A multilevel analysis of race, community disadvantage, and body mass index among adults in the US.
ABSTRACT This study examined the contributions of both individual socioeconomic status (SES) and community disadvantage in explaining the higher body mass index (BMI) of black adults in the US. Data from a national survey of adults (1986 American's Changing Lives Study) were combined with tract-level community data from the 1980 census. Results of multilevel regression analyses showed that black women had an age-adjusted BMI score three points higher than non-black women. Individual SES (income, education, assets) was negatively associated with BMI in women, but it only reduced the association between race and BMI from 2.99 to 2.50. Adding community socioeconomic disadvantage index further reduced the race coefficient slightly from 2.50 to 2.21. Nevertheless, living in communities with higher socioeconomic disadvantage was associated with higher BMI net of age, race, individual SES, smoking, physical activity, stress, and social support. Community income inequality (Gini) had an independent positive association with BMI, but did not substantially reduce racial differences among women. Community percent black was not associated with BMI. Results for men demonstrated no statistically significant racial differences in BMI, and no association between BMI and either individual SES or community disadvantage. Although individual SES and community socioeconomic disadvantage each partly explained the higher average BMI among black women, clear racial disparities persisted. Moreover, race, individual SES, community socioeconomic disadvantage, and individual health behaviors were each independent predictors of BMI among women. Unexplained within- and between-community variance in BMI remained among both women and men, with most unexplained variation due to within-community variance. Because our evidence for women suggests that the determinants of obesity are multiple and multilevel, attempts to address this growing social problem will similarly require a multi-faceted and multilevel approach.
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ABSTRACT: We employ multi-level statistical methods to 1993-2002 Behavioral Risk Factor Surveillance Survey, 1990 Census, and other data to estimate how neighborhood and metropolitan-level economic, physical and social factors influenced weight among non-elderly adults residing in the United States controlling for individual characteristics and behaviors. Several individual-level characteristics are estimated to have been positively (non-Latino black, Latino, low income, age) and negatively (married, employed, female, Asian, college graduate) associated with height-adjusted weight; and ethno-racial and economic segregation and the density of fast-food restaurants positively, and the price of fast-food negatively, influenced weight – even after controlling for diet, exercise, and smoking. These three behaviors explain more than 15% of the variation in weight, but were also influenced by several area-level factors. Thus, while individual characteristics and behaviors influenced body weight, area-level factors both directly (physiologically) and indirectly (psychologically) did also. We conclude by discussing how housing, labor market, and food and drug administration policies may counter the obesity epidemic in the United States in light of our empirical results.
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ABSTRACT: We use data from the 1980 to 2004 waves of the National Longitudinal Survey of Youth, 1979 Cohort to examine the association between the ethnic density of metropolitan areas and obesity among U.S. blacks and Latinos. Although minority groups’ obesity rates tend to be higher in metropolitan areas containing many co-ethnics, controlling for other areal characteristics and unobserved time-constant confounders via fixed-effects models dramatically alters this association. In the fixed-effects models, higher levels of co-ethnic density are inversely associated with black males’ obesity risk and unrelated to the obesity risk of black females, Latinas, and Latino males. For most groups, marrying and having children increases the risk of obesity.Health & Place 01/2015; 31. DOI:10.1016/j.healthplace.2014.12.006 · 2.44 Impact Factor
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ABSTRACT: Understanding mechanisms linking neighborhood context to health behaviors may provide targets for increasing lifestyle intervention effectiveness. Although associations between home neighborhood and obesogenic behaviors have been studied, less is known about the role of worksite neighborhood. To evaluate associations between worksite neighborhood context at baseline (2006) and change in obesogenic behaviors of adult employees at follow-up (2007-2009) in a worksite randomized trial to prevent weight gain. Worksite property values were used as an indicator of worksite neighborhood SES (NSES). Worksite neighborhood built environment attributes associated with walkability were evaluated as explanatory factors in relationships among worksite NSES, diet, and physical activity behaviors of employees. Behavioral data were collected at baseline (2005-2007) and follow-up (2007-2009). Multilevel linear and logistic models were constructed adjusting for covariates and accounting for clustering within worksites. Product-of-coefficients methods were used to assess mediation. Analyses were performed after study completion (2011-2012). Higher worksite NSES was associated with more walking (OR=1.16, 95% CI=1.03, 1.30, p=0.01). Higher density of residential units surrounding worksites was associated with more walking and eating five or more daily servings of fruits and vegetables, independent of worksite NSES. Residential density partially explained relationships among worksite NSES, fruit and vegetable consumption, and walking. Worksite neighborhood context may influence employees' obesogenic behaviors. Furthermore, residential density around worksites could be an indicator of access to dietary and physical activity-related infrastructure in urban areas. This may be important given the popularity of worksites as venues for obesity prevention efforts. Published by Elsevier Inc.American Journal of Preventive Medicine 11/2014; 48(1). DOI:10.1016/j.amepre.2014.08.025 · 4.28 Impact Factor