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      Change in Obesity Prevalence across the United States Is Influenced by Recreational and Healthcare Contexts, Food Environments, and Hispanic Populations

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          Abstract

          Objective

          To examine change in county-level adult obesity prevalence between 2004 and 2009 and identify associated community characteristics.

          Methods

          Change in county-level adult (≥20 years) obesity prevalence was calculated for a 5-year period (2004–2009). Community measures of economic, healthcare, recreational, food environment, population structure, and education contexts were also calculated. Regression analysis was used to assess community characteristics associated (p<0.01) with change in adult obesity prevalence.

          Results

          Mean±SD change in obesity prevalence was 5.1±2.4%. Obesity prevalence decreased in 1.4% (n = 44) and increased in 98% (n = 3,060) of counties from 2004–2009. Results showed that both baseline levels and increases in physically inactive adults were associated with greater increases in obesity prevalence, while baseline levels of and increases in physician density and grocery store/supercenter density were related to smaller increases in obesity rates. Baseline levels of the Hispanic population share were negatively linked to changing obesity levels, while places with greater Hispanic population growth saw greater increases in obesity.

          Conclusions

          Most counties in the U.S. experienced increases in adult obesity prevalence from 2004 to 2009. Findings suggest that community-based interventions targeting adult obesity need to incorporate a range of community factors, such as levels of physical inactivity, access to physicians, availability of food outlets, and ethnic/racial population composition.

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          Most cited references18

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          Neighborhoods and obesity.

          This review critically summarizes the literature on neighborhood determinants of obesity and proposes a conceptual framework to guide future inquiry. Thirty-seven studies met all inclusion criteria and revealed that the influence of neighborhood-level factors appears mixed. Neighborhood-level measures of economic resources were associated with obesity in 15 studies, while the associations between neighborhood income inequality and racial composition with obesity were mixed. Availability of healthy versus unhealthy food was inconsistently related to obesity, while neighborhood features that discourage physical activity were consistently associated with increased body mass index. Theoretical explanations for neighborhood-obesity effects and recommendations for strengthening the literature are presented.
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            Obesity prevalence and the local food environment.

            Disparities in access to healthy foods have been identified particularly in the United States. Fewer studies have measured the effects these disparities have on diet-related health outcomes. This study measured the association between the presence of food establishments and obesity among 1295 adults living in the southern region of the United States. The prevalence of obesity was lower in areas that had supermarkets and higher in area with small grocery stores or fast food restaurants. Our findings are consistent with other studies showing that types of food stores and restaurants influence food choices and, subsequently, diet-related health outcomes.
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              Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt.

              The American "stroke belt" has contributed to the study of stroke. However, U.S. geographic patterns of diabetes have not been as specifically characterized. This study identifies a geographically coherent region of the U.S. where the prevalence of diagnosed diabetes is especially high, called the "diabetes belt." In 2010, data from the 2007 and 2008 Behavioral Risk Factor Surveillance System were combined with county-level diagnosed diabetes prevalence estimates. Counties in close proximity with an estimated prevalence of diagnosed diabetes ≥11.0% were considered to define the diabetes belt. Prevalence of risk factors in the diabetes belt was compared to that in the rest of the U.S. The fraction of the excess risk associated with living in the diabetes belt associated with selected risk factors, both modifiable (sedentary lifestyle, obesity) and nonmodifiable (age, gender, race/ethnicity, education), was calculated. A diabetes belt consisting of 644 counties in 15 mostly southern states was identified. People in the diabetes belt were more likely to be non-Hispanic African-American, lead a sedentary lifestyle, and be obese than in the rest of the U.S. Thirty percent of the excess risk was associated with modifiable risk factors, and 37% with nonmodifiable factors. Nearly one third of the difference in diabetes prevalence between the diabetes belt and the rest of the U.S. is associated with sedentary lifestyle and obesity. Culturally appropriate interventions aimed at decreasing obesity and sedentary lifestyle in counties within the diabetes belt should be considered. Published by Elsevier Inc.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                5 February 2016
                2016
                : 11
                : 2
                : e0148394
                Affiliations
                [1 ]Pennington Biomedical Research Center, Baton Rouge, Louisiana, 70808, United States of America
                [2 ]Department of Sociology, Louisiana State University, Baton Rouge, Louisiana, 70803, United States of America
                University of Rhode Island, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: CAM TS CKM STB SBH. Analyzed the data: CAM. Wrote the paper: CAM TS CKM STB SBH. Acquired the data: CAM. Critically revised the manuscript for important intellectual content: CAM TS STB CKM SBH.

                Article
                PONE-D-15-37910
                10.1371/journal.pone.0148394
                4743954
                26849803
                861c84d1-a07a-491a-a586-99b1ddff4efa
                © 2016 Myers et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 August 2015
                : 17 January 2016
                Page count
                Figures: 2, Tables: 1, Pages: 12
                Funding
                This research was partially supported by 1) 11GRNT7750027 from the American Heart Association, 2) NORC Center Grant #2P30DK072476 entitled “Nutritional Programming: Environmental and Molecular Interactions” sponsored by NIDDK, 3) 1 U54 GM104940 from the National Institute of General Medical Sciences of the National Institutes of Health, which funds the Louisiana Clinical and Translational Science Center, and 4) 1 F32 HL123242 from the National Heart Lung and Blood Institute of the National Institutes of Health. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Obesity
                Medicine and Health Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Obesity
                People and Places
                Population Groupings
                Age Groups
                Adults
                People and Places
                Population Groupings
                Ethnicities
                Hispanic People
                Research and Analysis Methods
                Research Design
                Survey Research
                Census
                Medicine and Health Sciences
                Health Care
                Health Care Providers
                Medical Doctors
                Physicians
                People and Places
                Population Groupings
                Professions
                Medical Doctors
                Physicians
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Population Density
                Medicine and Health Sciences
                Health Care
                Health Services Research
                Social Sciences
                Economics
                Health Economics
                Health Insurance
                Medicine and Health Sciences
                Health Care
                Health Economics
                Health Insurance
                Custom metadata
                All data used in this analysis are fully available from national sources, including the Center for Disease Control and Prevention, the United States (U.S.) Census Bureau, U.S. Department of Agriculture, and U.S. Department of Health and Human Services. The dataset used for this study is included as a supplementary file.

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                Uncategorized

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