Individual- and Area-Level Socioeconomic Status Variables as Predictors of Mortality in a Cohort of 179,383 Persons

Kyle Steenland1 , Jane Henley2, Eugenia Calle2 and Michael Thun2

1 Rollins School of Public Health, Emory University, Atlanta, GA.
2 American Cancer Society, Atlanta, GA.

Received for publication September 10, 2003; accepted for publication December 9, 2003.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The authors have studied whether area-level socioeconomic status predicts mortality independently of individual-level socioeconomic status in 179,383 persons in the American Cancer Society Nutrition Cohort, followed for mortality from 1992 to 2000 (17,383 deaths). They used an area-level variable based on census blocks that was an average of home value, income, education, and occupation. Education was the individual-level socioeconomic status variable. The authors studied socioeconomic status-mortality trends with each socioeconomic status variable adjusted for the other. For all causes, an individual’s education was strongly and inversely associated with mortality; a weak but significant inverse trend was also present for area-level socioeconomic status. A similar pattern was seen for all-vascular disease. For all cancers, there was again a significant inverse trend with education but no trend with area-level socioeconomic status. Adjustment for conventional (non-socioeconomic status) individual-level risk factors diminished the effect of both socioeconomic status variables, although significant trends remained for men between education and all-cause, all-cancer, and all-vascular disease mortality. Study data indicate that the predictive value of area-level socioeconomic status variables varies by cause of death but is less important than individual-level socioeconomic status variables. Multivariate models that consider socioeconomic status as a potential confounder may not need to consider area-level socioeconomic status if data are available on individual-level education.

cohort studies; heart diseases; mortality; multilevel model; social class

Abbreviations: Abbreviation: ICD-9, International Classification of Diseases, Ninth Revision.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
In recent years, there has been considerable interest in whether area-level indicators of socioeconomic status predict health outcomes over and above the individual-level variables (16). However, data sets with sufficient size and detail to permit these types of analyses are relatively rare. We have studied the association of mortality with both individual- and area-level indicators of socioeconomic status in an American Cancer Society cohort. This study population has the advantage of being larger than many databases in which the relative impact of area-level and individual-level socioeconomic status variables has been considered and the further advantage of more recent follow-up.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
We studied the American Cancer Society Nutrition Cohort, which includes 184,194 persons (86,406 men, 97,788 women) aged 50–74 years at enrollment in 1992 (7). This is a subgroup of the American Cancer Society Cancer Prevention Study II cohort, enrolled in 1982. Address information was available in 1997 for 181,159 people alive at that time and in 1992 for 3,016 people who died between 1992 and 1997. Addresses were matched with census block data available from a private firm (Claritas, Inc., San Diego, California; http://www.census.claritas.com). There were 180,648 matches (98.1 percent). After further elimination of a few individuals missing education data, a cohort of 179,383 individuals was available for this study. We used as our area-level variable a composite census block variable, created by Claritas, that is a weighted average of household income (17 levels), home value (13 levels), occupation (13 categories), and education (years of education for those over age 25 years, five levels), based on 1990 Census data updated using a variety of sources by Claritas. This composite variable ranged from 0 to 100, with 100 representing the highest socioeconomic status level.

We used self-reported education (five categories) as our individual-level socioeconomic status variable, based on a questionnaire completed in 1982. We also considered but elected not to use occupation as a socioeconomic status variable, because occupation had little relation to mortality in this cohort after controlling for education. No data on income were available.

The Nutrition Cohort has been followed for mortality from 1992 to 2000. We studied all-cause mortality (17,383 deaths, 11,390 men and 5,993 women), all-vascular disease mortality (6,077 deaths; International Classification of Diseases, Ninth Revision (ICD-9), codes 390–459), coronary heart disease mortality (a subset of all-vascular mortality; 3,451 deaths; ICD-9 codes 410–414), stroke mortality (a subset of all-vascular mortality; 944 deaths; ICD-9 codes 430–438), and all-cancer mortality (7,097 deaths; ICD-9 codes 140–195 and 199–208).

Individual-level risk factors included in models as potential confounders were ascertained in the Nutrition Cohort baseline interview in 1992–1993 and included 1) race (White vs. other, only 1 percent of the cohort was Black); 2) marital status (widowed, not widowed); 3) smoking history (never, current by cigarettes per day: <20, 20, >20, former by age at quitting: <40, 40–44, 45–49, 50–54, >=55 years, and ever pipe/cigar smoking (men only)); 4) body mass index (<18.5, 18.5–24, 25–29, >=30, missing); 5) alcohol consumption (not current, current <1 drink/day, current 1 drink/day, current >1 drink/day, missing); 6) a physical activity index; and 7) diet (categories of fruits/vegetables, meats, fat consumption). Models for women also included menopausal status (pre- and postmenopause by age at menopause (<40, 40–44, 45–49, 50–54, >=55 years)) and the use of postmenopausal hormones (never, current, and former by years of use (<5, 5–9, >=10 years)). Models for coronary heart disease also included the use of aspirin (never, <15 days per month, >=15 days per month), blood pressure-lowering medication (none, current use), and cholesterol-lowering medication (none, current use).

Analyses were conducted via Cox regression. Follow-up time since 1992 was the time variable in these analyses, and age was controlled by stratification on 1-year age groups. Tests for trend between socioeconomic status and mortality were calculated via a test of significance of the coefficient for a continuous variable for either education or area-level socioeconomic status. To transform the categorical variable to a continuous one, we used either years of education (8 for less than high school, 12 for high school graduate, 14 for some college, 16 for college graduate, 18 for postgraduate study) or the midpoints of the area-level variable categories. Coefficients for the continuous variable, with the corresponding confidence intervals, are also presented for all models.

The focus of the analysis was on whether the area-level socioeconomic status variable and the education variable predicted mortality after adjustment for each other and for age. As it is likely that most traditional risk factors are intermediate variables on a pathway between socioeconomic status and outcome, analyses that adjust for these risk factors will underestimate the full impact of socioeconomic status. However, the degree to which the effect of socioeconomic status variables is attenuated by traditional risk factors, as well as by each other, is of interest. Therefore, we used models with 1) a single socioeconomic status variable and age, 2) both socioeconomic status variables and age, and 3) both socioeconomic status variables, age, and conventional risk factors.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Table 1 gives descriptive statistics for the cohort. Study participants were relatively well educated compared with the general population, yet varied substantially in both education and area-level socioeconomic status. The Spearman correlation coefficient between the level of education and area-level socioeconomic status was 0.32 (0.36 in men, 0.28 in women). This correlation is not so strong as to preclude an independent association with either variable after adjustment for the other. The medians of the area-level socioeconomic status variable for less than high school education, high school education, some college, college degree, and graduate school education were 48, 49, 53, 58, and 59, respectively, for men and 48, 51, 54, 58, and 58, respectively, for women.


View this table:
[in this window]
[in a new window]
 
TABLE 1. Descriptive statistics for the Cancer Prevention Study II Nutrition Cohort, 1992–2000
 
Table 2 shows the relation of all-cause mortality with individual-level education and the area-level variable (the composite socioeconomic status score for census block). For both men and women, both area-level and individual-level socioeconomic status variables were important predictors of mortality, with strong trends of increasing mortality by lower socioeconomic status, in analyses adjusted for age only. When both variables were included simultaneously in the model, rate ratios for education were largely unchanged, while rate ratios for the area-level variable were diminished, although trends were still apparent. The slope of the trend with education, adjusted for age and area-level socioeconomic status, indicated an approximate 7 percent decrease in the rate ratio for men for each year of education, while the corresponding value for women was 3 percent. The corresponding values for a 10-unit change in area-level socioeconomic status score were 1 percent for both men and women. Finally, adjustment for conventional risk factors eliminated trends with either socioeconomic status variable except for education among men.


View this table:
[in this window]
[in a new window]
 
TABLE 2. All-cause mortality, Cancer Prevention Study II Nutrition Cohort, 1992–2000
 
Table 3 shows the results for all-vascular disease. For both men and women, both socioeconomic status variables showed statistically significant trends, after adjustment for each other ("minimal adjustment"). Trends were stronger for education than the area-level variable. Further adjustment for conventional risk factors eliminated the statistical significance of all trends except that with education for men.


View this table:
[in this window]
[in a new window]
 
TABLE 3. All-vascular disease mortality (ICD-9* codes 390–459), Cancer Prevention Study II Nutrition Cohort, 1992–2000
 
Table 4 shows the results for coronary heart disease. Patterns are similar to those for all-vascular disease; coronary heart disease is the major component of all-vascular disease (57 percent of all vascular deaths). Table 5 shows the results for stroke, another subset of all-vascular disease. Patterns are again similar to those for all-vascular disease, but trends are not as pronounced and are not generally statistically significant. Results from separate analyses of all other vascular diseases, not shown, showed similar patterns.


View this table:
[in this window]
[in a new window]
 
TABLE 4. Coronary heart disease mortality (ICD-9* codes 410–414), Cancer Prevention Study II Nutrition Cohort, 1992–2000
 

View this table:
[in this window]
[in a new window]
 
TABLE 5. Stroke mortality (ICD-9* codes 430–438), Cancer Prevention Study II Nutrition Cohort, 1992–2000
 
Table 6 shows the results for all-cancer mortality. Whereas education is inversely associated with death rates from all cancers combined after adjustment for area-level socioeconomic status, the converse is not true. There are no trends with area-level socioeconomic status after adjustment for education. Multivariate analyses with adjustment for conventional risk factors showed a significant inverse trend for education among men. There is a weak but significant positive trend between the area-level variable and cancer mortality among men in multivariate analyses. Additional analyses were run for lung, breast, and prostate cancer (not presented). Consistent with the all-cancer findings, there were no significant trends for area-level variables after adjustment for education.


View this table:
[in this window]
[in a new window]
 
TABLE 6. All-cancer mortality (ICD-9* codes 140–195 and 199–208), Cancer Prevention Study II Nutrition Cohort, 1992–2000
 
Table 7 shows the data for all-cause mortality for those aged less than 65 years. Rate ratios for those with low socioeconomic status tend to be higher than those in table 2 (all-cause mortality not restricted by age), but the patterns seen in table 2 remain: stronger trends for education than for the area-level socioeconomic status variable. Unlike table 2, table 7 shows that trends with the area-level socioeconomic status variable (after control for education) are no longer statistically significant, because of smaller sample size.


View this table:
[in this window]
[in a new window]
 
TABLE 7. All-cause mortality under age 65 years, Cancer Prevention Study II Nutrition Cohort, 1992–2000
 
Additional analyses were conducted after excluding those with prevalent cancer or prevalent vascular disease at baseline. The patterns exhibited in these analyses for the socioeconomic status variables were similar to those presented (data not shown). Other additional analyses, not shown, were conducted in which a combination of three levels of the individual-level and three levels of the area-level variable was used. Model likelihoods did not improve, indicating no advantage of these models over models in which both variables were included separately.


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Individual-level education showed significant inverse trends with all-cause mortality (men and women), all-vascular disease mortality (men and women), and all-cancer mortality (men only), after adjustment for area-level socioeconomic status and age. The area-level socioeconomic status variable was a less important predictor of mortality for all of these outcomes. However, after adjustment for education, significant inverse trends with the area-level socioeconomic status variable were apparent for all-cause and vascular disease mortality for men and women in age-adjusted analyses. Area-level trends for vascular disease mortality were also seen for coronary heart disease, the largest component of vascular disease. No trend with the area-level variable was seen for all cancers after adjustment for education. Cancer is not a homogenous category, and some area-level trends may exist independently of trends with individual-level socioeconomic status for some specific cancers for either males or females. However, we did not find any such independent trends for either men or women when we looked at lung, prostate, and breast cancer specifically.

Adjustment for conventional risk factors in multivariate analyses eliminated the significant trends for area-level socioeconomic status for women and men for all-cause and vascular disease mortality, while individual-level education remained an important predictor for men but not women, for both of these causes and for all-cancer mortality. The weaker association of education with mortality among women than men is consistent with other reports (810) and may partly reflect misclassification of married women’s socioeconomic status when that classification ignores the husband’s socioeconomic status (8, 11). Inclusion of conventional risk factors when analyzing for the effects of socioeconomic status variables may not be appropriate, as conventional risk factors are likely to be intermediate variables on a pathway from socioeconomic status to disease.

Our results are reasonably consistent with those from three other US mortality studies (24). In particular, the large follow-up study by Anderson et al. (2) (239,000 people from the National Longitudinal Mortality Study) was consistent with our study regarding findings for all-cause mortality. The authors found that both types of socioeconomic status variables were independently important, but the individual-level variable was more important than the area-level variable. The authors did not present data on specific causes.

Waitzman and Smith (4) studied mortality among 10,000 persons from the First National Health and Nutrition Examination Survey from 1971 to 1974 who were followed through 1987, and they found that area-level variables were associated with death from a variety of causes after adjustment for individual-level socioeconomic status variables, but only for those less than 55 years of age. We had virtually no data on those aged less than 55 years (1 percent of our population at baseline). Among subjects aged less than 65 years, trends for both socioeconomic status variables appeared somewhat stronger than for the entire cohort for all-cause mortality, although some trends were not statistically significant because of smaller sample size.

De Roux et al. (1) followed 13,000 people for 9 years and found strong trends between coronary heart disease incidence and an area-level socioeconomic status variable, after adjusting for individual-level socioeconomic status variables and conventional risk factors. This may imply that the area-level variable captures some information about unspecified risk factors for disease, as opposed to information about treatment (mortality analysis cannot separate these two determinants of death).

Both individual-level and area-level socioeconomic status variables presumably act on mortality through a complex web of intermediate risk factors including conventional risk factors (smoking, obesity) as well as factors affecting access to medical care and quality of medical care. Our data, along with those of others, suggest that individual-level socioeconomic status variables are stronger predictors of several outcomes than are area-level socioeconomic status variables, but that area-level variables retain important predictive power for vascular disease mortality even after controlling for individual-level socioeconomic status. The fact that area-level socioeconomic status variables continue to retain some predictive power after adjustment for individual-level socioeconomic status variables would suggest either that they are capturing residual confounding at the individual level not fully controlled by individual-level socioeconomic status, or that ecologic variables themselves in fact have independent predictive power because they are capturing community-wide factors that influence mortality (e.g., access to medical care, stress resulting from community-wide poverty). The fact that vascular disease (and its major component, coronary heart disease) appears to be the outcome most influenced by area-level socioeconomic status is consistent with either hypothesis, as vascular disease is known to be affected by many individual-level risk factors and to be strongly influenced by medical care.

In conclusion, our data suggest that the predictive value of area-level socioeconomic status variables varies by cause of death but is less important than individual-level socioeconomic status variables. Further, if considered as a potential confounder, area-level socioeconomic status would generally not need to be included in multivariate models that included individual-level socioeconomic status and conventional risk factors for most causes of death, with the exception of male vascular disease.


    NOTES
 
Correspondence to Dr. Kyle Steenland, Department of Environmental and Occupational Health, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA 30322 (e-mail: nsteenl{at}sph.emlry.edu). Back


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. De Roux A, Merkin S, Arnett D, et al. Neighborhood of residence and incidence of coronary heart disease. N Engl J Med 2001;345:99–106. [Abstract/Free Full Text]
  2. Anderson R, Sorlie P, Backlund E, et al. Mortality effects of community socioeconomic status. Epidemiology 1996;8:42–7.[ISI]
  3. Yen I, Kaplan G. Neighborhood social environment and risk of death: multilevel evidence from the Alameda County Study. Am J Epidemiol 1999;149:898–907.[Abstract]
  4. Waitzman NJ, Smith KR. Phantom of the area: poverty-area residence and mortality in the United States. Am J Public Health 1998;88:973–6.[Abstract]
  5. Smith G, Hart C, Watt G, et al. Individual social class, area-based deprivation, cardiovascular disease risk factors, and mortality: the Renfrew and Paisley study. J Epidemiol Community Health 1997;52:399–405.[ISI]
  6. Kölegård Stjärne M, Diderichsen F, Reuterwall C, et al. Socioeconomic context in area of living and risk of myocardial infarction: results from Stockholm Heart Epidemiology Program (SHEEP). J Epidemiol Community Health 2002;56:29–35.[Abstract/Free Full Text]
  7. Calle E, Rodriguez C, Jacobs E, et al. The American Cancer Society Cancer Prevention Study II Nutrition Cohort. Cancer 2002;94:500–11.[CrossRef][ISI][Medline]
  8. Steenland K, Henley J, Thun M. All cause and cause-specific death rates by educational status for two million people in two American Cancer Society cohorts, 1959–1996. Am J Epidemiol 2002;156:11–21.[Abstract/Free Full Text]
  9. Pappas G, Queen S, Hadden W, et al. The increasing disparity in mortality between socioeconomic groups in the US, 1960–1986. N Engl J Med 1993;329:103–9.[Abstract/Free Full Text]
  10. Feldman J, Makuc D, Kleinman J, et al. National trends in educational differentials in mortality. Am J Epidemiol 1989;129:919–33.[Abstract]
  11. Koskinen S, Martelin T. Why are socioeconomic mortality differences smaller among women than among men? Soc Sci Med 1994;38:1385–96.[CrossRef][ISI][Medline]