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Community Policies and Programs to Prevent Obesity and Child Adiposity

      Introduction

      Evidence regarding impact of community policies and programs (CPPs) to prevent child obesity is limited, and which combinations of strategies and components are most important is not understood. The Healthy Communities Study was an observational study to assess relationships of characteristics and intensity of CPPs with adiposity, diet, and physical activity in children, taking advantage of variation across the U.S. in community actions to prevent child obesity. The study examined the association of CPPs to prevent child obesity with measured BMI and waist circumference, hypothesizing that communities with more-comprehensive CPPs would have children with lower adiposity.

      Methods

      The study included 130 communities selected by probability-based sampling or because of known CPPs targeting child obesity. Data were collected at home visits on 5,138 children during 2013–2015. CPPs were scored for multiple attributes to create a CPP intensity score. A CPP target behavior score reflected the number of distinct target behaviors addressed. Scores were standardized with the smallest observed score across communities being 0 and the largest 1. Multilevel regression analysis in 2016 adjusted for community, household, and individual characteristics.

      Results

      Higher CPP target behavior score was significantly associated with lower BMI and waist circumference in a dose−response relationship, with magnitude for the past 3 years of CPPs of 0.843 (p=0.013) for BMI and 1.783 cm (p=0.020) for waist circumference.

      Conclusions

      This study provides plausible evidence that comprehensive CPPs targeting a greater number of distinct physical activity and nutrition behaviors were associated with lower child adiposity.
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      References

        • Ogden C.L.
        • Carroll M.D.
        • Kit B.K.
        • Flegal K.M.
        Prevalence of childhood and adult obesity in the United States, 2011-2012.
        JAMA. 2014; 311: 806-814https://doi.org/10.1001/jama.2014.732
        • Shashaj B.
        • Bedogni G.
        • Graziani M.P.
        • et al.
        Origin of cardiovascular risk in overweight preschool children: a cohort study of cardiometabolic factors at the onset of obesity.
        JAMA Pediatr. 2014; 168: 917-924https://doi.org/10.1001/jamapediatrics.2014.900
        • Finkelstein E.A.
        • Graham W.C.
        • Malhorta R.
        Lifetime direct medical costs of childhood obesity.
        Pediatrics. 2014; 133: 854-862https://doi.org/10.1542/peds.2014-0063
        • Sanchez-Vaznaugh E.V.
        • Sanchez B.N.
        • Baek J.
        • Crawford P.B.
        ‘Competitive’ food and beverage policies: are they influencing childhood overweigth trends?.
        Health Aff (Millwood). 2010; 29: 436-446https://doi.org/10.1377/hlthaff.2009.0745
        • Madsen K.A.
        • Weedn A.E.
        • Crawford P.B.
        Disparities in peaks, plateaus, and declines in prevalence of high BMI among adolescents.
        Pediatrics. 2010; 126: 434-443https://doi.org/10.1542/peds.2009-3411
        • Wolfenden L.
        • Wyse R.
        • Nichols M.
        • Allender S.
        • Millar L.
        • McElduff P.
        A systematic review and meta-analysis of whole of community interventions to prevent excessive population weight gain.
        Prev Med. 2014; 62: 193-200https://doi.org/10.1016/j.ypmed.2014.01.031
        • Hennessy E.
        • Oh A.
        • Agurs-Collins T.
        • et al.
        State-level school competitive food and beverage laws are associated with children’s weight status.
        J Sch Health. 2014; 84: 609-616https://doi.org/10.1111/josh.12181
        • Nanney M.S.
        • Nelson T.
        • Wall M.
        • Haddad T.A.
        • Kubik M.
        • Story M.
        State school nutrition and physical activity policy environments and youth obesity.
        Am J Prev Med. 2010; 38: 9-16https://doi.org/10.1016/j.amepre.2009.08.031
        • Nanney M.S.
        • MacLehose R.
        • Kubik M.Y.
        • Davey C.S.
        • Coombes B.
        • Nelson T.F.
        Recommended school policies are assicated with student sugary drink and fruit vegetable intake.
        Prev Med. 2014; 62: 179-181https://doi.org/10.1016/j.ypmed.2014.01.026
        • McKinnon R.A.
        • Orleans C.T.
        • Kumanyika S.K.
        • et al.
        Considerations for an obesity policy agenda.
        Am J Prev Med. 2009; 36: 351-357https://doi.org/10.1016/j.amepre.2008.11.017
        • Sallis J.F.
        • Story M.
        • Lou D.
        Study design and analytic strategies for environmental and policy research on obesity, physical activity, and diet.
        Am J Prev Med. 2008; 36: S72-S77https://doi.org/10.1016/j.amepre.2008.10.006
        • National Academy of Medicine
        Evaluating Obesity Prevention Efforts: A Plan for Measuring Progress.
        The National Academies Press, Washington DC2013
        • Hunter C.M.
        • McKinnon R.A.
        • Esposito L.
        News from the NIH: research to evaluate “natural” experiments related to obesity and diabetes.
        Trans Behav Med. 2014; 4: 127-129https://doi.org/10.1007/s13142-013-0250-z
        • Strauss W.J.
        • Sroka C.J.
        • Frongillo E.A.
        • et al.
        Statistical design features of the Healthy Communities Study.
        Am J Prev Med. 2015; 49: 624-630https://doi.org/10.1016/j.amepre.2015.06.021
        • John L.V.
        • Gregoriou M.
        • Pate R.R.
        • et al.
        Operational implementation of the Healthy Communities Study: how communities shape children’s health.
        Am J Prev Med. 2015; 49: 631-635https://doi.org/10.1016/j.amepre.2015.06.019
        • Fawcett S.B.
        • Collie-Akers V.L.
        • Schultz J.A.
        Measuring community programs and policies and their intensity in the Healthy Communities Study.
        Am J Prev Med. 2015; 49: 636-641https://doi.org/10.1016/j.amepre.2015.06.027
        • Collie-Akers V.L.
        • Fawcett S.B.
        • Schultz J.A.
        Measuring progress of collaborative action in a community health effort.
        Rev Panam Salud Publ. 2013; 34: 422-428
        • Sroka C.J.
        • McIver K.L.
        • Sagatov R.D.F.
        • Arteaga S.S.
        • Frongillo E.A.
        Weight status measures collected in the Healthy Communities Study: protocols and analyses.
        Am J Prev Med. 2015; 49: 642-646https://doi.org/10.1016/j.amepre.2015.07.001
        • National Academy of Medicine
        Accelerating Progress in Obesity Prevention: Solving the Weight of the Nation.
        The National Academies Press, Washington, DC2012
        • CDC
        National Health and Nutrition Examination Survey (NHANES): Anthropometry Procedures Manual.
        DHHS; January, Washington, DC2013
        • Ali O.
        • Cerjak D.
        • Kent J.W.
        • James R.
        • Blangero J.
        • Zhang Y.
        Obesity, central adiposity and cardiometabolic risk factors in children and adolescents: a family-based study.
        Pediatr Obes. 2014; 9: e58-e62https://doi.org/10.1111/j.2047-6310.2014.218.x
        • Ochiai H.
        • Shirasawa T.
        • Nishimura R.
        • et al.
        Relationship of body mass index to percent body fat and waist circumference among schoolchildren in Japan—the influence of gender and obesity: a population-based cross-sectional study.
        BMC Public Health. 2010; 10: 493https://doi.org/10.1186/1471-2458-10-493
        • Garnett S.P.
        • Baur L.A.
        • Srinivasan S.
        • Lee J.W.
        • Cowell C.T.
        Body mass index and waist circumference in midchildhood and adverse cardiovascular disease risk clustering in adolescence.
        Am J Clin Nutr. 2007; 86: 549-555
        • Freedman D.S.
        • Ogden C.L.
        • Blanck H.M.
        • Borrud L.G.
        • Dietz W.H.
        The abilities of body mass index and skinfold thicknesses to identify children with low or elevated levels of dual-energy x-ray absorptiometry determined body fatness.
        J Pediatr. 2013; 163: 160-166https://doi.org/10.1016/j.jpeds.2012.12.093
        • Heo M.
        • Wylie-Rosett J.
        • Pietrobelli A.
        • Kabat G.C.
        • Rohan T.E.
        • Faith M.S.U.S.
        pediatric population-level associations of DXA-measured percentage of body fat with four BMI metrics with cutoffs.
        Int J Obes (Lond). 2014; 38: 60-68https://doi.org/10.1038/ijo.2013.134
        • Javed A.
        • Jumean M.
        • Murad M.H.
        • et al.
        Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta-analysis.
        Pediatr Obes. 2015; 10: 234-244https://doi.org/10.1111/ijpo.242
        • Weber D.R.
        • Leonard M.B.
        • Shults J.
        • Zemel B.S.
        A comparison of fat and lean body mass index to BMI for the identification of metabolic syndrome in children and adolescents.
        J Clin Endocrinol Metab. 2014; 99: 3208-3216https://doi.org/10.1210/jc.2014-1684
        • Tibshirani R.
        Regression shrinkage and selection via the lasso.
        J R Stat Soc Series B Stat Methodol. 1996; 58: 267-288
        • van Buuren S.
        • Groothuis-Oudshoorn K.
        Mice: multivariate imputation by chained equations in R.
        J Stat Software. 2011; 45: 3https://doi.org/10.18637/jss.v045.i03
        • Wolfenden L.
        • Wyse R.
        • Nichols M.
        • Allender S.
        • Millar L.
        • McElduff P.
        A systematic review and meta-analysis of whole of community interventions to prevent excessive population weight gain.
        Prev Med. 2014; 62: 193-200https://doi.org/10.1016/j.ypmed.2014.01.031
        • Sobol-Goldberg S.
        • Rabinowitz J.
        • Gross R.
        School-based obesity prevention programs: a meta-analysis of randomized controlled trials.
        Obesity (Silver Spring). 2013; 21: 2422-2428https://doi.org/10.1002/oby.20515
        • Vasques C.
        • Magalhães P.
        • Cortinhas A.
        • Mota P.
        • Leitão J.
        • Lopes V.P.
        Effects of intervention programs on child and adolescent BMI: a meta-analysis study.
        J Phys Act Health. 2014; 11: 426-444https://doi.org/10.1123/jpah.2012-0035
        • Shroff M.
        • Jones S.J.
        • Frongillo E.A.
        • Howlett M.
        Policy instruments used by states seeking to improve school food environment.
        Am J Public Health. 2012; 102: 222-229https://doi.org/10.2105/AJPH.2011.300338
        • Fawcett S.
        • Schultz J.
        • Watson-Thompson J.
        • Fox M.
        • Bremby R.
        Building multisectoral partnerships for population health and health equity.
        Prev Chronic Dis. 2010; 7 (Accessed November 2010): A118
        • Sobal J.
        • Rauschenbach B.S.
        • Frongillo E.A.
        Marital status, fatness, and obesity.
        Soc Sci Med. 1992; 35: 915-923https://doi.org/10.1016/0277-9536(92)90106-Z
        • Frongillo E.A.
        Understanding obesity and program participation in the context of poverty and food insecurity.
        J Nutr. 2003; 133: 2117-2118
        • Lucas R.M.
        • McMichael A.J.
        Association or causation: evaluating links between “environment and disease”.
        Bull World Health Organ. 2005; 83: 792-795