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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
  1. Daniel J Dutton1,
  2. Lindsay McLaren2
  1. 1The Prentice Institute for Global Population & Economy, Lethbridge, Alberta, Canada
  2. 2Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
  1. Correspondence to Daniel J Dutton, The Prentice Institute for Global Population & Economy, Suite L1184 (Library Information Network Centre), 4401 University Drive West, Lethbridge, Alberta, Canada T1K 3M4; daniel.dutton{at}


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.

Discussion Equalisation of common correlates of BMI between regions cannot be reasonably expected to reduce differences in the BMI distributions between regions.


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