In the discussion that follows, I will use the body of gerontological research on inequality by race (White compared to African American) in health outcomes among older people in the United States to illustrate theoretical and methodological issues in applied gerontological research. Applied resarch in general contributes infromation to help solve practical problems to improve the human condition rather than acquiring knowledge for its own sake. The United States experience can inform research on unequal health outcomes across segments of the older Turkish population.

Before the 1960's, difference by race in health and survival among older people received little research attention (Gefland, 1994; Markides & Black, 1996). Dowd and Bengtson's (1978) double jeopardy hypothesis, suggesting that minority status and age lead to declining health, stimulated research on racial inequality in health in the 1980's. Availability of longitudinal panel or multi-wave cross-sectional data in the 1990's and before led to studies (e.g., Jackson, 1991) assessing whether health inequality by race persists or diminishes over time as people age (Escarce, Epstein, Colby, & Schwartz, 1993). Despite declining poverty among older people resulting from Social Security (supplemental retirement income), Medicare (federal medical insurance for people 65 and older), Medicaid (health care payment for those in poverty), civil rights reform, and continuing work remains to understand racial inequality in health outcomes (e.g., Clayton & Byrd, 2001; Fiscella, Franks, Gold, & Clancy, 2000; Gornick, 2003; Hummer, Benjamins, & Rogers, 2004; Probst, Moore, Glover, & Samuels, 2004).

In the discussion that follows, I review studies that seek to answer Dowd and Bengtson's (1978) question whether racial inequality in health outcomes reduces or increases as people age, whether samples of African Americans are adequate to represent within-group and geographic difference, and I suggest conceptual and methodological expansion to better understand the dynamics of racial inequality in health outcomes among older Americans.

Theoretically-guided empirical research seeks to answer whether inequality in health outcomes among African compared to White Americans is consistent or whether it increases or decreases as people age. Based upon a Los Angeles County sample, Dowd and Bengtson (1978) evaluated the double jeopardy (e.g., age and race combine to increase inequality over time) and age as leveler (e.g., increasing age tends to decrease racial inequality) hypotheses. They found that while increasing age decreased racial differences in contact with relatives and life satisfaction, it increased racial differences in health and income. A year later, Bengtson (1979) expanded the variables hypothesized to result in inequality by race to include age, gender and social class. Over a decade later, Markides, Liang and Jackson (1990) also expanded double to multiple indicators of "jeopardy." As a stream of research develops investigators tend to add either additional variables hypothesized to results in a outcome or they expand the range of outcomes.

Whether studies are based upon cross-sectional or longitudinal data also affects investigations of the double jeopardy hypothesis. Analyses of cross-sectional data tend to support double jeopardy (e.g., Dowd & Bengtson, 1981; Jackson, Kolody, & Wood, 1982) while those of longitudinal data (e.g., Ferraro, 1987; Markides, 1981, 1983; Markides, Timbers & Osberg, 1984; Ward, 1983) find little support for the hypothesis. However, Dowd and Bengtson's  (1978) work was important because it led to subsequent research including additional theoretical concepts such as cumulative disadvantage across the life course or persistent racial inequality in health outcomes with advancing age. Additional concepts tied to the age as leveler hypothesis include selective survival (e.g., Crimmins, Hayward & Seeman, 2004; Markides, 1983; Markides & Machalek, 1984) and the racial crossover in mortality as reasons for diminished difference by race in selected outcomes in late life. The racial crossover denotes lower mortality rates among African Americans aged 75 and over compared to Whites (Corti et al., 1999; Guralnik, Land, Blazer, Fillenbaum, & Branch, 1993; Keil et al., 1995; Manton, Poss & Wing,1979; Markides & Machalek, 1984; Mendes de Leon et al., 1997; Wild, Laws, Fortmann, Varady, & Byrne, 1995; Wing, Manton, Stallard, Hames, & Tyroler, 1985). Methodologically, still others explored the effect of mid-life health outcomes in later life. For example, Hayward, Crimmins, Miles, and Yang (2000) contend that mid-life conditions increase the likelihood of late-life mortality in African Americans, distorting later-life mortality comparisons by race.

Other studies by Ferraro and colleagues (Ferraro & Farmer, 1996; Ferraro, Thorpe, McCabe, Kelly-Moore, & Jiang, 2006) find support through analysis of longitudinal data for the notion that disability, hospitalization transitions, and late onset conditions among African compared to White Americans are cumulative and persistent as people age. Their analyses include longitudinal data from the National Health and Nutrition Examination Survey (Ferraro & Farmer, 1996; Ferraro et al., 2006) and the North Carolina EPESE sample.

The studies cited above support selective survival (Crimmins et al., 2004; Markides, 1983; Markides & Machalek, 1984), the racial crossover in late-life mortality (Corti et al., 1999; Wild et al., 1995; Wing et al., 1985), persistent racial differences in disability (Kelley-Moore & Ferraro, 2004; Mendes de Leon et al., 1997; Mendes de Leon, Barnes, Bienias, Skarupski, & Exans, 2005), and cumulative disadvantage (Ferraro & Farmer, 1996; Ferraro et al., 2006; Kelley-Moore & Ferraro, 2004). Missing is an analysis of the effect of these outcomes on each other. Perhaps persistent inequality grounded in double or multiple jeopardy, selective survival, cumulative disadvantage, and the racial crossover can be integrated in a theoretical exploration of the precursors of racial inequality in health among older adults of different ages and across selected health outcomes.

Apart from studies including the analysis of data to evaluate the significance of theoretical concepts predicting racial inequality in health outcomes among older people, others offer purely theoretical explanations of variables that intervene in the relationship between race and inequality. Dressler (1993) suggests that social closure, characterized by the acquisition of status-enhancing material objects and behaviors to preserve racial dominance, intervenes in the relationship between race and health inequality or survival. Clark (2004) contends that allostatic burden resulting from repeated physiologic responses to racist threats affects health outcomes negatively among African Americans. Hummer (1996) adds social structural, individual/experiential, and social support variables to the explanation of racial health inequity. He suggests that racially-linked socioeconomic stratification affects access to health care resources as well as exposure to environmental threats to health. Individually, coping behaviors, stress, and social roles and support mediate both institutional racism and personal racist threats. Mendes de Leon and Glass (2004) include racism and discrimination, social and personal resources, and personal and interpersonal mechanisms such as Clark's allostatic burden with psychosocial resources to explain racial differences in health and well-being.

In addition to conceptually-guided empirical research and theoretical discussions, most studies of racial differences in health outcomes evaluate the prediction of various indicators of health inequality by race according to different health outcomes. The variety of predictors and health outcomes discourages conclusions about the importance of specific predictors.

Multiple key word searches (e.g., race, black, aged, older adults, health, health outcomes, health status, disease, illness) of electronic databases (e.g., AARP's Ageline, Medline, and Proquest), citation accounts included with full-text records in selected electronic data bases (e.g., Proceedings of the National Academy of Sciences, JSTOR), and journal tables of contents were used to identify published studies of racial inequality in health among older adults. Only full-text articles describing quantitative studies (e.g., those assessing the statistical significance of effects, differences, or relationships among independent and dependent variables) of adults aged sixty years and over were reviewed. Sixty-three studies, including the quantitative studies discussed above, were identified.

Careful sampling that represents variability in health outcomes in the population is critical for the validity of study results. Markides and Black (1996) contend that conclusions about racial differences in health outcomes are often based upon non-representative samples. Early work looked at racial differences in heart disease, mortality, subjective health and disability based upon federal Census data (Ferraro, 1987; Mutchler & Burr, 1991; Manton et al., 1979) and the results of the Evans County, GA, (Wing et al., 1985) and Charleston, SC, (Keil et al., 1989) Heart Studies. When the number of African Americans included in the Census samples were described (Ferraro, 1987; Mutchler & Burr, 1991), they hovered around 300 or 850 respondents. The Evans County African American sample was larger, at just over 1,500, and 399 African Americans were included in the Charleston Heart Study. Begun in 1948, the Framingham (Massachusetts) Study was supported by the National Heart Institute (later the National Heart, Lung, and Blood Institute). The Institute continues to support the Study and an effort is underway to add a cohort of 3,500 grandchildren of the original 1948 cohort. The Framingham sample was supplemented in 1994 by 504 racially-diverse respondents to reflect demographic change in the Massachusetts community.

According to Nietert, Sutherland, Keil and Bachman (2006), the original Charleston cohort of 2,181 adults over age 35 was supplemented in 1963 by a peer-nominated sample of 102 upper-SES African-American men. Cohorts of both studies, also supported by the National Institutes of Health, continued to be examined as late as 1995 with mortality ascertained by National Center for Health Statistics National Death Index and Social Security Death index data. Several studies based upon one or both samples in the Evans County, GA, and Charleston, SC, Heart Studies were identified in the search. Wing et al. (1985) include 1,507 Evans County African Americans in their study of mortality while Keil et al. (1989) describe the number of African Americans in the Charleston sample as 399. In a later study, Keil et al. (1995) included 726 African-American men from the combined samples. Finally, Nietert et al. analyzed data from 867 African American men and women in the Charleston sample in their follow-up study of racial differences in mortality risk.

Based upon data from the Social Security Administration's Longitudinal Retirement History Study and that from the 1986 Medicare Annual Beneficiary File combined with the Health Insurance Skeleton Eligibility Write-Off File, Clark and Maddox (1992) and Escarce et al. (1993), respectively, found African to be more likely than White Americans to have lower functional status and to receive fewer medical procedures and diagnostic tests. Although the sample of African American older adults in the Clark and Maddox sample was modest (566), the number included in the Escarce et al. analysis was very large (94,086). Their only independent variable, however, was race; offering no explanation of differences.

Numerous studies resulted from data gathered through coordinated national or multi-site initiatives. Ferraro and colleagues, including Farmer (1996), Kelley-Moore (2001), and Ferraro et al. (2006), identified racial differences in mortality, chronic illness, disability, subjective health, and hospitalization based upon the Centers for Disease Control's National Center for Health Statistic's National Health and Nutrition Examination Survey (NHANES), including the Epidemiologic Follow-up Study. They relied on successive waves of cross-sectional data gathered from 1971 onward. Beginning with their 1996 study, the three studies include 873, 814, and 801 African Americans, respectively.

Several studies of racial differences in health outcomes base their results wholly or in part on the African American sub-sample in the North Carolina Established Populations for the Epidemiologic Studies of the Elderly (EPESE). The number of African Americans is described as 2,260 (Mendes de Leon et al., 1997), 2,261 (Corti et al., 1999), 1,876 (Mendes de Leon, Gold, Glass, Kaplan, & George, 2001), 2,260 (Bohannon, Fillenbaum, Pieper, Hanlon, & Blazer, 2002), 1,355 (Kelley-Moore & Ferraro, 2004), and 1,691 (Sachs-Ericsson & Blazer, 2005). The difference in sample size may be due to difference across studies in missing values of variables or attrition.

Other studies drawn from federally-sponsored initiatives include Miller, McFall and Campbell's (1994) analysis of racial differences in long term care help including 473 African Americans and the Miller et al. (1997) study of physician visits and hospital stays among 513 respondents from the National Long Term Care Survey (NLTCS). The NLTCS, conducted through Duke University and administered through the Census Bureau with funding from the National Institute on Aging, includes a series of cross-sectional surveys begun in 1982 with subsequent surveys at 5-year intervals. Manton and Gu (2001) base their analysis of disability trends by race upon 5 years of NLTCS data but they fail to include the number of African Americans in their combined samples.

Other federally-supported data gathering initiatives include the Centers for Disease Control's National Center for Health Statistic's National Health Interview Survey (NHIS). Miller et al. (1997) included 555 African Americans drawn from the 1984 NHIS. Others combined multiple waves of NHIS data. Liao, McGee, Cao and Cooper (1999) compared functional ability, self-reported conditions, doctor visits and hospital days by race including 1,454 older African American decedents from 1986-1994 NHIS waves. Hummer, Benjamins and Rogers (2004) included 9,297 older African-Americans drawn from NHIS 1989-1994 cross-sectional waves in their analysis of racial differences in self-reported health, activity limitations, and mortality risk.

Finally, selected researchers relied upon African Americans included in the National Institute on Aging-sponsored Health and Retirement Survey (HRS) conducted through the Survey Research Center of the University of Michigan. In 1994, HRS data were supplemented by the Study of Assets and Health Dynamics among the Oldest Old (AHEAD), consisting of older adults born before 1924 aged 70 years and older at baseline. Hayward et al. (2000) constructed a sample of 1,587 African Americans from 1992 and 1994 HRS waves in their study of racial difference in disease incidence and prevalence, disability, strength and dexterity. Dunlop, Manheim, Song and Chang (2002) included 810 African-American AHEAD respondents in their analysis of racial difference in physician visits, hospital admission, outpatient surgery, home health care and nursing home stays. Moody-Ayers, Mehta, Lindquist, Sands and Covinsky (2005) based their analysis of racial differences in functional decline on 779 African Americans from 1993 AHEAD data. Sloan and Wang (2005) combined 4 waves of AHEAD data in their analysis of racial differences in cognitive function to include 19,964 respondents. The number of African Americans is not specified.

Other work stems from federally-supported data gathering initiatives that surfaced only once in our search. Gallo, Cooper-Patrick and Lesikar (1998) analyzed data from 203 and 519 elderly African American people drawn from Baltimore and Durham samples, respectively, of the multi-site National Institutes of Mental Health's Epidemiologic Catchment Area Program. The work of Miller et al. (1997) includes, among other data, 421 African American older adults drawn from the National Medical Expenditures Survey. Lee, Gehlbach, Hosmer, Reti and Baker (1997) included 288,008 African Americans in their analysis of office visits, hospital treatment phase, and receipt of 84 treatments. Based upon samples of 382 and 290 African Americans drawn from the Americans Changing Lives (ACL) and the National Survey of Families and Households (NSFH), respectively, Robert and Ruel (2006) examined racial differences in self-rated health. The National Institute on Aging supported ACL is administered through the University of Michigan Institute for Social Research while the NSFH is supported by the National Institute for Child Health and Human Development and administered through the Center for Demography of the University of Wisconsin.

Still other studies were based upon samples of African-American older adults selected from cities or geographical regions. For example, Mendes de Leon et al. (2005) and Skarupski et al. (2005) included 2,724 and 2,656 elderly African Americans, respectively, from the Chicago Health and Aging Project. Data from the Washington, D.C., Aging, Stress and Health Study formed the basis of Kahn and Fazio's (2005) analysis of difference by race in self-rated health, fatal and chronic conditions, functional impairment and depression. There were 579 older African Americans in their sample. Helzner et al. (2005) included 765 African Americans in their analysis of racial difference in hearing loss among Pittsburgh and Memphis Medicare beneficiaries. Regional analyses include the work of Mitchell, Mathews and Griffin (1997), looking at difference by race in receipt of primary and specialty medical care, financial assistance and personal care services. Their sample included 656 African Americans selected from the 33-counties of largely rural Eastern North Carolina. In the same region, O'Malley et al. (2001) included 965 African-American elderly women living in 10 counties in their analysis of racial differences in self-reported mammogram recommendation by a physician.

Twenty-five studies looking at differences by race in disease- or condition-specific outcomes in patient populations were identified. Studies supported by the Veterans Administration include only older men because of cohort differences by gender across branches of military service.

Studies of patient populations are different. Even though they may be drawn from multi-center clinical trials, authors seldom claim that their samples of patients represent others in some broader geographical area with the same disease or condition. Consequently, beyond statistical power assessment--seldom if ever included in analyses--describing either the number of African-American older adults included in these studies or other attributes of these samples with an eye towards the degree to which they represent the African-American older adult population seems pointless. Nonetheless the studies include Akerley, Moritz, Ryan, Henderson, and Zarcharski (1993), Al-Othman et al. (2003), Arnold et al. (2005), Bach, Cramer, Warren, and Begg (1999), Barnes et al. (2005), Bohnstedt, Fox, and Kohatsu (1994), Conigliaro et al. (2000), Dardik, Bowman, Gordon, Hsieh, and Perler (2000), Daumit, Hermann, Coresh, and Powe (1999), Dignam et al. (1999), Dominitz, Maunard, Billingsley, and Boyko (2002), Govindarajan, Shah, Erkman, and Hutchins (2003) (medical records analysis), Groeneveld, Heidenreich, and Garber (2003), Heimann et al. (1997), Heisler, Smith, Hayward, Krein, and Kerr (2003), Hoffman et al. (2001), Ibrahim, Siminoff, Burant, and Kwoh (2001, 2002), Jha et al. (2003),  Lannin et al. (1998), Marcella and Miller (2001) (tumor registry and Census data), Philbin and Disalvo (1998), Rathore et al. (2003), Rothenberg, Pearson, Zwanziger, and Mukamel (2004), Sayegh et al. (1999), Shadlen et al. (2006), and Smith et al. (2005).

Findings of the studies reviewed suggest that disadvantage underlying racial inequality in health outcomes is cumulative and persistent across the life course. This is consistent logically with the notion that higher mortality due to serious illness during middle to late-middle age among African Americans feeds both the survival of robust individuals and differential survival implicit in the well-documented mortality cross-over among African and White Americans aged 75 and older.

Conceptual advancement beyond Dowd and Bengtson's (1978) question whether age levels or exacerbates inequality in health outcomes by race is certainly evident. For example, Hayward et al. (2000) point out that the effect of race is selective in prevalence estimates for specific diseases (e.g., higher prevalence of hypertension among African American males aged 51 to 61 compared to Whites). Further, they acknowledge multidimensional aspects of race itself, including a confluence of biological, cultural, economic, political, and legal factors as well as geographic origin.

There are also methodological challenges associated with conceptual complexity beyond Dowd and Bengtson's (1978) largely dichotomous (e.g., convergent versus divergent racial inequality with ages) question. Such challenges are important because processes such as selective survival or higher mortality among younger less-robust individuals skew the distribution of surviving older African Americans according to disability, prevalence across chronic condition, or mortality. The challenge of controlling early adult mortality, morbidity, or socioeconomic variability that impacts racial difference in later-life mortality in study designs remains. Ideally, a longitudinal analysis including successive racially-representative national random samples of cohorts re-interviewed sequentially is an ideal approach. Given regional variability in higher premature mortality rates among African American males (e.g., Mansfield, Wilson, Kobrinski & Mitchell, 1999) as well as small numbers of older African Americans with higher levels of education (e.g., Manton & Gu, 2001) initial over-sampling to insure survival of numbers adequate statistically in later stages in longitudinal cohort designs becomes problematic. For example, only 1 respondent from the first wave in 1980 of the National Survey of Black Americans survived to be included in the third follow-up in 1992 (ICPSR, 2007).

Current longitudinal studies such as the ongoing Framingham study of successive cohorts enable investigators to control the effect of race-specific morbidity, mortality, or mortality risk in younger cohorts on subsequent older adult morbidity or mortality by age and race. However, African Americans living in Framingham, Massachusetts, likely represent a select group. The work of Ferraro et al. (2006) using 4 waves of data from the National Health and Nutrition Examination Survey suggests a start in this direction although the focus is upon hospital-linked mortality. Indeed, the promise and need for longitudinal data and analyses capable of contributing to conceptual clarity by sorting out the linked and difficult-to-capture effects of change in social, cultural, economic and other factors over the life course (early adulthood onward) is clear. As stated by Hayward et al. (2000:913), "By their very nature, chronic conditions typically grow out of socioeconomic conditions over a lengthy life-cycle period rather than from circumstances at a single point in time."

The description of sample sizes suggests that studies with sufficiently large numbers of older African-American respondents are limited in either the measures included (e.g., Escarce et al., 1993; Lee et al., 1997) or in geographical representation (e.g., Manton & Gu, 2001; Mendes De Leon & Glass, 2004). Several studies with what appear to be adequate samples of African Americans can become problematic when a relatively large number of variables are included in multivariate models. Manton and Gu (2001), for example caution the reader about large standard errors resulting from small samples and they characterize their estimates for African Americans as having poor precision.

Authors often fail to consider that empty cells in multivariate models can result when observations of variables, such as education and income with distributions skewed towards lower values are combined with those of other variables. Returning to the work of Manton and Gu (2001), it is apparent from their descriptive data that the percentage of African American college graduates and disabled persons are in the single digits, certainly resulting in very low cell values and extremely large standard errors when the effects of education and disability are combined, statistically, with only one additional variable. Without over-sampling to include adequate numbers in categories of variables with skewed distributions, researchers will fail to uncover meaningful and statistically significant relationships or effects (Crimmins et al., 2004; Hummer et al, 2004; Mendes de Leon & Glass, 2004; Pickle, Mungiole, Jones, & White, 1996). It is interesting that our review of studies revealed no reference to statistical power in multivariate analyses, beyond Ferraro and colleagues' (1996, 2001) reference to others' descriptions.

Mendes de Leon and Glass (2004) assert that the methodological advantages of the EPESE and Alameda County studies are offset by their representation of only small geographic areas. North Carolina EPESE data appear frequently in our review because they include a large number of African-American respondents. The North Carolina sample was drawn from counties close or adjacent to the Raleigh-Durham metropolitan area. Given the high concentration of African Americans in largely rural eastern North Carolina, Kelley-Moore and Ferraro's (2004) paraphrasing of NC EPESE data documentation suggesting that this sample reflects urban and rural counties in the Southeast is tenuous at best. The same could be said for other widely-used data sources, such as the Charleston, SC, and Evans County, GA, Heart Study. Finally, although Mendes de Leon et al. (2005) and Skarupski et al. (2005) have well over 2,500 elderly African Americans in their studies drawn from the Chicago Health and Aging Project, generalizing results beyond Chicago is problematic.

Still other studies are limited by the geographic distribution of older African Americans sharing an attribute. Ferraro and colleagues (1996, 2001), for example, include a dichotomous measure of rural residence along with race among the independent variables in their analyses of NHANES data. Coward, Netzer and Peek (1998) point out that over 90 percent of older rural African Americans live in the South, and over three-quarters of these live in only seven states. Undoubtedly, the 10 percent of older rural African Americans living elsewhere are likely different from the 90 percent living in the South. Clearly, there is room for better geographic as well as socioeconomic representation among African and White Americans in sample designs of large federally-funded research initiatives.

What are the implications of this review for research on inequality in health outcomes across Turkish ethnic groups of older adults? Sampling methods (consideration of geographical and regional variability) and sample sizes that represent variability in the older population must be considered carefully. There is also a need for conceptual expansion to reach more complete explanation.

Several authors (e.g., Escarce et al., 1993; Fiscella et al., 2000; Gornik, 2003; Miller et al., 1997), for example, suggest that cultural and behavioral variables be included in studies exploring racial differences in health outcomes. Both Govindarajan et al. (2003) and Groeneveld et al. (2003) suggest that cultural and behavioral indicators are needed in clinical research to better understand their relevance for cardiac and colorectal cancer outcomes, respectively. The contention here is that socioeconomic or structural variables (e.g., income or poverty status, education, insurance coverage) explain only part of the difference in health outcomes in older people by race. Some of the remaining difference can be explained by cultural and behavioral variables (e.g., attitudes toward screening and prevention, knowledge about disease, attitudes about treatment effectiveness).

Only one study exploring the relevance of race for late-stage breast cancer presentation in older women (Lannin et al., 1998) was identified that met this criterion. They found that preliminary measures of cultural/behavioral variables explained variation in late-stage breast cancer presentation not accounted for by race while controlling the effects of socioeconomic status. Clearly, there is a place for multidimensional measures of psychosocial variables derived using sound psychometric and multi-method approaches. Such variables include but are not limited to health or disease-specific knowledge, perception of racism or discrimination, and religious beliefs (e.g.,  Mitchell, Lannin, Mathews, & Swanson, 2002).

Jim Mitchell, Ph.D.

Professor, Sociology and Family Medicine

Director, Center on Aging, East Carolina University School of Medicine

Associate Director, Institute on Aging, UNC-Chapel Hill

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