title: How important are determinants of obesity measured at the individual level for explaining geographic variation in body mass index distributions? Observational evidence from Canada using Quantile Regression and Blinder-Oaxaca Decomposition.
abstract: BACKGROUND
  Obesity prevalence varies between geographic regions in Canada. The reasons for this variation are unclear but most likely implicate both individual-level and population-level factors. The objective of this study was to examine whether equalising correlates of body mass index (BMI) across these geographic regions could be reasonably expected to reduce differences in BMI distributions between regions.
  
  
  METHODS
  Using data from three cycles of the Canadian Community Health Survey (CCHS) 2001, 2003 and 2007 for males and females, we modelled between-region BMI cross-sectionally using quantile regression and Blinder-Oaxaca decomposition of the quantile regression results.
  
  
  RESULTS
  We show that while individual-level variables (ie, age, income, education, physical activity level, fruit and vegetable consumption, smoking status, drinking status, family doctor status, rural status, employment in the past 12 months and marital status) may be Caucasian important correlates of BMI within geographic regions, those variables are not capable of explaining variation in BMI between regions.
  
  
  CONCLUSIONS
  Equalisation of common correlates of BMI between regions cannot be reasonably expected to reduce differences in the BMI distributions between regions.
authors.affiliation: The Prentice Institute for Global Population & Economy, Lethbridge, Alberta, Canada.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Dutton
authors.affiliation: Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.
authors.forename: Lindsay
authors.initials: L
authors.lastname: McLaren
mesh_terms: D000293:Adolescent; D000328:Adult; D015992:Body Mass Index; D002170:Canada / Q000453:epidemiology; D005260:Female; D006306:Health Surveys; D006801:Humans; D007182:Income / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009765:Obesity / Q000453:epidemiology* / Q000523:psychology; D011159:Population Surveillance / Q000379:methods*; D015995:Prevalence; D012044:Regression Analysis; D012737:Sex Factors; D012959:Socioeconomic Factors; D011795:Surveys and Questionnaires; D044465:White People / Q000706:statistics & numerical data*; D055815:Young Adult
keywords: OBESITY; PUBLIC HEALTH; SOCIAL EPIDEMIOLOGY