1 Department of Health and Social Behavior, Harvard School of Public Health, Boston, MA.
2 Center for Society and Population Health, School of Public Health, University of Texas, Houston Health Science Center, Houston, TX.
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
aged; cohort studies; mortality; socioeconomic factors; survival analysis
Abbreviations: CI, confidence interval; EPESE, Established Populations for Epidemiologic Studies of the Elderly; SES, socio-economic status
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Evidence relating SES to mortality among elderly individuals is less consistent. Attenuation of the SES-mortality relation with age has been observed in most studies that have compared older and younger populations (11, 2
, 4
6
, 12
, 13
, 16
, 17
, 22
, 25
, 28
, 30
, 32
, 33
, 35
, 36
); in some studies, socioeconomic differentials in mortality persist at older ages (6
, 17
, 25
, 28
, 32
, 33
, 38
), but in others, no associations or modest associations are found (4
, 5
, 30
, 39
). There have been few empirical attempts to explore behavioral and biologic pathways by which SES and mortality are linked in the elderly. The underlying mechanisms that produce SES-mortality gradients may vary in importance over the life course (40
). For example, alcohol abuse may lead to increased risk of suicide, homicide, and accidents, which are prevalent causes of death in early adulthood (41
); social disengagement and the resulting lack of mental stimulation and support provided by one's social network may be critical in late life (42
, 43
). When attempts to delineate pathways are undertaken, differential adjustments for confounding or mediating variables render it difficult to assess the consistency of the SES-mortality association across studies. Finally, the extent to which observed differences in the relation between SES and mortality arise from the use of different SES indices rather than from actual intercommunity differences is unclear. With few exceptions (2
, 18
, 22
), there are no direct comparisons of the relative ability of different SES measures to predict mortality in a given elderly population, even though education, income, and occupation may operate through different pathways to affect health (44
).
We examined the impact of education, income, and occupational prestige on all-cause mortality in four community-based elderly cohorts. We addressed these questions: Are SES-mortality patterns similar across communities? Which SES indicator is the strongest predictor of mortality? Do associations differ by gender, age, race, marital status, or urban/rural residence? Can SES-mortality associations be accounted for by SES differences in morbidity, behaviors, or other social conditions?
![]() |
MATERIALS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The availability of common measures of not only SES but also potential confounders and mediating variables provided an opportunity to compare findings for different types of communities. New Haven represents an urban community of diverse ethnic and SES origins. Many of the men formerly worked in skilled manufacturing jobs, while many women worked in service industries. East Boston is an urban community of predominantly Italian Americans in which persons of both genders have a work history similar to that of New Haven men. The Iowa sample, mostly persons of Scandinavian and German extraction, is drawn from rural communities where farming is the primary occupation for men and homemaking is primary for women. The North Carolina cohort contains a mixture of urban and rural residents, with many Blacks. Comparisons across cohorts thus permit an assessment of the consistency of the SES-mortality association among older men and women and across communities characterized by varying degrees of urbanization and ethnic homogeneity.
Measures
Predictors were measured by self-report or proxy report at baseline.
Socioeconomic status. Education. Education, defined as years of schooling completed, was categorized as 07, 89, 1012, 13, and unknown.
Income. Income, defined as household income from all sources in the year before baseline, was categorized as $0$4,999, $5,000$9,999, $10,000$14,999, $15,000, and unknown. Income was not adjusted for household size.
Occupational prestige. Usual lifetime occupation was coded using three-digit US Census occupational codes from the 1970 Bureau of the Census Index of Industries and Occupations, which were then grouped into hierarchical categories using an established prestige scale, the Duncan Socioeconomic Index (46). The 1970 time frame was chosen because it was the period when many respondents were at the height of their careers. Prestige rankings derived from the total labor force were used. Rankings were divided into quartiles based on gender-specific distributions across the combined sites; quartiles were ordered from low prestige ("1") to high prestige ("4").
Covariates. Demographic covariates. Demographic covariates included gender, age, race (White/Nonwhite; New Haven and North Carolina only), and degree of urbanization (urban/rural; North Carolina only).
Behavioral covariates. Behavioral covariates included pack-years of smoking (number of years for which the person smoked multiplied by number of cigarette packs per day); body mass index (defined as weight (kg)/height (m)2 and categorized as <20, 20<30, and 30); alcohol consumption in the past month (none, <1 ounce/day (<30 ml/day), or
1 ounce/day (
30 ml/day) (47
)); physical activity (defined in appendix 1); number of social ties (presence of a spouse, contact with two or more relatives or friends, attendance at religious services, and membership in other groups (48
)); and access to health care (defined as availability of a regular health care provider).
Health status. Health status included number of chronic conditions (the sum of "yes" responses to items asking whether a physician had ever diagnosed high blood pressure, heart attack, stroke, diabetes, cancer, a broken hip, or other broken bones); depressive symptoms (Center for Epidemiologic Studies Depression Scale (49) score (appendix 2)); cognitive function (Short Portable Mental Status Questionnaire (50
) score); and physical function, measured with three self-report scales. The modified Katz Activities of Daily Living Scale (51
) assessed the ability to perform basic activities without assistance; scores were dichotomized into no disability versus any disability. Items from the Rosow-Breslau Functional Health Scale (52
) assessed gross mobility. A physical performance scale measured difficulty in pushing/pulling large objects; in stooping, crouching, and kneeling; in reaching above shoulder level; and in writing/handling small objects (53
). The latter scales were scored for the number of activities for which difficulty was reported.
Mortality. Dates of death were obtained from proxy informants and newspaper obituaries. Study records were matched to the National Death Index, and death certificates were obtained for nearly all deaths. Ascertainment through 9 years of follow-up was virtually complete (99 percent).
Analysis
The percentage of respondents who were deceased at the end of 9 years was computed for each SES level. Wald tests from logistic regression models (54) that took vital status at 9 years as an outcome and included baseline age as a covariate were used to assess the statistical significance of SES-mortality relations. Proportional hazards regression (55
, 56
) was then used to quantify the impact of education, income, and occupational prestige on mortality while adjusting for other covariates, and Wald tests were used to test the statistical significance of these associations. Survival time was defined as time from the baseline interview to the date of death or 9 years later, whichever was earlier. Initial models adjusted for demographic factors; additional models also controlled for health status and behaviors. Covariates were modeled as continuous when preliminary analyses suggested that it was valid to do so. Analyses were stratified by gender. Additional stratification by age, race, marital status, and urban/rural residence was undertaken to determine whether these factors modified SES-mortality relations.
Computing was done using the SUDAAN statistical package, version 7.0 (57). The generalized estimating equations approach (58
) was used to adjust standard errors for clustering induced by the sampling scheme. Estimates were weighted to reflect differential sampling, coverage, and response rates within sampling strata.
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
|
|
|
We next determined the degree to which observed SES-mortality relations could be accounted for by socioeconomic variations in baseline health status (table 5). Among men, the income-mortality association persisted in three of the four cohorts after adjustment for health status. On the other hand, the education-mortality association persisted only in New Haven, and the occupation-mortality association persisted only in East Boston. Among women, the income- mortality association persisted in two of three cohorts, but the strong education- and occupational prestige-mortality associations in North Carolina were no longer observed. Health-adjusted income-mortality associations generally did not follow a monotonic pattern in either gender; although respondents with incomes of $5,000$14,999 had significantly greater mortality than those with the highest incomes, they had comparable or somewhat greater mortality than those with the lowest incomes. Surprisingly, in East Boston women, weak inverse SES-mortality gradients (i.e., decreasing mortality with increasing SES) became more pronounced after adjustment for health status.
|
|
|
In North Carolina, SES gradients in mortality were somewhat stronger among unmarried women than among married women. In New Haven, however, SES-mortality associations were much stronger in married women than in unmarried women. There was no effect modification by marital status among East Boston or Iowa women. We found few differences in the pattern of SES-mortality associations by marital status among men.
The North Carolina cohort contained a mix of urban and rural residents. For women, much stronger SES-mortality associations were observed among urban dwellers than among rural dwellers. In urban women, these associations were as follows: for education of 07 years vs. 13 years, hazard ratio = 1.62 (95 percent confidence interval (CI): 1.22, 2.14); for income of $0$4,999 vs.
$15,000, hazard ratio = 2.31 (95 percent CI: 1.58, 3.36); and for the lowest quartile of Duncan Socioeconomic Index versus the highest, hazard ratio = 1.46 (95 percent CI: 1.11, 1.92). In rural women, the corresponding figures were: for education, hazard ratio = 1.18 (95 percent CI: 0.80, 1.73); for income, hazard ratio = 1.75 (95 percent CI: 0.96, 3.22); and for Duncan Socioeconomic Index, hazard ratio = 1.13 (95 percent CI: 0.76, 1.67). For men, however, mortality gradients by education and income occurred in both urban and rural respondents, and occupational prestige more strongly predicted mortality in rural respondents than in urban respondents.
To test the sensitivity of the findings to alternative SES specifications, we repeated our analyses after reclassifying education, income, and occupational prestige into quartiles according to site- and gender-specific distributions. The pattern of results was very similar to that reported above.
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
However, it is likely that health-damaging behaviors and poor health status constitute steps in the causal pathway between low SES and high mortality. While the mechanisms by which SES affects the development and maintenance of social and health behaviors are not entirely clear (34, 61
), it is likely that adjustment for a more comprehensive and precisely measured set of health-behavior and health-status indicators than was available in this study (or many others) would eliminate SES-mortality associations, because there must be a set of defined mediators, including behaviors and physical health parameters, that serve as links between SES and mortality. Thus, the association between SES and mortality will be underestimated in models that factor out the effects of these intervening variables (33
, 60
). For this reason, controlling for health behaviors or health status when quantifying the strength of SES-mortality gradients may be inappropriate. Analyses adjusted for demographic factors may provide the best estimates of the impact of SES on mortality.
In this study, there were intercommunity differences in the strength of the SES-mortality associations. Associations were stronger in New Haven and North Carolina than in East Boston and Iowa. What might account for such intercommunity variation? We measured SES in an identical fashion across sites, minimizing the likelihood that intercommunity variation was attributable to differences in operationalizing SES. However, it is possible that intercommunity differences could be explained by the relative deprivation hypothesis (62). A low income may be more deleterious in communities where the majority of the population is wealthy than in poorer communities, either because of invidious social comparison processes or because of economic barriers to purchasing goods and services at prices geared toward more affluent residents. Nevertheless, when we reclassified education, income, and occupational prestige into quartiles according to site- and gender-specific distributions, we found similar heterogeneity of results across communities; this suggests that differences in relative deprivation are not responsible for intercommunity variations in the strength of SES-mortality associations.
Our analyses also suggest that differences in SES-mortality associations are not entirely accounted for by intercommunity variations in social patterning of behaviors such as social interaction, smoking, alcohol consumption, and physical activity, since adjustments for these factors produced proportionately similar reductions in hazard ratios across cohorts. Indeed, even after data were controlled for health status itself, intercommunity differences in SES-mortality associations persisted. The communities that exhibited the strongest SES gradients before adjustment also exhibited the strongest SES gradients after adjustment.
Income is thought to promote health partly because it provides access to material goods and services. Such access may not be as closely tied to income among the elderly as it is among working-age persons, since informal social networks, government subsidies for housing and medical care, and accumulated assets may provide post-retirement resources that were obtained with earned income at earlier ages. This explanation has been posited for the apparent attenuation of the SES-mortality relation among older persons as compared with younger persons (59, 63
), and it could account for this finding in our data. If income is a less valid marker of economic resources in East Boston or Iowa than in New Haven or North Carolina, this could also explain the stronger income-mortality associations observed in the latter communities. New Haven and North Carolina residents are more diverse with respect to ethnicity, urbanization, and occupational history than residents of East Boston and Iowa. It is possible that elderly East Bostonians, primarily Italian Americans who live in close proximity to each other and to the main neighborhood health clinic, or Iowans, who live in a rural, agrarian community, have access to more diverse social and economic resources than is reflected by traditional SES measures. The possibility that traditional SES measures have less salience in rural settings is supported by the fact that these measures were far less predictive of mortality among rural women than among urban women in North Carolina. However, this explanation is rendered less compelling by two pronounced intracommunity gender differences. In East Boston, lower-SES men were at significantly increased risk of death compared with higher-SES men, whereas this was not true for women. In addition, the striking effect modification of the SES-mortality relation by urban/rural residence among North Carolina women was not observed among the men.
Of the SES indicators considered here, income was the most consistently associated with mortality among elderly men and women. This is somewhat unexpected, since the cohorts were, for the most part, composed of persons who were not working, and incomes were reported within restricted ranges. For men, low education and occupational prestige also predicted mortality in the majority of cohorts (in demographic-adjusted analyses). For women, however, low education and occupational prestige were predictive of increased mortality in just one of the cohorts. One reason may be that respondents estimated household income from all sources, including spouses, whereas education and occupation measures focused on the individual alone. Among the birth cohorts of 19001920, women's education and occupational status may be less indicative of household SES than their husbands' education and occupational status or a combined measure of their status and their husbands' status (63, 64
). Because information on spouse's occupation was not available, we could not classify female respondents by their husbands' occupations. While SES inequalities in mortality are generally weaker in women than in men (3
, 10
, 12
, 13
, 16
, 33
, 36
, 65
), steeper gradients are observed when women are classified by their husbands' occupations than when they are classified by their own (66
). Moreover, within strata defined by women's own occupations, there are gradients by husband's occupation (66
). Using 1980 Finnish census data linked to mortality records, Koskinen and Martelin (67
) found smaller educational and occupational mortality gradients among working-age women than among working-age men, but only for married individuals. In unmarried persons, SES-mortality gradients were equally steep for both genders. Consistent with this finding, we also observed stronger SES-mortality associations among unmarried women than among married women. However, this was true only in North Carolina.
Because mortality differentials based on income at only one time point are not as reliable as results based on accumulated wealth or income over many years (9), our estimates of the income-mortality association may be conservative. Stronger income-mortality associations have been observed when earnings have been averaged over multiple years (20
, 68
, 69
). On the other hand, it is possible that we overestimated the impact of income on mortality, as the relation between income (or occupation) and health may be bidirectional. Persistently low income or occupational status may adversely affect health, and conversely, ill health may lead to reduced income or occupational status (7
). However, the fact that education, which is usually obtained prior to major changes in health, was as predictive of mortality as income and occupational prestige (at least for men) suggests that reverse causation does not entirely account for observed gradients. Education may be associated with lower mortality because it promotes access to and ability to use health-relevant information, including adoption of a healthy lifestyle and preventive health care, as well as higher income potential and occupational achievement (44
, 61
).
Men with Duncan Socioeconomic Index scores in the lowest quartile were 25 percent more likely to die than the highest-scoring men, after adjustment for demographic factors. This finding is unique in that occupational prestige has been neglected in epidemiologic investigations of SES and mortality in this country. European researchers report strong links between occupational status, as measured by the British Registrar General's Scale, and mortality (2329
). However, because the Registrar General's Scale was designed to provide decreasing mortality rates with increasing social class, there are built-in associations between this scale and health (44
). Use of Duncan Socioeconomic Index scores to measure occupational prestige in the EPESE cohorts avoided this tautology and was appropriate, since this classification system was developed in 1970, when the respondents were in the middle of their working years. The Duncan Socioeconomic Index may not measure prestige accurately in younger birth cohorts, since rankings change over time. Researchers wishing to examine relations between occupation and health in younger generations should use an updated version of the Duncan Socioeconomic Index (44
). Occupational prestige may protect against mortality because it is a source of self-esteem and other psychological rewards as well as a source of financial gain (61
).
Unexpectedly, in East Boston and Iowa, women in the lowest quartile of occupational prestige experienced lower mortality than did women in the highest quartile, although the association in Iowa was statistically significant only after adjustment for health status. The reason for this anomalous finding is unclear. Perhaps having a high-prestige job in a community where many of one's peers do not entails some level of psychosocial stress that adversely affects health, especially if that job is combined with child-rearing responsibilities, which fell mainly on women in the EPESE generation.
In summary, this study examined the relation between SES and mortality in four community-dwelling elderly populations. Higher SES, whether measured by education, by income, or by occupational prestige, was generally associated with reduced mortality over a 9-year period. Findings varied by gender and by community. SES-mortality gradients were more similar in men and women when household income rather than individual educational or occupational attainment was considered. Future research should continue to investigate the relative validity of traditional SES measures for men and women and develop more balanced assessment tools, including indicators of wealth among the elderly. Researchers should focus not only on delineating individual characteristics but also on community attributes that mediate or modify pathways through which socioeconomic conditions are associated with disease and death.
![]() |
APPENDIX 1 |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Response choices: For items 1 and 2: 1 = frequently; 2 = sometimes; 3 = rarely; 4 = never. For item 3: 1 = yes; 2 = no.
Physical activity level = number of items with the response choice "1."
Iowa
How often do you:
Response choices: 0 = do not do; 1 = every day; 2 = several times a week; 3 = once a week; 4 = several times a month; 5 = once a month or less.
Physical activity level = number of items with response choices of "1" or "2" (for items 1 and 2) or of "1," "2," or "3" (for item 3).
New Haven, Connecticut
In the last month, how often have you done:
Response choices: 1 = often; 2 = sometimes; 3 = never.
Physical activity level = number of items with the response choice "1."
North Carolina
Questions on physical activity were not asked.
![]() |
APPENDIX 2 |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
(*Reverse-scored.)
At some study sites, abbreviated versions of the scale were used. Scores were obtained by summing response choices across the items indicated below.
East Boston: items 6, 7, 11, 12, 14, 15, 16, 18, 19, and 20. Response choices: 1 = yes; 0 = no.
Iowa: items 2, 6, 7, 11, 12, 14, 15, 16, 18, 19, and 20. Response choices: 0 = hardly ever; 1 = some of the time; 2 = most of the time.
New Haven: items 120. Response choices: 0 = rarely or none of the time; 1 = some of the time; 2 = much of the time; 3 = most or all of the time. (This is the full-length scale.)
North Carolina: items 120. Response choices: 1 = yes; 0 = no.
![]() |
ACKNOWLEDGMENTS |
---|
The authors thank Dr. Paul R. Markowitz for helpful comments on earlier versions of the manuscript.
![]() |
NOTES |
---|
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|