Judy H. Ng, PhD

 To develop an easy-to-interpret, patient-reported Functional Limitations Index (FLI) that can be used to assess and monitor the full spectrum of functioning in a community-dwelling population.

 Observational design using nationally representative survey data.

 We used self-rated health as a criterion for empirically assigning weights to 5 National Health Interview Survey items assessing difficulty with seeing, hearing, walking, cognition, and self-care. In addition to succinctly summarizing cumulative limitations, we addressed 2 main questions: (1) Which limitations have stronger associations with self-rated health? and (2) How does severity (from 0, no difficulty, to 3, unable to do) relate to self-rated health? We generated a respondent-level summary score based on a model predicting self-rated health from the 5 linearly scored (0-3) items and used splines to account for nonlinear severity—self-rated health associations.

 The strongest association of specific functional limitations with self-rated health involved mobility; the weakest associations involved sensory limitations. The association of severity with self-rated health was nonlinear and largest moving from 

. Nationally, 5% of noninstitutionalized adults were considered 

. Great mobility limitations (defined as 

) most distinguished limitation groups (present in 0% of 

 The FLI is an easy-to-administer, easy-to-interpret, and valid summary measure of disability that health plans and health care organizations can use for quality-of-care monitoring across a variety of settings to improve care for patients with disabilities.

The 5-item Functional Limitations Index is an easily administered and easily interpretable measure that distinguishes patients along the disability continuum (least to most limited).

The strongest associations of specific functional limitations with self-rated health involved mobility and self-care, possibly reflecting acute illness sequalae.

The weakest associations involved sensory limitations, possibly due to long-term adaptation through accommodations.

Greater severity was associated with worse self-rated health, with the largest increase from no difficulty to somewhat difficult, possibly reflecting challenging adjustment to new health problems.

Limitations groups were most distinguished by being unable to or having a lot of difficulty walking or climbing stairs.

An estimated 12% to 30% of Americans have a disability,

 In clinical populations, greater disability is associated with higher costs and lower quality of life.

 There is a national commitment to improving the health and health care of persons with disabilities,

 who make up one of the largest groups of underserved adults in the United States.

 This requires a consistent, efficient means of identifying and monitoring the spectrum of community-dwelling individuals with disabilities.

Some widely used measures of disability are used to classify working-aged adults as unable to work because of a health condition (eg, CMS original entitlement code); such measures do not capture individuals with disabilities who are able to work or who developed disabilities after age 65 years. Other measures involve a clinical diagnosis (eg, the Sheehan Disability Scale

) or are limited to 1 or 2 domains (eg, SF-36 Physical Function

 score of mobility), limiting their usefulness in the general population.

Responding to the need for a standardized, broadly applicable measure of disability,

 a group of disability measurement experts developed the International Classification of Functioning, Disability and Health (ICF) to classify the consequences of disease and complement the 

 functioning, defined here as activity limitations, results from dynamic interactions among health conditions, body function and body structure impairments, participation in social roles, and environmental and personal factors. The ICF model focuses on functioning independent of health condition.

 For example, a patient with severe pain from a back injury and a patient with cerebral palsy may both have limited mobility, whereas a full-time student with depression may experience concentration limitations and a part-time worker with depression may not. The ICF model treats the first 2 people the same and the second 2 as distinct.

In this paper, we describe the development of a brief, easy-to-administer, and easy-to-interpret summary measure of patient functioning that health care organizations and providers can use to assess and monitor a wide spectrum of functioning independent of clinical diagnoses. In keeping with the ICF model, we focus on functional limitations independent of disease or condition. We considered only items in the 6-item Short Set of Disability Questions included in the National Health Interview Survey (NHIS) since 2010, as this measure was based on the ICF model.

 This short set of questions reliably and validly

 measures limitations associated with undertaking basic activities.

 It represents the majority of individuals (but not all) with functional limitations and assesses the most frequently occurring (but not all) domains of functional limitation.

We describe an empirically informed method of combining the items in this set to preserve distinctions among different types and severities of functional limitations while maximizing the correlation to self-rated health. Typically, individuals are classified as having a disability if they have at least 1 serious functional limitation.

 Another approach would be to classify individuals experiencing difficulty with at least 2 domains or activities of daily living as having a disability.

 Both of these dichotomous approaches ignore the possibility that the severity and different types of limitations may be associated with different levels of well-being and medical need.

Because the effect of a disability on well-being and medical need is likely to fall on a continuum, a continuous measure is needed to better capture the effect of disability and its severity, even if the continuous score is ultimately categorized for some purposes. One way to create a continuous measure is to count the number of limitations that a person reports

; although simple, a count assumes that each functional domain implies the same level of medical need or requires the same degree of intervention. Alternatively, functioning domains could be weighted to reflect their associations with health, using published weights (eg, those used in the World Health Organization [WHO] Disability Assessment Schedule

) or specific weights for the specific items in a given survey. For example, Altman and Bernstein

 constructed a weighted summary measure of disability based on 8 functions from the 2001-2005 NHIS, based on untested assumptions about the relative effects of different functional limitations and levels of severity.

Here we describe the development and application of a weighted summary measure of functioning with empirically derived weights reflecting the association between several functional limitations and self-rated health. Self-rated health was chosen as a criterion variable because it predicts health care utilization, morbidity, illness recovery, functional decline, and mortality,

 and because it is widely available in survey data. To inform our model, we addressed 2 questions: (1) Which limitations have stronger associations with self-rated health? and (2) How does the association of limitations with health vary by level of severity? Measuring the importance of severity in different patient functioning domains has practical clinical and policy implications for monitoring and improvement of quality of care.

&#8203;&#8203;&#8203;&#8203;&#8203;&#8203;

Data are from the 2014-2015 NHIS Adult Functioning and Disability (AFD) supplement, fielded with the Sample Adult module to a randomly chosen subset of all sampled adults (18,303 in 2014 and 16,939 in 2015).

We excluded 1818 AFD respondents (5.2%) who were missing all 6 WHO functional limitation items. We imputed a 0 (no difficulty) for 91 (0.3%) respondents who completed some but not all functional limitation items, as it is common for respondents to misunderstand long sequences of similar items as a “check-all-that-apply” format.

 Sampling weights incorporating study design and nonresponse were applied to all analyses, and robust variance estimation was used.

The NHIS-AFD module includes the following 6 functional limitation questions: vision (difficulty seeing), hearing (difficulty hearing), mobility (difficulty walking or climbing steps), communication (difficulty communicating), cognition (difficulty remembering or concentrating), and self-care (difficulty washing all over or dressing). All items had response options of no difficulty (level 0), some difficulty (level 1), a lot of difficulty (level 2), and unable to do (level 3). We sought to create a brief index based on these 6 well-validated items.

Constructing a Summary Functional Limitations Score

We developed a linear regression model that predicted self-rated health from the functional limitation items. The final model retained 5 functional limitation items scored linearly (0-3). Communication was excluded from the final model because an initial model that included all 6 items yielded a coefficient for communication that did not significantly differ from zero (

]). For each functional domain, we added a linear spline term that allowed the estimated difference between 

 (level 1) to differ from the estimated difference between 

Covariate-adjusted self-rated health scores were estimated using recycled predictions.

 The covariate-adjusted score is the expected self-rated health score for a given person if the person’s sociodemographic characteristics corresponded to those of the average person in the population. The coefficients of the functional limitations from the final model shown in 

 can be used to derive a predicted self-rated health score for every adult AFD respondent based on their self-reported vision, hearing, mobility, cognition, and self-care items (eAppendix B). We created a standardized (

 score) score, the Functional Limitations Index (FLI),

by subtracting the sample mean from each score and dividing the difference by the sample SD. The mean index score was 0 with an SD of 1. We classified respondents into 1 of 3 limitation groups based on the index scores. The cut points defining these groups were 1 SD apart on the index. Because the groups were meant to distinguish those with limitations, all cut points were below 0 (

). These cut points were selected to meet the potentially competing goals of having reasonably sized limitation groups (to ensure reliable subgroup estimates) and representing distinct levels of limitations. Most respondents were without limitations because the NHIS represents the general population. Respondents with no or very little limitation were assigned 0 or just above 0 scores. Although a small proportion of individuals with more limitations had large negative scores, there were no large positive scores; thus, the distribution of scores on the index is left-skewed.

As a preliminary validation and illustration of our approach, we compared the functional limitations and sociodemographic characteristics of the representative noninstitutionalized US adult population who were categorized as 

 per our FLI. We also determined which socio&shy;demographic characteristics were associated with which limitation group using bivariate and multivariate logistic regression models to predict being classified 

Predicting Self-rated Health From Functional Limitations

Negative coefficients for each functional limitation indicated a negative association with self-rated health (0-100 scale, where 0 represented poor health and 100 excellent health). Each retained functional limitation main effect coefficient was negative and statistically significant (

&thinsp;<&thinsp;.05). The strongest predictor of self-rated health was mobility limitation (—15 points for each additional severity level), followed by self-care (–10 points), cognition (–7 points), vision (–5 points), and hearing (–3 points).

, the negative association with functional limitations diminished (Figure 2). For example, a respondent with a single level 1 (

) mobility limitation was predicted to have a 15-point lower self-rated health score than those with no such limitation, but a respondent with a single level 2 (

) mobility limitation was predicted to have one 22 points (—15&thinsp;×&thinsp;2&thinsp;+&thinsp;8) lower, and a respondent with a single level 3 (

) limitation was predicted to have one 29 points (—15&thinsp;×&thinsp;3&thinsp;+&thinsp;8&thinsp;×&thinsp;2) lower. This means that the difference in quality of life between reporting 

 (15 points) was greater than the difference associated with reporting 

 an activity (7 points). Although the association with self-rated health diminished with increased severity in general, the largest change in self-rated health associated with moving from 

We used the model predicting self-rated health to assign the FLI score and create 3 limitation groups: 5% of noninstitutionalized US adults, hereafter referred to as adults, were considered 

 score&thinsp;>&thinsp;—2 and&thinsp;≤&thinsp;–1), and 87% 

 score&thinsp;>&thinsp;—1). As shown in 

, the proportion of adults with no limitations (

) ranged from 82% (for hearing) to 97% (for self-care). The proportion reporting great limitations (

) ranged from less than 1% (for self-care) to 6% (for mobility). Great mobility limitations most distinguished limitation groups: They were present in 0% of the 

Distribution of the Number of Functional Limitations Among Limitation Groups

 presents the proportion of adults with any limitation or great limitations in none, 1, 2, and 3 or more areas of functioning, overall and by limitation group. Overall, 65% of adults had no limitations, 22% had 1 limitation, 9% had 2 limitations, and 4% had 3 or more limitations. About 75% of the 

 group had 0 limitations, 23% had a single limitation, and 2% had 2 limitations. Of the 

, 60% had 2 limitations, 30% had 1 limitation, and 10% had 3 or more limitations. Of the 

, 62% had 3 or more limitations and most of the remainder (31%) had 2 limitations.

Overall, 8% of adults had at least 1 great limitation and 1% had 2 or more. Of the 

, 2% had a single great limitation and the remainder had no great limitation. Of the 

, 31% had 1 great limitation and 2% had 2. Of the 

, 76% had at least 1 great limitation, 14% had 2 great limitations, and 3% had 3 or more great limitations.

Distribution of Types of Limitations Among Limitation Groups

 had any functional limitations, most often 

 in a single area of sensory functioning (hearing or vision, 9%-13%), and rarely any mobility or self-care limitation (<5%). The 

 all had at least 1 limitation, and most had 2 limitations (usually 

 but occasionally great limitation). A majority (96%) of the 

 had mobility limitations; when a great limitation existed, it was usually in the realm of mobility. Approximately one-third or more of the 

 group had a sensory limitation (33%-45% with any limitation) and rarely a self-care limitation (5%). A majority of the 

 had 3 or more limitations, including at least 1 great limitation. Nearly all (99%) of the 

 had mobility limitations; a majority had great mobility limitations along with cognitive, hearing, and vision limitations. More than half of the 

 group (60%-75% with any limitation) had a sensory limitation and approximately half (47%) a self-care limitation.

Sociodemographic Characteristics of the 3 Limitation Groups

 were more often male, younger, and Hispanic or Asian and lived in the Northeast (

 were more often married and socioeconomically advantaged; that is, they more often had a bachelor’s degree or more, reported an income of at least $75,000, and utilized private rather than public insurance.

The 5-item FLI is an easy-to-administer, easy-to-interpret, valid measure of functioning that health care organizations can use to identify and monitor the care and outcomes of individuals with disabilities of different severities independent of health conditions.

Our approach to scoring the FLI improves on other methods used to measure and summarize disability by utilizing patient function over clinical diagnoses and allowing empirical evidence to drive the weighting of different functional limitations and their severities for the summary score. By adjusting for socio&shy;demographic characteristics, the FLI represents variation in health solely due to functional limitations; it can be used as a continuous measure of disability or to create limitation groups for analysis.

Self-rated health has a strong negative association with each of 5 functional limitations, with mobility limitations having the strongest association, followed by limitations in self-care, cognition, vision, and hearing. Our results are consistent with those of Balestroni and Bertolotti,

 who found that mobility was a stronger predictor of the EuroQol EQ-5D, a health-related quality-of-life measure, than self-care or daily activities limitations.

The disability paradox is the phenomenon wherein individuals with disabilities report greater well-being than unimpaired individuals assume they would experience in the same situation. In a 2014 survey, largely unimpaired community-dwelling adults cited vision loss as the limitation they believed would have the greatest effect on their day-to-day life, followed by memory loss; they ranked hearing loss as least likely to have a large effect.

 Our finding that visual limitations are not strongly associated with self-rated health suggests that the disability paradox may be especially relevant for visual sensory limitation; the relatively small impact of hearing loss is consistent with unimpaired individuals’ assessment. The associations between sensory limitations (vision and hearing) and self-rated health may in part reflect long-term adaptation to accommodations (eg, eyeglasses and hearing aids). In contrast, mobility and self-care limitations may reflect acute illness sequelae, including physical deconditioning, early mortality, and falls resulting in loss of independence and ability to participate in social activities.

 The large coefficients corresponding to a change from 

 limitation may reflect the difficulty of adjusting to the new onset of a health problem, whereas the smaller coefficients corresponding to differences between 

 limitations may reflect some combination of worsening of an existing health problem and the success of environmental adaptations to functional limitations.

This study has some limitations. Although the 6 NHIS functional limitation items are likely to capture most adults with disabilities, they probably underrepresent adults with psychiatric and cognitive disabilities associated with higher-order functioning limitations (eg, learning, decision making).

 Mental health disability is potentially very important, especially among those who also report disability due to a chronic condition.

 Information on US adults with intellectual disabilities is not routinely collected, despite recognized disparities in care.

 We did not evaluate other ICF items (eg, anxiety, depression) given our interest in a function-based measure. The FLI does not distinguish between permanent and temporary disability.

 Finally, although the construction of the FLI was supported by a strong theoretical foundation and the pattern of associations between limitation groups and sociodemographic characteristics provides preliminary evidence of the validity of the measure, additional validation work (eg, investigation of the convergent, discriminant, and predictive qualities of the index) is needed prior to any high-stakes application.

Policy makers and clinical researchers must understand how varying levels of disability influence quality of life. As individuals experience disability at higher levels of severity, the negative association between limitations and self-rated health increases, but to a diminishing extent. Dichotomous or summed counts of disability ignore this phenomenon. A key strength of the FLI is that because it empirically summarizes multiple functional limitation items and severity levels on the same ordinal scale as a single dimension, it can meaningfully facilitate research into variability in quality of and access to care by improving the insight into the relative contributions of different areas of functioning, severity, and additivity. The FLI may also help assess heterogeneity in disability-related disparities for different population subgroups. This method can be used as a guideline and be adapted to other populations of interest, such as Medicare or Medicaid beneficiaries. Policy makers might also find the FLI useful for assessing programmatic outcomes. For example, the association between disability and general health might be used to monitor change over time related to policy changes, such as the Americans with Disabilities Act, or programs, such as the Social Security Disability Insurance program.&ensp;

The authors would like to thank Biayna Darabidian for help with preparation of the manuscript.

 RAND Corporation, Santa Monica, CA (MM, DA, MNE, NO, MKB), and Pittsburgh, PA (SCM); American Hospital Association (PG), Chicago, IL; National Committee for Quality Assurance (JHN), Washington, DC.

Articles

We describe an easy-to-interpret, patient-reported Functional Limitations Index that can be used to monitor care of patients along the disability continuum.

The Development of a Patient-Reported Functional Limitations Index

 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.

 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.

 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.

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.

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&mdash;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.

 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.

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,

including documented disparities in the delivery of primary and preventive services,

 Increasingly, patient-reported outcomes, including functional health outcomes, are being used as quality indicators.

 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&mdash;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.

 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&mdash;by midcentury. 

 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.

 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. 

 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.

 Further, there is limited evidence for Native Hawaiians/Pacific Islanders because they are often included with Asians.

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 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.

 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.

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).

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 χ

 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 

 values. Thus, at the alpha testing level of .05, only 

 values <.003 (.05/14) were considered significant.

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 (

). Asians had higher levels of education than whites, but also reported lower levels of home ownership and higher levels of Medicaid eligibility.

Health and functional health status as measured by VR-12 PCS and MCS scores, number of ADL impairments, and number of chronic conditions were generally worse among those of racial/ethnic minority backgrounds (Table 1) than whites. Hispanics, blacks, American Indians/Alaskan Natives, and multiracial individuals had slightly lower mean PCS scores than other racial/ethnic groups. All minority groups had lower mean MCS scores than whites. The mean number of functional ADL limitations and chronic conditions was slightly higher among Hispanics, blacks, American Indians/Alaskan Natives, Native Hawaiians/Pacific Islanders, and multiracial individuals than other groups. With the exception of lower mean MCS scores, Asians had similar or better functional status on most measures than whites. Native Hawaiians/Pacific Islanders had functional status similar to whites.

The prevalence of 14 adverse health indicators by race/ ethnicity appears in 

. Adjusted ORs for all racial/ethnic minority groups relative to whites appear in 

. A higher proportion of those of racial/ethnic minority backgrounds had fair or poor perceived health compared with whites (Table 2). Blacks (41.9%) and Hispanics (45.8%) had the highest prevalence of fair or poor perceived health, while whites had the lowest prevalence (24.1%). The proportion of beneficiaries in the sickest quartile of PCS scores ranged from 33.7% and 32.5% for American Indians/Alaskan Natives and multiracial individuals respectively to 18.6% and 21.3% for Asians and Native Hawaiians/Pacific Islanders. A higher proportion of all racial/ethnic minority groups also had poorer mental health (sickest quartile of MCS scores) than did whites, with the most pronounced absolute difference of 19 and 15 percentage points among Hispanics and American Indians/Alaskan Natives.

Adjusting for sociodemographic factors and region eliminated some differences between whites and racial/ethnic minority groups, but did not greatly alter these patterns (Table 3). Racial/ethnic minority groups generally had significantly higher odds of fair or poor perceived health than whites, from an OR of 1.30 for Hispanics and American Indians/Alaskan Natives (95% CI, 1.24-1.37 and 1.13-1.49, respectively) to an OR of 1.40 (95% CI, 1.32-1.49) for multiracial individuals. After adjustment, differences in the proportion of individuals who were in the lowest quartile of PCS were no longer significant for most groups. Moreover, Asians and Native Hawaiians/ Pacific Islanders were significantly less likely than whites to have low PCS scores (OR, 0.52 [95% CI, 0.48-0.56] and OR 0.67 [95% CI, 0.55-0.81], respectively). All racial/ethnic minority groups had significantly higher adjusted odds of poor mental health than whites (from OR 1.22 [95% CI, 1.14-131] to 1.53 [95% CI, 1.32-1.78]).

. Racial/ethnic minority groups were generally more likely than whites to report chest pain; shortness of breath; and foot, arthritis, and depression symptoms (Table 2). Prevalence of symptoms was usually highest among blacks, Hispanics, American Indians/Alaskan Natives, Native Hawaiians/Pacific Islanders, and multiracial individuals. Conversely, Asians had the lowest prevalence of arthritis symptoms and were similar to whites in prevalence of shortness- of-breath symptoms.

Most, but not all, significant findings in bivariate comparisons remained significant after multivariate adjustment, but patterns were largely similar (Table 3). Hispanics, blacks, American Indians/Alaskan Natives, Native Hawaiians/Pacific Islanders, and multiracial individuals had significantly higher odds than whites of most symptoms. Conversely, Asians had significantly lower odds than whites for shortness of breath (OR, 0.85; 95% CI, 0.80-0.90) and arthritis symptoms (OR, 0.60; 95% CI, 0.56-0.65).

. Hispanics and Native Hawaiians/Pacific Islanders had the highest prevalence of vision and hearing problems. All racial/ethnic groups except Asians had a higher prevalence of obesity than whites. Blacks had the highest prevalence of obesity relative to whites (40.6% vs 28.6%), while Asians had a much lower prevalence than whites (8.1% vs 28.6%). Most groups had rates of smoking that were similar to, or lower than, those of whites.

Adjusting for sociodemographic factors did not greatly alter the patterns for vision problems (Table 3). Most racial/ ethnic minority groups had significantly higher odds of vision problems than whites (from OR 1.36 [95% CI, 1.27-1.45] to 1.67 [95% CI, 1.32-2.10]). Racial/ethnic minority groups also had significantly higher odds of hearing problems than whites (from OR 1.18 [95% CI, 1.08-1.30] to 1.68 [95% CI, 1.30- 2.17]). Reflecting the patterns observed in bivariate results, most groups except blacks and Asians had few significant differences from whites in obesity; blacks retained significantly higher odds of obesity than whites (OR 1. 43; 95% CI, 1.38-1.48), while Asians retained significantly lower odds (OR 0.22; 95% CI, 0.19-0.25). Except for multiracial individuals, most racial/ethnic groups also had either no significant difference or lower odds of being smokers than did whites.

. Blacks, American Indians/Alaskan Natives, Native Hawaiians/Pacific Islanders, and multiracial individuals had worse functioning relative to whites, while Asians had better functioning (Table 2). The prevalence of multimorbidity (more than 3 chronic conditions) was similar among most groups (within approximately 

5-7 percentage points of one another), with Asians reporting the lowest prevalence.

Adjustment did not greatly alter these patterns (Table 3). Blacks, American Indians/Alaskan Natives, and multiracial individuals had significantly higher odds of ADL impairment than whites (from OR 1.11 [95% CI, 1.07-1.15] for blacks to OR 1.37 [95% CI, 1.20-1.57] for American Indians/Alaskan Natives and multiracial individuals), while Asians had significantly lower odds than whites (OR 0.63; 95% CI, 0.59-0.67). Most racial/ethnic groups did not report significantly different odds for multimorbidity than whites, while Asians and Hispanics retained significantly lower odds than whites (OR 0.66 [95% CI, 0.61-0.69] and OR 0.90 [95%CI, 0.86-0.94], respectively).

Findings from this nationally representative survey of Medicare MC members 65 years and older provide new evidence of significant racial/ethnic disparities in health status indicators. To our knowledge, this is one of the first national assessments of health and functional status among racially and ethnically diverse Medicare beneficiaries. The results provide unique national estimates of prevalence for many of these health indicators among older adults and add evidence to the literature on health status—based disparities among older adults. Even after controlling for key sociodemographic factors, race/ethnicity was significantly associated with most adverse outcomes on 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 ADL disability.

Unlike much earlier research, this study assesses health indicators that are critical end points of care but not routinely assessed (eg, functional health status), and not just receipt of health services. These indicators reflect quality-of-life issues that are especially important for older adults. Moreover, while the findings are consistent with prior Medicare disparities studies, many prior studies were typically limited to blacks, whites, or Hispanics, while this study examined smaller groups (eg, Native Hawaiians, American Indians, Asians, and multiracial individuals—a growing and heterogeneous group) typically excluded from national comparisons because of small numbers or unreliable race/ethnicity data.

 This is critical, given the growing diversity of the aging, with proportions of elderly from many racial/ethnic subgroups expected to grow faster than those of blacks and whites.

By providing a snapshot of disparities and health quality across diverse indicators, these data are useful in highlighting areas of greatest need for improving quality and equitable care in older US adults, particularly mental health status and health symptoms, where differences between minorities and whites were the most pronounced. These symptoms typically result from common chronic conditions such as diabetes, heart failure, and arthritis that are often the target of quality measurement and improvement efforts. Older adults from minority racial/ethnic groups may have unique needs, including diverse health beliefs or attitudes,

 and may report or perceive health status differently.

Teasing out these dynamics and changing business practices to include culturally appropriate care could potentially improve care delivery and close gaps in quality of care.

 Examples of culturally appropriate care include improved language helpline support, availability of educational materials in multiple languages, and availability of non-English pharmacy labeling.

Promising analytic techniques that pool information across time may also increase the power to compare smaller racial/ethnic groups.

 Future research should also consider changes over time, using baseline and 2-year follow-up data from the survey.

This study had several limitations. First is the capture of race and ethnic categories. Whereas the HOS distinguishes major federal categories of race and ethnicity (including distinguishing between Asians and Native Hawaiians/Pacific Islanders), it does not distinguish many important subgroups within each category. For example, Vietnamese and Spanishspeaking- only elderly may be higher-risk subgroups within Asian and Hispanic populations.

 The study also excluded 11% of elderly respondents who returned a survey without self-reported race. Although self-reported race is considered more reliable than administrative race data (which was available in the study), it often includes more missing values than administrative race (this was the case with our data). Furthermore, while the HOS is offered in English, Spanish, and Chinese, it is possible that respondents or proxy respondents who do not have sufficient proficiency in these languages were reluctant to respond. These factors could introduce bias and limit our ability to generalize results to all racial and ethnic groups.

This study examines disparities in important patient-reported functional outcomes not routinely assessed among diverse racial/ethnic groups in Medicare managed care.

Judy H. Ng, PhD

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