estimated deaths attributable to social factors in the united states

Estimated Deaths Attributable to Social Factors in theUnited StatesSandro Galea, MD, DrPH, Melissa Tracy, MPH, Katherine J. Hoggatt, PhD, Charles DiMaggio, PhD, and Adam Karpati, MD, MPH

In 1993, an article provocatively titled ‘‘ActualCauses of Death in the United States’’ offereda new conceptualization of cause-of-deathclassification, one that acknowledged andquantified the contributions of behavior ratherthan the more typical pathological explanationsrecorded on death certificates.1 The authors,McGinnis and Foege, found that the mostprominent contributor to mortality in 1990 wastobacco (400000 deaths), followed by diet andactivity patterns (300000 deaths). A decadelater, updated findings by Mokdad et al.2 usingdata from 2000 showed progress in some areasbut the growing contribution of obesogenicbehavior (poor diet and physical inactivity). De-spite controversy over the methods used toderive the attributable numbers of deaths andthe validity of their estimates, especially in thearticle by Mokdad et al., the findings of botharticles have been influential, are frequently citedand debated in the peer-reviewed literature,3---12

and have been cited in discussions of nationalpublic health priorities.13

In a 2004 editorial accompanying the articleby Mokdad et al., McGinnis and Foege notedthat although

it is also important to better capture and applyevidence about the centrality of social circum-stances to health status and outcomes . . . thedata are still not crisp enough to quantify thecontributions [of social circumstances] in thesame fashion as many other factors.14(p1264)

In the past 15 years, there has been growinginterest in the social determinants of health,and several proposed frameworks describe theeffects on individual and population health ofsocial factors at multiple levels, including be-havioral factors, features of an individual’ssocial network and neighborhood, and socialand economic policies.15,16 Numerous studieshave demonstrated a link between mortality andsocial factors such as poverty and low education.Although the proposed causal chain linkingadverse social factors to poor health is compli-cated, the evidence points to mechanisms

including risky health behaviors (e.g., smoking),inadequate access to health care, and poornutrition, housing conditions, or work environ-ments.17---20 Social relationships have also beenlinked to mortality, as social ties influence healthbehaviors and social support buffers againststress, which in turn affects immune function,cardiovascular activity, and the progression ofexisting disease.21,22 Negative social interactions,including discrimination, have been linked toelevated mortality rates, potentially through ad-verse effects on mental and physical health aswell as decreased access to resources.23,24 Fi-nally, characteristics of one’s residential envi-ronment may influence mortality through in-vestment in health and social services in thecommunity, effects of the built environment, andexposure to violence, stress, and social normsthat promote adverse health behaviors.25---28

To date, few studies have provided popula-tion estimates of deaths attributable to socialfactors. For example, 1 study estimated thatover 1 million deaths from 1996 to 2002would have been avoided if all adults in the US

population had at least a college education.29

Other studies have estimated attributable frac-tions for mortality of 2% to 6% for poverty(depending on the year and data source),30,319%to 25% for income inequality (depending on agegroup),32 and 18% to 25% for low neighbor-hood socioeconomic status (depending on gen-der and racial/ethnic group).33 Building on theseprevious efforts, we aimed to estimate the num-ber of deaths in the United States attributable tosocial factors, using a systematic review of theavailable literature combined with vital statisticsdata.

METHODS

To calculate the number of deaths attribut-able to social factors in the United States, wefirst estimated the relative risk (RR) of mortalityassociated with each social factor and obtainedan estimate of the prevalence of each socialfactor in the United States. These estimateswere used to calculate the population-attribut-able fraction of mortality for each factor, which

Objectives. We estimated the number of deaths attributable to social factors in

the United States.

Methods. We conducted a MEDLINE search for all English-language articles

published between 1980 and 2007 with estimates of the relation between social

factors and adult all-cause mortality. We calculated summary relative risk

estimates of mortality, and we obtained and used prevalence estimates for each

social factor to calculate the population-attributable fraction for each factor. We

then calculated the number of deaths attributable to each social factor in the

United States in 2000.

Results. Approximately 245000 deaths in the United States in 2000 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.

Conclusions. The estimated number of deaths attributable to social factors in

the United States is comparable to the number attributed to pathophysiological

and behavioral causes. These findings argue for a broader public health

conceptualization of the causes of mortality and an expansive policy approach

that considers how social factors can be addressed to improve the health of

populations. (Am J Public Health. Published online ahead of print June 16, 2011:

e1–e10. doi:10.2105/AJPH.2010.300086)

RESEARCH AND PRACTICE

Published online ahead of print June 16, 2011 | American Journal of Public Health Galea et al. | Peer Reviewed | Research and Practice | e1

http://ajph.aphapublications.org/cgi/doi/10.2105/AJPH.2010.300086The latest version is at Published Ahead of Print on June 16, 2011, as 10.2105/AJPH.2010.300086

Copyright 2011 by the American Public Health Association

http://ajph.aphapublications.org/cgi/doi/10.2105/AJPH.2010.300086

was multiplied by the total number of deathsin the United States in 2000 to estimate thenumber of deaths attributable to each socialfactor.

Relative Risk Estimates

We conducted a MEDLINE search for allEnglish-language articles published between1980 and 2007 with estimates of the relationbetween individual- and area-level social fac-tors and adult all-cause mortality. Individual-level social factors included education, poverty,health insurance status, employment statusand job stress, social support, racism or dis-crimination, housing conditions, and earlychildhood stressors. Area-level social factorsincluded area-level poverty, income inequality,deteriorating built environment, racial segre-gation, crime and violence, social capital, andavailability of open or green spaces. We iden-tified these articles to extract RR estimates fromindependent samples that could be combinedthrough meta-analysis to obtain summary RRestimates for the relations between each socialfactor and mortality.

The included studies presented data suffi-cient for calculating an estimate of the associ-ation between at least 1 of the social factors ofinterest and adult all-cause mortality, usingunweighted counts, RR estimates, regressioncoefficients, or mortality rates. For studies thatconcerned multiple levels of analysis, we in-cluded only those that appropriately usedmultilevel analytic methods. Figure 1 summa-rizes the studies that we considered, included,and excluded. We excluded articles presentingresults from studies conducted outside of theUnited States, those limited to participants witha history of disease or using composite mea-sures of socioeconomic status, and those thatdid not use adult all-cause mortality as anoutcome measure. We also excluded reviewarticles, articles providing insufficient data tocalculate RR estimates, and articles presentingdata only for proxy measures of the socialfactors of interest. This left a total of 120eligible studies.

Further criteria for inclusion in the meta-analyses included the presentation of SE orother variance estimates to allow calculationof an approximate 95% confidence interval(CI) for a dichotomous contrast in the socialfactor of interest (e.g., low vs high educational

attainment). Additionally, RR estimates unad-justed for potential mediators of the relationbetween the social factor and mortality weredesired; however, this requirement was relaxedfor estimates of the effect of area-level socialfactors since nearly all estimates in the litera-ture were adjusted. Finally, we decided a priorito limit meta-analyses to social factors forwhich at least 3 RR estimates from separatestudies were available.

We extracted RR estimates from theremaining 60 studies for the following factors:education, poverty, social support, area-levelpoverty, income inequality, and racial segre-gation. Because meta-analyses must be con-ducted on nonoverlapping samples,34 weexcluded an additional 13 articles because theyprovided only estimates for samples alreadyrepresented by other articles. When multiplearticles provided data for the same sample, weselected estimates incorporating the largest sam-ple size, the longest duration of follow-up, andthe fewest restrictions on the sample in terms of

age group or gender; additionally, we preferredestimates incorporating person---time data fromlongitudinal studies. The final 47 studies used inthe meta-analyses are summarized in Table 1.

From each of the 47 articles, we extractedunadjusted RR estimates if provided; other-wise, we calculated RR estimates using un-weighted or weighted counts, regression co-efficients, or mortality rates according tostandard methods.34 The cutpoints used fordichotomous contrasts for each social factor,which are summarized in Table 2, were basedon definitions most commonly used in the in-cluded studies and the literature on these socialfactors more generally.

When possible, we extracted age-specificestimates for 2 broad age groups, those aged25 to 64 years at baseline and those aged65 years or older at baseline. Although someevidence suggests that the relation between thesocial factors of interest and mortality de-creases with age,19 most deaths occur amongolder individuals. Altogether, we extracted 68

FIGURE 1—Flow diagram of studies considered for meta-analyses to derive summary relative

risk (RR) estimates for each social factor in relation to mortality.


e2 | Research and Practice | Peer Reviewed | Galea et al. American Journal of Public Health | Published online ahead of print June 16, 2011

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estimates from the 47 articles, as summarized inFigure 1. We calculated summary statistics foreach social factor using Comprehensive Meta-Analysis version 2 (Biostat, Englewood, NJ). Weused random-effects models for all summaryestimates, taking into account unmeasured het-erogeneity in effect estimates across studies andallowing greater weight to be given to studiesconducted on smaller samples than when usingfixed-effects models.34

Prevalence Estimates and Mortality Data

Estimates of the prevalence of each socialfactor in the US adult population (aged ‡25years) were obtained from the 2000 US Cen-sus,75,77 except for the prevalence of low socialsupport, which was obtained from the ThirdNational Health and Nutrition Examination Sur-vey (NHANES III).76 To derive prevalence esti-mates, we used cutpoints as similar as possible tothose used when calculating dichotomous con-trasts for each of the included studies. Table 2summarizes the definitions and sources of dataused to obtain prevalence estimates for each

social factor, and prevalence estimates for eachfactor are presented in Table 3.

We obtained the total number of deaths in2000 from all causes by age group from theNational Vital Statistics Report.81 Because theaverage duration of follow-up for studies pro-viding RR estimates for samples aged 25 to 64years at baseline was 10 years, we includeddeaths among persons aged 25 to 74 years forthis age group, which was similar to the methodused by Hahn et al.30

Calculation of Population Attributable

Fraction and Sensitivity Analyses

We calculated the population-attributablefraction (PAF ) for each social factor using thefollowing formula:

ð1Þ PAF ¼ pðRR � 1ÞpðRR � 1Þ þ 1

;

where RR is the summary RR estimate formortality derived from the meta-analyses de-scribed and p is the prevalence of the socialfactor in the US population in 2000. The

population-attributable fraction represents theproportion of all deaths that can be attributedto the social factor (i.e., the proportion of alldeaths that would not have occurred in theabsence of the social factor).19,82---84 The popu-lation-attributable fraction was then multipliedby the total number of deaths in the relevant agegroup to arrive at the number of deaths attrib-utable to the social factor in that age group.

We conducted sensitivity analyses to assessthe robustness of the summary RR estimate foreach social factor using alternate cut points ormultiple categories (e.g., tertiles) rather thana dichotomous contrast in exposure levels.

RESULTS

Table 3 provides the summary RR estimatesand corresponding 95% CIs derived frommeta-analyses of the relations between eachsocial factor and adult all-cause mortality. RRsfor mortality associated with low education andpoverty were higher for individuals aged 25to 64 years than they were for those aged 65

TABLE 2—Definitions Used to Calculate Dichotomous Contrasts for the Association Between Each Social Factor and Adult

All-Cause Mortality and Prevalence Estimates for Each Social Factor: United States, 2000

Definition for RR Estimates From Studies

Prevalence Estimates Used in PAF Calculationsa

Social Factor Definition Source

Low education < high school vs ‡ high school

diploma or equivalent

% of adult population with

< high school education

US Census Bureau Summary File 375

Poverty Annual household income of

< $10 000 vs ‡ $10 000b

% of adult population living below the

poverty level


Low social support ‘‘Low’’ vs ‘‘high’’ score on a social

network indexc

% of adult population with score of 0 or

1 on social network indexc

National Health and Nutrition Examination

Survey III76

Area-level poverty ‡ 20% of population living below

poverty level vs < 20% living below

poverty leveld

% of adult population living in counties

with ‡ 20% of population living

below the poverty level


Income inequality Gini coefficient 1 SD above the mean

vs the mean value


with Gini coefficient at or above the

25th percentilee

US Census Bureau, derived from household

income data75

Racial segregation % Black 1 SD above the mean vs

the mean value


with ‡ 25% of population reporting

their race/ethnicity as non-Hispanic Black


Note. PAF = population attributable fraction; RR = relative risk.aAdult population was defined as those aged ‡ 25 years.b$10 000 roughly corresponded to the poverty threshold for a family of 4 in the early to mid-1980s,78 when many of the included studies were conducted.cSocial network indices in most included studies were based on that developed by Berkman and Syme79; low scores indicated few social ties. The social network index included in NHANES III, from which theprevalence estimate was derived, ranged from 0 to 4, and included indicators of marriage or partnership, contact with friends and relatives, frequency of church or religious service attendance, andparticipation in voluntary organizations.76

d20% or more of population living below the poverty level corresponds to the criteria for a ‘‘poverty area’’ put forth by the US Census Bureau.80

eA Gini coefficient in the top 25th percentile (0.459) represents areas with the highest levels of income inequality in the United States.



years or older; for example, the RR was 1.75(95% CI=1.51, 2.04) for poor versus nonpoorindividuals aged 25 to 64 years, but the RR was1.40 (95% CI=1.37, 1.43) for poor versus non-poor individuals aged 65 years or older. Adverselevels for all social factors considered wereassociated with increased mortality, although theRR of mortality for low education among thoseaged 65 years or older was not statisticallysignificant (RR=1.23; 95% CI=0.86, 1.76).

Table 3 also shows the population-attribut-able fraction for each social factor. Thesefractions ranged from 1.7% for area-level pov-erty to 11.5% for less than a high schooleducation among those aged 25 to 64 years.From these population-attributable fractionestimates and mortality data, we estimated thatapproximately 245000 deaths in the United

States in 2000 were attributable to low edu-cation, 133000 to poverty, 162000 to lowsocial support, 39000 to area-level poverty,119000 to income inequality, and 176000 toracial segregation.

Our sensitivity analyses showed that thesummary RR estimates varied in magnitudebut not direction depending on the choice ofcutpoints and categories for the exposure.Among those aged 25 to 64 years, summaryRR estimates for the relation between loweducation and mortality ranged from 1.17 to2.45, those for poverty ranged from 1.20 to2.39, and those for low social support rangedfrom 1.08 to 1.54. For area-level poverty,summary RR estimates ranged from 1.10 to1.15, those for income inequality ranged from1.14 to 1.32, and those for racial segregation

ranged from 1.47 to 2.54. Complete results ofthe sensitivity analyses are available from theauthors upon request.

DISCUSSION

We found that in 2000, approximately245000 deaths in the United States wereattributable to low education, 176000 to racialsegregation, 162000 to low social support,133000 to individual-level poverty,119000 toincome inequality, and 39000 to area-levelpoverty. These mortality estimates are compa-rable to deaths from the leading pathophysio-logical causes. For example, the number ofdeaths we calculated as attributable to loweducation is comparable to the number causedby acute myocardial infarction (192898), a sub-set of heart disease, which was the leading causeof death in the United States in 2000.81 Thenumber of deaths attributable to racial segrega-tion is comparable to the number from cerebro-vascular disease (167661), the third leadingcause of death in 2000, and the number attrib-utable to low social support is comparable todeaths from lung cancer (155521).

Our estimates of the number of deathsattributable to social factors can be looselycompared with previous estimates, althoughour approach differs methodologically fromprior efforts. Woolf et al. reported that anaverage of 196000 deaths would have beenavoided each year from 1996 to 2002 if alladults in the United States had had a collegeeducation, compared with our estimate of245000 deaths attributable to having less thana high school education in 2000.29 Our num-bers are higher because we included deathsamong those aged 65 years or older, whereasWoolf et al. included deaths only among in-dividuals aged 25 to 64 years. Hahn et al.estimated that 6% of deaths in the United Statesin 1991 could be attributed to poverty, corre-sponding to 91000 deaths among those aged 25years or older,30 whereas Muennig et al. esti-mated that 2.3% of deaths in the United Statesin 2000 could be attributed to poverty, corre-sponding to 54000 deaths.31 Although ourestimated population-attributable fraction formortality attributable to poverty was 4.5%,roughly between these 2 previous estimates, ourestimate of the number of deaths attributable topoverty (133000) was higher than the estimate

TABLE 3—Calculation of the Number of US Deaths in 2000 Attributable to Each

Social Factor

Social Factor and

Age Group RR (95% CI)a Prevalence, %b PAF, %c Total Deaths,d No.

Deaths Attributable to

Social Factor,e No.

Individual-level factors

Low education

‡ 25 y 244 526

25–64 y 1.81 (1.64, 2.00) 16.1 11.5 972 645 112 209

‡ 65 y 1.23 (0.86, 1.76) 34.5 7.4 1 799 825 132 317

Poverty

‡ 25 y 133 250

25–64 y 1.75 (1.51, 2.04) 9.5 6.7 972 645 64 692

‡ 65 y 1.40 (1.37, 1.43) 9.9 3.8 1 799 825 68 558

Low social support

‡ 25 y 161 522

25–64 y 1.34 (1.23, 1.47) 21.0 6.7 972 645 64 819

‡ 65 y 1.34 (1.16, 1.55) 16.7 5.4 1 799 825 96 703

Area-level factorsf

Area-level poverty 1.22 (1.17, 1.28) 7.8 1.7 2 331 261 39 330

Income inequality 1.17 (1.06, 1.29) 31.7 5.1 2 331 261 119 208

Racial segregation 1.59 (1.31, 1.94) 13.8 7.5 2 331 261 175 520

Note. CI = confidence interval; PAF = population attributable fraction; RR = relative risk.aSummary relative risk estimates derived from meta-analyses as described in Methods.bPrevalence of each social factor in the US population of the relevant age, according to 2000 US Census data, except lowsocial support, according to data from the Third National Health and Nutrition Examination Survey, as reported in Ford et al.,2006.76

cPAF = ([p(RR–1)] / [p(RR–1) + 1])· 100, where p is the prevalence expressed as a proportion and RR is the relative risk estimate forthe group of interest.dTotal number of deaths in each age group in 2000, from Minino et al., 2002.81 For low education, poverty, and low social support,deaths for the younger age group include deaths for those aged 25–74 to account for an average of 10 of follow-up time in studiesused to calculate the summary relative risk estimate.eDeaths attributable to social factor = (PAF/100)· total deaths in 2000. Numbers reflect the use of a nonrounded populationattributable fraction in calculations.fAge group for all area-level factors was ‡ 25 y.



by Hahn et al. This higher estimate is partlybecause of differences in age stratification andthe use of deaths among all races rather thanthose among Whites and Blacks only,30 andpartly because we estimated deaths for a laterperiod in which there was a greater number ofdeaths overall. By contrast, our estimates of thepopulation-attributable fraction for mortality as-sociated with area-level poverty (1.7%) and withincome inequality (5.1%) are not directly com-parable to those reported in previous studies,which looked at excess mortality in neighbor-hoods with medium and low levels of socioeco-nomic status (encompassing a broader array offactors, including educational level and medianhousing value)33 and at the percentage of mor-tality in areas with high levels of income in-equality that could be attributed to that incomeinequality.32

Several issues should be considered wheninterpreting these findings. Limited availabilityof data from US samples prevented us fromconsidering some social factors and, in somecases, forced us to base our RR estimates onsmall numbers of studies. Previous analyses1,2

also relied on small numbers of studies in somecases to derive their attributable risk estimates;we suggest that our approach of conductinga systematic meta-analysis allowed us to capturethe relations as accurately as possible. The 6social factors considered are highly interrelated;thus, deaths attributed to each factor are notnecessarily mutually exclusive. In addition, in theabsence of stratum-specific estimates we couldnot assess possible heterogeneity in effects, andwhen only adjusted RR estimates were availablewe likely underestimated the true number ofdeaths because the RR estimates on which webased our calculations were derived by controllingfor mediating variables rather than confounders.Our methods assume that the relations betweensocial factors and mortality that were estimated inthe 1980s and 1990s, when most of the includedstudies were conducted, still applied in 2000,the year used for our prevalence and mortalitydata. Although subgroup analyses we conductedsuggested no differences in the relation betweeneach social factor and mortality in differenttime periods, others have suggested that dispar-ities in mortality by socioeconomic status havebeen increasing during the past few decades.85

Our meta-analytic results are only as validand reliable as the studies upon which they are

based. Although many of the RR estimates usedin the meta-analyses were derived from na-tional samples, some were conducted in specificpopulations or areas of the country. Thesesamples may not reflect the target population––specifically, the adult US population––used tocalculate the number of attributable deaths.The measures and definitions used to operation-alize the social factors of interest were not alwaysconsistent across studies, although sensitivityanalyses suggested no substantial differences inRR estimates when using alternate cutpoints.Additionally, the approach used to calculate thepopulation attributable fraction is not strictly validin the presence of confounding or effect modifi-cation, although it may provide reasonably accu-rate results in practice despite methodologicallimitations.83,84 We used unadjusted estimatesand stratified by covariates whenever possiblebut were restricted to adjusted estimates for thearea-level social factors considered.

The extent of the potential bias in ourestimates depends on whether there is residualconfounding present in the adjusted estimatesand the degree of effect measure modifica-tion.86 Although the bias in the presence ofconfounding alone may be predictable––incom-plete control of positive confounding leads to anoverestimate of the RR that translates into anoverestimate of the attributable numbers ofdeaths––it is difficult to predict the direction ofbias when there is heterogeneity in the RRestimates.83 This difficulty reflects a methodo-logical limitation inherent in using adjusted RRestimates to derive population attributable frac-tion estimates: residual confounding and effectheterogeneity will affect summary estimates ofthe RRs and lead to bias in the population-attributable fraction estimates. Ultimately, how-ever, this concern applies to all studies that reston meta-analytic techniques, including the onesthat we build on in conducting this work. Oneconclusion that can be drawn from our work isthat individual study results may not be usefulfor synthetic analyses such as ours unless thesestudies provide detailed data summaries andsubgroup estimates in addition to the final,multiply adjusted estimates.

Finally, we limited our analysis to structuralsocial factors that are largely features of individualexperiences or group context. We did not con-sider stress processes that may explain the linkbetween social factors and mortality. In many

ways, stress processes may be considered a medi-ator of some of the factors we study, so weaccounted for them. However, stress processesmay also mediate the relation between other‘‘nonsocial’’ factors and mortality, so we mighthave underestimated the contribution of socialfactors to mortality in the United States. Thisanalysis suggests that, within a multifactorialframework, social causes can be linked to deathas readily as can pathophysiological and behav-ioral causes. All of these factors contribute sub-stantially to the burden of disease in the UnitedStates, and all need focused research efforts andpublic health efforts to mitigate their conse-quences. j

About the AuthorsAt the time of this study, Sandro Galea and Melissa Tracywere with the Department of Epidemiology, University ofMichigan, Ann Arbor, and the Department of Epidemiol-ogy, Mailman School of Public Health, Columbia Univer-sity, New York, NY. Katherine J. Hoggatt was with theDepartment of Epidemiology, University of Michigan, AnnArbor. Charles DiMaggio is with the Department ofEpidemiology, Mailman School of Public Health, ColumbiaUniversity. Adam Karpati is with the Department of Healthand Mental Hygiene, New York, NY.

Correspondence should be sent to Sandro Galea, MD,DrPH, Department of Epidemiology, Columbia UniversityMailman School of Public Health, 722 168th St, Room1508, New York, NY 10032-3727 (e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the ‘‘Reprints/Eprints’’ link.

This article was accepted November 19, 2010.

ContributorsS. Galea wrote the first draft of the article, superviseddata analysis, and serves as study guarantor. M. Tracywas responsible for data collection and analysis, withinput from K. J. Hoggatt. C. DiMaggio contributed to themeta-analytic methods. S. Galea and A. Karpati concep-tualized the study and its design. All authors providedcritical edits on drafts of the article and approved thefinal version.

AcknowledgmentsThis study was funded in part by the National Institutes ofHealth (grants DA 022720, DA 022720-S1, DA017642, MH 082729, and MH 078152) to S. Galea.

We thank Brian Gluck and Aditi Sagdeo for their helpwith data collection and Jenna Johnson for researchassistance.

Human Participant ProtectionNo protocol approval was necessary because there wereno human participants directly engaged in this work.

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