Article

Socioeconomic Disparities In Health: Pathways And Policies

Harvard University, Cambridge, Massachusetts, United States
Health Affairs (Impact Factor: 4.97). 03/2002; 21(2):60-76. DOI: 10.1377/hlthaff.21.2.60
Source: PubMed

ABSTRACT

Socioeconomic status (SES) underlies three major determinants of health: health care, environmental exposure, and health behavior. In addition, chronic stress associated with lower SES may also increase morbidity and mortality. Reducing SES disparities in health will require policy initiatives addressing the components of socioeconomic status (income, education, and occupation) as well as the pathways by which these affect health. Lessons for U.S. policy approaches are taken from the Acheson Commission in England, which was charged with reducing health disparities in that country.

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    • "This could reduce the probability to receive a medical diagnosis or treatment despite functional limitations. Less education, as in our study, could be an underlying determinant and act as a barrier in health care utilization in this subgroup[38]. More research is needed to explain and verify this finding . "
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    ABSTRACT: Public health monitoring depends on valid health and disability estimates in the population 65+ years. This is hampered by high non-participation rates in this age group. There is limited insight into size and direction of potential baseline selection bias. We analyzed baseline non-participation in a register-based random sample of 1481 inner-city residents 65+ years, invited to a health examination survey according to demographics available for the entire sample, self-report information as available and reasons for non-participation. One year after recruitment, non-responders were revisited to assess their reasons. Five groups defined by participation status were differentiated: participants (N = 299), persons who had died or moved (N = 173), those who declined participation, but answered a short questionnaire (N = 384), those who declined participation and the short questionnaire (N = 324), and non-responders (N = 301). The results confirm substantial baseline selection bias with significant underrepresentation of persons 85+ years, persons in residential care or from disadvantaged neighborhoods, with lower education, foreign citizenship, or lower health-related quality of life. Finally, reasons for non-participation could be identified for 78 % of all non-participants, including 183 non-responders. A diversity in health problems and barriers to participation exists among non-participants. Innovative study designs are needed for public health monitoring in aging populations.
    Full-text · Article · Dec 2016 · BMC Geriatrics
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    • "Finally, multiple authors have suggested that Gene × SES interactions may differ in strength across different populations or societies (Bates, Hansell, Martin, & Wright, 2015; Bates et al., 2013; Hanscombe et al., 2012; Tucker-Drob, Briley, & Harden, 2013; Turkheimer & Horn, 2014). Those supporting this type of explanation have pointed to higher social stratification in access to education (Hauser, 1970) and relatively modest social health (Adler & Newman, 2002) and social-welfare support (DeNavas-Walt & Proctor, 2014) in the United States compared with Australia and Western Europe. "
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    ABSTRACT: A core hypothesis in developmental theory predicts that genetic influences on intelligence and academic achievement are suppressed under conditions of socioeconomic privation and more fully realized under conditions of socioeconomic advantage: a Gene × Childhood Socioeconomic Status (SES) interaction. Tests of this hypothesis have produced apparently inconsistent results. We performed a meta-analysis of tests of Gene × SES interaction on intelligence and academic-achievement test scores, allowing for stratification by nation (United States vs. non-United States), and we conducted rigorous tests for publication bias and between-studies heterogeneity. In U.S. studies, we found clear support for moderately sized Gene × SES effects. In studies from Western Europe and Australia, where social policies ensure more uniform access to high-quality education and health care, Gene × SES effects were zero or reversed.
    Full-text · Article · Dec 2015 · Psychological Science
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    • "Specifically, the behav- 126 iors were binge drinking (i.e., consuming 5 or more alcoholic beverages at one 127 time in the last week), cigarette smoking in the past 30 days, use of other tobac- 128 co products such as chewing tobacco or snuff in the past 30 days, participating in 129 physical activity in the past 7 days, using marijuana in the past 30 days, visiting 130 the doctor and dentist for preventive care in the past year, and eating at fast food 131 restaurants 3 or more times in the past 7 days. All behaviors were dichotomized 132 such that a value of 1 represented the less healthy form (i.e., being a current cig- 133 arette smoker; not going to the doctor for a preventive visit; did not engage in 134 physical activity), whereas a value of 0 represented more positive behavior 135 (i.e., not a current cigarette smoker; went to the doctor during the past year; en- 136 gaged in physical activity).Cutler and Lleras-Muney, 2008;Caspi et al., 1997;Adler and Newman, 2002;141 Goodman, 1999;Starfield et al., 2002). Confounding variables were measured 142 using data from Waves I and IV. "
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    ABSTRACT: Objective: We documented health-related behavior clustering among US young adults and assessed the extent to which educational attainment was associated with the identified clusters. Methods: Using data from Wave IV of the National Longitudinal Study of Adolescent to Adult Health (Add Health), we performed latent class analysis on 8 health-related behaviors (n=14,338), documenting clustering of behavior separately by gender. Subsequently, we used multinomial logistic regression and estimated associations between educational attainment and the health-related behavior clusters. Results: Twenty-eight percent of young women grouped into the most favorable health behavior cluster, while 22% grouped into a very high-risk cluster. A larger percentage of young men (40%) grouped into the highest risk cluster. Individuals with educational attainment at the college and advanced degree levels exhibited much lower risk of being in the unhealthy behavioral clusters than individuals with lower educational attainment, net of a range of confounders. Conclusion: Substantial fractions of US young adults, particularly those with less than college degrees, exhibit unhealthy behavior profiles. Efforts to improve health among young adults should focus particular attention on the clustering of poor health-related behavior, especially among individuals who have less than a college degree.
    Full-text · Article · Dec 2015 · Preventive Medicine
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