Int J Health Geogr - Spatial pattern of body mass index among adults in the diabetes study of Northern California (DISTANCE).

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Resumo

CKGROUND: The role that environmental factors, such as neighborhood socioeconomics, food, and physical environment, play in the risk of obesity and chronic diseases is not well quantified. Understanding how spatial distribution of disease risk factors overlap with that of environmental (contextual) characteristics may inform health interventions and policies aimed at reducing the environment risk factors. We evaluated the extent to which spatial clustering of extreme body mass index (BMI) values among a large sample of adults with diabetes was explained by individual characteristics and contextual factors.METHODS: We quantified spatial clustering of BMI among 15,854 adults with diabetes from the Diabetes Study of Northern California (DISTANCE) cohort using the Global and Local Moran's I spatial statistic. As a null model, we assessed the amount of clustering when BMI values were randomly assigned. To evaluate predictors of spatial clustering, we estimated two linear models to estimate BMI residuals. First we included individual factors (demographic and socioeconomic characteristics). Then we added contextual factors (neighborhood deprivation, food environment) that may be associated with BMI. We assessed the amount of clustering that remained using BMI residuals.RESULTS: Global Moran's I indicated significant clustering of extreme BMI values; however, after accounting for individual socioeconomic and demographic characteristics, there was no longer significant clustering. Twelve percent of the sample clustered in extreme high or low BMI clusters, whereas, only 2.67% of the sample was clustered when BMI values were randomly assigned. After accounting for individual characteristics, we found clustering of 3.8% while accounting for neighborhood characteristics resulted in 6.0% clustering of BMI. After additional adjustment of neighborhood characteristics, clustering was reduced to 3.4%, effectively accounting for spatial clustering of BMI.CONCLUSIONS: We found substantial clustering of extreme high and low BMI values in Northern California among adults with diabetes. Individual characteristics explained somewhat more of clustering of the BMI values than did neighborhood characteristics. These findings, although cross-sectional, may suggest that selection into neighborhoods as the primary explanation of why individuals with extreme BMI values live close to one another. Further studies are needed to assess causes of extreme BMI clustering, and to identify any community level role to influence behavior change.

Resumo Limpo

ckground role environment factor neighborhood socioeconom food physic environ play risk obes chronic diseas well quantifi understand spatial distribut diseas risk factor overlap environment contextu characterist may inform health intervent polici aim reduc environ risk factor evalu extent spatial cluster extrem bodi mass index bmi valu among larg sampl adult diabet explain individu characterist contextu factorsmethod quantifi spatial cluster bmi among adult diabet diabet studi northern california distanc cohort use global local moran spatial statist null model assess amount cluster bmi valu random assign evalu predictor spatial cluster estim two linear model estim bmi residu first includ individu factor demograph socioeconom characterist ad contextu factor neighborhood depriv food environ may associ bmi assess amount cluster remain use bmi residualsresult global moran indic signific cluster extrem bmi valu howev account individu socioeconom demograph characterist longer signific cluster twelv percent sampl cluster extrem high low bmi cluster wherea sampl cluster bmi valu random assign account individu characterist found cluster account neighborhood characterist result cluster bmi addit adjust neighborhood characterist cluster reduc effect account spatial cluster bmiconclus found substanti cluster extrem high low bmi valu northern california among adult diabet individu characterist explain somewhat cluster bmi valu neighborhood characterist find although crosssect may suggest select neighborhood primari explan individu extrem bmi valu live close one anoth studi need assess caus extrem bmi cluster identifi communiti level role influenc behavior chang

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