title: Spatial pattern of body mass index among adults in the diabetes study of Northern California (DISTANCE).
abstract: BACKGROUND
  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.
authors.affiliation: School of Public Health, Division of Community Health and Child Development, University of California, 207-B University Hall, #7360, Berkeley, CA 94720-7360, USA. blaraia@berkeley.edu.
authors.forename: Barbara A
authors.initials: BA
authors.lastname: Laraia
authors.forename: Samuel D
authors.initials: SD
authors.lastname: Blanchard
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Karter
authors.forename: Jessica C
authors.initials: JC
authors.lastname: Jones-Smith
authors.forename: Margaret
authors.initials: M
authors.lastname: Warton
authors.forename: Ellen
authors.initials: E
authors.lastname: Kersten
authors.forename: Michael
authors.initials: M
authors.lastname: Jerrett
authors.forename: Howard H
authors.initials: HH
authors.lastname: Moffet
authors.forename: Nancy
authors.initials: N
authors.lastname: Adler
authors.forename: Dean
authors.initials: D
authors.lastname: Schillinger
authors.forename: Maggi
authors.initials: M
authors.lastname: Kelly
mesh_terms: D000328:Adult; D000368:Aged; D015992:Body Mass Index; D002140:California / Q000453:epidemiology; D016000:Cluster Analysis; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D003625:Data Collection / Q000379:methods / Q000706:statistics & numerical data; D003920:Diabetes Mellitus / Q000175:diagnosis / Q000453:epidemiology*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D012111:Residence Characteristics / Q000706:statistics & numerical data; D062206:Spatial Analysis