Social Epidemiology: Social Determinants of Health in the United States: Are We Losing Ground?

Harvard Center for Population and Development Studies, Harvard School of Public Health, USA.
Annual Review of Public Health (Impact Factor: 6.47). 05/2009; 30(1):27-41. DOI: 10.1146/annurev.publhealth.031308.100310
Source: PubMed


The United States ranks in the lower tiers of OECD countries in life expectancy, and recent studies indicate that socioeconomic inequalities in health have been widening in the past decades. Over this period, many rigorous longitudinal studies have identified important social, behavioral, and environmental conditions that might reduce health disparities if we could design effective interventions and make specific policy changes to modify them. Often, however, neither our policy changes nor our interventions are as effective as we hoped they would be on the basis of findings from observational studies. Reviewed here are issues related to causal inference and potential explanations for the discrepancy between observational and experimental studies. We conclude that more attention needs to be devoted to (a) identifying the correct etiologic period within a life-course perspective and (b) understanding the dynamic interplay between interventions and the social, economic, and environmental contexts in which interventions are delivered.

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    • "The need for theoretical models describing the relationship of researcher identities within communities is made clearer with an understanding of social determinants and power structures long identified by social epidemiology, i.e. poverty, status hierarchies, racism, and corporate-industrial policies, among others (Berkman, 2009; Krieger, 2004; Marmot, 2009; Navarro, 2009; Phelan et al., 2010; Wallerstein et al., 2011; Williams, 2012). Post-colonial theory has added explanatory models for communities of color, the particular histories of genocide, forced migrations, appropriation of lands, and attempted assimilations of indigenous cultures and languages (Duran and Duran, 1995; King et al., 2009); as well as the more hidden micro-aggressions (Walters et al., 2009) and hegemonic discourse that reinforces internalized oppression (Gaventa and Cornwall, 2008). "

    Critical Sociology 01/2014; 41(7-8). DOI:10.1177/0896920513516025
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    • "It may also confer beneficial social connections leading to a quality job that offers health insurance. The quality job may also improve socio-economic status, a risk factor that is also associated with improved biomarker profiles (Alley et al., 2006; Berkman, 2009; Cohen et al., 1997; Davey Smith, Harbord, & Ebrahim, 2004; Ernst & Resch, 1993; Kawachi & Berkman, 2001; Markowe et al., 1985; Muennig, Sohler, & Mahato, 2007; Pradhan, Manson, Rifai, Buring, & Ridker, 2001; Ridker, Hennekens, Buring, & Rifai, 2000). Together, these small impacts may produce add up to large changes in some biomarker profiles, or may add up to small changes in many biomarker profiles. "
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    ABSTRACT: Higher social capital is associated with improved mental and physical health and reduced risk of premature mortality. We explored the relationship between five measures of structural social capital and 1) intermediate health outcomes (elevated C-reactive protein, cholesterol, blood pressure, and serum fibrinogen) and 2) distal outcomes (cardiovascular and all cause mortality). We did so using the National Health and Nutrition Examination Survey III 1988-1994 linked to the National Death Index with mortality follow-up through 2006. We employed ordinary least squares regression for the intermediate outcomes, seemingly unrelated regression (SUR) to consider combined effects, and Cox proportionate hazards models for mortality outcomes. We then performed extensive sensitivity analyses, exploring the contribution of various variables and reverse causality. We find that our measures of social capital did not predict statistically significant changes in the laboratory biomarkers we study. Nevertheless, belonging to organizations or attending church >12 times per year were associated with reduced all cause mortality (hazard ratio [HR] = 0.81, 95% confidence interval [CI] = 0.70-0.93 and HR = 0.72, 95% CI = 0.60-0.86, respectively). In SUR analyses, however, combined laboratory values were significant for all measures of social capital we study with the exception of visits to neighbors. This suggests that some forms of structural social capital improve survival through small changes in multiple measures of biological risk factors rather than moderate or large changes in any one measure.
    Social Science [?] Medicine 05/2013; 85:18-26. DOI:10.1016/j.socscimed.2013.02.007 · 2.89 Impact Factor
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    • "The " accumulation of risk " conceptual model posits that the accumulated socioeconomic disadvantage (or advantage) through the life-course, rather than SES at any particular age, is associated with adult health outcomes. And, the " social mobility " conceptual model posits that stability or mobility across SES levels through the life-course is associated with adult health outcomes (Berkman, 2009; Cohen, Janicki-Deverts, Chen, & Matthews, 2010; Kuh, Ben- Shlomo, Lynch, Hallqvist, & Power, 2003; Loucks et al., 2010; Pollitt et al., 2005; Shavers, 2007). Most researchers assessing the association of SES and adult obesity, however, utilize only adult SES indicators, and often measured once (McLaren, 2007; Monteiro et al., 2004). "
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    ABSTRACT: Objectives: To elucidate the association between life-course socioeconomic status (SES) and obesity among older (aged 60 and older) Singaporean Chinese men and women. Methods: Data from the Social Isolation, Health and Lifestyles Survey (single-stage stratified random sampling design) was utilized. Obesity (body mass index >27.4 kg/m(2)) was assessed for 1,530 men and 2,036 women. Childhood (family financial status while growing up), adult (education), and older adult (housing type) SES indicators were used to define the accumulation of risk (cumulative socioeconomic disadvantage), social mobility (8 trajectories using the 3 SES indicators), and sensitive period (independent effect of each SES indicator) conceptual models. Association between the 3 life-course SES conceptual models and obesity was assessed using logistic regression analysis. Results: Among women and men, low childhood SES lowered the odds of obesity. Low adult SES increased the odds of obesity only among women. There was no association between cumulative socioeconomic disadvantage and obesity. Women experiencing upward social mobility had lower odds of obesity relative to both those experiencing low SES and high SES through the life-course. Discussion: Association of the life-course SES conceptual models with obesity among older Singaporeans is different from that reported among younger Western populations, suggesting the association to be context specific. The different conceptual models complement each other.
    The Journals of Gerontology Series B Psychological Sciences and Social Sciences 11/2012; 68(1). DOI:10.1093/geronb/gbs102 · 3.21 Impact Factor
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