4.7 Article

Explained and Unexplained Regional Variation in Canadian Obesity Prevalence

Journal

OBESITY
Volume 19, Issue 7, Pages 1460-1468

Publisher

WILEY-BLACKWELL
DOI: 10.1038/oby.2010.339

Keywords

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Funding

  1. Population Health Intervention Research Centre (PHIRC) within the Population Health Intervention Research Network (PHIRNET)
  2. Alberta Innovates-Health Solutions (formerly the Alberta Heritage Foundation for Medical Research)

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The objective of our study was to examine sociodemographic and behavioral variables underlying the geographic variation of obesity in Canada. We aimed to quantify the share of regional variation in average BMI attributable to commonly cited determinants of obesity and the remaining share, which is attributable to the idiosyncrasies of the regional environment (regional effects). Using data from the Canadian Community Health Survey (CCHS) (2004), ordinary least squares (OLS) regression, and Blinder-Oaxaca decomposition to decompose the difference in mean BMI between regions, we quantify two parts of the difference: a share explained by different levels of the covariates and a share explained by those covariates having different effects on BMI in the different regions, using the Atlantic provinces as the reference group. We observed that some differences (e. g., average BMI for males in Quebec compared to the Atlantic provinces) are mostly explained by the different levels of socio-demographic and behavioral covariates, while others (e. g., average BMI for females in Quebec compared to the Atlantic provinces) are mostly explained by the different effects of the covariates on BMI. In the latter scenario, even if covariates were made to be identical in the different regions, the difference in average BMI would persist. Thus, targeting covariates in different regions through plans like physical activity or nutrition policy, income equalization, or education subsidies will have ambiguous effects for addressing disparate obesity levels, being plausible policy options in some regions but less so in others. Future research and policy would benefit from identifying these region-specific attributes that have local implications for BMI.

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