prescription drug coverage for all Medicare beneficiaries, whether they were enrolled in FFS or managed care (Medicare Advantage) plans. The availability of drug coverage beginning in 2006 served to free up budgets for FFS Medicare enrollees that could be used to make copayments for colorectal cancer (CRC) screening using endoscopy (colonoscopy or sigmoidoscopy). In 2007, Medicare eliminated the copayments required by seniors for CRC screening by endoscopy. Later in 2008, CRC screening by colonoscopy became part of the gold standard for CRC screening. This legitimized its use and offered even further encouragement to seniors, who may have been reluctant to undergo the procedure because of the non-pecuniary risks associated with it. In addition, 37 CRC screening interventions occurred during this timeframe to enhance compliance with screening standards. Using multilevel analysis of individuals’ endoscopy utilization, derived from 100% FFS Medicare claims, along with county-level market and contextual factors, we compare the periods before and after the MMA (2001–2005 to 2006–2009) to determine whether disparities in the utilization of endoscopic CRC screening occurred or changed over the decade. We examined Blacks, Asians, and Hispanics relative to Whites, and Females relative to Males (with race or ethnicity combined). We examined each state separately for evidence of disparities within states, to avoid confounding by geographic disparities. We expected that the net effect of the policy changes and the targeted interventions over the decade would be to increase CRC screening by endoscopy, reducing disparities. We saw improvements over time (reduced disparities relative to Whites) for Blacks and Hispanics residing in several states, and improvements over time for Females relative to Males in many states. For the vast majority of states, however, disparities persisted with Whites and Males exhibiting greater rates of utilization than other groups. States that undertook the interventions were more likely to have had improvements in disparities or positive disparities for women and minorities. While some gains were made over this time period, the gains were unevenly distributed across the USA and more work needs to be done to reduce remaining disparities.
The hospital admission for ambulatory care sensitive conditions (ACSCs) is a validated indicator of impeded access to good primary and preventive care services. The authors examine the predictors of ACSC admissions in small geographic areas in two cross-sections spanning an 11-year time interval (1995-2005). Using hospital discharge data from the Healthcare Cost and Utilization Project of the Agency for Healthcare Research and Quality for Arizona, California, Massachusetts, Maryland, New Jersey, and New York for the years 1995 and 2005, the study includes a multivariate cross-sectional design, using compositional factors describing the hospitalized populations and the contextual factors, all aggregated at the primary care service area level. The study uses ordinary least squares regressions with and without state fixed effects, adjusting for heteroscedasticity. Data is pooled over 2 years to assess the statistically significant changes in associations over time. ACSC admission rates were inversely related to the availability of local primary care physicians, and managed care was associated with declines in ACSC admissions for the elderly. Minorities, aged elderly, and percent under federal poverty level were found to be associated with higher ACSC rates. The comparative analysis for 2 years highlights significant declines in the association with ACSC rates of several factors including percent minorities and rurality. The two policy-driven factors, primary care physician capacity and Medicare-managed care penetration, were not found significantly more effective over time. Using small area analysis, the study indicates that improvements in socioeconomic conditions and geographic access may have helped improve the quality of primary care received by the elderly over the last decade, particularly among some minority groups.
This study investigates whether variation in Medicare Advantage plan performance on comprehensive diabetes care is explained by the case mix of plans. Using data on 513 Medicare Advantage plan-year observations for 2007 and 2008, the authors estimate multivariate regressions for 3 diabetes care quality measures: (1) hemoglobin screening, (2) low-density lipoprotein screening, and (3) retinal eye exam. Plan case mix is measured with the percentage of a plan's enrollees who have type 1 diabetes with and without comorbidities and the percentage of a plan's enrollees who have type 2 diabetes with and without comorbidities. Plans with a higher percentage of enrollees with type 1 diabetes with comorbidity and plans with a higher percentage of enrollees with type 2 diabetes without comorbidity have lower performance, on average. Finding evidence of a relationship between case mix and Healthcare Effectiveness Data and Information Set performance reinforces the argument for developing standardized risk adjustment or stratification methods in public reporting and pay-for-performance efforts.
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Ethnic minorities and some other patient groups consistently report lower scores on patient surveys, but the reasons for this are unclear. This study examined whether low scores of ethnic minority and other socio-demographic groups reflect their concentration in poorly performing primary care practices, and whether any remaining differences are consistent across practices. Using data from the 2009 English General Practice Patient Survey (2 163 456 respondents from 8267 general practices) this study examined associations between patient socio-demographic characteristics and 11 measures of patient-reported experience. South Asian and Chinese patients, younger patients, and those in poor health reported a less positive primary care experience than White patients, older patients and those in better health. For doctor communication, about half of the overall difference associated with South Asian patients (ranging from -6 to -9 percentage points) could be explained by their concentration in practices with low scores, but the other half arose because they reported less positive experiences than White patients in the same practices. Practices varied considerably in the direction and extent of ethnic differences. In some practices ethnic minority patients reported better experience than White patients. Differences associated with gender, Black ethnicity and deprivation were small and inconsistent. Substantial ethnic differences in patient experience exist in a national healthcare system providing universal coverage. Improving the experience of patients in low-scoring practices would not only improve the quality of care provided to their White patients but it would also substantially reduce ethnic group differences in patient experience. There were large variations in the experiences reported by ethnic minority patients in different practices: practices with high patient experience scores from ethnic minority patients could be studied as models for quality improvement.
Racial disparities in American health care outcomes are well documented. We investigated racial disparities in hospital mortality and adverse discharge disposition after brain tumor craniotomies performed in the United States from 1988 to 2004. We explored potential explanations for the disparities. The data source was the Nationwide Inpatient Sample. We used multivariate ordinal logistic regression corrected for clustering by hospital and adjusted for age, sex, primary payer for care, income in postal code of residence, geographic region, admission type and source, medical comorbidity, treatment year, hospital case volume, and disease-specific factors. Random-effects pooling was also used. A total of 99 665 craniotomies were studied. Hospital mortality and adverse discharge disposition (any discharge other than directly home) were more likely in black patients than others for all tumor types. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) for blacks were: hospital craniotomy mortality (OR, 1.64; 95% CI, 1.32-2.03; P < .001), and adverse discharge disposition (OR, 1.43; 95% CI, 1.31-1.56; P < .001). Medicaid patients had higher mortality, while private-pay patients had lower mortality. Hospital annual case volume was lower for black and Hispanic patients and for those with Medicaid as the primary payer in pooled analyses, whereas patients with private insurance received care at higher-volume hospitals. Black patients generally presented with higher disease severity, including more emergency or urgent admissions (OR, 1.71; 95% CI, 1.54-1.89; P < .001); more hemiparesis and hemiplegia for primary tumors, meningiomas, and metastases (P < .04 for all); and more hydrocephalus for acoustic neuromas (P = .1). Black patients died more often or had an adverse discharge disposition after tumor craniotomies in the United States in the period studied (1988-2004). Blacks had more severe disease at presentation and were treated at lower-volume hospitals for surgery. Other socially defined patient groups also showed disparities in access and outcomes of care.
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Underuse of coronary angiography is common among patients with acute myocardial infarction (AMI) and the magnitude of underuse varies across geographic areas. To examine the influence of patient demographic, clinical and hospital characteristics on underuse of coronary angiography, and the contribution of these factors to variation in underuse across geographic regions. Cohort study using data from the Cooperative Cardiovascular Project. Nine thousand four hundred fifty-eight patients in 95 hospital referral regions (HRRs) hospitalized for AMI in 1994 to 1995 and for whom angiography was rated necessary. Odds ratios (95% confidence intervals) associated with underuse of angiography according to patient and hospital characteristics. The difference between low and high rates of underuse of angiography across regions after controlling for regional differences in patient and hospital characteristics. Of those for whom angiography was rated necessary, 42% did not undergo the procedure. Underuse of angiography was associated with several patient demographic and hospital attributes (eg, female gender, black race, treatment in a hospital without angiography, treatment by a general practitioner) as well as with prevalent clinical characteristics, such as renal insufficiency, congestive heart failure, prior coronary artery bypass surgery, and chronic obstructive pulmonary disease. Across HRRs, variation in underuse ranged from 24.0% to 58.3%. The difference between low and high rates did not decline significantly after controlling for regional differences in patient or hospital characteristics. At the patient-level, rates of necessary angiography may be improved if we address disparities in care related to sociodemographic characteristics and to the technological capabilities of hospitals. In addition, practice guidelines should be updated to reflect clinical concerns about the risks and benefits of angiography and subsequent revascularization in certain patient sub-groups, both to provide appropriate guidance to physicians and to facilitate better estimates of underuse. The causes of regional variation in underuse do not appear to be related to regional differences in patient or hospital characteristics, and therefore, require further study.
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The health implications of regional differences in Medicare spending are unknown. To determine whether regions with higher Medicare spending achieve better survival, functional status, or satisfaction with care. Cohort study. National study of Medicare beneficiaries. Patients hospitalized between 1993 and 1995 for hip fracture (n = 614,503), colorectal cancer (n = 195,429), or acute myocardial infarction (n = 159,393) and a representative sample (n = 18,190) drawn from the Medicare Current Beneficiary Survey (MCBS) (1992-1995). EXPOSURE MEASUREMENT: End-of-life spending reflects the component of regional variation in Medicare spending that is unrelated to regional differences in illness. Each cohort member's exposure to different levels of spending was therefore defined by the level of end-of-life spending in his or her hospital referral region of residence (n = 306). 5-year mortality rate (all four cohorts), change in functional status (MCBS cohort), and satisfaction (MCBS cohort). Cohort members were similar in baseline health status, but those in regions with higher end-of-life spending received 60% more care. Each 10% increase in regional end-of-life spending was associated with the following relative risks for death: hip fracture cohort, 1.003 (95% CI, 0.999 to 1.006); colorectal cancer cohort, 1.012 (CI, 1.004 to 1.019); acute myocardial infarction cohort, 1.007 (CI, 1.001 to 1.014); and MCBS cohort, 1.01 (CI, 0.99 to 1.03). There were no differences in the rate of decline in functional status across spending levels and no consistent differences in satisfaction. Medicare enrollees in higher-spending regions receive more care than those in lower-spending regions but do not have better health outcomes or satisfaction with care. Efforts to reduce spending should proceed with caution, but policies to better manage further spending growth are warranted.
There are wide disparities between blacks and whites in the use of many Medicare services. We studied the effects of race and income on mortality and use of services. We linked 1990 census data on median income according to ZIP Code with 1993 Medicare administrative data for 26.3 million beneficiaries 65 years of age or older (24.2 million whites and 2.1 million blacks). We calculated age-adjusted mortality rates and age- and sex-adjusted rates of various diagnoses and procedures according to race and income and computed black:white ratios. The 1993 Medicare Current Beneficiary Survey was used to validate the results and determine rates of immunization against influenza. For mortality, the black:white ratios were 1.19 for men and 1.16 for women (P<0.001 for both). For hospital discharges, the ratio was 1.14 (P<0.001), and for visits to physicians for ambulatory care it was 0.89 (P<0.001). For every 100 women, there were 26.0 mammograms among whites and 17.1 mammograms among blacks. As compared with mammography rates in the respective most affluent group, rates in the least affluent group were 33 percent lower among whites and 22 percent lower among blacks. The black:white rate ratio was 2.45 for bilateral orchiectomy and 3.64 for amputations of all or part of the lower limb (P<0.001 for both). For every 1000 beneficiaries, there were 515 influenza immunizations among whites and 313 among blacks. As compared with immunization rates in the respective most affluent group, rates in the least affluent group were 26 percent lower among whites and 39 percent lower among blacks. Adjusting the mortality and utilization rates for differences in income generally reduced the racial differences, but the effect was relatively small. Race and income have substantial effects on mortality and use of services among Medicare beneficiaries. Providing health insurance is not enough to ensure that the program is used effectively and equitably by all beneficiaries.
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To examine national variation in use of the Medicare hospice benefit by older individuals before their death, and to identify individual characteristics and local market factors associated with hospice use. Retrospective analysis of Medicare administrative data. Hospice care. Older Medicare enrollees who died in 1996. Rate of hospice use per 1,000 older Medicare beneficiary deaths. Overall, 155 of every 1,000 older Medicare beneficiaries who die use hospice before death. This rate is significantly higher among younger older persons (P < .001), non-blacks (P < .001), persons living in wealthier areas (P < .001), and persons in urban areas (P < .001). Areas with a higher proportion of non-cancer diagnoses among hospice users have higher rates of hospice use for both cancer and non-cancer reasons than areas with a majority of hospice users having cancer diagnoses (P < .001). Hospice use is higher in areas with fewer hospital beds per capita (P < .001), areas with lower in-hospital death rates (P < .001), and areas with higher HMO enrollment (P < .001). Rates of hospice use are also positively related to average reimbursements for health care (P < .001) and to physicians per capita (P < .001). In the largest metropolitan statistical areas (MSAs), rates of hospice use vary more than 11-fold from a low of 35.15 (Portland, ME) to a high of 397.2 per 1,000 deaths (Ft. Lauderdale, FL). The wide variation in hospice use suggests that there is great potential to increase the number of users of the Medicare hospice benefit.
Substantial racial disparities in the use of some health services exist; however, much less is known about racial disparities in the quality of care. To assess racial disparities in the quality of care for enrollees in Medicare managed care health plans. Observational study, using the 1998 Health Plan Employer Data and Information Set (HEDIS), which summarized performance in calendar year 1997 for 4 measures of quality of care (breast cancer screening, eye examinations for patients with diabetes, beta-blocker use after myocardial infarction, and follow-up after hospitalization for mental illness). A total of 305 574 (7.7%) beneficiaries who were enrolled in Medicare managed care health plans had data for at least 1 of the 4 HEDIS measures and were aged 65 years or older. Rates of breast cancer screening, eye examinations for patients with diabetes, beta-blocker use after myocardial infarction, and follow-up after hospitalization for mental illness. Blacks were less likely than whites to receive breast cancer screening (62.9% vs 70.9%; P<.001), eye examinations for patients with diabetes (43.6% vs 50.4%; P =.02), beta-blocker medication after myocardial infarction (64.1% vs 73.8%; P<.005), and follow-up after hospitalization for mental illness (33.2 vs 54.0%; P<.001). After adjustment for potential confounding factors, racial disparities were still statistically significant for eye examinations for patients with diabetes, beta-blocker use after myocardial infarction, and follow-up after hospitalization for mental illness. Among Medicare beneficiaries enrolled in managed care health plans, blacks received poorer quality of care than whites.
This paper examines racial variation in quality of and access to care experienced by elderly persons enrolled in Medicare+Choice plans. We used eight individual-level Health Plan Employer Data and Information Set (HEDIS) measures to compare whites with blacks, Asians, Hispanics, and Native Americans. Across all measures, black enrollees received lower-quality care. Hispanics and Native Americans were less likely to receive some types of care but were as likely or more likely to receive other types of care. Asians received equal or better care for all measures. It is important that studies of health care quality include all racial subgroups since the black/white patterns may not apply.
To examine state variability in diabetes care for Medicare beneficiaries and the impact of certain beneficiary characteristics on those variations. Medicare beneficiaries with diabetes, aged 18-75 years, were identified from 1997 to 1999 claims data. Claims data were used to construct rates for three quality of care measures (HbA(1c) tests, eye examinations, and lipid profiles). Person-level variables (e.g., age, sex, race, and socioeconomic status) were used to adjust state rates using logistic regression. A third of 2 million beneficiaries with diabetes aged 18-75 years did not have annual HbA(1c) tests, biennial eye examinations, or biennial lipid profiles. There was wide variability in the measures among states (e.g., receipt of HbA(1c) tests ranged from 52 to 83%). Adjustment using person-level variables reduced the variance in HbA(1c) tests, eye examinations, and lipid profiles by 30, 23, and 27%, respectively, but considerable variability remained. The impact of the adjustment variables was also inconsistent across measures. Opportunities remain for improvement in diabetes care. Large variations in care among states were reduced significantly by adjustment for characteristics of state residents. However, much variability remained unexplained. Variability of measures within states and variable impact of the adjustment variables argues against systems effects operating with uniformity on the three measures. These findings suggest that a single approach to quality improvement is unlikely to be effective. Further understanding variability will be important to improving quality.