Racial/ethnic differences in activities of daily living disability in older adults with arthritis: A longitudinal study




To investigate racial/ethnic differences in disability onset among older Americans with arthritis. Factors amenable to clinical and public health intervention that may explain racial/ethnic differences in incident disability were examined.


We analyzed longitudinal data (1998–2004) from a national representative sample of 5,818 non-Hispanic whites, 1,001 African Americans, 228 Hispanics interviewed in Spanish (Hispanic/Spanish), and 210 Hispanics interviewed in English (Hispanic/English), with arthritis and age ≥51 years who did not have baseline disability. Disability in activities of daily living (ADL) was identified from report of inability, avoidance, or needing assistance to perform ≥1 ADL task.


Over the period of 6 years, 28.0% of African Americans, 28.5% of Hispanic/Spanish, 19.1% of Hispanic/English, and 16.2% of whites developed disability. The demographic-adjusted disability hazard ratios (AHR) were significantly greater among African Americans (AHR 1.94, 95% confidence interval [95% CI] 1.51–2.38) and Hispanic/Spanish (AHR 2.03, 95% CI 1.35–2.71), but not significantly increased for Hispanic/English (AHR 1.41, 95% CI 0.82–2.00) compared with whites. Differences in health factors (comorbid conditions, functional limitations, and behaviors) explained over half the excess risk among African Americans and Hispanic/Spanish. Medical access factors (education, income, wealth, and health insurance) were substantial mediators of racial/ethnic differences in all minority groups.


Racial/ethnic differences in the development of disability among older adults with arthritis were largely attenuated by health and medical access factors. Lack of health insurance was particularly problematic. At the clinical level, treatment of comorbid conditions, functional limitations, and promotion of physical activity and weight maintenance should be a priority to prevent the development of disability, especially in minority populations.


Arthritis and other rheumatic conditions are common chronic conditions among elderly Americans. They are the leading cause of disability, limiting daily activities for more than 7 million Americans (1). As the US population ages, the impact of arthritis is expected to increase. Data from the National Health Interview Survey (NHIS) showed that the prevalence of arthritis increased from nearly 1 in every 6 people in 1990 to 1 in every 5 people in 2002 (2, 3). Although evidence showed decreasing disability trends in the general population (4–6), disability was increasing among persons with arthritis. Arthritis-related activity limitations rates almost tripled from 2.8% in 1990 to 7.8% in 2002 based on age-adjusted rates from NHIS data (2, 3).

The racial/ethnic composition of the US population is also changing. Minority (nonwhite) populations will increase from 30.6% of the US population in 2000 to 49.9% by the year 2050. The fastest increase is among people of Hispanic origin; this segment is projected to increase from 12.6% of the US population in 2000 to 24.4% by 2050 (7). Among persons with arthritis, African American and Hispanic minority groups had substantially higher rates of disability than their white counterparts (3, 8–12).

In order to advance public health efforts to promote equitable health outcomes, it is crucial to identify modifiable factors that contribute to racial/ethnic differences among persons with arthritis. This study addressed this question by examining racial/ethnic differences in the development of disability in activities of daily living (ADL) among older adults with arthritis. Longitudinal data from the 1998–2004 Health and Retirement Study (HRS), a national representative sample of community living adults, were used to answer the following research questions: 1) For older adults with arthritis who were free of disability at baseline (1998), what are the national rates at which racial/ethnic groups develop ADL disability? 2) How much of the observed racial/ethnic differences in the development of disability can be explained by demographic, health, and medical access factors? 3) What are the significant risk factors that predict the development of ADL disability among older adults with arthritis?


Data and study sample.

This study used data from the HRS, which is sponsored by the National Institute of Aging and conducted by the University of Michigan. The HRS cohort is based on a national representative sample of noninstitutionalized older Americans. The ongoing HRS collects biennial in-depth information on medical access, finance, health, and sociodemographics (13).

Public release HRS data from 1998, 2000, 2002, and 2004 were used. The baseline arthritis cohort included 9,943 respondents age ≥51 who self-identified as Hispanic/Latino, African American/black, and/or white/Caucasian. To draw inferences about incidence of ADL disability, we restricted our analyses to 7,257 respondents who were without baseline ADL disability and were alive at the subsequent 2000 interview. Excluded by design were 1,340 people with baseline ADL disability, 375 decedents prior to 2000, 480 baseline proxy interviews, and 411 nonrespondents by 2000 interview. For analytical purposes, another 80 people with missing data in either HRS 2000 ADL disability status or baseline explanatory variables were excluded.


Baseline arthritis was determined by an affirmative answer to the 1998 HRS question, “Have you ever had or has a doctor ever told you that you have arthritis or rheumatism?” Self-reported arthritis is relevant from a public policy perspective because many persons with arthritis do not see a health care provider for their symptoms (14).


This study relies on the Disablement Process Model of Verbrugge and Jette (15). Along the main pathway of the disablement process are pathology, representing cellular disturbances; impairment, referring to organ level abnormalities; functional limitations, representing restrictions in basic performance; and finally disability, a gap between a person's capabilities and environmental demands (e.g., limitations in ADL tasks). Factors that influence the ongoing process include personal, environmental lifestyle/behavioral (e.g., health behaviors), and physical/social environmental (e.g., medical access) factors.

The HRS monitors 6 basic ADL tasks essential for independent living: dressing, walking across a room, transferring in/out of bed, bathing, eating, and toileting. Disability in an ADL task expected to last for at least 3 months was ascertained from self-reported inability, avoidance, or needing assistance from a person or using an assistive device in carrying out such tasks. This assessment of ADL disability captures chronic dependence in self-care activities that could jeopardize a person's ability to live independently. For the purpose of analysis, the development of ADL disability was identified by the first report of disability in ≥1 ADL tasks at a subsequent 2000, 2002, or 2004 interview.


Baseline (1998) demographic factors consisted of race/ethnicity, age, sex, marital status, and living arrangement (live alone or with others). We used the HRS race/ethnicity information to classify people into 4 mutually exclusive groups: English-speaking Hispanic, Spanish-speaking Hispanic, (non-Hispanic) African American, and (non-Hispanic) white. We distinguished English-speaking Hispanic from Spanish-speaking Hispanic adults because prior literature (16) suggested that persons from different linguistic groups might have different views of health and disease or disability. People from other racial/ethnic groups were excluded from analyses due to small subgroups (n = 109). Marital status and living situation were assessed at all biennial interviews (1998, 2000, 2002, or 2004).

Health factors assessed at each interview included comorbid chronic conditions, functional limitations, and health behaviors. In addition to arthritis, chronic conditions were ascertained by self-report of physician diagnosis of conditions including cancer, diabetes, heart disease, hypertension, pulmonary disease, or stroke. The presence of depressive symptoms was determined by an abbreviated Center for Epidemiologic Studies Depression scale (CES-D) assessment (17). Persons with high depressive symptoms were identified with a cut point of ≥5 on the 8 CES-D items, which accounted for the upper 10 percentile of persons with CES-D information. Bad vision was defined as poor or legally blind eyesight.

Functional limitations assessed at each interview included physical and instrumental activities of daily living (IADL) task limitations. Physical limitations were assessed from self-reported inability or avoidance of any of the 4 tasks of walking several blocks, climbing several stairs without rest, pulling or pushing large objects, and lifting or carrying weights over 10 pounds. IADL limitations were ascertained from reports of receiving help, cannot do, or do not do because of physical, mental, emotional, or memory problems in any of the 5 social tasks, which included preparing hot meals, grocery shopping, using the telephone, taking medication, or managing money.

Health behaviors assessed at each interview consisted of current smoking, current alcohol consumption, weight status, and regular vigorous physical activity. Current smoking was ascertained from a positive response to “Do you smoke cigarettes now?” Alcohol consumption was based on a positive response to the question, “Do you ever drink any alcoholic beverages such as beer, wine, or liquor?” Weight status was determined from weight gain or loss of ≥10 pounds, obesity, or being underweight. Body mass index (BMI; weight [kg]/height [m2]), calculated from self-reported height and weight, was used to define obesity (BMI ≥30) and underweight (BMI <20). Regular vigorous physical activity was ascertained from the report of participation at least 3 times a week over the past 12 months in activities such as sports, heavy housework, or a job that involves physical labor.

Medical access factors assessed at each interview consisted of education, wealth, family income, and health insurance. Education, a measure of human capital, was dichotomized as 12 or more years versus fewer completed years of education. For analytic purposes, family income (all sources received by the respondent and spouse/partner during the preceding year) and wealth (the sum of housing and nonhousing assets) were dichotomized using the lowest baseline HRS population-weighted quartiles (18). If only partial income or wealth information was provided during the interview, dichotomized values were based on imputed estimates developed by the University of Michigan. Health insurance was classified into 4 mutually exclusive groups: any private insurance coverage, Medicaid enrollment, Medicare or other government insurance programs (such as CHAMPUS, CHAMPVA, or the Veterans Administration without additional Medicaid or private insurance coverage), and no coverage.

Statistical analysis.

The HRS is a national probability sample. All analyses used person-weights, stratum, and sampling codes for the 1998 HRS data developed at the University of Michigan to provide valid inferences of the US population. Analyses were restricted to 1998 HRS self-respondents. Nonrespondents (including proxy interviews) compared with respondents tended to disproportionately be African American or Hispanic adults. We adjusted for potential bias due to missing interview information and/or nonresponse by handling respondents with completed data as another stage of sampling to obtain adjusted sampling weights, using standard sampling methodology (19). Statistical testing was conducted at a nominal 5% alpha significance level.

Bivariate analyses were employed to examine whether there are differences in the baseline demographic, health, and medical access factors between minority groups and whites using chi-square tests. We calculated unadjusted 6-year cumulative risks of incident disability by race/ethnicity. All analyses adjusted for the complex sampling design using SUDAAN software version 9.0 (20).

Survival analysis for discrete data was used to analyze racial/ethnic differences in the development of ADL disability. The development of ADL disability was measured in discrete rather than continuous time because ADL disability was monitored only at 2-year intervals (1998–2000, 2000–2002, and 2002–2004). A discrete hazard rate modeled the probability of developing disability by the next 2-year interview given a disability-free status and risk profile at the current interview. To investigate whether demographic, health, and medical access factors explained the racial/ethnic differences in development of disability, we employed a series of hierarchical survival models to estimate race/ethnicity related discrete hazard rate of disability by first controlling for demographic factors, and then sequentially adding to the model health and medical access factors. To be included in the analyses, a person's disability status must be known at the start and the end of a 2-year period. All completed interview pairs (1998/2000, 2000/2002, and 2002/2004) from a person prior to death contributed to the analyses. This method specifically accounts for repeated measures on the same individual and uses time-varying covariates. We used the SAS GENMOD procedure (SAS Institute, Cary, NC) with a complementary log-log link to estimate the discrete hazard model. To account for the complex sampling design, variance was estimated using balanced repeated replication, a form of bootstrapping (19, 21). Hazard ratios and associated 95% confidence intervals (95% CIs) estimated from the discrete hazard model were reported.


Our sample of 7,257 respondents represented 24.6 million older Americans with self-reported arthritis free of baseline disability, comprising 85.5% whites, 9.3% African Americans, 2.4% Hispanics/Spanish, and 2.9% Hispanics/English. This population was predominantly female (61.5%) with an average baseline age of 66.7 years. By 2004 (6 years post-baseline interview) 908 (12.5%) people from the 7,257 study cohort had died. The mortality rate was the highest among African Americans (13.8%), followed by whites (12.4%), Hispanic/English (11.4%), and Hispanic/Spanish (9.7%). The nonresponse rates among the survivors were 4.2% and 5.7% for the 2002 and 2004 interviews, respectively.

Information on the baseline characteristics, stratified by the 4 racial/ethnic groups, is presented in Table 1. Compared with whites, African Americans were more likely to be unmarried and live alone, while the Hispanic/Spanish subgroup was more likely to live with someone else. The Hispanic/English group was disproportionately younger than the other 3 subgroups and tended to be unmarried.

Table 1. Frequency of baseline (1998) characteristics among 7,257 participants with arthritis at risk of incident activities of daily living disability in the Health and Retirement Study*
Baseline characteristicsAfrican American (n = 1,001)Hispanic/Spanish (n = 228)Hispanic/English (n = 210)White (n = 5,818)
  • *

    Values are the population percentage. Statistical test of minority group compared with white reference group from chi-square test.

  • Unadjusted P < 0.05.

  • Chi-square result over multiple risk factor categories.

  • §

    Unadjusted P < 0.01.

 Female sex66.3567.1662.8060.74
 Age, years
 Live alone32.63§15.09§25.0025.48
Health factors
 Chronic conditions
  Heart disease19.609.64§17.1120.67
  High depressive symptoms17.39§34.10§17.069.06
  Pulmonary disease6.18§6.372.78§9.09
  Vision (poor/legally blind)10.01§8.356.654.58
 Functional limitation
  Physical function33.92§39.5926.2022.43
  IADL disability14.40§15.519.236.81
 Health behaviors
  Current smoker19.9013.9717.7016.08
  Current alcohol use16.39§15.34§30.6232.20
  Lack of regular vigorous physical activity64.98§72.0358.5755.33
   Gain >10 pounds19.7717.8316.6615.31
   Loss <10 pounds20.0315.6616.4315.53
Medical access factors
 Low education (≤11 years)53.53§85.60§50.17§24.36
 Low income54.63§71.90§41.38§22.59
 Low net wealth58.53§71.32§50.76§20.59
 Health insurance

The complex patterns of racial/ethnic differences in health factors that emerged are shown in Table 1. For comorbid chronic conditions, all 3 minority groups (especially the Hispanic/Spanish and African American groups) compared with whites, were generally less likely to report life-threatening conditions (cancer, heart disease, and stroke) as well as pulmonary disease, but were more likely to report other chronic conditions (diabetes, high depressive symptoms, hypertension, and poor vision). For functional limitations, physical limitation was 50% greater among African Americans and 75% greater among Hispanic/Spanish, and IADL limitation was 100% greater among African Americans and Hispanic/Spanish than their white counterparts. The prevalence of functional limitations among Hispanic/English was similar to whites. Health behaviors, which included current smoking, alcohol abstinence, less participation in regular vigorous physical activities, and weight problems such as obesity and weight loss were more often reported by African Americans. Hispanic/Spanish compared with whites were more likely to abstain from alcohol use. The Hispanic/English group was more comparable with whites in terms of health behavior.

There were also substantial racial/ethnic differences in baseline medical access factors, as shown in Table 1. All minority groups compared with whites had fewer medical access resources in terms of low education, low income, and low wealth. Minorities were more likely to be uninsured or have Medicaid enrollment, while ∼60% of whites reported private insurance coverage. The most economically disadvantaged group was the Hispanic/Spanish. More than 85% of Hispanic/Spanish had less than a high school education and only 19.7% of this minority group had private insurance coverage.

After 6 years, 17.7% of the total arthritis cohort developed ADL disability. The 6-year cumulative ADL disability incidence rates were significantly higher among African Americans (28.0%) and Hispanic/Spanish (28.5%) compared with whites (16.2%). A similar disability rate was reported in the Hispanic/English group as in the white group, 19.1% and 16.2%, respectively (Figure 1).

Figure 1.

Six-year cumulative rates of onset disability among older Americans with arthritis from Health and Retirement Study participants age ≤51. ADL = activities of daily life. Error bars show 95% confidence intervals.

Table 2 shows the hazard ratios (HRs) for developing ADL disability over the 6-year period while controlling for demographics for the 3 minority groups relative to the white group. The demographic-adjusted disability HRs were significantly higher among the African American group (adjusted HR 1.94, 95% CI 1.51–2.38) and Hispanic/Spanish group (adjusted HR 2.03, 95% CI 1.35–2.71), and moderately but not significantly elevated for the Hispanic/English group (adjusted HR 1.41, 95% CI 0.82–2.00) compared with the white group with arthritis.

Table 2. Hazard ratios (HRs) of developing activities of daily living (ADL) disability (Health and Retirement Study 1998–2004)*
Adjustment factorsAfrican American (n = 1,001)Hispanic/Spanish (n = 228)Hispanic/English (n = 210)White (n = 5,818)
  • *

    Values are the adjusted HR (95% confidence interval).

  • Adjustment factors include: demographic factors (race/ethnicity, age, sex, marital status, living arrangement), health factors (chronic diseases: cancer, diabetes, heart disease, high depressive symptoms, hypertension, pulmonary disease, stroke, vision problem; functional limitations: physical limitations, incident ADL limitations; health behaviors: smoking, alcohol use, exercise, weight problems), and medical access factors (education, income, wealth, health insurance).

  • Reference.

  • §

    Associated race/ethnicity incident disability hazard rate compared with white reference group is significant at a 0.05 alpha level of testing.

Demographic factors1.94 (1.51–2.38)§2.03 (1.35–2.71)§1.41 (0.82–2.00)1.00
 Demographics + chronic diseases1.68 (1.27–2.08)§1.72 (1.10–2.33)§1.38 (0.77–1.98)1.00
 Demographics + functional limitations1.68 (1.31–2.05)§1.67 (1.20–2.14)§1.39 (0.92–1.86)1.00
 Demographics + health behaviors1.63 (1.29–1.97)§1.69 (1.12–2.27)§1.41 (0.82–2.00)1.00
Demographic + all health factors1.42 (1.10–1.74)§1.41 (1.00–1.82)1.42 (0.91–1.93)1.00
Demographic + health + medical access factors1.31 (1.01–1.61)§1.20 (0.82–1.58)1.32 (0.88–1.76)1.00

Separately adjusting for health factor differences related to chronic conditions, functional limitations, or health behaviors each explained 28–35% of the excess HRs for African Americans and Hispanic/Spanish, but had little influence on Hispanic/English disability differences. Taken together, health factors reduced the excess HRs among African Americans 55% (to 1.42), and among Hispanic/Spanish 60% (to 1.41), but the adjusted hazard ratio of Hispanic/English was almost unchanged. It was notable that after adjusting for demographic and health factors, the excess risk was similar across all 3 minority groups (1.41–1.42) compared with whites.

Finally, analyses that additionally control for medical access factors further reduced the excess risk among African Americans by an additional 12% (to 1.31), among Hispanic/Spanish an additional 20% (to 1.20), and among Hispanic/English an additional 24% (to 1.32) even after accounting for differences due to health and demographic factors (Table 2). Recognizing that the impact of medical access factors may overlap with that of health factors, sensitivity analyses that controlled for medical access factors and demographics (without the influence of health factors) were conducted. Medical access factors alone explained 60% of excess risk for African Americans, 95% for Hispanic/Spanish, and 73% for Hispanic/English. This finding reflects the influence of substantial economic disparities among minorities compared with whites indicated in Table 1. However, economic disparities and types of insurance held also are likely to partially reflect the health of individuals. Sensitivity analyses were also conducted in an additional 1,569 people who developed arthritis after the baseline interview. The results (data not shown) were essentially identical to those presented in Table 2.

Table 3 presents the results from the full discrete hazard model, showing the relative impact of demographics, health, and medical access factors on the development of ADL disability. Besides race/ethnicity, age was the only significant predictor of ADL disability among demographic factors. The risk of disability increased dramatically for people with older age (71–80 years, adjusted HR 1.38; ≥81 years, adjusted HR 3.27). Among the health factors, the strongest risk factor was IADL disability (adjusted HR 2.74), followed by lack of regular vigorous physical activities and weight loss (adjusted HR 1.87 and 1.84, respectively). Other health factors that significantly increased the risk of ADL disability included stroke (adjusted HR 1.68), obesity (adjusted HR 1.52), weight gain (adjusted HR 1.50), diabetes (adjusted HR 1.47), pulmonary disease (adjusted HR 1.33), limitation in physical function (adjusted HR 1.32), high depressive symptoms (adjusted HR1.29), current smoker (adjusted HR 1.26), and hypertension (adjusted HR 1.17). Alcohol consumption was associated with lower likelihood for developing ADL disability (adjusted HR 0.64).

Table 3. Adjusted hazard ratio (HR) for developing activities of daily living disability (Health and Retirement Study 1998–2004)*
  • *

    Values are the adjusted HR (95% confidence interval). IADL = incident activities of daily living.

  • Associated adjusted hazard is significantly different from unity at 0.05 alpha level.

Second interval (2000–2002)0.82 (0.70–0.94)
Third interval (2002–2004)0.84 (0.68–0.99)
 African American1.31 (1.01–1.61)
 Hispanic/Spanish1.20 (0.82–1.58)
 Hispanic/English1.32 (0.88–1.76)
 Female sex1.06 (0.90–1.22)
 Age 61–70 years0.80 (0.61–0.99)
 Age 71–80 years1.38 (1.07–1.70)
 Age ≥81 years3.27 (2.49–4.06)
 Unmarried0.85 (0.63–1.08)
 Live alone1.04 (0.86–1.22)
Health factors
 Chronic conditions
  Cancer1.03 (0.86–1.19)
  Diabetes1.47 (1.20–1.75)
  Heart disease1.06 (0.87–1.24)
  High depressive symptoms1.29 (1.02–1.57)
  Hypertension1.17 (1.00–1.35)
  Pulmonary disease1.33 (1.08–1.58)
  Stroke1.68 (1.28–2.07)
  Vision (poor/legally blind)1.11 (0.89–1.34)
 Functional limitation
  Physical function1.32 (1.15–1.49)
  IADL disability2.74 (2.30–3.17)
 Health behaviors
  Current smoker1.26 (1.00–1.53)
  Current alcohol use0.64 (0.50–0.78)
  Lack of vigorous physical activities1.87 (1.50–2.24)
   Obese1.52 (1.30–1.74)
   Underweight1.33 (0.95–1.71)
   Gain >10 pounds1.50 (1.25–1.75)
   Loss >10 pounds1.84 (1.58–2.10)
Medical access factors
 Low education (≤11 years)0.92 (0.78–1.06)
 Low income1.12 (0.91–1.33)
 Low net wealth1.06 (0.89–1.24)
 Health insurance
  Medicaid1.65 (1.18–2.11)
  Medicare/CHAMPS/CHAMPVA/VA1.41 (1.11–1.71)
  None0.82 (0.50–1.14)

Medical access factors were not significant predictors of developing disability after controlling for other risk factors except for holding public health insurance. Both Medicaid enrollment (adjusted HR 1.65) and Medicare or other public health insurance (adjusted HR 1.41) were associated with greater risk of ADL disability relative to holding private insurance coverage, after adjusting for demographic and health factors. At least part of this large, positive effect of the Medicaid variable may reflect the fact that low income people in poor health are more likely to be put on Medicaid in order to cover otherwise uninsured medical expenses. We also noted that if we removed the insurance variables, the low income variable would become statistically significant (adjusted HR 1.22, 95% CI 1.02–1.43).


This study provided evidence of racial/ethnic differences in the development of ADL disability among older Americans with arthritis who had no ADL disability at baseline. Overall, 1 out of 6 people in this arthritis cohort reported disability in at least 1 ADL task over the 6-year followup period. However, there were substantial and complex differences across race/ethnicity categories. The incident rates of ADL disability among African Americans (28.0%) and Hispanic/Spanish (28.5%) were more than 1.7 times that of whites (16.2%). But Hispanic/English reported a rate (19.1%) similar to whites.

Racial and ethnic differences in the prevalence of disability for persons with arthritis have long been a concern (8–11, 22). However, national data on racial/ethnic differences in the development of disability among persons with arthritis are sparse. One study by Shih et al (12) based on national data showed higher rates in the development of disability among African Americans and Hispanics compared with whites based on the 1998–2000 HRS and noted that factors contributing to disability differed across racial/ethnic groups.

This present study added to the current arthritis literature by exploring the contribution of health and medical access factors to racial/ethnic differences in the development of disability. In addition, we considered acculturation, which can influence health status (23, 24). The present study used the language of preferred interview as a proxy for acculturation. Participants who choose to respond in their native language (rather than the language of the host country) usually have lower levels of acculturation. Language barriers may limit opportunities for integration with the host culture and reduce social acceptance resulting from being identified as Hispanic. While many cultural features are not captured by this attribute, it differentiates 2 distinct populations that may have different experiences which influence disability. For example, disadvantages stemming from limited educational and occupational choices, and social stress related to poverty may contribute to the greater disability burden experienced by the Hispanic/Spanish group compared with the Hispanic/English group.

To guide a public health response to promote equitable health outcomes, we investigated the relative influence of health and medical access factors on racial/ethnic differences in the development of disability. Disability developed most frequently among African Americans and Hispanic/Spanish, whose risk was almost doubled compared with whites after controlling for demographic differences. Differences in health factors related to chronic comorbid conditions, functional limitations, and health behaviors explained over half of that excess risk for developing disability among African Americans and Hispanic/Spanish. In contrast, Hispanic/English had an insignificant, but slightly elevated risk for developing disability than did whites; little of that excess was attenuated by differences in health factors. The limited influence of health factors on elevated Hispanic/English disability rates as compared with whites reflected the strongly similar profiles on functional limitations and health behaviors, and few differences in chronic conditions for Hispanic/English versus whites (Table 1). Hispanic/Spanish had poorer health profiles than Hispanic/English. Once individual health factors were controlled for in the analyses, the higher disability incidence for Spanish-speaking versus English-speaking Hispanics was eliminated.

Medical access factors were substantial mediators of racial/ethnic differences in the development of disability. African American, Hispanic/Spanish, and Hispanic/English minorities compared with whites had disproportionately fewer economic resources in terms of education, income, and wealth. Minorities were also more likely to be uninsured or rely on Medicaid coverage. Hispanic/Spanish adults were the most economically disadvantaged in every category. Even after controlling for health factors, medical access factors explained an additional 12% excess risk among African Americans and 20% among Hispanic/Spanish. Similarly, medical access factors had an attenuating effect on the Hispanic/English disability risk compared with whites.

There is a large body of literature that reported that lower income is directly related to poorer health outcomes (25, 26). However, it was not just poverty but the social gradient (i.e., place in society) that affected health outcomes (26). It was found that in the US, non-English-speaking immigrants experience tremendous discrimination not only in schools and the work-place but also in healthcare settings and in society in general (27–30). Lack of private insurance may indicate poorer quality of health care received. Individuals in lower tier health plans commonly have fewer choices with regard to health services, which can compromise their quality of care. For example, fewer joint replacements are done in racial/ethnic minority groups than in whites (31), although whether or not this is due to medical access, discrimination, or other factors is unclear. The measured economic disparities in our study may additionally reflect unmeasured differences in social gradient, which explained the large excess risk of disability related to medical access factors for minorities in general and Hispanic/Spanish adults in particular.

Several limitations common to the use of secondary databases may affect our findings. First, data were self-reported. However, self-reported information is standard in epidemiologic research and its reliability has been documented (32, 33). Second, no information on disease severity was available. However, we used limitations in physical functioning and IADL tasks, likely consequences of disease, as surrogates for disease severity measures. Including functional limitations also adjusted for where individuals started on the disability spectrum. Third, although this study demonstrated that racial/ethnic differences in disability were largely due to health and medical access factors, these findings could be confounded by unmeasured environmental factors contributing to disability. Such factors may include occupation, job demands, poorer living conditions, and segregation. Additionally, Hispanic/Spanish and Hispanic/English language differences may reflect distinct paradigms concerning health and illness (34, 35). Finally, dividing Hispanics into 2 groups based on interview language was a gross distinction of cultural differences. It is also likely that there are important cultural differences aggregated into the African American and white subgroups. Therefore analyses adjusted for common correlates of race/ethnicity (e.g., education, income, and wealth).

In summary, these national data from the HRS indicated that among older adults with arthritis, excess racial/ethnic rates for developing ADL disabilities were largely explained by differences in health and medical access factors. Differences in economic resources between groups were great. Lack of health insurance was especially problematic given limited income and wealth available to minority populations to enable medical access. Differences in health factors were especially great for African Americans and Spanish-speaking Hispanics and exacerbated excess disability for those groups when compared with whites. At the clinical level, not only should treatment of comorbid conditions be considered, but also disease prevention, prevention and treatment of functional limitations, and promotion of healthy behaviors should be a priority for all patients with arthritis to prevent the development of disability. Future research should be directed at how to more effectively deliver such programs especially to minority populations. Policy recommendations that provide incentives to address these factors should be considered.


Ms Song had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study design. Song, Rowland Chang, Dunlop.

Acquisition of data. Song.

Analysis and interpretation of data. Song, Huan Chang, Tirodkar, Rowland Chang, Manheim, Dunlop.

Manuscript preparation. Song, Huan Chang, Tirodkar, Rowland Chang, Manheim, Dunlop.

Statistical analysis. Song, Dunlop.