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The American Journal of Managed Care March 2014
STABLE Results: Warfarin Home Monitoring Achieves Excellent INR Control
Grace DeSantis, PhD; Jackie Hogan-Schlientz, RN, BSN; Gary Liska, BS; Shari Kipp, BS; Ramarion Sallee; Mark Wurster, MD; Kenneth Kupfer, PhD; and Jack Ansell, MD
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Beyond Black and White: Race/Ethnicity and Health Status Among Older Adults
Judy H. Ng, PhD; Arlene S. Bierman, MD, MS; Marc N. Elliott, PhD; Rachel L. Wilson, MPH; Chengfei Xia, MS; and Sarah Hudson Scholle, DrPH
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Rebecca J. Williams, DrPh; Andrew L. Masica, MD; Mary Ann McBurnie, PhD; Leif I. Solberg, MD; Steffani R. Bailey, PhD; Brian Hazlehurst, PhD; Stephen E. Kurtz, PhD; Andrew E. Williams, PhD; Jon E. Puro, MHA-PA; and Victor J. Stevens, PhD
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Beyond Black and White: Race/Ethnicity and Health Status Among Older Adults

Judy H. Ng, PhD; Arlene S. Bierman, MD, MS; Marc N. Elliott, PhD; Rachel L. Wilson, MPH; Chengfei Xia, MS; and Sarah Hudson Scholle, DrPH
This study examines disparities in important patient-reported functional outcomes not routinely assessed among diverse racial/ethnic groups in Medicare managed care.
Objectives: This study examined physical and mental health, health symptoms, sensory and functional limitations, risk factors, and multimorbidity among older Medicare managed care members to assess disparities associated with race/ethnicity.

Study Design and Methods: We used data on 236,289 older adults from 208 Medicare plans who completed the 2012 Medicare Health Outcomes Survey to compare 14 health indicators across non-Hispanic whites, blacks, American Indians/Alaskan Natives, Asians, Native Hawaiians/Pacific Islanders, multiracial individuals, and Hispanics. Logistic regression models that clustered on the plan estimated the risk of indicators of adverse health and functional status.

Results: Even after controlling for key patient sociodemographic factors, race/ethnicity was significantly associated with most adverse health indicators. Except for Asians, all racial/ethnic minority groups were significantly more likely than whites to report poor mental health status, presence of most health symptoms, sensory limitations, and activities-of-daily-living disability. Important differences were observed across racial and ethnic groups.

Conclusions: Despite some exceptions, elders of racial/ethnic minority background are generally at higher risk than non-Hispanic whites for a broad range of adverse health and functional outcomes that are not routinely assessed. Limitations include bias related to self-reported data and respondent recall. Future research should consider ethnic subgroup variations; employing newer techniques to improve estimates for smaller groups; and prioritizing and identifying opportunities for care improvement of diverse enrollee groups by considering specific needs. To improve the health status of the elderly, service delivery targeting the needs of specific population groups, coupled with culturally appropriate care for racial/ ethnic minorities, should also be considered.

Am J Manag Care. 2014;20(3):239-248
Critical patient-reported outcomes of care are not routinely assessed among diverse elderly racial/ethnic groups in Medicare managed care.
  • Certain non-white racial/ethnic groups—blacks, Hispanics, American Indians, and Native Hawaiians—had worse functional status, health symptoms, and risk factors than whites. Asians were an exception.

  • As the proportion of older adults from non-white groups grows more rapidly than whites, it will be crucial to understand health outcomes in diverse enrollee populations.

  • To improve the health status of the elderly and reduce disparities, service delivery targeting the needs of specific population groups should be considered.
The maintenance and improvement of health and functioning is a major goal in providing care to older adults in the United States.1 Improving health and eliminating disparities in quality of care are a primary objective of Healthy People 2020, a national initiative aimed at helping to improve health-related quality of life and other aspects of population health that have been identified as health system priorities by the Institute of Medicine.2,3 Further, the Affordable Care Act (ACA) includes provisions to address health disparities, including enhanced understanding and data collection regarding care for specific racial and ethnic groups in federally supported health programs like Medicare managed care (MC).4 Moving forward, MC plans will have strong incentives to address healthcare disparities, given that payment will be tied to their ability to provide better clinical quality and patient experiences for all members, as measured by common performance metrics (eg, Healthcare Effectiveness Data and Information Set or Consumer Assessment of Healthcare Providers and Systems measures). Among older adults, those of racial and ethnic minority backgrounds have been found to receive lower quality of care than whites,5-19 including documented disparities in the delivery of primary and preventive services,7-9 use of selected treatments,10-12 and patient experiences with care.15-19 Increasingly, patient-reported outcomes, including functional health outcomes, are being used as quality indicators.20 However, little is known about how functional health outcomes vary across racial and ethnic groups. An understanding of patterns of health and functional status across racial and ethnic groups is needed to develop targeted interventions aimed at improving health outcomes.

Moreover, existing Medicare disparities research has focused primarily on blacks and whites—and, to a lesser extent, Hispanics—with much less analysis of the experiences of other racial/ethnic groups: American Indians/Alaskan Natives, Asians, Native Hawaiians/Pacific Islanders, or individuals of more than 1 race.5,21,22 The proportion of elderly people within these other groups is expected to increase more quickly than that of blacks or whites by more than double—or even triple—by midcentury. 23 While studies have documented disparities among these other groups, often they do not simultaneously compare groups within a single study, and draw conclusions based on varying studies and sources, wherein populations are not necessarily comparable because of differences among data sources or health measures used.21,22 Because of sample size limitations, many smaller racial/ethnic groups are also excluded from national comparative assessments (eg, Native Hawaiian/ Pacific Islander, American Indian/Alaskan Native, and Asian populations). When included, there is often insufficient data for a large proportion of health indicators used in such assessments. 21-23 Studies that do include more diverse racial/ethnic groups have revealed somewhat mixed findings: studies generally find lower-quality care among minority groups compared with whites, but there is evidence that Asians fare better than whites for some aspects of healthcare.24-29 Further, there is limited evidence for Native Hawaiians/Pacific Islanders because they are often included with Asians.23

This study examined racial/ethnic differences in a large variety of health indicators and functional status measures among adults 65 years and older from a diversity of racial/ ethnic groups, using data from a nationally representative sample of Medicare beneficiaries enrolled in Medicare health plans. It sought to (1) assess racial/ethnic differences in the prevalence of 14 health status indicators across diverse racial/ ethnic groups within a single study; and (2) examine whether health status differs by race/ethnicity even after adjusting for patients’ age, sex, socioeconomic status, region, and length of enrollment in their Medicare plan. Unlike much earlier research, this study used data on critical end points of care that are not routinely assessed (eg, functional health status), rather than health services receipt. The study also examined estimates for smaller groups often excluded from Medicare comparisons because of insufficient data (eg, Native Hawaiian, American Indian, and Asian populations).


Data and Sample

Data are from the 2012 baseline Medicare Health Outcomes Survey (HOS), an annual mail survey with telephone follow-up sponsored by the Centers for Medicare & Medicaid Services. The survey collected health and demographic information from a nationally representative sample of Medicare beneficiaries enrolled in Medicare health plans, and had an overall response rate of 47.5%. Race/ethnicity was self-reported. The core measure of health was the Veterans-RAND 12- Item Short Form Survey (VR-12), an instrument embedded within the HOS that allows calculation of 0-100 Physical Component Summary (PCS) and Mental Component Summary (MCS) scores, with 0 representing the worst health and 100 representing the best health for each.30,31 Other measures of health status in the survey assessed perceived health, presence of health symptoms, sensory limitations, risk factors, functional impairments, and chronic conditions.

The study sample included 236,289 elderly Medicare health plan members from 208 plans in 2012, who were 65 years and older, residing in the 50 US states or the District of Columbia, and who self-reported their race/ethnicity.

Analytic Variables

The main independent variable was Medicare beneficiaries’ self-reported race/ethnicity. Seven race/ethnicity categories were created based on respondents’ self-report of Latino or Hispanic descent and race (white, black, American Indian/ Alaskan Native, Asian, Native Hawaiian/Pacific Islander). A person identifying as Hispanic was coded “Hispanic” irrespective of race. Among those not self-identifying as Hispanic, a person reporting a single race was coded in the corresponding race group; a person reporting more than 1 race was coded as multiracial. The resulting race/ethnicity categories were Hispanic (of any race), non-Hispanic white, non-Hispanic black, non-Hispanic American Indian/Alaskan Native, non- Hispanic Asian, non-Hispanic Native Hawaiian/Pacific Islander, and non-Hispanic multiracial (individuals reporting more than 1 race).

The main dependent variables were 14 health status indicators from the HOS, assessing general health status, presence of health symptoms, sensory limitations, risk factors, functioning, and presence of chronic conditions.

Three measures of general health status and functioning were assessed: self-report of perceived health, VR-12 PCS score, and VR-12 MCS score. To focus on indicators of worsethan- average health, each measure was dichotomized as an adverse health indicator. Perceived health was dichotomized as fair or poor (vs excellent, very good, or good) health. The PCS measure was coded as poor physical health—scoring in the lowest quartile of the PCS score distribution (<30.11) versus higher. The MCS measure was similarly coded as lowest quartile (<45.05) versus higher. The choice to dichotomize the PCS and MCS health variables was motivated partly by the desire to understand racial/ethnic disparities for risk of being in the poorest health, focusing on the most vulnerable of the elderly, rather than capturing distinctions between average and better than average health.

Respondents reported the presence or absence of 5 health symptoms: chest pain symptoms in the last 4 weeks when exercising or resting (vs no symptoms); shortness of breath symptoms in the last 4 weeks (when lying flat, sitting, walking <1 block, or climbing 1 flight of stairs vs no symptoms); foot symptoms in the last 4 weeks (numbness, tingling, inability to feel hot/cold, or nonhealing sores on feet vs no symptoms); arthritis pain the last 4 weeks (severe or moderate pain vs mild, very mild, or no pain); and depressed mood for much of the last year (yes or no).

Respondents were asked to report vision problems (ability to see well enough to read newspapers, with glasses or contacts if needed) or hearing problems (ability to hear most things, with a hearing aid if needed). Respondents were classified as obese (body mass index [BMI] >30 kg/m2) versus not, based on self-reported weight and height. Current smoking status was dichotomized as smoking every day or some days versus not smoking at all.

Adverse indicators included activity-of-daily-living (ADL) disability and multimorbidity. Disability was assessed through a single dichotomous indicator of any (vs no) selfreported difficulty, or inability to perform any of 6 ADLs: bathing, dressing, eating, getting in or out of chairs, walking, and toileting. A multimorbidity indicator was defined as reporting 4 or more (vs 3 or fewer) of 14 chronic conditions: hypertension, angina pectoris or coronary artery disease, congestive heart failure, heart attack, other heart condition, stroke, respiratory conditions, inflammatory bowel disease, hip/knee arthritis, hand/wrist arthritis, osteoporosis, sciatica, diabetes, and any cancer.

Covariates were age (in years), gender, marital status (married vs not), education (some college or higher vs no college), Medicaid eligibility (based on administrative data), length of enrollment in Medicare health plan (in months), and census region (New England, Mid-Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain, or Pacific).

Data Analysis

Analyses were performed using SAS version 9.1 (SAS Institute, Cary, North Carolina). The patient was the unit of analysis. The unadjusted prevalence of all 14 adverse health indicators was compared by race/ethnicity using the χ2 test, and the difference in prevalence of an indicator for each racial/ ethnic group, relative to non-Hispanic whites, was reported. To further examine the association of race/ethnicity with health, 14 multivariate logistic regressions were used to predict each health indicator from all racial/ethnic indicators other than non-Hispanic whites and the full set of covariates described above, accounting for clustering of members within plans using PROC GENMOD. Multivariate results were reported as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Because this study examined 14 dependent variables for each individual, a Bonferroni correction was used to interpret P values. Thus, at the alpha testing level of .05, only P values <.003 (.05/14) were considered significant.


Patient Population

The 236,289 elderly Medicare health plan members in the study sample included 176,994 (74.9%) white; 20,553 (8.7%) Hispanic; 22,729 (9.6%) black; 1054 (0.5%) American Indian/ Alaskan Native; 8228 (3.5%) Asian; 729 (0.3%) Native Hawaiian/Pacific Islander; and 6002 (2.5%) multiracial (>1 race) survey respondents. Blacks, Hispanics, American Indians/Alaskan Natives, Native Hawaiians/Pacific Islanders, and multiracial respondents had lower levels of education and homeownership than did whites, and higher levels of Medicaid eligibility (Table 1). Asians had higher levels of education than whites, but also reported lower levels of home ownership and higher levels of Medicaid eligibility.

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