Unexplained Variance of Obesity Levels Across Canada

As in most countries, the population levels of obesity in Canada vary considerably from province to province (as they do within provinces). Although there are many “theories” on why this may be the case there has been little work done on trying to unravel the “explained” and “unexplained” regional variation based on a comparison of factors known to affect obesity levels such as socio-economic status, urban-rural distribution, and other variables.

This issue was now addressed by Daniel Dutton and Lindsay McLaren from the University of Calgary, Alberta, in a paper just published in OBESITY.

Using data from the nationally representative Canadian Community Health Survey (CCHS) (2004), the researchers attempted to decompose the difference in mean BMI between regions, into differences explained by different levels of the covariates between regions and a share explained by those covariates having different effects on BMI in the different regions.

Canada was split into five regions for this analysis: British Columbia, the Prairies (Alberta, Saskatchewan, and Manitoba), Ontario, Quebec, and the Atlantic provinces (Nova Scotia, New Brunswick, Prince Edward Island, and Newfoundland). The Atlantic provinces, which currently have the highest obesity rates in Canada, were used as the reference group.

While some differences between provinces (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, 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.

One example of a surprising difference between regions is that the impact of increased fruit and vegetable consumption on BMI is substantially stronger in Ontario and Quebec women than in Atlantic women.

The authors have the following explanation to offer regarding this finding:

“One possibility is that the quality of fruits and vegetables consumed differs by region. For example, fruit servings in the Atlantic provinces may consist of more canned fruits (due to a climate less conducive to growing a variety of fruits or geographical distance affecting the efficiency of transporting perishables to the region), which are often packed in syrup, adding to the calorie count, compared to fresh fruits. Another plausible explanation is that consumption of other foods varies regionally, and differentially offsets the impact of fruit and vegetable consumption. For example, if high levels of consumption of fruits and vegetables in the Atlantic region are associated with higher consumption of food overall (including less healthy foods), perhaps reflecting dietary social norms, then we would observe different returns to the consumption of fruits and vegetables.”

Thus, even if covariates (e.g. promoting the consumption of fruit and vegetables) were made to be identical in the different regions, the difference in average BMI between regions would still persist.

As the authors note:

“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.”

Therefore, while some drivers of obesity may best be addressed by federal policies, each region may have to adopt their own strategies to fully address the obesity problem – what works well in one province may have little to no effect in others.

It appears that what applies in clinical practice, also applies for efforts at the regional level: one size does not fit all.

Edmonton, Alberta

Dutton DJ, & McLaren L (2011). Explained and Unexplained Regional Variation in Canadian Obesity Prevalence. Obesity (Silver Spring, Md.) PMID: 21253004