Background Since 1997, all managed-care plans administered byMedicare have reported on quality-of-care measures from theHealth Plan Employer Data and Information Set (HEDIS). Studiesof early data found that blacks received care that was of lowerquality than that received by whites. In this study, we assessedchanges over time in the overall quality of care and in themagnitude of racial disparities in nine measures of clinicalperformance.
Methods In order to compare the quality of care for elderlywhite and black beneficiaries enrolled in Medicare managed-careplans who were eligible for at least one of nine HEDIS measures,we analyzed 1.8 million individual-level observations from 183health plans from 1997 to 2003. For each measure, we assessedwhether the magnitude of the racial disparity had changed overtime with the use of multivariable models that adjusted forthe age, sex, health plan, Medicaid eligibility, and socioeconomicposition of beneficiaries on the basis of their area of residence.
Results During the seven-year study period, clinical performanceimproved on all measures for both white enrollees and blackenrollees (P<0.001). The gap between white beneficiariesand black beneficiaries narrowed for seven HEDIS measures (P<0.01).However, racial disparities did not decrease for glucose controlamong patients with diabetes (increasing from 4 percent to 7percent, P<0.001) or for cholesterol control among patientswith cardiovascular disorders (increasing from 14 percent to17 percent; change not significant, P=0.72).
Conclusions The measured quality of care for elderly Medicarebeneficiaries in managed-care plans improved substantially from1997 to 2003. Racial disparities declined for most, but notall, HEDIS measures we studied. Future research should examinefactors that contributed to the narrowing of racial disparitieson some measures and focus on interventions to eliminate persistentdisparities in the quality of care.
Despite decades of impressive scientific and clinical innovations,substantial deficiencies persist in the quality of health carein the United States,1,2,3 and troubling disparities exist inthe quality of care for racial and ethnic minorities.4,5,6,7,8Efforts to improve the quality of health care and attempts toreduce disparities in treatment may be connected, because variationsin appropriate care that are not caused by clinical factorsor by the informed preferences of patients are, by definition,indicators of suboptimal care.9
Recently, signs of an improved quality of care have been evidentboth within and outside managed-care settings.10,11,12 Theseimprovements may be related to efforts to measure and reportclinical performance. However, little is known about whethergeneral improvements in the quality of care are also accompaniedby reductions in racial and ethnic disparities. Quality-improvementefforts may reduce such disparities, but they also may haveno effect or even increase disparities.
A recent study of patients with end-stage renal disease suggestedthat broadly targeted interventions to improve the quality ofcare were associated with reduced racial disparities in hemodialysisdosing.13 However, an analysis of the administration of influenzavaccine to Medicare beneficiaries showed that higher rates ofvaccination in managed-care plans, as compared with the ratesin fee-for-service plans, were not associated with reduced disparitieson the basis of race.7
Within the Medicare program, managed-care plans provide an opportunesetting to examine the relation between improvements in thequality of health care and changes in racial disparities. Healthplans are particularly well positioned to improve care, becausethey finance and monitor the provision of health services toenrollees.12,14 Since 1997, all health plans participating inMedicare have been required to submit publicly reported performancemeasures from the Health Plan Employer Data and InformationSet (HEDIS) developed by the National Committee for QualityAssurance (NCQA). In previous studies of HEDIS measures, blackenrollees were less likely to receive beta-blockers after myocardialinfarction, eye examinations after receiving a diagnosis ofdiabetes, follow-up care after hospitalization for mental illness,and influenza vaccinations.6,8
In this study, we report on trends in the quality of care providedto enrollees in Medicare managed-care plans from 1997 to 2003and assess whether racial disparities in quality changed duringthis period. Evaluation of HEDIS performance trends by racecan provide important information about whether broad improvementsare associated with narrowed or widened racial disparities inthe quality of medical care.15
Methods
Study Population
We obtained HEDIS data for Medicare managed-care plans fromthe Centers for Medicare and Medicaid Services (CMS) coveringseven reporting years (1998 to 2004) with information regardingclinical care that was delivered from 1997 to 2003. These datacontained 2,691,482 observations for enrollees who were eligiblefor at least one of the nine HEDIS indicators described in Table 1.Each observation included the patient's health identificationcode and health plan, as well as variables indicating eligibilityfor and adherence to each HEDIS measure.
Table 1. Description of HEDIS Measures of Quality of Care.
NCQA developed detailed specifications for measures that definecriteria for inclusion in the sample and the method for thecalculation of adherence to each HEDIS quality indicator. Toensure that health plans prepared data in accordance with NCQAspecifications and that the data would be valid for use in healthplan comparisons, the CMS conducted two audits of HEDIS reportingby Medicare managed-care plans during 1998. The audits includeda review of data systems, interviews with health plan personnel,and a centralized review of medical records. In the initialphase of these audits, 90.3 to 96.6 percent of health plansthat reported data were fully compliant with the technical specificationsof the three HEDIS measures of effectiveness of care. Aftercompletion of the audit, all plan-reported rates were within1 percentage point of audit-derived rates.16
Using the health identification code, we matched each enrolleewith HEDIS data on at least one measure with the file of Medicareenrollees for the corresponding year to obtain demographic informationon the race, age, sex, and ZIP Code of residence of beneficiariesand to ascertain whether they also had Medicaid coverage. Weachieved a match rate of 96 percent, or 2,573,166 observations.We excluded 229,938 observations for enrollees who were underthe age of 65 years, 201,323 observations for enrollees whowere of a race or ethnic background other than black or white,and 44,150 observations for enrollees who died during the yearof measurement (with some overlap of enrollees in these threeexclusion categories). This process yielded a total study sampleof 2,122,809 observations, of which 9.2 percent were for blackenrollees. To reduce the likelihood that trends in performanceand disparities might be a result of the entrance and exit ofhealth plans from Medicare, our primary analysis excluded 319,358observations from health plans with less than five years ofcontinuous participation in Medicare managed care. (In a secondaryanalysis, we did not exclude health plans with less than fiveyears of participation and instead included observations foreligible patients from all health plans that participated inMedicare managed care for at least one year during the studyperiod. Results were similar and are not shown.)
The primary study sample included 1,803,451 observations (9.4percent for black enrollees) from 183 health plans for the nineHEDIS indicators. The sample size of observations over the entirestudy period ranged from 79,133 for the beta-blocker measureto 1,035,946 for the breast-cancer-screening measure.
Study Variables
Our dependent variables were the receipt of each HEDIS indicator(Table 1) by eligible enrollees. Our chief independent variablewas black or white race, and these designations are highly accuratein Medicare enrollment data.17 Covariates included age, sex,enrollment in Medicaid, the percentage of persons 65 years ofage or older within the enrollee's ZIP Code with an income ofless than the federal poverty level, the percentage of persons65 years of age or older in the enrollee's ZIP Code who hadattended college, and urban residence. Data on poverty, educationallevel, and urban residence within a particular ZIP Code wereobtained from the 2000 U.S. Census.
Statistical Analysis
We assessed demographic and socioeconomic characteristics ofthe population that was eligible for each HEDIS measure. Forwhite enrollees and black enrollees in each year, we calculatedthe performance for each HEDIS measure as the percentage ofeligible enrollees who were reported to have achieved the performancemeasure.
To determine adjusted rate differences between white enrolleesand black enrollees, we fitted separate linear models predictingreceipt of each HEDIS measure to each year's data. To assesstrends, we fitted models to combined data from the first andlast usable year for each measure. We assessed the overall trendin the quality of care by testing the significance of the yeareffect in the model without a race-by-year interaction. To assesschanges in racial disparity on the risk-difference scale, wetested the significance of a race-by-year interaction term.
In order to determine the adjusted effect of variables regardingdemographic characteristics, health plan, and socioeconomicfactors on racial disparities in the quality of care, we fittedthree versions of each of these linear regression models. Adjustedrates and differences then corresponded to those predicted atthe mean values of the adjuster variables. The first model adjustedfor age and sex. The second model added to the first model variablesfor rural residence and health plan (defined as a Medicare managed-carecontract). Because each contract is typically limited to a specificstate (except for a few health plans that serve contiguous areasin adjacent states) and the addition of variables by state didnot significantly alter regression results after controllingfor health plan, we did not include further geographic controls.The third model added to the second model variables for Medicaideligibility and ZIP-Code-level variables for income and education.All analyses were performed using SAS statistical software (version9.1) and are reported with two-tailed P values. Our study protocolwas approved by the Human Studies Committee of Harvard MedicalSchool and the CMS Privacy Board.
Results
The demographic and socioeconomic characteristics of the enrolleeswho were eligible for each of the four categories of HEDIS measuresduring the initial and final years of measurement are shownin Table 2. As compared with the proportion of white enrollees,a higher proportion of black enrollees was female, eligiblefor Medicaid, and living in urban areas that had higher ratesof poverty and lower rates of educational attainment. Blackenrollees made up a higher proportion of the sample for diabetes-relatedmeasures — a finding that probably reflects the higherprevalence of this disease among blacks. The demographic characteristicsof white enrollees and black enrollees were very stable duringthe seven-year study period, except for an increase in the percentageof Medicaid recipients and a decrease in the percentage of whitewomen in urban areas who were eligible for the breast-cancer-screeningmeasure.
Table 2. Demographic Characteristics of the Study Population as Measured by HEDIS, by Year.
The quality of care improved during the study period on allmeasures for both blacks and whites (P<0.001 for all timetrends) (Table 3). For black enrollees, the absolute improvementranged from 6 percent (for the completion of mammography) to43 percent (for a level of low-density lipoprotein [LDL] cholesterol<130 mg per deciliter for patients with diabetes). For whiteenrollees, the absolute improvement ranged from 3 percent (forcompletion of mammography) to 37 percent (for LDL cholesterol<130 mg per deciliter for enrollees with diabetes).
Table 3. Adherence to HEDIS Measures by Race and Year.
The disparity between blacks and whites narrowed significantlyfor seven of the nine measures in the study (P<0.01). However,for control of levels of glycosylated hemoglobin, the disparitybetween blacks and whites increased from 4 percent to 7 percent(P<0.001). For the measure of the percentage of enrolleeswho achieved an LDL cholesterol level of less than 130 mg perdeciliter after a myocardial infarction or a coronary procedure,racial disparities were statistically unchanged (P=0.72).
Table 4 summarizes the results of the multivariable models.Adjustment of the HEDIS performance rates for age and sex (model1) had little effect on estimated disparities. Additional adjustmentfor the enrollee's health plan and rural residence (model 2)reduced the disparities between blacks and whites in the initialand final year for six of the nine HEDIS measures and renderedthe race-by-year interaction for control of glycosylated hemoglobinlevels no longer statistically significant. Additional adjustmentfor the socioeconomic indicators of Medicaid coverage and residencein high-poverty and low-education areas (model 3) further reducedthe magnitude of disparities between blacks and whites in boththe initial and the final year. In all models, however, thedecrease between the initial and the final year in the magnitudeof disparities remained statistically significant for sevenof the nine HEDIS measures we studied (P<0.01 for all race-by-yearinteraction terms).
Table 4. Racial Differences in HEDIS Performance Adjusted for Demographic, Socioeconomic, and Health-Plan Effects.
Figure 1 illustrates two patterns of time trends in the qualityof care for blacks and whites who were enrolled in Medicaremanaged care. For the testing of LDL cholesterol among enrolleeswith diabetes (Figure 1A), improvements for white enrolleesand black enrollees were accompanied by a reduction in the racialdisparity between these two groups from 1999 to 2003. In contrast,for the control in levels of LDL cholesterol below 130 mg perdeciliter after a myocardial infarction or a coronary procedure(Figure 1B), clinical performance improved substantially forboth white enrollees and black enrollees but with no reductionin the disparity between blacks and whites from 1999 to 2002.
Figure 1. Trends in Receipt of Two HEDIS Measures for Enrollees in Medicare Managed-Care Plans, by Race.
Panel A shows large overall improvements in the quality of care by year and a significant narrowing of the disparity between blacks and whites in testing for levels of low-density lipoprotein (LDL) cholesterol among patients with diabetes (P<0.001). Panel B also shows large overall improvements in the quality of care by year but no narrowing of the disparity between races in the control of LDL cholesterol below a level of 130 mg per deciliter after myocardial infarction (MI) or a coronary revascularization procedure (P=0.72).
Discussion
In this time-trend analysis of nine clinical performance measuresfor enrollees in Medicare managed-care plans from 1997 to 2003,quality of care improved on all nine measures and was accompaniedby a significant reduction in the disparities between blacksand whites on seven of the measures. Both trends were substantialand were not explained by changes in the sociodemographic characteristicsof enrollees or in the health plans that participated in theMedicare managed-care program during the study years. In contrast,racial disparities did not decrease over time for two HEDISmeasures assessing clinical outcomes for diabetes and heartdisease.
An adjustment for rural residence and health plan narrowed theobserved magnitude of racial disparities for most HEDIS measures— a finding suggesting that part of the racial disparitywas related to the disproportionate enrollment of black beneficiariesin health plans or in regions with lower performance on thesemeasures. Even the adjusted models, however, showed decreasedracial disparities over time.
In spite of observed improvements, performance as measured byHEDIS indicators approached or exceeded 90 percent on only threemeasures (testing of glycosylated hemoglobin and LDL cholesterolfor patients with diabetes and the frequency of prescribingbeta-blockers for patients with cardiovascular disorders). Onthe other six measures, performance was less than 82 percentfor both white enrollees and black enrollees. For these importantclinical services, gaps between actual and optimal care remainedsubstantial.2
Although racial disparities decreased to 2 percent or less forfive of the six process measures, disparities remained at 7percent or greater for the three measures assessing clinicaloutcomes (control of LDL cholesterol for enrollees with eitherdiabetes or heart disease and control of glycosylated hemoglobin)in the most recent study years. Although we controlled for socioeconomicvariables, the financial burden of the use of lipid-loweringand glucose-lowering medications may have contributed to thegreater disparity we observed on these outcome measures, whichoften require sustained therapy in addition to intermittenttesting.18,19
Our findings are consistent with the proposition that improvementsin the quality of care are associated with reductions in racialdisparities.9 By increasing the consistency of the deliveryof care, interventions such as the use of reminder systems,disease management programs, and feedback to health care providersmay decrease variation on the basis of nonclinical factors suchas race.20,21,22,23,24 A greater awareness among beneficiariesor their health care providers about appropriate services forbreast-cancer screening, diabetes control, and cardiovascularcare could also explain our results.
We believe that the observed declines in racial disparitieswere unlikely to have resulted from specific health plan programstailored to improve care for black enrollees. The representativesof nearly half of the health plans who responded to a recentsurvey did not collect data regarding race and ethnic backgroundof enrollees.25 In addition, efforts by health plans to developprograms to eliminate disparities in the quality of care onthe basis of racial and ethnic factors are relatively recent.26Since late 2003, the CMS has provided data regarding enrollees'race and ethnic background to participating health plans andhas required that they conduct at least one project to reducedisparities.27 However, the reduction in disparities we observedlargely preceded these efforts.
The strengths of this study were the inclusion of a large, nationallyrepresentative sample of enrollees and the use of quality measuresthat have been audited and publicly reported by health plansfor several years. Since all health plans participating in Medicarewere required to report data regarding the quality of care,we avoided the selection bias associated with voluntary reportingprograms.28 We were able to adjust for health plan effects andseveral measures of socioeconomic position that may have confoundedor mediated the relationship between race and the quality ofcare. By limiting our primary analysis to plans with five ormore consecutive years of participation in Medicare, we addressedthe possibility that changes in the quality of care or reductionsin disparities might be an artifact of health plans' selectivelyentering or exiting the Medicare program.
Our study had several limitations. It was not designed to addressthe factors that may have caused the observed results or todetermine whether similar trends would have been observed foraspects of the quality of care beyond those assessed by thepublic reporting of HEDIS measures. Furthermore, patients inMedicare fee-for-service and non-Medicare settings were notincluded in the HEDIS data set. Previous studies have shownsimilar racial disparities in fee-for-service and managed-caresettings,7,29 but whether our finding of decreasing racial disparitiesover time extends beyond Medicare managed care remains an openquestion.
Because enrollment data for Medicare did not reliably identifyenrollees who were Hispanic, Asian, or Native American duringour study years,17 we chose not to analyze trends among theseethnic and racial groups. Such studies are clearly needed. Thedata also lacked detailed clinical information to provide risk-adjustedoutcome measures. Although unmeasured clinical factors mightpartially explain cross-sectional differences in outcome measuresby race, such factors would be less likely to explain changesin racial disparities over time.
Several studies have suggested that racial differences in thequality and outcomes of care may be related to differences inthe site of care between white and minority patients.30,31 Althoughwe were able to analyze the contribution of health plans toracial variation in the quality of care, the current HEDIS reportingprotocol does not collect information on providers and practiceswithin plans. In addition, the attitudes of patients about healthin general or about their ability to modify their diet or physicalactivity32,33 may contribute to racial disparities in clinicaloutcomes, but these variables were not available in our analysis.
Our findings have two important policy implications. For policymakers,health plan leaders, purchasers, and providers who are concernedwith improving the quality of care and with reducing disparities,appropriate data are essential to gauge progress on each ofthese objectives. Measures of quality should be stratified byrace, ethnic background, and socioeconomic position —an approach that is now rarely possible with publicly reporteddata on the quality of care.9,34,35 Second, although racialdisparities decreased on some measures of quality, interventionsthat are focused on black enrollees or their health care providersmay still be necessary to eliminate the disparities that remain.
In summary, improvements in the quality of care among enrolleesin Medicare managed-care plans since 1997 have been accompaniedby reduced racial disparities in most, but not all, measuresof clinical performance we studied. Effective collaborativeefforts by policymakers, health-plan administrators, clinicians,and patients may be needed to eliminate these disparities entirely.
Supported by an institutional National Research Service Award(5 T32 HP11001-15) from the Health Resources and Services Administration,by the Primary Care Research Fund of Brigham and Women's Hospital,and by a grant (P01-HS10803) from the Agency for HealthcareResearch and Quality.
We are indebted to Robert E. Wolf and Matthew Cioffi for statisticaland programming contributions; to Kimberly Elmo and Judy Gilesfor help with data acquisition; and to the Research Data AssistanceCenter at the University of Minnesota.
Source Information
From the Division of General Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital (A.N.T., E.C.S., J.Z.A.); the Department of Health Care Policy, Harvard Medical School (A.N.T., A.M.Z., J.Z.A.); and the Department of Health Policy and Management, Harvard School of Public Health (E.C.S., J.Z.A.) — all in Boston.
Address reprint requests to Dr. Ayanian at the Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave., Boston, MA 02115, or at ayanian{at}hcp.med.harvard.edu.
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