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Social Conditions and Distress in Elderly Persons

Findings From the MacArthur Studies of Successful Aging

^a Health and Social Behavior and
^b Epidemiology, Harvard School of Public Health, Boston, Massachusetts
^c Division of Geriatrics, UCLA School of Medicine, Los Angeles, California

Laura D. Kubzansky, Department of Health and Social Behavior, Harvard School of Public Health, 677 Huntington Ave., Boston, MA 02115-6096 E-mail: lkubzans{at}hsph.harvard.edu.

Decision Editor: Toni C. Antonucci, PhD

	Abstract

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The purpose of this study was to determine separate and joint associations of race/ethnicity and socioeconomic status (SES) with psychological distress among older high-functioning adults and to examine 2 psychosocial resources that may explain these associations. Participants were 70–79-year-old individuals (n = 1,189) participating in the MacArthur Studies of Successful Aging program, a 3-site study of community-dwelling men and women. Participants represented the top third of their peers in terms of functional ability in 1988. Additive and interactive models were used to examine cross-sectional associations among race/ethnicity, SES, and distress. Although decreases in distress generally occur with aging, findings suggest that social structural factors can influence distress even among elderly people. Blacks were less distressed than Whites when SES was controlled. There was a gradient between education and distress among Whites but not among Blacks. Measures of social support and control did not mediate effects of race/ethnicity on distress. These results differ from those of previous studies and indicate that age and functional status should be considered in examinations of relationships among race/ethnicity, SES, and distress.

SOCIAL conditions, such as an individual's position in different systems of status and hierarchy, have powerful and overarching effects on many aspects of individual experience. As a result, even emotions, which may feel highly personal and unique to the individual, are conditioned by external social forces and may be socially patterned. Kemper 1993 argued that many human emotions can be understood as responses to the power and/or status meanings and implications of situations. Previous research has found that Black men and women and those who come from lower socioeconomic status (SES) groups generally report higher levels of distress than do other individuals (Kessler and Neighbors 1986; Mirowsky and Ross 1980; Thoits 1982; Turner and Noh 1983; Ulbrich, Warheit, and Zimmerman 1989; Warheit, Holzer, and Schwab 1973). Investigators have examined two competing hypotheses to explain these relationships. One hypothesis suggests that higher levels of distress are primarily a function of low SES. According to this hypothesis, because Blacks tend to be more socioeconomically disadvantaged than Whites, race/ethnicity is not an independent determinant of psychological distress but serves as a proxy for SES. Although numerous studies have presented empirical evidence supporting this hypothesis (Antunes, Gordon, Gaitz, and Scott 1974; Neff 1984; Roberts, Stevenson, and Breslow 1981; Warheit, Holzer, and Arey 1975; Warheit et al. 1973; Weissman and Myers 1978), Kessler and Neighbors 1986 argued that SES does not fully capture the stresses to which Blacks are exposed by virtue of their minority status. Thus, they hypothesized that the effects of race/ethnicity and SES are interactive, whereby low-SES blacks experience higher levels of distress than other individuals. Using data from eight epidemiological surveys, Kessler and Neighbors 1986 found that among people with low incomes, Blacks reported significantly more psychological distress than Whites, whereas Black-White differences in distress were smaller among individuals with more income. However, further evidence for an independent effect of race/ethnicity on distress among low-SES individuals has been mixed (Cockerham 1990; Ulbrich et al. 1989).

Many of the studies examining relationships among race/ethnicity, SES, and psychological distress were conducted in samples of respondents aged 18 and older with the average age around 40 years. Whether effects found in these studies hold for individuals who are significantly older has yet to be determined. Generally, research has suggested that older adults experience less distress than younger people (Carstensen and Charles 1998). Thus, it is possible that the effects of race/ethnicity and SES on distress in older adults are attenuated or insignificant. If effects are simply attenuated, however, we might still expect to see a similar pattern of effects in older adults whereby race/ethnicity and SES interact to influence distress, albeit at somewhat lower levels than those reported in younger populations. Moreover, in an older cohort, physical health status may be an important potential confound, given the high prevalence of chronic conditions among individuals aged 65 and older. Other research has indicated that Blacks bear a higher burden of disease, disability, and premature mortality (Mutchler and Burr 1991; Williams, Lavizzo-Mourey, and Warren 1994), although some studies have described a survival effect whereby Blacks who survive to old age are hardier and more resilient than younger Blacks (Gibson 1991; Manton, Patrick, and Johnson 1987; Markides and Mindel 1987; Wing, Manton, Stallard, Hames, and Tyroler 1985). In addition, health status is associated with both SES (Adler et al. 1994) and psychological distress (Aneshensel, Frerichs, and Huba 1984). Given the unequal distribution of these conditions, health may confound or mediate the relationships among race/ethnicity, SES, and psychological distress among older adults. Our purpose in the current study was to determine the separate and joint effects of race/ethnicity and SES on psychological distress among older high-functioning adults who have less chronic disease than other elderly people and to examine two psychosocial resources that may explain some of these effects.

Social structural conditions, such as race/ethnicity or SES, pattern variations in exposure or vulnerability to behavioral, psychosocial, material, and environmental risk factors and resources. For example, Williams and colleagues 1994 suggested that race/ethnicity affects health by virtue of the social identity, obligations, and access to resources that are shaped by membership in a given social category. Thus, if different racial/ethnic groups systematically experience differential health outcomes, it is presumably because of the health behaviors; stress; access to medical care; and range of material, social, psychological, cultural, and religious resources that are influenced by membership in these groups (Williams et al. 1994). However, the effects of category membership may not be monolithic. For example, whether the effects of race/ethnicity on psychological health are uniform across age groups has yet to be determined. Pearlin and Schooler 1978 and others have suggested that fewer psychological and social coping resources are available to lower status groups, and as a result, individuals in these groups are more vulnerable to the impact of life stressors (Thoits 1982; Ulbrich et al. 1989). Because both a sense of control and social support have been found to buffer the negative health effects of stress, researchers have argued that unequal distributions of these coping resources may account for status differences in psychological distress (Thoits 1982, Thoits 1995; Turner and Noh 1983). However, clear evidence for this is lacking, and one purpose of the present study is to test this hypothesis (Aneshensel 1992; Thoits 1995).

Belief in personal control is a learned expectation that outcomes are contingent on one's own choices and actions and that one can master or effectively alter one's environment (Ross and Wu 1995; M. Seeman and Seeman 1983). A greater sense of control has been linked to more positive health outcomes (Kessler, Turner, and House 1988; Mirowsky and Ross 1990; Rodin 1986; Turner and Noh 1983; Turner and Roszell 1994). Control may influence health, both through enhancing health-related behaviors, and also through physiological mechanisms that are triggered by experiences of uncontrollability and demoralization (Rodin and Timko 1992; Rowe and Kahn 1987; M. Seeman and Seeman 1983). Previous research has suggested that control is inversely distributed by SES (Mirowsky and Ross 1989; Pearlin, Lieberman, Menaghan, and Mullen 1981; Ross and Wu 1995; Thoits 1995). Similarly, minority group members also report lower levels of control, although findings in this domain have not always been consistent (Miller 1995; Mirowsky and Ross 1989; Thoits 1995; Turner and Roszell 1994). Ross and Wu 1995 argued that education (which is associated with higher levels of SES) enables one to develop communication and analytic skills, to develop the ability to gather and interpret information, and to solve problems. These skills then increase one's ability to control various life events and outcomes. By contrast, those with less education may acquire less effective problem-solving skills and then believe (perhaps accurately) that powerful others and unpredictable forces control their lives.

Social support and social networks of mutual obligation, which engender a sense that one is cared for and loved, esteemed and valued, are also associated with positive health outcomes (Berkman and Breslow 1983; Berkman and Syme 1979; House, Landis, and Umberson 1988; T. E. Seeman et al. 1993). Social support is associated with decreases in depression, anxiety, and other psychological problems (Blazer 1983; Kessler and McLeod 1985; LaRocco, House, and French 1980) and also increases the likelihood of a person's practicing many protective health behaviors (Berkman and Mullen 1997; Umberson 1987). In the United States and Great Britain, higher levels of social support are generally associated with higher levels of SES, although variations in levels of support across race/ethnicity groups have been small and inconsistent (Berkman and Breslow 1983; Berkman and Mullen 1997; Eckenrode 1983; Ross and Mirowsky 1989; Ross and Wu 1995; Vaux 1988).

In the present investigation we examined the relationships among race/ethnicity, SES, and psychological distress among elderly high-functioning adults and tested whether differential distribution of psychosocial resources can explain any differences found. On the basis of the Kessler and Neighbors 1986 findings, we hypothesized an interactive effect of race/ethnicity and SES on distress in an elderly high-functioning sample. We predicted that lower SES Blacks would experience the highest levels of distress. We designed this study to examine the generalizability of previous findings to older individuals so that we could determine whether social structure influences distress even at older ages, which are generally associated with decreases in distress. In addition, we took account of potential confounding by functional status, which has a strong relationship to depression (Berkman et al. 1986). Replication of previous findings in the current sample would suggest that negative effects of race/ethnicity and SES occur in older individuals as well as younger and middle-aged individuals and are not solely attributable to poor functioning. In addition, replication would suggest that previously identified patterns regarding the effects of race/ethnicity and SES on distress remain in effect despite the possibly increased hardiness of older Blacks. To be consistent with previous work in this area, the measure of psychological distress includes indicators of depression, anxiety, and somatization. Psychological distress is considered conceptually distinct from mental illness in that the term does not include problems like personality disorders, organic mental disorders, extreme mood swings (bipolar disorders), alcoholism, or schizophrenia (Mirowsky and Ross 1989).

	Methods

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Data and Participant Selection
Data for these analyses come from the MacArthur Research Network on Successful Aging Community Study, a three-site longitudinal study of men and women aged 70–79 years. Details of the study design are available elsewhere (Berkman et al. 1993). Participants were drawn from three community-based studies of individuals in Durham, NC; East Boston, MA; and New Haven, CT, who are part of the Established Populations for Epidemiologic Studies of the Elderly (EPESE; Cornoni-Huntley, Brock, Ostfeld, Taylor, and Wallace 1986). For the MacArthur study, participants were selected on the basis of physical and cognitive function at the 1988 EPESE interview. Age range was restricted to minimize the effects of age in subsequent analyses. Age-eligible men and women () were screened on the basis of six criteria to identify approximately the top third of participants in terms of functional ability. Inclusion criteria included (a) no self-reported disabilities on the Katz Activities of Daily Living Scale (Katz, Downs, Cash, and Grotz 1970); (b) no more than one self-reported disability on eight items tapping gross mobility and physical performance (Nagi 1976; Rosow and Breslau 1966); (c) ability to hold a semitandem balance for 10 s (Tinetti, Williams, and Mayewski 1986); and (d) ability to stand from a seated position five times within 20 s (Tinetti et al. 1986). Participants were also required to meet two criteria for cognitive function including scoring 6 or higher on the nine-item Short Portable Mental Status Questionnaire (Pfeiffer 1975) and recalling three or more of six elements on a delayed recall of a short story.

Of the 1,313 participants who met criteria, 1,189 (90.8%) agreed to participate and provided informed consent. Among the high-functioning participants, 290 (24.4%) were from New Haven, 472 (39.7%) were from East Boston, and 427 (35.9%) were from Durham. Of these, 182 (81.6%) of the Black participants were from Durham. Of the participants who were age eligible but did not meet the screening criteria (), small random subsamples of participants were selected who were functioning in the middle tertile (medium functioning; ) and who had major impairments in either physical and/or cognitive functioning (low functioning; ). Participants for these two subsamples were selected in order to match their age and sex distributions to those of the high-functioning group. Data collection was completed between 1988 and 1989 and included a 90-min face-to-face interview covering detailed assessments of physical performance, measures of cognitive performance, health status, and social and psychological characteristics. In the current investigation we focused primarily on cross-sectional data for the high-functioning group (), but also presented some selected comparative analyses using data from the medium- and low-functioning participants ().

Measures
Sociodemographic characteristics
Sociodemographic variables used in these analyses include sex, race/ethnicity, SES, and marital status. Race/ethnicity was coded White or non-White with 223 of the 228 non-White individuals identifying themselves as Black. As a result, race/ethnicity was recoded as White or Black, excluding the remaining 5 non-White individuals from the categorization. Following the technique used by Kessler and Neighbors 1986, we used a multidimensional measure of SES by considering income and education as separate indicators of SES. Income was measured according to yearly levels and was categorized into groups of roughly $5,000 increments. The lowest category included income ranges of less than $5,000, and the highest category include incomes of $50,000 or more. Educational attainment was coded as the highest level of education achieved. To further explore particular effects, we also categorized educational attainment into three groups according to level of education achieved relative to major educational markers such as a high school diploma or college degree: 8th grade or less (completed middle school but did not go to high school), 9th–11th grade (some high school but did not obtain high school diploma), 12th grade or more (obtained a high school diploma and some further education). Marital status was coded as currently married or not married.

Psychosocial measures
To be consistent with previous research in this area (Cockerham 1990; Kessler and Neighbors 1986), we used symptom measures of depression, somatization, and anxiety obtained from subscales of the Hopkins' Symptom Checklist (Derogatis, Lipman, Rickels, Uhlenhuth, and Covi 1973) to measure psychological distress. Depression scores ranged from 11 to 31, somatization scores ranged from 12 to 32, and anxiety scores ranged from 7 to 21. Reported internal consistency reliability coefficients () ranged from .84 to .87, and test-retest reliability ranged from .75 to .82 (Derogatis, Lipman, Rickels, Uhlenhuth, and Covi 1974). Other research in this area has argued that each type of scale provides some information about psychopathology independent of the other, suggesting the importance of using multiple scales in a comprehensive analysis (Mirowsky and Ross 1989). A sense of control was measured with a seven-item scale of personal mastery (Pearlin and Schooler 1978). Items for this scale, which were chosen on the basis of factor analytic techniques assess the extent to which individuals regard their life chances as being under their own control versus being fatalistically ruled. Summary scores reflect the average of nonmissing item responses weighted by the number of items answered (). Scores on this measure ranged from 11 to 28.

Measures of social networks and support were drawn from various sources including work at Yale University, University of Michigan, and Duke University (Antonucci and Akiyama 1987; Blazer 1982; T. Seeman and Berkman 1988; T. E. Seeman and Syme 1987). These measures were obtained from a battery of questions about respondents' perceptions of their social network, the presence of particular types of social ties, and the extent to which these ties provide emotional and instrumental support and/or are sources of demands and criticism for respondents. The measure of social ties includes measures of marital status, number of friends and relatives (including children) to whom the respondent feels close, number of groups with whom the respondent volunteers, frequency of attendance of clubs and organizations, and frequency of attendance of religious services or other activities with the respondent's religious group. Scores on this measure ranged from 9 to 47. Because the ties measure assesses an abstract construct and is not an indicator of a latent construct, calculation of an internal consistency reliability coefficient was deemed inappropriate (Blalock 1971; Heise 1972). The 2-month test-retest correlation for this measure was .86. Social support measures focus on the quality of emotional and instrumental support (T. E. Seeman, Berkman, Blazer, and Rowe 1994). The measure of emotional support was based on the average reported frequency with which members of the respondent's social network made the respondent feel loved and listened to when he or she had a problem. Scores on this measure ranged from 0 to 3. The internal consistency reliability coefficient for this measure was .68, and the 2-month test-retest correlation was .73. The measure of instrumental support was based on the average reported frequency with which network members helped with daily tasks and provided information. Scores on this measure ranged from 0 to 3. The internal consistency reliability coefficient for this measure was .67, and the 2-month test-retest correlation was somewhat low at .44 (possibly because of greater variability in such support over time in response to variations in need for this type of assistance).

Analyses
We first examined mean levels of income, education, and psychosocial factors among Blacks and Whites. Following Kessler and Neighbors 1986, we estimated three models to examine the effects of race/ethnicity versus SES on distress. In the first model, we estimated only the effects of race/ethnicity on distress: . In the second model, we estimated a linear additive regression equation that looked at the simultaneous effects of race/ethnicity and SES on distress: . Then, to test the interactive perspective, we estimated a third model: , where D is a measure of distress, R is a dichotomous variable for race/ethnicity coded 1 for Blacks and 0 for Whites, SES is a measure of social status, and R x SES is a multiplicative interaction between R and SES. In all models SES is a multidimensional measure that includes income and education as separate indicators. Thus, we report the effect of race/ethnicity alone on distress, the effect of race/ethnicity on distress while controlling for SES, and also the effect of interactions between race/ethnicity and SES on distress.

We examined the relationship between gender and distress and found that women reported more anxiety, , p < .01, and more somatization, , p < .05, than men but did not differ in reported levels of depression, , p >.4. However, models that stratified by gender suggested that the effects of race/ethnicity and SES did not differ across men and women. Thus, similar to previous research in this area, all analyses presented here controlled for sex and marital status. Because we had only a 10-year age span, there are few age differences in the cohort. Therefore, we did not control for age. Because the Black participants were concentrated in one site (Durham) we also ran within-site analyses to see if any differences emerged. Findings were identical to those of the larger sample, so we reported the results from all three sites combined. Support for the Kessler and Neighbors 1986 results would lead us to expect that in the third model b₁ will be positive, indicating that Blacks are more distressed than Whites. We would expect b₂ to be negative, demonstrating that increased SES is associated with decreased distress. And we would expect b₃ to be negative, which would suggest that race/ethnicity differences in distress become smaller as individuals move up the SES hierarchy. For any models that included interaction terms, we expressed all terms as deviations from their means before we entered them in the models, so that the effects of race/ethnicity and SES could be interpreted in the presence of an interaction (Finney, Mitchell, Cronkite, and Moos 1984).

To assess whether effects of race/ethnicity and SES could be explained by psychosocial resources, we estimated the following model: , where PS is a measure of psychosocial resources. A significant value for b₃ with an attenuation in b₁ from the original model would suggest that psychosocial resources explain some of the effects of race/ethnicity on distress. If b₃ was significant but not associated with an attenuation in b₁, this would suggest an effect of psychosocial resources on distress levels independent of race/ethnicity.

	Results

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Race/Ethnicity, SES, and Distress
Table 1 shows that Blacks had less income and fewer years of education than Whites, whereas psychosocial resources did not differ consistently between Blacks and Whites. For example, Blacks reported feeling less sense of control but had larger social networks than Whites. Regression analyses that examined the gross effect of race/ethnicity on the three measures of distress showed that Blacks were significantly less distressed than Whites on every measure of distress. For example, Blacks reported less anxiety (, p < .001), less depression (, p < .001), and less somatization (, p < .05) than Whites. When SES was included in the model, race/ethnicity remained in the model, suggesting that Blacks were significantly less distressed than Whites even when SES was controlled. For example, when we controlled for income and education, Blacks evidenced less anxiety (, p < .001), less depression (, p < .001), and less somatization (, p < .01) than Whites. We found no independent effect of SES. However, the interactive model suggested that the effects of race/ethnicity on distress depended on education. Table 2 reports the results of analyses in which we examined the gross effect of race on distress, the additive effects of race and SES on distress, and the interactive effects of race and SES on distress, while controlling for sex and marital status.

View larger version (17K): [in this window] [in a new window]   Figure 1. Joint effects of race/ethnicity and education on depressed mood.

View larger version (15K): [in this window] [in a new window]   Figure 2. Joint effects of race/ethnicity and education on anxiety.

View larger version (16K): [in this window] [in a new window]   Figure 3. Joint effects of race/ethnicity and education on somatization.

Psychosocial Resources and Distress
To better understand some of the relationships among race/ethnicity, SES, and distress, we examined whether social support and/or mastery (control) accounted for some of the race/ethnicity differentials we identified in the first set of analyses. Three measures of social support and social ties were used: number of ties, level of emotional support, and level of instrumental support. Each support measure was included in separate models estimating the joint effects of race/ethnicity and SES as described previously (). Of the three measures of social ties or support, only emotional support was consistently associated with distress (see Table 3 ). For example, less emotional support was associated with greater depression (, p < .01), greater somatization (, p < .05), and greater anxiety (, p < .08). The effect of race/ethnicity on distress was only slightly attenuated when emotional support was included in the model, maintaining its strong predictive power (e.g., depression, , p < .01). In fact, there was only a weak association between race/ethnicity and levels of support. Thus, we did not have evidence to suggest that emotional support might be mediating the effect of race/ethnicity on distress. The measure of network ties was associated with depression only, and instrumental support was not associated with any of the distress measures (data not shown). There was a main effect of race/ethnicity for network ties only, suggesting that Blacks had more ties than Whites. With further analyses, we examined whether there was a joint effect of race/ethnicity and social ties or support on distress and found no evidence of an interaction.

	Discussion

TOP Abstract Methods Results Discussion References

 
Previous research has suggested that race/ethnicity and SES combine nonadditively to create distress, so that Blacks are more distressed than Whites at lower SES levels. Our results, however, did not replicate these findings. In a study of high-functioning elderly individuals, we found that race/ethnicity affected distress when SES was controlled, with Blacks being less distressed than Whites when additivity was assumed. Although an interactive perspective proved valuable, the direction of effects was unexpected. We found joint effects of race/ethnicity and education, where Blacks were less distressed than Whites at lower levels of education and distress levels converged at higher levels of education. In fact, although there was a gradient between education and distress among Whites, there was little effect of education on distress for Blacks. Blacks at higher levels of education did not report feeling significantly less distressed than less well-educated Blacks. Although the pattern was similar for the relationship among race/ethnicity, income, and distress, the results were not significant. Among elderly people, income is a less useful indicator of SES because wealth (e.g., assets such as home ownership and other property) may be peaking at a time when income is dropping. Thus, education may be a more accurate indicator of SES among older, retired individuals (Haan, Kaplan, and Syme 1989; House et al. 1990; Kubzansky, Berkman, Glass, and Seeman 1998; Ross and Wu 1995). Our data also suggest that both social support and a sense of control were independently associated with distress. Fewer psychosocial resources predicted higher levels of distress. However, psychosocial resources were not consistently distributed differentially between Whites and Blacks and did not explain the effect of race/ethnicity on distress. Similarly, comparison of effects in the high-functioning sample with those in the medium- and low-functioning sample tentatively suggests that our findings cannot be attributed to differential functional status between Blacks and Whites.

It may be that the development of effective coping resources is one determinant of who survives to old age, so that these psychosocial factors do not differ across race/ethnicity among elderly individuals. More generally, our findings may reflect some kind of survival phenomenon, where elderly Blacks differ from younger Blacks in important ways. Some investigators have suggested that the greater distress reported by lower status Blacks may be due to their sense of frustrated aspirations and truncated mobility resulting from their lower status (Kessler and Neighbors 1986; Parker and Kleiner 1966). However, whereas younger, lower status Blacks may be powerfully engaged with these issues, older Blacks may have come to terms with the conditions of their life, developed effective coping skills, and have available a larger array of social resources, including higher levels of integration in their communities. Alternatively, the lower distress of Blacks versus Whites at lower levels of education may be a reflection of differential patterns of aging between Blacks and Whites. Older Blacks may be accorded higher status within their communities and may remain more critically engaged with other individuals than older Whites (Chatters, Taylor, and Jackson 1985; Glass, Seeman, Herzog, Kahn, and Berkman 1995; Taylor and Chatters 1986). Although Blacks and Whites did not differ in their levels of social support in this study, it may be that the level of involvement in the community and sense of importance differ and that these factors are related to experienced distress.

Some of the financial and social resources that lead to the alleviation of distress as one moves up the social hierarchy may not be equally available to Black versus White individuals. Another plausible explanation may be related to a gap between aspirations and attainments, which more highly educated Blacks are more likely to experience than Whites (Jones 1997). With increasing levels of education, Whites may find that they are able to fulfill many of their goals, whereas institutional and individual racism and discrimination may obstruct the ability of more highly educated Blacks to fulfill their goals. Rising levels of education and associated increases in aspirations may be accompanied by increased levels of frustration among Blacks, which would eliminate an education gradient in distress for these individuals. Unfortunately, we cannot examine these hypotheses in greater detail in the present study.

In disadvantaged groups, benefits as well as disadvantages may be conferred by category membership. There may be health-enhancing cultural resources and strengths that are unique to members of minority racial/ethnic groups or to which they have greater access (Williams et al. 1994). For example, investigators have reported that levels of religious involvement are higher among Blacks and other ethnic groups than among Whites (e.g., Mirowsky and Ross 1980; Williams 1997). Religious involvement may then serve as a protective resource for these groups, enhancing both their spiritual and social reserves. In the current study we could not examine this question fully; although frequency of involvement with a religious organization was included in our measure of social ties, we did not have a measure of spiritual involvement or of the respondents' level of emotional engagement in a religious community.

The strengths of this study include a sample drawn from population-based cohorts reflecting substantial geographic and socioeconomic heterogeneity. Furthermore, because most people in the study were high functioning, it is unlikely that poor health or functional disability affected reported distress or psychological and social activities. At the same time, such high-functioning elders represent a third of the larger population cohort from which they came. Although we found no support for a different effect in the lower functioning groups, this finding should be confirmed in other studies.

Although the direction of effects differed, our findings are similar to previous work in that the effect of race/ethnicity on distress differed across levels of educational attainment (Cockerham 1990; Kessler and Neighbors 1986). Thus, this study does provide further support for the notion that additive models examining the relationships between race/ethnicity, SES, and distress may not be sufficient. Despite the general reduction in distress among older adults (Carstensen and Charles 1998), our findings suggest that membership in particular race/ethnicity groups continues to have systematic effects on psychological health among elderly people. Moreover, our data confirm that multiple SES categories may jointly influence distress, and these effects could not be explained by a differential distributions of two psychosocial coping resources. Group membership as summarized by racial/ethnic social categories is associated with a social identity, a set of obligations, and the accessibility of resources, which individually or jointly may influence health outcomes. To clarify how membership in specific racial/ethnic categories influences health outcomes, researchers will need to unpack the various psychological and social ramifications of category membership and explicitly examine their effects on health.

	Acknowledgments

 
This work was supported by the MacArthur Foundation Research Network on Successful Aging. The authors also acknowledge contributions from discussions with colleagues at meetings of the MacArthur Successful Aging and the Socioeconomic Status and Health networks. The article was approved by appropriate institutional review boards at Yale University, Massachusetts General Hospital, and Duke University.

Received for publication May 4, 1999. Accepted for publication January 25, 2000.

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