1 Department of Epidemiology and Center for Social Epidemiology and Population Health, University of Michigan, Ann Arbor, MI.
2 School of Public Health, Queensland University of Technology, Brisbane, Queensland, Australia.
3 Center for Biostatistics in AIDS Research, School of Public Health, Harvard University, Boston, MA.
4 Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI.
Received for publication March 10, 2003; accepted for publication September 3, 2003.
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ABSTRACT |
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adult; cardiovascular diseases; child; mortality; risk; social class; socioeconomic factors; women
Abbreviations: Abbreviations: CI, confidence interval; HR, hazard ratio; ICD-9, International Classification of Diseases, Ninth Revision.
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INTRODUCTION |
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Although the inverse relation between adult socioeconomic position and health has been observed in both men and women, most of the evidence linking low socioeconomic position to poorer health outcomes is derived from middle-aged male populations (14, 15). Evidence is accumulating, however, that the complex interplay of biologic, social, economic, behavioral, and psychological life course processes may operate differently for men and women for some health outcomes, and so it may be worthwhile to consider men and women separately (16). Of the 24 studies that have examined life course socioeconomic position in relation to cardiovascular disease, less than half have even included women and then usually in relatively small numbers (8). Furthermore, the assessment of womens socioeconomic position has often been based on the socioeconomic characteristics of her household and/or partner, so it is important to include indicators of her own position (17).
In addition, there may also be other aspects of the socially defined structural roles of women that may be associated with poorer health outcomes, such as the demands of child or elder care. To examine questions about the contribution of life course socioeconomic processes to womens mortality, we took advantage of data collected in the Alameda County Study between 1965 and 1996, which included information on a womans own childhood socioeconomic conditions, education, and occupation. In addition, we used information on her household income and the occupation of her husband/partner in relation to her risk of all-cause, cardiovascular disease, and noncardiovascular disease mortality.
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MATERIALS AND METHODS |
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Cause-specific mortality was ascertained primarily through computer linkage with the California Master Death Index, supplemented with searches of the National Death Index. Follow-up for mortality was completed through December 1996 with a total of 1,163 deaths documented among females in this study. Earlier investigations estimated that this accounted for approximately 96 percent of the deaths occurring within the study population (18). International Classification of Diseases, Ninth Revision (ICD-9), codes 390459 listed as the primary cause of death were considered cardiovascular disease outcomes (n = 580). Separate analyses were performed within this group for deaths due to ischemic heart disease (ICD-9 codes 410414) and cerebrovascular disease (ICD-9 codes 430438), but as the findings were similar we present results for the combined category of cardiovascular disease only. An analysis of premature mortality was restricted to subjects aged 45 years or younger at baseline (n = 1,753), with a total of 247 deaths recorded (cardiovascular disease-related deaths = 72).
Assessment of life course socioeconomic indicators
The questionnaires sent at each wave of data collection were similar in content and collected information on socioeconomic and demographic factors, physical and psychological health, medical conditions, personal habits, activities, and home life.
Childhood
Socioeconomic position in childhood was assessed at baseline by asking subjects to indicate their fathers education and occupation. Detailed occupational codes corresponding to the US Census were derived from the questionnaires and later collapsed by the authors into two broad categoriesnonmanual (high socioeconomic position) and manual (low socioeconomic position). When information on the fathers occupation was missing (n = 247), we categorized respondents according to their fathers education, where low childhood socioeconomic position was defined as the fathers having 8 years of education or less.
Education
Women in this study ranged in age from 17 to 94 years. As average levels of education have increased in a cohort-specific fashion and the socioeconomic implications of attaining a certain level of education have changed over time, we attempted to account for cohort effects by ranking womens education relative to their broad birth cohort as being younger or older than 55 years at baseline. Sensitivity analyses showed that using narrower age bands did not substantively alter the conclusions of the study. Younger women who reported not completing at least 12 years of schooling and older women who did not finish at least 8 years were categorized in the lowest education group, while any subject who reported completing more than 12 years was classified as having the highest education.
Occupation
An occupation was recorded for all subjects who reported employment outside the home. At baseline, all retirees were asked to report their occupation when they were last employed, and this continued over the course of follow-up. Additionally, all married women were asked to report their husbands occupation. The job titles were then coded according to the relevant census year classifications and collapsed into broader occupational categories by the authors (J. B. D., J. W. L.): professionals (doctors, lawyers, professors, scientists, engineers, architects, etc.); other nonmanual positions (secretaries, stenographers, bookkeepers, typists, office workers, cashiers, tellers, collectors, messengers, and salespersons); skilled manual positions (foremen, machinists, electricians, carpenters, mechanics, craftsman, military enlisted men, and protective service workers); and unskilled manual positions (truck and bus drivers, operatives and apprentices in industry, bartenders, waiters, cooks, other service workers, gardeners, longshoremen, laborers, sharecroppers, and private household workers).
The job classifications for women were further collapsed to nonmanual versus manual positions because of the relatively small number of women employed in skilled manual positions. Housewives were considered a separate occupational category. These categories were updated at each wave of data collection and used in the time-dependent covariate analyses to account for women who may have changed occupational status, such as when they reentered the labor force after working in the home providing child care.
Income
Subjects were asked to report the gross household income from all sources for the year prior to the survey. Income categories were created on the basis of approximate tertiles in the distribution of income among study respondents. At baseline, subjects reporting an annual income of less than $5,000 before taxes were categorized as living in low-income households, those who reported incomes from $5,000 to $10,000 were considered as living in medium-income households, while earnings of greater than $10,000 were categorized as high income. Reported incomes during subsequent waves of data collection were adjusted to constant dollars using the consumer price index.
Covariates
At baseline and at each subsequent wave of data collection, subjects were classified as current, former, or never smokers. A measure of physical activity was also created according to the intensity, frequency, and duration of reported participation in different types of leisure time activity, such as swimming, walking, active sports, or gardening. Weight (in pounds) and height (in feet and inches) were self-reported by subjects and then converted to their metric equivalent for the calculation of body mass index. Women with a body mass index of greater than 30 kg/m2 were categorized as obese. Previous research using the Alameda County data has shown that these covariates are related to both socioeconomic position and mortality.
Statistical analysis
All statistical procedures were performed using SAS version 6.12 software (SAS Institute, Inc., Cary, North Carolina). Cox proportional regression analyses were used to estimate relative mortality hazards and 95 percent confidence intervals for all exposures of interest. Additionally, a time-dependent hazard regression analysis was performed to account for changes in selected exposures over time, such as income and health behaviors. Survival time was calculated as the number of years between baseline survey completion and date of death.
RESULTS
Table 1 shows demographic, behavioral, and health characteristics among study participants in 1965. The age range of subjects at the baseline examination was from 17 to 94 years, with a mean age of approximately 44 years. The majority of participants were White (84.3 percent), with the remaining subjects of African, Asian, and Hispanic descent. The resulting racial composition of the study subjects was similar to the population of Alameda County as a whole at that time. Most participants reported being married at the time of examination, never smoking cigarettes, and being moderately to highly physically active; they were not considered obese. An earlier published report has characterized the Alameda County Study participants in greater detail (19).
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DISCUSSION |
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There are several potential limitations that should be considered in the interpretation of the results of this study. First, there are a number of issues to bear in mind in regard to the measurement of socioeconomic position over the life course. Our measure of childhood socioeconomic position was based on recall at the baseline examination that likely would have resulted in nondifferential exposure misclassification, which may underestimate the effects of childhood disadvantage. A recent review of studies of childhood socioeconomic position and adult cardiovascular disease has shown that studies measuring socioeconomic position in childhood showed stronger associations between childhood socioeconomic position and outcomes than studies relying on adult recall of childhood socioeconomic position (8). Furthermore, we were forced to create fairly crude categorizations of childhood socioeconomic position into a simple high/low dichotomy because of the particular occupational distribution of the fathers in this sample.
It is also possible that the educational and occupational classification of women in this study was imprecise. There were enormous secular changes in education over the life course of these cohorts of women. At baseline, women were aged 1794 years and thus were born between 1871 and 1948. Achieving a high school education for a woman born in the 1900s had a very different social meaning than the same objective level of education for a woman born in the 1930s. We attempted to deal with this issue by assigning each woman an educational rank relative to her birth cohort. Nevertheless, this is a fairly crude attempt to adjust for such massive secular changes in education.
In regard to measuring a womans occupation, there are two potential sources of imprecision. First, the occupational classification schemes available for the 1960, 1970, and 1980 censuses may be more sensitive for male than female jobs, so that our collapsed occupational categories were not as accurate a representation of womens work as they might have been. Evidence for this is found in the fact that occupation was unrelated to mortality in unadjusted models, although the estimates did strengthen when premature mortality was examined, suggesting that the occupational classification may have been more sensitive for younger than older women, many of whom were already retired at baseline. A second source of imprecision may have arisen from missing data on occupational changes among women over the life course. Most occupational change occurred in the women who were classified as working in the home at baseline (46.7 percent). Some of these women were of retirement age (16.9 percent) and so would not have changed occupational classifications. Of the remainder, some rejoined the workforce over the 31 years of the study (23.3 percent), but 15.1 percent had missing data on their subsequent occupations. This may have meant that we underestimated the effects of occupation on womens mortality experience. Finally, occupational classification of women may have been more imprecise for older than younger cohorts, because cohorts of older women had more constrained access to different types of work. The final issue in regard to measuring socioeconomic position is that, because household income may have been relatively accurately reported, the smaller measurement error in that indicator of adult socioeconomic position may have made it appear to be a stronger predictor of mortality than other life course socioeconomic position indicators, potentially measured with greater error. Nevertheless, these potential measurement issues cannot explain why childhood socioeconomic position was associated with cardiovascular disease but not with noncardiovascular disease mortality, when examining this association was one of the primary objectives of this study. It also cannot explain why education was strongly linked to noncardiovascular disease mortality but not to cardiovascular disease mortality.
Other measurement limitations include the inability to accurately capture smoking, body mass index, and levels of physical activity. Although assessment of these behavioral pathways was not the primary goal of the study, it is likely that these effects were underestimated because of self-report and measurement error. In addition, the period under study was one where epidemic levels of coronary heart disease in the United States declined sharply. The period was also characterized by major shifts in the socioeconomic patterning of major cardiovascular disease risk factors such as smoking (40, 41), so that both secular and socioeconomic shifts in risk factors between 1965 and the 1990s complicate the understanding of the behavioral pathways that may help mediate the effects of early life socioeconomic position on cardiovascular disease mortality. Finally, the sample used here is unlikely to be representative of the United States as a whole, but it is an unselected population and may be more socioeconomically diverse than the only previous US study of mortality conducted among nurses (32).
Our findings are consistent with those from other studies on women. An inverse association between childhood socioeconomic position and cardiovascular disease was reported among 117,006 subjects participating in the US Nurses Heath Study (32). Kuh et al. (39) also observed a strong inverse association between the fathers social class and premature mortality (2654 years) in a large post-World War II birth cohort in the United Kingdom.
A number of hypotheses have been generated to explain the effect of early life socioeconomic position on adult disease risk. In 1977 and 1978, Forsdahl (42, 43) first speculated that persons growing up in a deprived environment, followed by later affluence, were at an increased risk for developing coronary heart disease and that the relation was mediated primarily through serum cholesterol concentrations, although studies of upward mobility in affluent countries have tended to show reduced rather than elevated risk (35). Others have highlighted the role of early life conditions on the development of insulin resistance (4446) or of hemostatic factors, such as increased levels of plasma fibrinogen (47, 48), as a potential explanation for the findings linking lower childhood socioeconomic position to adult health.
In addition to childhood social class, certain markers of low socioeconomic position later in life were also associated with increased mortality in our study. Women who reported completing the lowest number of years of schooling had a greater risk of noncardiovascular disease-related death compared with women who reported achieving the highest level of education. Annual household income was inversely associated with both cardiovascular disease-related and noncardiovascular disease-related mortality in all analyses, but it was stronger for cardiovascular disease-related deaths. The subjects occupation was unrelated to mortality in this study. However, among married women, there was some suggestion of an increased risk of death depending upon the husbands occupation. The lowest risk was observed among women married to professionals, and the highest risk was observed among women with husbands who worked in unskilled manual positions, even after adjustment for other individual and household measures of socioeconomic position.
Our findings are generally consistent with the literature, linking low socioeconomic position in adulthood to poorer general health and adverse health outcomes in women (29, 3639, 49). Previous research in the area, however, has tended to rely primarily on the husbands or household measures of socioeconomic position rather than on the womans own measures, and this is especially true for women working within the home. Our results indicate an independent contribution of both individual and household measures of adult socioeconomic position to risk of death in women, which suggests that the results of previous studies that cannot account for multiple measures of socioeconomic position should be interpreted with caution because of the possibility of residual confounding by other socioeconomic predictors.
Additionally, a small number of studies of women have attempted to evaluate the independent effect of both childhood and adult measures of socioeconomic position on adult health (3739). In a previously mentioned study of employed women in the United Kingdom, the authors of the paper created a measure of lifetime social class by summing the number of times a womans class location was considered manual or nonmanual (37). Unfortunately, as the study population consisted of employed women, housewives were not included. Our study is the first to examine women working within the home in the analyses of socioeconomic differences in mortality.
The results of the current investigation suggest that both individual and household markers of adult socioeconomic position are consequential considerations in studies of womens health, and the results demonstrate the utility of multiple measures of socioeconomic position from across different life course stages. In our analyses, the associations among most measures of socioeconomic position held, although somewhat attenuated, after controlling for the other socioeconomic measures. This suggests that studies that are dependent on a single measure of socioeconomic position are missing potentially valuable aspects of the relation between socioeconomic position and adult health outcomes as they play out over the life course. Moreover, as most studies rely on largely self-reported information, those that use multiple predictors of socioeconomic position may be less prone to measurement error resulting in nondifferential misclassification bias. Problems of measurement error may be exacerbated in studies focusing specifically on women, as a majority of studies use household measures of social position rather than individual measures. It has been suggested that the measures commonly used to characterize an individuals social position better predict the adult health outcomes and mortality for men than those for women (14).
An additional strength of our study is the ability to update information on the markers of socioeconomic position over an extended period of follow-up. The estimates derived from the time-dependent covariate analyses were similar to those generated from baseline data; however, allowance was made for changes in risk factors over time.
Our results generally indicate modest relative associations between the measures of socioeconomic position and mortality. This is to be somewhat expected in a study with a protracted period of follow-up, where death eventually occurs in approximately 45 percent of the total population. The subanalyses of those women aged 45 years or younger highlight the importance of the socioeconomic position on premature mortality. The results from this analysis suggest that early life markers of social class may play a greater role in predicting premature mortality. However, we cannot know if this is truly the case or an artifact of measurement closer to the time of death. It is also possible that the results may be subject to survivor bias, so that any effects of life course socioeconomic conditions were expressed in premature mortality, before older women could be recruited into the cohort in 1965. Nearly 8 percent of the study population was 70 years or older in 1965.
In summary, our results indicate that both childhood and adult markers of socioeconomic position are important factors in cardiovascular disease mortality, but it appears that lower socioeconomic position later in life has a larger impact on overall mortality. This suggests the potential for different socioeconomic markers from different life course stages to be linked through different mechanisms that may ultimately affect mortality risk. These findings highlight the need to incorporate multiple markers of socioeconomic position over the life course, to more adequately characterize the links between different dimensions of life course socioeconomic position and subsequent mortality.
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ACKNOWLEDGMENTS |
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NOTES |
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REFERENCES |
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