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The spatial clustering of obesity: does the built environment matter?
Author(s) -
Huang R.,
Moudon A. V.,
Cook A. J.,
Drewnowski A.
Publication year - 2015
Publication title -
journal of human nutrition and dietetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.951
H-Index - 70
eISSN - 1365-277X
pISSN - 0952-3871
DOI - 10.1111/jhn.12279
Subject(s) - neighbourhood (mathematics) , scan statistic , obesity , medicine , statistic , body mass index , built environment , socioeconomic status , geocoding , demography , environmental health , cluster (spacecraft) , spatial analysis , statistics , geography , cartography , mathematics , population , mathematical analysis , civil engineering , pathology , sociology , computer science , engineering , programming language
Background Obesity rates in the USA show distinct geographical patterns. The present study used spatial cluster detection methods and individual‐level data to locate obesity clusters and to analyse them in relation to the neighbourhood built environment. Methods The 2008–2009 Seattle Obesity Study provided data on the self‐reported height, weight, and sociodemographic characteristics of 1602 King County adults. Home addresses were geocoded. Clusters of high or low body mass index were identified using Anselin's Local Moran's I and a spatial scan statistic with regression models that searched for unmeasured neighbourhood‐level factors from residuals, adjusting for measured individual‐level covariates. Spatially continuous values of objectively measured features of the local neighbourhood built environment (SmartMaps) were constructed for seven variables obtained from tax rolls and commercial databases. Results Both the Local Moran's I and a spatial scan statistic identified similar spatial concentrations of obesity. High and low obesity clusters were attenuated after adjusting for age, gender, race, education and income, and they disappeared once neighbourhood residential property values and residential density were included in the model. Conclusions Using individual‐level data to detect obesity clusters with two cluster detection methods, the present study showed that the spatial concentration of obesity was wholly explained by neighbourhood composition and socioeconomic characteristics. These characteristics may serve to more precisely locate obesity prevention and intervention programmes.