Racial Variation In Quality Of Care Among Medicare+Choice Enrollees Black/white patterns of racial disparities in health care do not necessarily apply to Asians, Hispanics, and Native Americans. by Beth A. Virnig, Nicole Lurie, Zhen Huang, Dorothea Musgrave, A. Marshall McBean, and Bryan Dowd ABSTRACT: 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. Since the landmark 1985 Report of the Secretary s Task Force on Black and Minority Health, minority Americans have been consistently shown to have poorer health status and worse health outcomes than white Americans have. 1 These differences have remained so persistent that Healthy People 2010 has as one of its two overarching goals the elimination of racial and ethnic disparities in health. 2 These health status differences likely arise from several factors, including differences in access to care and in the quality of care received. Racial and ethnic disparities have now been documented across health conditions and in hospital, ambulatory care, and community settings. 3 This phenomenon has perhaps been best documented in the Medicare fee-for-service (FFS) program, through which a majority of Americans age sixty-five and older receive basic health care coverage. 4 Differences in care patterns for black and white Medicare FFS beneficiaries have been found for cancer treatment, treatment after acute myocardial infarction, use of surgical procedures, hospice use, and preventive care. 5 Beth Virnig is an assistant professor, Zhen Huang is a research fellow, Marshall McBean is a professor, and Bryan Dowd is Mayo Professor of Public Health, in the Division of Health Services Research and Policy, University of Minnesota School of Public Health, in Minneapolis. Nicole Lurie is senior natural scientist and Paul O Neill Alcoa Professor at RAND in Arlington, Virginia. Dorothea Musgrave is a health services officer with the U.S. Public Health Service in Baltimore. 224 November/December 2002 2002 Project HOPE The People-to-People Health Foundation, Downloaded Inc. from HealthAffairs.org on December 05, 2018.
Racial Variation Racial disparities also have been documented among beneficiaries in the Medicare+Choice (M+C) program in 2001. Eric Schneider and colleagues used data from the Medicare Current Beneficiary Survey (MCBS) to demonstrate that disparities in adult vaccination rates between blacks and whites persisted despite the presence of managed care. 6 More recently (in 2002) Schneider and colleagues reported that differences in quality of care are also present for black and white M+C enrollees based on 1998 Health Plan Employer Data and Information Set (HEDIS) data. 7 Less is known about disparities in access and quality for other ethnic populations. The expected growth in the Hispanic and Asian populations makes increasing knowledge about these populations important. There is very little information at all regarding access to and quality of care for Native Americans despite their documented poor health status. In this paper we analyze data from the health plans reporting on quality of care for M+C beneficiaries, using data from 2000 HEDIS reports for major population subgroups in the United States: whites, African Americans, Hispanics, Asians, and Native Americans. HEDIS is a nationally accepted system used to measure performance in health plans. We combine individual-level HEDIS data with Centers for Medicare and Medicaid Services (CMS) enrollment data to examine whether racial/ethnic differences in receipt of care previously observed in the FFS system persist within managed care plans. These data are unique in that they are one of the first reports of variation in quality in managed care for Hispanics, Asians, and Native Americans. Using data from 2000, we update the 2002 analyses of Schneider and colleagues, which were limited to comparisons between whites and blacks. 8 Data And Study Methods Data. We merged individual-level HEDIS data for reporting year 2000 (based on 1999 experience) with health plan enrollment data obtained from the CMS. All but one of the contracting entities required by the CMS to submit individual-level data submitted the data for the reporting year. The 301 M+C plans submitted 7,498,496 records with HEDIS information. The average number of records per M+C contract was 27,875 (1,189 484,738 records per contract). Individual records submitted by plans were identified via the Health Insurance Claim (HIC) number, a unique identifier used by Medicare. The HICs were merged with the 1999 Medicare Denominator file to obtain information on the age, race, sex, and state and county of residency. The HICs also were merged with the Group Health Plan (GHP) Masterfile to confirm that each submitted record showed corresponding plan enrollment during the given contract year. People were excluded from this analysis if they did not have a valid HIC number (424,707 records, 5.66 percent of total submitted records). Records also were excluded if they matched with the Medicare Denominator file but had unknown HEALTH AFFAIRS ~ Volume 21, Number 6 225
race (29,982 records) or if there was no evidence of managed care enrollment in either the Denominator file or the GHP Masterfile (18,547 records). Entire plans and their enrollees were excluded from this analysis if their submitted records failed to achieve at least a 95 percent match on HIC numbers (9.63 percent of total contracts, representing 677,701 submitted records). Study measures. For this analysis six quality-of-care measures were selected: breast cancer screening mammogram; control of cholesterol after acute cardiovascular events; use of beta-blockers after acute myocardial infarction (AMI); diabetes care (hemoglobin A1C testing, eye exams, and LDL testing); and control of high blood pressure. A single access-to-care measure was used (use of preventive or ambulatory services) because it had the least ambiguous interpretation. We imputed income indirectly based on the average household income by ZIP code for households with persons age sixty-five and older using figures from the 1990 U.S. Census Bureau, because data from the 2000 census are not yet available. 9 Statistical analysis. All analyses were conducted using SAS. All measures were adjusted for age and sex using direct standardization methods. 10 Logistic regression was used to estimate adjusted odds ratios. Income adjustment was conducted for all multivariate regression models to confirm that estimates of racial/ethnic variation were not explained by income differences. Unless noted, reported results do not include income adjustment. Study Results Across all measures, whites are the largest population group and Native Americans the smallest (Exhibit 1). The denominators for each measure vary, reflecting the differences in criteria for including individuals in that measure. Access to ambulatory care has the largest denominator, defined as all persons with continuous enrollment (no more than one gap in enrollment of up to forty-five days) during EXHIBIT 1 Number Of Persons Contributing To Each Quality Measure, By Race/Ethnicity, 2000 Measure White Black Asian Hispanic Native American Other Mammogram Cholesterol management after acute cardiovascular event 201,549 20,915 19,566 1,382 2,398 285 2,429 675 109 a 11,727 591 Beta-blocker after AMI Diabetes care (3 measures) 13,807 71,896 1,094 11,339 168 2,476 406 3,379 a 97 327 5,108 Controlling high blood pressure Access to ambulatory/preventive care 61,407 3,518,931 7,682 298,999 783 72,463 1,587 104,160 39 2,103 1,504 114,936 SOURCES: Health Plan Employer Data and Information Set (HEDIS) 2000 individual-level data; and Medicare 1999 Denominator data. NOTE: AMI is acute myocardial infarction. a Numbers are too small to report. 226 November/December 2002
Racial Variation calendar year 1999 and age sixty-five and older. The mammography measure is limited to females ages 52 69. The denominator population for the diabetes measures is the smallest, limited to persons with a diagnosis of diabetes in their medical or pharmacy claims. As has been previously reported, persons identified as black consistently received poorer health care relative to that of whites across all measures (Exhibit 2). Blacks with diabetes were less likely than whites were to have an annual eye exam or to have HbA1C testing. Blacks were also less likely to have an ambulatory/ preventive care visit in the measurement year. Hispanics were similarly less likely than whites were to have a mammogram, LDL cholesterol testing after an acute cardiovascular event or as part of their diabetes care, or an ambulatory or preventive care visit with a doctor. However, Hispanics were as likely as whites were to receive beta-blockers after an acute AMI and eye exams as part of their diabetes EXHIBIT 2 Likelihood That Persons Of Various Racial/Ethnic Groups Would Receive Various Measures Of Quality, Adjusted For Age And Sex, Odds Ratios And Rate Per 100 Medicare+Choice Enrollees, 2000 Odds ratio Measure White Black Asian Hispanic Native American Other Mammogram Cholesterol management after acute cardiovascular event Beta-blocker after AMI 0.84 a 0.64 a 0.64 b 5 1.20 1.16 0.74 a 0.72 b 0.97 0.41 b 4 1.24 1.16 Diabetes care, HbA1C testing Diabetes care, eye exam Diabetes care, LDL-C screening 0.72 a 0.78 a 0.65 a 1.31 b 1.83 a 1.30 b 0.93 0 0.88 0.85 1.73 0.45 1.23 b 1.46 a 1.20 b Control high blood pressure Access to ambulatory/preventive care 0.84 a 0.57 a 1.27 0 1.17 0.67 a 1.79 0.64 a 8 0.82 a Rate per 100 enrollees Mammogram Cholesterol management after acute cardiovascular event Beta-blocker after AMI 75.61 67.71 88.75 72.24 57.24 83.40 76.47 71.58 90.15 69.67 60.00 88.46 56.55 76.17 72.25 90.41 Diabetes care, HbA1C testing Diabetes care, eye exam Diabetes care, LDL-C screening 80.19 67.37 73.74 74.65 61.63 64.49 84.10 79.07 78.57 79.08 67.34 71.21 77.45 78.16 55.71 83.23 75.09 77.11 Control high blood pressure Access to ambulatory/preventive care 35.76 87.64 31.97 80.06 41.51 87.74 39.45 82.58 49.98 82.01 37.63 85.26 SOURCES: Health Plan Employer Data and Information Set (HEDIS) 2000 individual-level data; and Medicare 1999 Denominator data. NOTE: AMI is acute myocardial infarction. a p <.01 vs. whites. b p <.05 vs. whites. c Numbers are too small to report. HEALTH AFFAIRS ~ Volume 21, Number 6 227
Compared with white women, Hispanic women showed greater disparities in receipt of mammograms than did black women. care. Native Americans also were less likely to receive a mammogram, to have their LDL cholesterol checked as part of their diabetes care, and to have HbA1C testing (somewhat less likely, although this difference was not statistically significant). Both Native Americans and Hispanics were more likely than whites were to have their high blood pressure controlled. Asians and persons whose race was labeled other were as likely as or more likely than whites were to receive care in all domains. While Asians were equally likely to have an ambulatory/preventive care visit, persons labeled as other were less likely to have such a visit. Discussion As did Schneider and colleagues, we found significant differences in quality of care experienced by blacks compared with that of whites. 11 We show that these differences are also present for Hispanics and Native Americans, although the magnitudes and patterns may differ. For example, compared with white women, Hispanic and Native American women showed even greater disparities in receipt of a mammogram than did black women. This may be the result of different cultural attitudes and beliefs with regard to the effectiveness of mammography, or it may simply reflect the fact that most of the effort to increase breast cancer screening rates for minority women has focused on African Americans. The patterns for diabetes care are different. Black persons were the only racial group for which the rates were significantly lower than for whites. For all racial groups, except Native Americans, the rate of HbA1c testing is greater than the rates for either eye exams or LDL cholesterol screening. This difference probably reflects the fact that HbA1c testing has been a greater focus recently than LDL cholesterol screening or eye examination. Age and sex adjustment. Although global HEDIS measures are unadjusted, our analysis adjusts for age and sex. There is always a concern that failing to account for differing age and sex distributions could result in an erroneous conclusion. In this case, without adjustment, it is possible that differences attributed to race were actually due to differences in the age and sex composition of the populations being compared. The persistence of the racial effects after likely confounding factors (such as age and sex) are controlled for makes this less likely to be the case. Study limitations. There are several limitations to this study. First, M+C enrollees represent a self-selected population. The results of this analysis may not apply to Medicare beneficiaries in the FFS program. Second, data on race and ethnicity were obtained from the Medicare program and may be inaccurate in some cases. However, comparisons of the accuracy of Medicare s race codes with self-reports (using MCBS data) suggest that the primary error is mistakenly identifying some 228 November/December 2002
Racial Variation Asians, Native Americans, and Hispanics as white. 12 The impact of the misclassification has not been examined to date. Third, all racial groups are, by their very nature, heterogeneous. Thus, while Asians as a group appear to receive better-quality care, averages may mask major problems in access or quality thought to occur among some subgroups, such as Hmong or Vietnamese. Fourth, we do not have direct information on beneficiaries education or income. Our ability to use indirect estimates is also somewhat limited because ZIP code level estimates of income from the 2000 census are not yet available. As have others, we used 1990 census estimates of income, which may be out of date. Finally, the number of nonblack ethnic groups, particularly Native Americans, included in this analysis is small. Thus, some patterns that show large differences (for example, controlling high blood pressure) are not statistically significant but probably should be viewed as important. The lack of statistical significance is likely attributable to low power, as is evidenced by statistically significant differences of lesser magnitudes observed for other racial groups (for example, Hispanics, blacks, and Asians) for the same measure. This sample-size issue has implications for the ability to monitor quality of care in health plans for different racial/ ethnic subgroups. To the extent that HEDIS demands a random sample for analysis, numbers of persons in various ethnic groups will probably often be low, yielding insufficient power for meaningful analysis. This risks dismissing important problems. If sampling was stratified based on race/ethnicity (and weighted, if necessary), quality between groups could be assessed for many more measures. Starting in 2003, M+C plans will have data on the race/ethnicity of their plan members provided to them by the CMS. This should make it possible to incorporate stratified sampling into the HEDIS reporting process. We do not have information about the distribution of illness severity or comorbidity across plans. With the possible exception of mammography (where persons with a history of breast cancer may not be eligible for a mammogram), it is not obvious that observed racial/ethnic differences in quality can be explained by illness severity or comorbidity. Despite these limitations, these data highlight the need to examine disparities among multiple racial/ethnic groups. While continued documentation of disparities does little to advance our understanding of what to do about them, continued measurement of disparities in quality will be important in designing and evaluating interventions to address them. This work was supported by a contract from the Centers for Medicare and Medicaid Services. HEALTH AFFAIRS ~ Volume 21, Number 6 229
NOTES 1. U.S. Task Force on Black and Minority Health, Report of the Secretary s Task Force on Black and Minority Health (Washington: U.S. Department of Health and Human Services, 1985). 2. DHHS, Healthy People 2010: Understanding and Improving Health (Washington: U.S. Government Printing Office, 2000). 3. Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care, Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, ed. B.D. Smedley, A.Y. Stith, and A.R. Nelson (Washington: National Academy Press, 2002); and R.M. Mayberry, F. Mili, and E. Ofili, Racial and Ethinic Differences in Access to Medical Care, Medical Care Research and Review 57 no. 1 (Supp. 2000): 108 145. 4. M.E. Gornick et al., Thirty Years of Medicare: Impact on the Covered Population, Health Care Financing Review 18, no. 2 (1996): 179 237. 5. See, for example, P.B. Bach et al., Racial Differences in the Treatment of Early-Stage Lung Cancer, New England Journal of Medicine 341, no. 16 (1999): 1198 1205; C.N. Klabunde et al., Trends and Black/White Differences in Treatment for Nonmetastatic Prostate Cancer, Medical Care 36, no. 9 (1998): 1337 1348; J.G. Canto et al., Relation of Race and Sex to the Use of Reperfusion Therapy in Medicare Beneficiaries with Acute Myocardial Infarction, New England Journal of Medicine 342, no. 15 (2000): 1094 1100; and A.M. McBean and M. Gornick, Differences by Race in the Rates of Procedures Performed in Hospitals for Medicare Beneficiaries, Health Care Financing Review 15, no. 4 (1994): 77 90. 6. E.C. Schneider et al., Racial Disparity in Influenza Vaccination: Does Managed Care Narrow the Gap between African Americans and Whites? Journal of the American Medical Association 286, no. 12 (2001): 1455 1460. 7. E.C. Schneider, A.M. Zaslavsky, and A.M. Epstein, Racial Disparities in Quality of Care for Enrollees in Medicare Managed Care, Journal of the American Medical Association 287, no. 10 (2002): 1288 1294. 8. Ibid. 9. N. Krieger, Overcoming the Absence of Socioeconomic Data in Medical Records: Validation and Application of a Census-Based Methodology, American Journal of Public Health 82, no. 5 (1992): 703 710; and M.E. Gornick et al., Effects of Race and Income on Mortality and Use of Services among Medicare Beneficiaries, New England Journal of Medicine 335, no. 11 (1996): 791 799. 10. S. Selvin, Statistical Analysis of Epidemiologic Data (New York: Oxford University Press, 1991), 29 30. 11. Schneider et al., Racial Disparities in Quality of Care. 12. S.L. Arday et al., HCFA s Racial and Ethnic Data: Current Accuracy and Recent Improvements, Health Care Financing Review 21, no. 4 (2000): 107 116. 230 November/December 2002