Inequality in the Built Environment Underlies Key Health Disparities in Physical Activity and Obesity

Department of Nutrition, University of North Carolina at Chapel Hill, North Carolina, United States
PEDIATRICS (Impact Factor: 5.47). 02/2006; 117(2):417-24. DOI: 10.1542/peds.2005-0058
Source: PubMed


Environmental factors are suggested to play a major role in physical activity (PA) and other obesity-related behaviors, yet there is no national research on the relationship between disparity in access to recreational facilities and additional impact on PA and overweight patterns in US adolescents.
In a nationally representative cohort, we sought to assess the geographic and social distribution of PA facilities and how disparity in access might underlie population-level PA and overweight patterns.
Residential locations of US adolescents in wave I (1994-1995) of the National Longitudinal Study of Adolescent Health (N = 20745) were geocoded, and a 8.05-km buffer around each residence was drawn (N = 42857 census-block groups [19% of US block groups]). PA facilities, measured by national databases and satellite data, were linked with Geographic Information Systems technology to each respondent. Logistic-regression analyses tested the relationship of PA-related facilities with block-group socioeconomic status (SES) (at the community level) and the subsequent association of facilities with overweight and PA (at the individual level), controlling for population density.
Outcome measures were overweight (BMI > or = 95th percentile of the Centers for Disease Control and Prevention/National Center for Health Statistics growth curves) and achievement of > or = 5 bouts per week of moderate-vigorous PA.
Higher-SES block groups had a significantly greater relative odds of having 1 or more facilities. Low-SES and high-minority block groups were less likely to have facilities. Relative to zero facilities per block group, an increasing number of facilities was associated with decreased overweight and increased relative odds of achieving > or = 5 bouts per week of moderate-vigorous PA.
Lower-SES and high-minority block groups had reduced access to facilities, which in turn was associated with decreased PA and increased overweight. Inequality in availability of PA facilities may contribute to ethnic and SES disparities in PA and overweight patterns.

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Available from: Barry M Popkin, Jan 27, 2015
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    • "In response, researchers and policy makers have intervened on aspects of the neighborhood built environment to improve diet and increase physical activity (Sallis and Glanz, 2009; Rahman et al., 2011). Associations with BMI have been detected for a wide range of neighborhood-level exposures including food access (Morland et al., 2002; Spence et al., 2009; Galvez et al., 2009), walkability (Cohen et al., 2007; Gordon-Larsen et al., 2006), and neighborhood socio-economic status (Gordon-Larsen et al., 2006; Everson et al., 2002; Gordon-Larsen et al., 2003), but the evidence for these associations remains inconsistent (Caspi et al., 2012; Ding and Gebel, 2012). Despite the high prevalence of obesity in Hispanic populations and evidence of racial disparities in the association between built environmental factors and obesity outcomes (Duncan et al., 2012; Duncan et al., 2014a), there have been relatively few studies that investigate built environment influences among Hispanic youth. "
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    ABSTRACT: Objective: The purpose of this cross-sectional study was to establish neighborhood built environment correlates of adiposity as measured by dual x-ray absorptiometry (DXA). The utility and methodological gains of using this measure for built environment research was further investigated by comparing model fit across parallel models on BMI z-scores and waist circumference. Methods: Pre-existing data collected from 2001-2001 on 576 overweight and obese Hispanic youth were compiled with built environment data, and 2000 Census data for analyses conducted in 2012. Walking-distance buffers were built around participants' residential locations. Variables for park space, food access, walkability, and neighborhood socio-cultural aspects were entered into a multivariate regression model predicting percent body fat. Parallel models were built for BMI z-score, and waist circumference. Results: Significant associations were found between percent body fat and supermarket access for boys, and percent body fat and increased park space and decreased neighborhood linguistic isolation for girls. Neighborhood socio-cultural characteristics accounted for more variance in obesity compared to BMI z-score or waist circumference. Conclusion: Park access, food environment, and neighborhood socio-cultural characteristics are independent contributors to body fat in children, and the contribution of these risks differs by gender. There are incremental gains to using a more accurate measure of body fat in built environment obesity studies.
    05/2015; 2. DOI:10.1016/j.pmedr.2015.05.005
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    • "To date, the underlying factors for these disparities within the BBR remain poorly understood. Environmental factors play a key role in obesity-related behaviours (Gordon-Larsen et al. 2006) and children's weight. The vulnerability to environmental factors increases obesity risks of rural African American children through community and school environments (Grow et al. 2010). "
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    ABSTRACT: Background: Childhood obesity has been rising rapidly in the USA. The rate is higher among those at a lower socioeconomic status and racial/ethnic minority groups. In Alabama, nearly half of the children from rural African American families are overweight or obese. Studies suggest that children's eating behaviours and weight could be influenced by surrounding food environments. The purpose of this paper is to assess the community food environment and examine the associations with childhood obesity in Alabama's Black Belt region. Methods: This research uses both qualitative and quantitative methods. Weight status of 613 African American students in four elementary schools in a rural county of Alabama was assessed. We examined community food environments around children's home through GIS (Geographic Information System) and statistical methods. The interrelations between children's weight and community food environments are explored with multi-level models. Results: Approximately 42.1% of surveyed children were overweight or obese, much higher than the national average, 30.6%. In Model 1, convenience stores (3.44; P < 0.01), full service restaurants (8.99; P < 0.01) and supermarkets (-37.69; P < 0.01) were significantly associated with the percentile of body mass index. Fast food stores (-0.93; P = 0.88) were not related to children's weight. In Model 2, the additions of sociodemographic factors and school effects cause significant changes of the relationships between children's weight and four types of food outlets. The percentage of African American population (90.23, P < 0.01) and school (6.68, P < 0.01) were positively associated with children's weight; while median household income (-39.6; P < 0.01) was negatively related to it. Conclusion: Children's weight is influenced by community food environments, sociodemographic factors and school context. Findings suggest that policymakers and planners need to improve community food environments of low-income minority communities. Parents and schools should pay more attention to reduce the negative impacts of food environments on children.
    Child Care Health and Development 10/2014; 41(5). DOI:10.1111/cch.12204 · 1.69 Impact Factor
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    • "There were seven articles reviewed, of which five of the articles [52,54-57] identified issues of spatial inequality for cultural minorities at the neighbourhood level in accessing physician health care. The studies incorporated the Two Step Floating Catchment Area method along with other methods, such as questionnaire surveys or probability studies. "
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    ABSTRACT: The purpose of this paper is to review the current research on catchment areas of private general practices in different developed countries because healthcare reform, including primary health care, has featured prominently as an important political issue in a number of developed countries. The debates around health reform have had a significant health geographic focus. Conceptually, GP catchments describe the distribution, composition and profile of patients who access a general practitioner or a general practice (i.e. a site or facility comprising one or more general practitioners). Therefore, GP catchments provide important information into the geographic variation of access rates, utilisation of services and health outcomes by all of the population or different population groups in a defined area or aggregated area. This review highlights a wide range of diversity in the literature as to how GP catchments can be described, the indicators and measures used to frame the scale of catchments. Patient access to general practice health care services should be considered from a range of locational concepts, and not necessarily constrained by their place of residence. An analysis of catchment patterns of general practitioners should be considered as dynamic and multi-perspective. Geographic information systems provide opportunities to contribute valuable methodologies to study these relationships. However, researchers acknowledge that a conceptual framework for the analysis of GP catchments requires access to real world data. Recent studies have shown promising developments in the use of real world data, especially from studies in the UK. Understanding the catchment profiles of individual GP surgeries is important if governments are serious about patient choice being a key part of proposed primary health reforms. Future health planning should incorporate models of GP catchments as planning tools, at the micro level as well as the macro level, to assist policies on the allocation of resources so that opportunities for good health outcomes for all groups within society, especially those who have been systematically denied equitable access, are maximised.
    International Journal of Health Geographics 08/2014; 13(1):32. DOI:10.1186/1476-072X-13-32 · 2.62 Impact Factor
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