title: Using kernel density estimation to understand the influence of neighbourhood destinations on BMI.
abstract: OBJECTIVE
  Little is known about how the distribution of destinations in the local neighbourhood is related to body mass index (BMI). Kernel density estimation (KDE) is a spatial analysis technique that accounts for the location of features relative to each other. Using KDE, this study investigated whether individuals living near destinations (shops and service facilities) that are more intensely distributed rather than dispersed, have lower BMIs.
  
  
  METHODS
  A cross-sectional study of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia.
  
  
  METHODS
  Destinations were geocoded, and kernel density estimates of destination intensity were created using kernels of 400, 800 and 1200 m. Using multilevel linear regression, the association between destination intensity (classified in quintiles Q1(least)-Q5(most)) and BMI was estimated in models that adjusted for the following confounders: age, sex, country of birth, education, dominant household occupation, household type, disability/injury and area disadvantage. Separate models included a physical activity variable.
  
  
  RESULTS
  For kernels of 800 and 1200 m, there was an inverse relationship between BMI and more intensely distributed destinations (compared to areas with least destination intensity). Effects were significant at 1200 m: Q4, β -0.86, 95% CI -1.58 to -0.13, p=0.022; Q5, β -1.03 95% CI -1.65 to -0.41, p=0.001. Inclusion of physical activity in the models attenuated effects, although effects remained marginally significant for Q5 at 1200 m: β -0.77 95% CI -1.52, -0.02, p=0.045.
  
  
  CONCLUSIONS
  This study conducted within urban Melbourne, Australia, found that participants living in areas of greater destination intensity within 1200 m of home had lower BMIs. Effects were partly explained by physical activity. The results suggest that increasing the intensity of destination distribution could reduce BMI levels by encouraging higher levels of physical activity.
authors.affiliation: Department of Gender and Women's Health, Centre for Health and Society, Academic Centre for Health Inequity, Melbourne School of Population Health, University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Tania L
authors.initials: TL
authors.lastname: King
authors.affiliation: Department of Gender and Women's Health, Centre for Health and Society, Academic Centre for Health Inequity, Melbourne School of Population Health, University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Rebecca J
authors.initials: RJ
authors.lastname: Bentley
authors.affiliation: Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University, Burwood, Victoria, Australia.
authors.forename: Lukar E
authors.initials: LE
authors.lastname: Thornton
authors.affiliation: Department of Gender and Women's Health, Centre for Health and Society, Academic Centre for Health Inequity, Melbourne School of Population Health, University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Anne M
authors.initials: AM
authors.lastname: Kavanagh
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001315:Australia; D015992:Body Mass Index; D015986:Confounding Factors, Epidemiologic; D003430:Cross-Sectional Studies; D004779:Environment Design; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009043:Motor Activity; D012044:Regression Analysis; D012111:Residence Characteristics; D014505:Urban Population; D016138:Walking; D055815:Young Adult
keywords: EPIDEMIOLOGY; PUBLIC HEALTH; STATISTICS & RESEARCH METHODS