title: The spatial clustering of obesity: does the built environment matter?
abstract: 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.
authors.affiliation: Interdisciplinary Program for the PhD in Urban Design and Planning, University of Washington, Seattle, WA, USA.|The Urban Form Lab (UFL), Department of Urban Design and Planning, University of Washington, Seattle, WA, USA.
authors.forename: R
authors.initials: R
authors.lastname: Huang
authors.affiliation: The Urban Form Lab (UFL), Department of Urban Design and Planning, University of Washington, Seattle, WA, USA.
authors.forename: A V
authors.initials: AV
authors.lastname: Moudon
authors.affiliation: Biostatistics Unit, The Group Health Research Institute, Seattle, WA, USA.
authors.forename: A J
authors.initials: AJ
authors.lastname: Cook
authors.affiliation: Center for Public Health Nutrition, Department of Epidemiology, University of Washington, Seattle, WA, USA.
authors.forename: A
authors.initials: A
authors.lastname: Drewnowski
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D015992:Body Mass Index; D001835:Body Weight; D016000:Cluster Analysis; D004779:Environment Design / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000453:epidemiology*; D012111:Residence Characteristics / Q000706:statistics & numerical data*; D012959:Socioeconomic Factors; D062206:Spatial Analysis; D014861:Washington / Q000453:epidemiology; D055815:Young Adult
keywords: Local Moran's I; body mass index; built environment; geographic information systems; spatial scan statistic