Estimated Deaths Attributable to Social Factors in the United States

Sandro Galea, MD, DrPH; Melissa Tracy, MPH; Katherine J. Hoggatt, PhD; Charles DiMaggio, PhD; Adam Karpati, MD, MPH


Am J Public Health. 2011;101(8):1456-1465. 

In This Article


We found that in 2000, approximately 245000 deaths in the United States were attributable to low education, 176000 to racial segregation, 162000 to low social support, 133000 to individual-level poverty,119000 to income inequality, and 39000 to area-level poverty. These mortality estimates are comparable to deaths from the leading pathophysiological causes. For example, the number of deaths we calculated as attributable to low education is comparable to the number caused by acute myocardial infarction (192898), a subset of heart disease, which was the leading cause of death in the United States in 2000.[81] The number of deaths attributable to racial segregation is comparable to the number from cerebrovascular disease (167661), the third leading cause of death in 2000, and the number attributable to low social support is comparable to deaths from lung cancer (155521).

Our estimates of the number of deaths attributable to social factors can be loosely compared with previous estimates, although our approach differs methodologically from prior efforts. Woolf et al. reported that an average of 196000 deaths would have been avoided each year from 1996 to 2002 if all adults in the United States had had a college education, compared with our estimate of 245000 deaths attributable to having less than a high school education in 2000.[29] Our numbers are higher because we included deaths among those aged 65 years or older, whereas Woolf et al. included deaths only among individuals aged 25 to 64 years. Hahn et al. estimated that 6% of deaths in the United States in 1991 could be attributed to poverty, corresponding to 91000 deaths among those aged 25 years or older,[30] whereas Muennig et al. estimated that 2.3% of deaths in the United States in 2000 could be attributed to poverty, corresponding to 54000 deaths.[31] Although our estimated population-attributable fraction for mortality attributable to poverty was 4.5%, roughly between these 2 previous estimates, our estimate of the number of deaths attributable to poverty (133000) was higher than the estimate by Hahn et al. This higher estimate is partly because of differences in age stratification and the use of deaths among all races rather than those among Whites and Blacks only,[30] and partly because we estimated deaths for a later period in which there was a greater number of deaths overall. By contrast, our estimates of the population-attributable fraction for mortality associated with area-level poverty (1.7%) and with income inequality (5.1%) are not directly comparable to those reported in previous studies, which looked at excess mortality in neighborhoods with medium and low levels of socioeconomic status (encompassing a broader array of factors, including educational level and median housing value)[33] and at the percentage of mortality in areas with high levels of income inequality that could be attributed to that income inequality.[32]

Several issues should be considered when interpreting these findings. Limited availability of data from US samples prevented us from considering some social factors and, in some cases, forced us to base our RR estimates on small numbers of studies. Previous analyses[1,2] also relied on small numbers of studies in some cases to derive their attributable risk estimates; we suggest that our approach of conducting a systematic meta-analysis allowed us to capture the relations as accurately as possible. The 6 social factors considered are highly interrelated; thus, deaths attributed to each factor are not necessarily mutually exclusive. In addition, in the absence of stratum-specific estimates we could not assess possible heterogeneity in effects, and when only adjusted RR estimates were available we likely underestimated the true number of deaths because the RR estimates on which we based our calculations were derived by controlling for mediating variables rather than confounders. Our methods assume that the relations between social factors and mortality that were estimated in the 1980s and 1990s, when most of the included studies were conducted, still applied in 2000, the year used for our prevalence and mortality data. Although subgroup analyses we conducted suggested no differences in the relation between each social factor and mortality in different time periods, others have suggested that disparities in mortality by socioeconomic status have been increasing during the past few decades.[85]

Our meta-analytic results are only as valid and reliable as the studies upon which they are based. Although many of the RR estimates used in the meta-analyses were derived from national samples, some were conducted in specific populations or areas of the country. These samples may not reflect the target population–specifically, the adult US population–used to calculate the number of attributable deaths. The measures and definitions used to operationalize the social factors of interest were not always consistent across studies, although sensitivity analyses suggested no substantial differences in RR estimates when using alternate cutpoints. Additionally, the approach used to calculate the population attributable fraction is not strictly valid in the presence of confounding or effect modification, although it may provide reasonably accurate results in practice despite methodological limitations.[83,84] We used unadjusted estimates and stratified by covariates whenever possible but were restricted to adjusted estimates for the area-level social factors considered.

The extent of the potential bias in our estimates depends on whether there is residual confounding present in the adjusted estimates and the degree of effect measure modification.[86] Although the bias in the presence of confounding alone may be predictable–incomplete control of positive confounding leads to an overestimate of the RR that translates into an overestimate of the attributable numbers of deaths–it is difficult to predict the direction of bias when there is heterogeneity in the RR estimates.[83] This difficulty reflects a methodological limitation inherent in using adjusted RR estimates to derive population attributable fraction estimates: residual confounding and effect heterogeneity will affect summary estimates of the RRs and lead to bias in the population-attributable fraction estimates. Ultimately, however, this concern applies to all studies that rest on meta-analytic techniques, including the ones that we build on in conducting this work. One conclusion that can be drawn from our work is that individual study results may not be useful for synthetic analyses such as ours unless these studies provide detailed data summaries and subgroup estimates in addition to the final, multiply adjusted estimates.

Finally, we limited our analysis to structural social factors that are largely features of individual experiences or group context. We did not consider stress processes that may explain the link between social factors and mortality. In many ways, stress processes may be considered a mediator of some of the factors we study, so we accounted for them. However, stress processes may also mediate the relation between other "nonsocial" factors and mortality, so we might have underestimated the contribution of social factors to mortality in the United States. This analysis suggests that, within a multifactorial framework, social causes can be linked to death as readily as can pathophysiological and behavioral causes. All of these factors contribute substantially to the burden of disease in the United States, and all need focused research efforts and public health efforts to mitigate their consequences.


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