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1 Social Science & Medicine 62 (2006) Explaining US racial/ethnic disparities in health declines and mortality in late middle age: The roles of socioeconomic status, health behaviors, and health insurance Joseph J. Sudano a,b,, David W. Baker c a Center for Health Care Research and Policy, Case Western Reserve University at The MetroHealth System, Rammelkamp 236, 2500 MetroHealth Drive, Cleveland, OH , USA b Departments of Medicine and Epidemiology and Biostatistics, Case School of Medicine, Case Western Reserve University, Cleveland, OH, USA c Division of General Internal Medicine, Feinberg School of Medicine, Northwestern University, 676 N. St. Clair Street, Rm. 255, Chicago, IL 60611, USA Available online 2 August 2005 Abstract Pervasive health disparities continue to exist among racial/ethnic minority groups, but the factors related to these disparities have not been fully elucidated. We undertook this prospective cohort study to determine the independent contributions of socioeconomic status (SES), health behaviors, and health insurance in explaining racial/ethnic disparities in mortality and health declines. Our study period was , and our study population consists of a US nationally representative sample of 6286 non-hispanic whites (W), 1391 non-hispanic blacks (B), 405 Hispanics interviewed in English (H/E), and 318 Hispanics interviewed in Spanish (H/S), ages in 1992 in the Health and Retirement Study. The main outcome measures were death; major decline in self-reported overall health (SROH); and combined outcome of death or major decline in SROH. Crude mortality rates over the 6-year study period for W, B, H/E and H/S were 5.8%, 10.6%, 5.8%, and 4.4%, respectively. Rates of major decline in SROH were 14.6%, 23.2%, 22.1% and 39.4%, for W, B, H/E and H/S, respectively. Higher mortality rates for B versus W were mostly explained by worse baseline health. For major decline in SROH, education, income, and net worth independently explained more of the disparities for all three minority groups as compared to health behaviors and insurance, reducing the effect for B and H/E to non-significance, while leaving a significant elevated odds ratio for H/S. Without addressing the as-yet undetermined and pernicious effects of lower SES, public health initiatives that promote changing individual health behaviors and increasing rates of insurance coverage among blacks and Hispanics will not eliminate racial/ethnic health disparities. r 2005 Elsevier Ltd. All rights reserved. Keywords: Racial/ethnic health disparities; Hispanics; Socioeconomic status; Health behaviors; Insurance; Self-reported health declines; USA; Cohort study Corresponding author. Center for Health Care Research and Policy, The MetroHealth System, Rammelkamp 236, 2500 MetroHealth Drive, Cleveland, OH , USA. Tel.: ; fax: addresses: (J.J. Sudano), (D.W. Baker) /$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi: /j.socscimed

2 910 J.J. Sudano, D.W. Baker / Social Science & Medicine 62 (2006) Introduction Over the past several decades, studies have accumulated overwhelming evidence of mortality and health status disparities between racial and ethnic groups (Bassett & Krieger, 1986; Feldman & Fulwood, 1999; Geronimus, Bound, Waidmann, Hillemeier, & Burns, 1996; Schulz et al., 2000; Smith & Kington, 1997). Conceptual models have emerged in the literature that attempt to explain these differences (Mendes de Leon & Glass, 2004; Schulz, Williams, Israel, & Lempert, 2002). Generally, these explanatory models focus on how upstream macrosocial factors (e.g., racism and racial discrimination, racial residential segregation, social stratification) structure and limit the personal resources (e.g., educational, occupational, financial) available to racial and ethnic minority group members, and how in turn various downstream social and individual level mechanisms (e.g., stress exposure, lack of medical care, unhealthy lifestyles) impact health outcomes over time. Several key factors have emerged and been identified as the primary social pathways and processes that affect health. These include low socioeconomic status (SES) (Lantz et al., 1998; Schulz et al., 2002; Williams & Collins, 1995), adverse health behaviors (Black, Ray, & Markides, 1999; Lantz et al., 2001), and lack of health insurance (Baker, Sudano, Albert, Borawski, & Dor, 2001; Monheit & Vistnes, 2000). To the extent that these adverse health factors tend to cluster together and are more prevalent among racial/ethnic groups (Lynch, Kaplan, & Salonen, 1997), they may explain the observed disparities in health outcomes between minority group members when compared to majority group members, i.e., typically non-hispanic whites in the United States (Mendes de Leon & Glass, 2004). However, several limitations in the studies listed above have prevented a more complete understanding and elucidation of the causal relationships between these factors and the observed racial/ethnic disparities. These include: variations in study design (e.g., cross-sectional vs. longitudinal); focus on only one particular health outcome (typically mortality); the coarse or inadequate measurement of key factors (usually SES but also baseline health status); aggregating very distinct racial/ ethnic sub-groups (e.g., more or less acculturated Hispanic groups); or the omission of key factors from the analysis (e.g., insurance status). In the background section that follows, we first elaborate on several of these issues, and then finish with the research questions we investigate in this study. Background Any examination of racial/ethnic disparities in health outcomes must necessarily include an investigation of socioeconomic (SES) determinants because of the strong correlations between SES and health, and SES and race/ ethnicity. For example, because African Americans are disproportionately poorer and have less education compared to non-hispanic whites in the United States, SES differentials confound the relationship between race and health outcomes. Therefore, if inadequate or coarse controls for SES are employed, researchers will underestimate the effect of SES. This results in a residual race effect, leaving the causes of health disparities only partly explained, and often implicitly attributing the unexplained portion to some underlying genetic difference (Kaufman, Cooper, & McGee, 1997). Among the variables available to measure SES, education and income are the most used. Education is relatively easy to measure and stable over time; and it is positively related to a variety of health outcomes, both perceived and objectively measured (Ross & Mirowsky, 1999). Education contributes to an array of resources that are salutary to health, including cognitive and problem solving skills; self-directedness, self-efficacy and personal sense of control; healthy lifestyles; and more lucrative and health-sustaining occupations (Mirowsky & Ross, 1998; Ross & Wu, 1996). Moreover, these salutary relationships persist notwithstanding some salient racial and gender variations in financial returns from educational attainment (Williams & Collins, 1995). Regarding financial resources, income is the most commonly used whether measured at the individual or household level. There is a longstanding positive relationship between income and health in the literature (Lynch, Kaplan, & Salonen, 1997; Lynch, Kaplan, & Shema, 1997; Lynch & Kaplan, 2000), but the use of contemporaneous income (i.e., typically income measured in the year prior to a survey) does pose several measurement challenges when studying health outcomes over time. First, it is highly volatile over the lifecourse, particularly for those persons in the service and labor sectors of the economy, who are also disproportionately members of racial/ethnic minority groups. Second, current income may be susceptible to reverse causation in its relationship to health, where declines in health may produce lower income. Hence, some authors suggest measures of accumulated wealth may be better indicators of financial resources available for health and health-related decisions across the lifecourse, particularly for aging adults (Duncan, Daly, McDonough, & Williams, 2002). There are several pathways by which wealth may affect health. First, it may be a marker for an intergenerational transfer of advantage including health stock and lifechances during the developmental years, through childhood and adolescence, and into early adulthood (Lynch & Kaplan, 2000). Alternatively, the lack of wealth when measured in adulthood may be a marker for past economic hardship, and poorer health stock and fewer

3 J.J. Sudano, D.W. Baker / Social Science & Medicine 62 (2006) life-chances. Second, it can also be a measure of the ability to sustain unanticipated economic downturns such as job loss, lay offs, health shocks, and other events that require large expenditures (Chiteji, 2004). A discussion of the impact of wealth on health would be moot if there were no variation across racial/ethnic groups in the United States. That is far from reality: large differences in both income and wealth exist. For example, median household incomes for 1998 were $42,440, $25,350 and $28,330 for non-hispanic whites, non-hispanic blacks, and Hispanics, respectively (US- Census Bureau, 2004). Even more dramatic inequalities in wealth statistics existed in 1998: median net worth (including home equity) was $71,000, $6200, and $7190 and median non-home equity assets (liquid assets, savings, etc.) were $20,100, $1020 and $1820 for non- Hispanic whites, non-hispanic blacks, and Hispanics, respectively (Orzechowski & Sepielli, 2003). For these reasons, it is imperative that investigations of racial/ ethnic disparities in health include several measures of SES, including education, income and wealth. For residents of the United States where universal health insurance coverage does not yet exist, insurance status represents another important factor in health outcomes, particularly for older adults. Previous studies have shown that adults age years old who lack health insurance have higher risk-adjusted rates of decline in their overall health and physical functioning (Baker et al., 2001) and higher risk-adjusted mortality compared to individuals with private insurance (McWilliams, Zaslavsky, Meara, & Ayanian, 2004). Likewise, reduced access to care can have serious consequences for health outcomes via lack of preventive services use, delayed diagnosis of disease, and poor monitoring and control of chronic diseases (Committee on the Consequences of Uninsurance, 2002). Non-elderly Hispanics had the highest rates of uninsurance among any ethnic group in the United States in 1998: 32%, compared to 12% for whites and 19% for blacks (National Center for Health Statistics, 2004). These rates have improved only modestly over the past several years, with public insurance replacing most of the employer-sponsored decreases in coverage for blacks and Hispanics. Parenthetically, even with higher rates of insurance coverage albeit public in nature blacks and Hispanics continue to report greater barriers and reduced access to care when compared to white (Hargraves, 2004). Coupling these factors with lower satisfaction with the quality of care they do receive (Johnson, Saha, Arbelaez, Beach, & Cooper, 2004), Hispanics have higher insurance-related risks for adverse health outcomes compared to their non-hispanic white and black counterparts. This trend is expected to continue as employersponsored insurance coverage continues to drop and more individuals obtain public coverage or are forced into the ranks of the uninsured (Hargraves, 2004). Finally, the relationships between racial/ethnic group membership, SES, and health outcomes is sometimes paradoxical and often complex. For example, many members of Hispanic sub-groups (particularly Mexican- Americans) traditionally have an SES profile similar to that of African Americans, report poorer self-assessed health than non-hispanic whites, and yet have mortality rates similar to the non-hispanic white population (Franzini, Ribble, & Keddie, 2001; Markides & Coreil, 1986). This relationship seems to be even more pronounced for Hispanics who respond to interviews in Spanish (Finch, Hummer, Reindl, & Vega, 2002). No longitudinal study to date has simultaneously examined the contributions of (1) health status, (2) SES, (3) health behaviors, and (4) health insurance for explaining racial/ethnic disparities in mortality and health decline among a diverse, nationally representative sample of older adults. This study uses data from the Health and Retirement Study (HRS) to go beyond prior studies and to answer the following research questions: (1) For older adults, do health status, SES, health behaviors and health insurance differ significantly across US racial/ethnic groups? (2) How much of the observed racial/ethnic disparity in health outcomes is explained by each set of factors above and do these factors completely explain the observed disparity? (3) Is this relationship consistent across different health outcomes (mortality and health declines)? Methods Data and study population For this study we used data from Wave 1 (1992) and Wave 4 (1998) of the HRS, a longitudinal, nationally representative, multistage area probability sample of US households. Supported by the National Institute on Aging and conducted by the University of Michigan s Institute for Social Research, the first wave of data collection targeted non-institutionalized persons in the contiguous US ages (born between ) and their spouses. For the initial interview, in-home, face-to-face interviews were conducted for 7608 households yielding 12,652 individual respondents (an overall response rate of 82%). Subjects were subsequently interviewed every two years. By design, the HRS staff provided sampling and analytical weights only for the targeted age-eligible respondents and their ageeligible spouses/partners to adjust for over sampling and non-response bias. A more detailed description of the overall study design and sampling methods are provided elsewhere (Heeringa & Connor, 1995).

4 912 J.J. Sudano, D.W. Baker / Social Science & Medicine 62 (2006) Because of the HRS complex survey design and our desire to produce nationally representative estimates for this age group in our final analysis, we were necessarily limited to using only those respondents for whom sampling and analysis weights were provided. These were the targeted age-eligible respondents and spouses/partners mentioned above, ages 51 61, numbering 9824 individuals. Next, 75 respondents had incomplete records for either the 1992 or 1998 interview. An additional 1349 (13.7%) were lost to follow-up over the 6-year period. This left us with 8400 respondents in our final sample. Statistical analysis All analyses used the survey subroutines available in Stata 7.0 (Stata Corporation, College Station, TX) to account for sample weighting and to adjust variances for the complex survey design of the HRS. Multivariate logistic regression modeling was conducted using the svylogit procedure in Stata version 7.0. A p-value of 0.05 was used to determine statistical significance. Analytic scheme Our analysis was conducted in three stages. First, we conducted bivariate analysis of demographic, socioeconomic, health behavior and health status characteristics by race/ethnicity (Table 1). Second, we analyzed the distributional characteristics of outcome measures across racial/ethnic groups (Table 2). Third, multivariate analysis of each outcome measure employed 5 models. We first specified the unadjusted relative risk (model 1) for outcomes by racial/ethnic group compared to whites. Next, we specified a core model equation (model 2) that includes 1992 baseline control variables and covariates: age, sex, marital status, self-reported overall health (SROH), number of physical limitations, and number of chronic diseases. We then employed a series of nested logistic regression models where independent variables of interest (health behaviors, insurance, and SES) are sequentially added to the core model (models 3 5). This procedure allows us to examine the incremental reduction effects of these variables on the unadjusted racial/ethnic relative risks (Tables 3 5). Outcome measures Three outcome measures were used: death (all cause mortality) between , major decline in SROH between 1992 and 1998, and a combined measure of major decline or death over the study period. First, death was determined using National Death Index and household contacts. Second, SROH was assessed with the Excellent/Very Good/Good/Fair/Poor questionnaire response format at each interview. A major decline in SROH was defined as follows: (1) a decline from excellent/very good/good health in 1992 to fair/poor health in 1998; or (2) a decline from fair health in 1992 to poor in 1998 (Baker et al., 2001). Because this variable excludes individuals who died between 1992 and 1998, as well as individuals at the floor of health status in 1992, we also used a third outcome variable a combined major decline or death. We also used these three variables because it is possible that the effects of the independent variables of interest may be different for all-cause mortality compared to declines in health. Adjustments were made to odds ratios to more accurately reflect true relative risk using a published formula (Zhang & Yu, 1998). Main Independent variable: A short excursus into race and ethnicity Our operationalization of an individual s race/ethnicity was classification based on two self-reported selfidentification items in the HRS questionnaire. First, respondents were asked Do you consider yourself Hispanic or Latino? and second, Do you consider yourself primarily white or Caucasian, black or African American? If an individual answered yes to the first question, they were classified as Hispanic regardless of their answer to the subsequent question. In the dataset used for this analysis, 18 people identified themselves as Hispanic or Latino and black or African American and were classified as Hispanic. Many authors suggest caution when using race and ethnicity in health care research because it may be unclear what these variables represent. For example, studies may inadvertently use race/ethnicity as surrogates for income, education, occupation, health behaviors, or health insurance coverage if they do not have data to explicitly adjust for these variables. The detailed information in the HRS on SES, health behaviors and insurance status allows us to conceptualize race and ethnicity as complex social variables (Hummer, 1996). In essence, the labeling of persons as white, black, or Hispanic suggests a particular social status that to some degree marks the social contexts in which persons live out their lives. Elements of these social contexts might include variations in exposure to prejudice, discrimination and segregation, cultural differences and language barriers, health beliefs, and health care seeking behaviors (Herman, 1996; Williams, 1996). However, our ability to measure these domains is clearly limited by the HRS data and hence we use race/ ethnicity here as a surrogate for this complex of important social factors. Likewise, we recognize that the race/ethnicity categories available in our data imply a measure of homogeneity within groupings that may belie the empirical record. For example, there are important

5 J.J. Sudano, D.W. Baker / Social Science & Medicine 62 (2006) Table 1 Baseline 1992 study population characteristics by racial/ethnic group in the HRS (N ¼ 8400) Characteristic Whites (N ¼ 6286) Blacks (N ¼ 1391) Hispanic/English (N ¼ 405) Hispanic/Spanish (N ¼ 318) Age, mean years (SE) (0.05) (0.10) (0.20) (0.16) Female sex (%) ***,a Marital status (%) Never married ***,a 5.5**,a 3.7**,a Sep., div., widowed Married Education, mean years ***,b 10.7***,b 6.2***,b (SE) (0.07) (0.15) (0.29) (0.38) Income-to-needs ratio, mean ***,b 3.98***,b 2.23***,b (SE) (0.14) (0.11) (0.26) (0.19) Net worth in quintiles, mean ***,b 2.6***,b 1.7***,b (SE) (0.04) (0.05) (0.08) (0.09) Insurance status 1992, 1994, and 1996 Insured at all 3 interviews ***,a 67.3***,a 46.6***,a Intermittently insured Uninsured at all 3 interviews Smoking (%) Never ***,a ***,a Past smoker Current smoker Alcohol consumption pattern (%) Abstainer ***,a ***,a Moderate Heavy CAGE score (%) None ***,a One indicator Two or more indicators BMI quintiles (%) Lowest ***,a 15.5**,a 10.9***,a 2 nd rd th Highest Self-reported overall health (%) Poor ***,a 7.5***,a 16.7***,a Fair Good Very good Excellent Chronic disease count, mean ***,b (SE) (0.02) (0.05) (0.06) (0.09) Physical limitations count, mean ***,b 3.3**,b 4.0***,b (SE) (0.07) (0.15) (0.29) (0.26) Notes: Results are from weighted analyses using survey estimation procedures in Stata 7.0. Significance tests for categorical variables are based on the Pearson w 2 -statistic corrected for survey design and the test for continuous variables are based on the t-statistic. Income-to-needs ratio is based on household income in 1991 divided by the poverty guideline for a given household size. A value of 5.00 for example indicates a household income 5 times (or alternative, 500% of) the poverty guideline for a given household size. BMI ¼ Body mass index (kg/m 2 ) and was used in the construction of quintiles. *po0:05, **po0:01, ***po0:001. a Statistical significance is for trends compared to whites across all categories of the variables. b Statistical significance is compared to whites.

6 914 J.J. Sudano, D.W. Baker / Social Science & Medicine 62 (2006) cultural differences between Mexican Americans, Cuban Americans, and Puerto Ricans that could affect their health outcomes. However, in the HRS the number of Hispanics other than Mexican-American was too small to allow subgroup analysis. Combining these groups into a single category of Hispanics would ignore these differences, and hence we chose to divide Hispanics into two groups based on their choice of language for the initial interview in Wave 1 (1992). This method (a) differentiates two very different populations (Arcia, Skinner, Bailey, & Correa, 2001; Fiscella, Franks, Doescher, & Saver, 2002), and (b) uses language as a proxy for acculturation/assimilation (Arcia et al., 2001). We label these two Hispanic subgroups Hispanic/ English and Hispanic/Spanish. We also recognize that there are likely to be important cultural differences between whites and blacks of different ethnic backgrounds that may affect their health care use and that are not captured by other variables in the data. In sum, we recognize that our aggregation of persons into the socially constructed groupings provided is less than ideal. We chose therefore to include as many factors present in the HRS data that could be considered covariates of race/ethnicity (e.g., education, income, wealth, etc.) both between and within groups, and to conceptualize race/ethnicity as a surrogate for unmeasured social and cultural factors. Demographic variables Respondents age in years (continuous) was recorded from the 1992 interview. Marital status was categorized as married (referent group), separated/divorced/widowed, or never married; sex was categorized as female or male (referent group). SES measurement Educational attainment was measured in number of years of formal schooling completed. We computed an adjusted income measure for each respondent an income-to-needs ratio (INR) that incorporates total household income divided by the poverty guideline for a given size household. As an example, an INR of 3.0 is equivalent to an income 300% of the poverty guidelines for a given family size. Net worth as of 1992 was first measured as a continuous variable and then collapsed into quintiles. Health behavior and health insurance variables Health behaviors include a smoking behavior measure (never smoked as referent); past problem drinking behavior measured using the CAGE index (none as referent) (Mayfield, McLeod, & Hall, 1974); a measure of current drinking pattern based on number of drinks per day categorized as abstainer, moderate (referent), and heavy drinking; and body mass index (BMI) quintiles used as indicators of weight problems/obesity. Finally, we used a measure of intermittent insurance status, first determining the existence of any kind of health insurance coverage (public or private) in 1992, 1994, and 1996, and then categorizing individuals as (a) insured all three interviews (referent), (b) 1 2 reports of uninsurance, or (c) uninsured all three interviews. Health status variables All multivariate models adjusted for three health status variables based on self-reports from the 1992 interview. These included SROH (excellent health as referent), a count of total number of chronic diseases (hypertension, diabetes, health disease, chronic lung disease, cancer, arthritis, stroke, and visual difficulties) (Verbrugge, Lepkowski, & Imanaka, 1989), and number of physical limitations. Two sets of questions previously described by HRS investigators assess the number of physical limitations (Fillenbaum, Burchett, & Welsh, 1993) and include four items assessing difficulties with mobility (activities requiring large muscle strength) and six items assessing difficulties with agility (activities required to perform instrumental activities of daily living). We have reported details on the items in a previous study (Baker et al., 2001). Results Study population characteristics Our final sample included 6286 non-hispanic whites, 1391 non-hispanic blacks, 405 Hispanics interview in English, and 318 Hispanics interviewed in Spanish. Those lost to follow up were slightly more likely to be minority group members, and to have lower incomes and lower educational attainment compared to those who remained in the study. No other significant differences were found between these two populations in any other characteristics we investigated. There were large and significant differences between the racial/ethnic groups in SES and insurance status (Table 1). Blacks, Hispanic/English and Hispanic/ Spanish were more socioeconomically disadvantaged compared to Whites in each of the SES-component variables. Whites were somewhat more likely than blacks and Hispanic/English to be insured at all three interviews (82.3%, 71.7%, and 67.3%, respectively); Hispanic/Spanish were much less likely to be insured at all interviews (46.6%). The percentage of people who were uninsured at all three interviews showed the opposite trend (4.1%, 7.1%, 10.2%, and 27.7% for

7 J.J. Sudano, D.W. Baker / Social Science & Medicine 62 (2006) whites, blacks, Hispanic/English and Hispanic/Spanish, respectively). Differences in health behaviors across the three racial/ ethnic minority groups were more complex and countervailing when compared to whites. Blacks were more likely to be current smokers, while rates for Hispanic/ English and Hispanic/Spanish were similar to whites. There were no significant differences between Hispanic/ English respondents in either alcohol consumption patterns or in CAGE indicator scores compared to whites. Both blacks and Hispanic/Spanish respondents were more likely to report alcohol abstinence; however, they also were more likely to have histories of past drinking problems. All three minority groups were more likely to be overweight/obese compared to whites. Finally, all three minority groups were more likely to report fair or poor health and elevated mean numbers of physical limitations compared to whites. Only blacks reported elevated mean numbers of chronic diseases compared to Whites. Synopsis of outcomes: Death, major decline, and major decline or death A total of 581 deaths occurred over the study period, including 383 whites, 153 blacks, 29 Hispanic/English and 16 Hispanic/Spanish (Table 2). Blacks had unadjusted relative risks nearly twice that of whites (1.82, 95% CI ¼ ), while the relative risks for the 2 Hispanic groups were equal to or less than that for whites (0.99, 95% CI ¼ and 0.75, 95% CI ¼ , for Hispanic/English and Hispanic/ Spanish, respectively). Across the study period, 1285 persons reported a major decline in overall health, including 828 whites, 263 blacks, 80 Hispanic/English and 94 Hispanic/Spanish. All three minority groups had elevated risks for a major decline compared to whites. Relative risks for blacks and Hispanic//English were elevated compared to whites, and were similar in magnitude to each other (1.56, 95% CI ¼ and 1.52, 95% CI ¼ , respectively). Those for Hispanic/Spanish were more than 2.5 times that for whites (2.70, 95% CI ¼ ). A total of 1846 combined deaths and major declines occurred over the study period: 1211 for whites, 416 for blacks, 109 and 110 for Hispanic/English and Hispanic/ Spanish, respectively, with elevated relative risks for the three minority groups as expected (1.53, 95% CI ¼ ; 1.34, 95% CI ¼ ; and 1.93, 95% CI ¼ for blacks, English and Spanishspeaking Hispanics, respectively). Nested models for death For blacks, adding baseline variables (model 2) reduced the unadjusted relative risk to a statistically Table 2 Sample sizes, percentages and unadjusted relative risks for death, major decline in self-reported overall health (SROH), and combined outcome (major decline or death) by racial/ethnic group, in the HRS, Outcome Racial/Ethnic group membership White Black Hispanic/English Hispanic/Spanish Death as of 1998 (N ¼ 8400) Unweighted sample size No. of deaths (n ¼ 581) Weighted %, (95% CI) 5.8 ( ) 10.6*** ( ) 5.8 ( ) 4.4 ( ) Unadjusted RR (95% CI) Referent 1.82*** ( ) 0.99 ( ) 0.75 ( ) Major decline in SROH (N ¼ 7297) Unweighted sample size No. with major decline (n ¼ 1265) Weighted %, (95% CI) 14.6 ( ) 23.2*** ( ) 22.1** ( ) 39.4*** ( ) Unadjusted RR (95% CI) Referent 1.56*** ( ) 1.52** ( ) 2.70*** ( ) Major decline or death (N ¼ 8400) Unweighted sample size No. with major decline or death (n ¼ 1846) Weighted %, (95% CI) 18.9 ( ) 29.0*** ( ) 25.3** ( ) 36.4*** ( ) Unadjusted RR (95% CI) Referent 1.53*** ( ) 1.34** ( ) 1.93*** ( ) Notes: RR ¼ relative risk, CI ¼ confidence interval, Unadjusted RR is corrected relative risk following the formula of Zhang and Yu (see Methods section for citation), derived from logistic regression. All statistical tests are compared to the referent group ( white ). *po0:05; **po0:01; ***po0:001.

8 916 J.J. Sudano, D.W. Baker / Social Science & Medicine 62 (2006) Table 3 Nested logistic regression results showing changes in relative risk of death by 1998 for each racial/ethnic group, compared to whites, in the HRS, Nested models Relative risk and 95% CIs compared to whites White Black Hispanic/English Hispanic/Spanish Death as of 1998 (N ¼ 8400) Unadjusted relative risk Referent 1.82*** ( ) 0.99 ( ) 0.75 ( ) Baseline variables only a 1.24 ( ) 0.84 ( ) 0.47*** ( ) Baseline, health behaviors b 1.30* ( ) 0.88 ( ) 0.54*** ( ) Baseline, health behaviors, insurance c 1.31* ( ) 0.86 ( ) 0.50*** ( ) Baseline, health behaviors, insurance, SES d 1.27 ( ) 0.87 ( ) 0.53*** ( ) Notes: All statistical tests are compared to the referent group ( white ). *po0.05; **po0.01; ***po a Baseline variables include age, sex, and the following, as of the respondents 1992 interview: marital status, self-reported overall health, number of chronic diseases, and number of physical limitations. b Health behavior variables include the following, as of the respondents 1992 interview: smoking status, alcohol consumption pattern, CAGE score, and body mass index. c Insurance status ¼ continuously insured (referent), intermittently insured, uninsured at three consecutive interviews (1992, 1994, 1996). d SES includes educational attainment, income-to-needs ratio (total household income divided by the poverty guideline for a given size household) and net worth in quintiles (see Methods section for details) as of the 1992 interview. non-significant value of 1.24 (95% CI ¼ ) (Table 3). Neither the addition of health behaviors, insurance status, or SES (models 3 5) reduced the relative risks any further than the baseline model. For Hispanic/English, the unadjusted relative risk (a nonsignificant 0.99 compared to whites) declined somewhat (0.84, 95% CI ¼ ) after adding the baseline variables (model 2). Subsequent models produced similar results. For Hispanic/Spanish, the unadjusted relative risk was dramatically reduced by adding baseline controls: from 0.75 (95% CI ¼ ) to 0.47 (95% CI ¼ ), indicating over a 50% lower risk of death over the study period compared to whites. Adding health behaviors, insurance, and SES produced results similar to the baseline model; hence, all models produced statistically significant lower risks for this group compared to whites. Nested models for major decline Table 4 presents nested multivariate results for a major decline in health. Again, all three minority groups had elevated relative risks for a major decline in the unadjusted model (model1), with the risk for Hispanic/ Spanish nearly three times that for whites. Adding baseline variables reduced the risk for blacks over 50%, and approximately 25% and 30% for Hispanic/ English and Hispanic/Spanish, respectively; however, all three minority groups still had elevated risks compared to whites. These disparities were not explained by differences in health behaviors (model 2); the relative risk for blacks was reduced by only 0.03 compared to the baseline model, and the relative risks for Hispanics actually increased 0.04 for both subgroups. When insurance status was added to the baseline/health behavior model in model 4, small reductions in the relative risks were found for blacks (model 3 RR ¼ 1.28 to model 4 RR ¼ 1.22) and Hispanic/English (model 3 RR ¼ 1.38 to model 4 RR ¼ 1.36); for Hispanic/Spanish the reductions after controlling for insurance were larger (model 3 RR ¼ 2.50 to model 4 RR ¼ 2.28). When SES was added in model 5, the relative risks for blacks and Hispanic/English were markedly reduced and no longer statistically significant (1.07, 95% CI ¼ and 1.14, 95% CI ¼ , respectively). While the relative risk for Hispanic//Spanish also was markedly reduced, it remained elevated and significant (1.44, 95% CI ¼ ). Nested models for major decline or death As expected, the results for the combined outcome of major decline or death (Table 5) are an amalgamation of the prior multivariate outcome models of death and major decline. Baseline variables reduce the relative risks somewhat but not to statistically insignificant levels for all groups. Models containing health behaviors and insurance along with the baseline variables performed similarly to those containing the baseline variables alone. Adding SES in a final model including all variables reduced the risks for all three minority groups to insignificance.

9 J.J. Sudano, D.W. Baker / Social Science & Medicine 62 (2006) Table 4 Nested logistic regression results showing changes in relative risk of major decline in self-reported overall health between 1992 and 1998 for each racial/ethnic group, compared to whites, in the HRS, Nested models Relative risk and 95% CIs compared to whites White Black Hispanic/English Hispanic/Spanish Major decline in SROH (N ¼ 7297) Unadjusted relative risk Referent 1.59*** ( ) 1.52** ( ) 2.70*** ( ) Baseline variables only a 1.28** ( ) 1.38** ( ) 2.46*** ( ) Baseline, health behaviors b 1.25** ( ) 1.42** ( ) 2.50*** ( ) Baseline, health behaviors, insurance c 1.22* ( ) 1.36* ( ) 2.28*** ( ) Baseline, health behaviors, insurance, SES d 1.07 ( ) 1.14 ( ) 1.44** ( ) Notes: see Table 3 above. Table 5 Nested logistic regression results showing changes in relative risk of major decline in self-reported overall health between 1992 and 1998 or death by 1998 for each racial/ethnic group, compared to whites, in the HRS, Nested models Relative risk and 95% CIs compared to whites White Black Hispanic/English Hispanic/Spanish Major decline or death (N ¼ 8400) Unadjusted relative risk Referent 1.53*** ( ) 1.34** ( ) 1.93*** ( ) Baseline variables only a 1.24** ( ) 1.23* ( ) 1.68*** ( ) Baseline, health behaviors b 1.23** ( ) 1.27* ( ) 1.77*** ( ) Baseline, health behaviors, insurance c 1.21** ( ) 1.22 ( ) 1.62*** ( ) Baseline, health behaviors, insurance, SES d 1.10 ( ) 1.08 ( ) 1.20 ( ) Notes: see Table 3 above. Discussion In this study of US adults in late middle age, blacks and Hispanics had worse health outcomes over the 6-year follow-up period. However, the relationships between race/ethnicity and health outcomes were complex. Blacks had higher unadjusted mortality compared to whites, while Hispanics who preferred to be interviewed in English (Hispanic/English group) had similar unadjusted mortality compared to whites, and Hispanics who preferred to be interviewed in Spanish (Hispanic/Spanish group) actually had lower unadjusted mortality (Table 2). While all three of the non-white groups were more likely than whites to have a major decline in SROH, the rate was by far the highest among the Hispanic/Spanish group. Thus, the group with the lowest overall mortality had the highest rate of health decline. Our findings emphasize the importance of teasing apart disparities in mortality from disparities in other health outcomes (i.e., SROH, physical functioning, or other morbid events). Risk factors for death and health decline differ, as do the differences in the prevalence of these risk factors across racial/ethnic groups. As a consequence, the explanations for disparities may vary depending upon the health outcomes examined and upon the racial/ethnic groups compared. Two factors emerged as the predominant mediating causes of health disparities. The first was baseline health status. Compared to whites, blacks were approximately twice as likely as whites to report fair/poor health, had more chronic diseases, and reported more physical limitations (Table 1). The relative risk of death for blacks compared to whites dropped from 1.82 to 1.24 after we included baseline self-reported health, the number of chronic diseases, and the number of physical limitations (Table 3). Similarly, the risk of major decline in SROH dropped from 1.59 to 1.28 after these baseline health variables were included (Table 4). Differences in baseline health were also large for Hispanics. These differences in baseline health explained much of the higher rate of health decline for Hispanics compared to whites, although the power of baseline health for explaining differences in rates of health decline was less than that seen for blacks (Table 4). To understand racial and ethnic health disparities specifically for older adults we must remember that we are examining only a snapshot of an individuals life course. Hence, if we are going to reduce mortality rates for blacks in late

10 918 J.J. Sudano, D.W. Baker / Social Science & Medicine 62 (2006) middle-age, we must turn our attention upstream and address the importance of prenatal and early life influences, as well as the burden of illness that build up through early adulthood and middle age (Blackwell, Hayward, & Crimmins, 2001; Geronimus, 1992). The influence of interventions or policies designed to preserve or improve health in later life and reduce health disparities will be limited in their impact by the disparities that have already developed. Therefore, prospective studies of health disparities should carefully measure and adjust for differences in baseline health. This approach will limit residual and hence unexplained effects of race/ethnicity on health. Additionally, studies should emphasize that these endogenous baseline differences represent some combination of the direct and indirect effects of race/ethnicity on baseline health status that beg further research. The second dominant explanation for the worse health outcomes for blacks and Hispanics was SES. In contrast, health insurance and health behaviors explained little of the racial/ethnic differences in health outcomes. This should not be surprising. The power of a variable to explain disparities arises from absolute differences in the prevalence of a risk factor across groups and the magnitude of relative risk for adverse health outcomes conveyed by the risk factor. There were enormous differences in education, income, and net worth across the racial and ethnic groups (Table 1), and these variables were strong predictors of health outcomes in this study (data not shown) and other previous studies. Our results are also consistent with previous studies that have found large direct (or residual) effects of SES on health that were not explained by differences in health behaviors. There were relatively small differences in health behaviors, including smoking, alcohol use, and BMI across racial/ethnic groups in our study (Table 1), and therefore it is not surprising that health behaviors explained little or none of the disparities in outcomes. This dominant effect of SES as a cause of disparities is consistent with other studies (Smith & Kington, 1997; Williams & Collins, 1995). However, the mechanisms by which SES affects health remain unclear, and may include a panoply of social arrangements related to social position that conspire to create these disparities. For example, racism, institutionalized discrimination and residential segregation limit educational and occupational choices for many minority group members, shunting social mobility and limiting life chances that lead to better health (Nazroo, 2003; Williams & Collins, 2001). Secondly, racial/ethnic minority groups are disproportionately exposed to poorer environmental living conditions and worse working conditions (Light et al., 1995; Williams, 2002, 2003). Similarly, and particularly for African Americans, there is growing evidence that long-term exposure to social disorder and violence associated with life in poverty-stricken, highly segregated communities, may produce high allostatic loads, thereby increasing the risk for developing a variety of chronic and disabling physical and cognitive conditions (Massey, 2004). Third, individuals in the lower strata of society may experience an accumulation of disadvantages over the lifecourse, where a weathering of health leads to morbidity and/or mortality and becomes especially notable in older age (Geronimus, 1992; Lynch, Kaplan, & Shema, 1997). Fourth, the experience of discrimination and lower social standing of many minority groups may engender psycho-physiological responses that negatively affect perceptions of health, leading to what Vega and Rumbaut consider the somatization of personal and social problems (Vega & Rumbaut, 1991). To our knowledge, this is the first study to examine the degree to which differences in rates of health insurance coverage contribute to differences in racial/ ethnic health disparities. The absolute differences in health insurance coverage were relatively small for whites compared to blacks, intermediate for whites compared to the Hispanic/English group, and by far largest for whites compared to the Hispanic/Spanish group. As a result, the absolute decrease in the relative risk of a major decline in overall health when health insurance coverage was added to the model was smallest for blacks (declining from 1.25 to 1.22), intermediate for the Hispanic/English group (declining from 1.42 to 1.36), and by far largest for the Hispanic/Spanish group (declining from 2.50 to 2.28; Table 4). Thus, although lack of health insurance is an important population level risk factor for a decline in overall health and mortality, differences in insurance coverage across racial/ethnic subgroups explained only a small fraction of the disparities in outcomes. The results for the Hispanic/Spanish group were consistent with findings in many other studies including those indicating a Hispanic paradox (Franzini et al., 2001; Markides & Coreil, 1986) (see Hunt et al. (2003) and Palloni and Morenoff (2001) for contradictory findings). Specifically, we find that although Hispanic/ Spanish persons have an SES profile similar to and in this study even worse than blacks, their rates of mortality were over 50% lower than blacks, and also significantly lower than whites. However, this potentially salutary effect of ethnicity on mortality is offset by the markedly greater prevalence of major declines in self-reported health constituting a new Hispanic paradox with Hispanic/Spanish respondents reporting rates nearly three times that of whites. One possible explanation for these findings of worse self-reported health but lower mortality rates may lie in the relationships between occupations, physical exertion, and ethnicity. First, increased physical activity decreases the risk of cardiovascular events, regardless of

11 J.J. Sudano, D.W. Baker / Social Science & Medicine 62 (2006) whether the activity occurs on the job or during leisure time (Paffenbarger, Blair, & Lee, 2001; Steenland et al., 2000). Second, US Hispanics are more likely to work in occupational groups that require higher levels of physical activity (e.g., service, farming, construction, production) compared to their non-hispanic white counterparts; 60% versus 36% for Hispanics and non- Hispanic whites, respectively (Fronczek & Johnson, 2003). If in fact the Hispanic/Spanish respondents in our study are more likely to be engaged in these occupations that arguably require frequent, strenuous manual labor, then the aerobic component of these work-related activities may reduce the risk of cardiovascular disease but increase the risk of musculoskeletal problems that adversely affect overall health and physical functioning. To test this hypotheses, we first analyzed the racial/ ethnic occupational distribution, and then created a job physical difficulty scale, based on three items in the questionnaire that ask how often (none, some, most, or all of the time) the respondents job requires (1) physical effort, (2) lifting heavy loads, and (3) stooping, kneeling, and crouching (alpha for the scale was 0.84). Among individuals actively employed outside of the home in our study population, Hispanic/Spanish respondents were more likely to be in unskilled blue collar and service/ farm related occupations compared to their non- Hispanic white counterparts (81% versus 24%, respectively, data not shown). We then divided the job physical difficulty scale into quartiles, and the same pattern was found: Hispanic/Spanish respondents were more likely to be in the 4th (highest) quartile of job physical difficulty compared to whites (48% versus 26%, respectively, data not shown). Additionally, this pattern was consistent for both males and females, although the rates for males of both groups were elevated compared to females. Hence, we believe this suggests that the seemingly paradoxical finding of lower mortality and yet elevated rates of health declines among Hispanic/ Spanish respondents may result from the countervailing health-protective (aerobic) and health-detrimental (harsh physical demands) characteristics of their occupations. Future studies would benefit from a closer examination of these factors, perhaps including a focus on the onset or development of new physical limitations among older adults as an outcome measure. There are several limitations in our study. First, we did not adjust for attrition in our analysis. The 13.7% of study participants that were lost to follow up were more likely to be minority group members and to have lower SES and worse health at baseline. This could bias our results in unpredictable ways. Similarly, the disproportionate percentage of Hispanics who were lost to follow up may have contributed to the overall lower rates of death in this population as noticed in our study. One explanation advanced for the lower mortality rates for Hispanics, the salmon hypothesis, suggests Hispanics may leave the US and return to their native countries to die and hence not be recorded in the National Death Index, in a sense becoming statistically immortal. However, recent work suggests little evidence for this phenomena (Abraido-Lanza, Dohrenwend, Ng-Mak, & Turner, 1999). Second, all of our measures of health status are based on self-reports. To the extent that there are cultural or other differences in the way minority group members report these factors, some bias in our measurement of health status may have occurred. However, SROH has in general exhibited extremely good predictive value for future morbidity and mortality across a wide number of studies (Benyamini & Idler, 1999; Idler & Benyamini, 1997) including those with large numbers of Hispanic/ Latino respondents (McGee, Liao, Cao, & Cooper, 1999), suggesting that for the most part the validity of SROH does not vary across racial/ethnic groups (Subramanian, Acevedo-Garcia, & Osypuk, 2005). Similarly, more rigorous analyses of the factor structure of health related quality of life measurement scales (e.g., Short Form-36) suggest few differences across racial/ethnic groups (Peek, Ray, Patel, Stoebner-May, & Ottenbacher, 2004). These studies in toto suggest that in general, subjective measures of health are largely equivalent across the racial/ethnic groups in our study, with perhaps a small bias in the measurement of SROH in the Hispanic- Spanish subgroup (Stewart & Napoles-Springer, 2000). On the other hand, reliability and validity of the more objective measures of physical limitations and number of chronic conditions is more complex. Physical limitation counts based on self-reports have generally exhibited good reliability and validity across many different measurement scales and across different groups of respondents (McDowell & Newell, 1996). For chronic condition reports, however, the results vary widely across groups and across the type of condition, and are influenced by other factors such as access to health care. For example, people who say they have hypertension are highly likely to actually have the condition. Vargas et al. reported that 87% of people who reported being told by a physician that they had hypertension exhibited objective evidence of hypertension using a broad set of criteria (Vargas, Burt, Gillum, & Pamuk, 1997). The specificity of self-reported hypertension was similar for non-hispanic white adults, black adults, and Mexican-Americans, although the predictive value positive was somewhat lower for Hispanic adults, suggesting a higher proportion of Hispanic adults did not in fact have hypertension. Conversely, in NHANES III, Hispanic adults were more likely than non-hispanic white adults and black adults to be unaware of their hypertension (Havas et al., 1996). Therefore, there is the potential that our measures of chronic condition counts may have been biased in undetermined directions, and may have affected our results.

12 920 J.J. Sudano, D.W. Baker / Social Science & Medicine 62 (2006) Third, we recognize that our methodology of dividing Hispanics into two groups based on interview language is not sufficient as a multi-dimensional measure of acculturation/assimilation. However, evidence suggests over 70 percent of the variance in acculturation scores (based on acculturation scales) can be accounted for by the single variable of language proficiency (National Research Council, 2004). Hence, we believe that our categorization based on interview language differentiates between two different populations, and additionally represent the categorization of these two groups on an important dimension of acculturation. Future studies would benefit by including other factors (e.g., time in host country, generational status, ethnic loyalty) involved in the acculturation/assimilation process (National Research Council, 2004), as these factors may not only affect the responses of immigrant racial/ ethnic minorities such as Hispanics, but also immigrants in other comparison groups. Fourth, we adjust for differences in baseline covariates, but we did not adjust for time-dependent covariates with the exception of insurance status. However, adjusting for the time-dependence of our covariates can be problematic because some may change as a result of declines in health. Fifth, we recognize that some of the deaths reported in the HRS may have been attributable to non-health related factors such as motor vehicle accidents, homicide and suicide. However, based on 1999 data (National Center for Health Statistics, 2003), these causes of death (a) represent only a small percentage of the all-cause death rates across these groups in the United States (3.7%, 3.2%, and 3.9%, age-adjusted, for non-hispanic whites, blacks, and Hispanics, respectively), (b) do not differ dramatically across groups in the age range we studied (approximately, 63.9, 72.0, and 54.3 deaths per 100,000 population for those years old, non- Hispanic whites, blacks, and Hispanics, respectively) and (c) should not have significantly affected our results and hence our conclusions. Sixth, while highlighting the primacy of SES in explaining declines in health, our analysis only explored three dimensions of SES at the individual level (education, income, and net worth). Future studies may benefit by expanding the focus to include the occupational dimension of individual SES, and neighborhood dimensions of SES. Similarly, future studies may benefit from investigating the roles of SES in explaining health disparities along the income/wealth distribution. These efforts should contribute to a better understanding of the social-structural and institutional mechanisms mediating the relationship between SES and health. Finally, our results show that much of the health disparities we observed at the end of the study period are in large measure explained by the health disparities that have already developed as of the baseline interview. Future studies are needed that focus on how SES factors contribute to health disparities across the lifecourse. These studies will necessarily be long and expensive, but would provide much needed clarification of the social, economic, and political environments which determine the accumulation and distribution of exposures and resources over the lifecourse and ultimately shape patterns of adult health (Lynch & Kaplan, 2000). Our findings, along with previous research (House et al., 2000; Lantz et al., 1998), suggest that without addressing the as-yet undetermined and pernicious effects of lower SES, public health initiatives that promote changing individual health behaviors and increasing rates of insurance coverage among blacks and Hispanics will result in only modest decreases in racial/ethnic disparities. We do view reductions in adverse health behaviors and increasing insurance coverage as laudable goals in the public health sector, and they will result in better health for all. However, these policies should be considered efforts through which the rising tide will raise all boats and not as panaceas for reducing the disparities in health that exist for racial/ethnic minority groups in our society. We all must recognize that only through a more equitable distribution of resources positively related to health can we ultimately eliminate racial/ethnic health disparities. 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