This study estimated life expectancy and health adjusted life expectancy by BMI category for Canadian men and women, age 20 and over, during the period 2000–2005. Our findings indicate that there are important health expectancy differences between BMI categories, as well as between the sexes for those in the lower excess weight categories. When estimated at 20 years of age, both sexes have significant losses of LE and HALE in the underweight and higher obesity classes and significant gains in LE and HALE in the overweight category when compared to those in the normal weight category. Women and men at 20 also demonstrate progressively higher proportions of life spent in nonoptimal health in excess weight categories as compared to those of normal weight.
Women and men in the obese class 1 category do not share the same HALE experience at 20: the former have a significantly lower HALE while there is no effect for the latter. Furthermore, men in the obese class 1 category saw progressive increases in the proportion of life spent in nonoptimal health with each successive age group that were more substantial than those of normal weight men, while women in this BMI category experienced increases proportional to normal weight women in all age groups. Finally, although overweight men and women both had significantly higher HALE at age 20, overweight women experienced a larger increase in proportion of life spent in nonoptimal health compared to normal weight women, whereas this increase was relatively small when comparing normal weight and overweight men.
This study adds to the current literature on life expectancy and health expectancy by BMI category by using available datasets to estimate age-sex-specific mortality and HRQL by BMI category in Canada. When our results are compared to those of the only other study that examined health expectancy by BMI category using a representative sample of populations at the country level , we found similar patterns in life expectancy and proportion of life spent in nonoptimal health (measured as LwD/LE in this other study, where LwD is Life with Disability) amongst normal weight, overweight, and obese men and women. Ours is the first study to our knowledge to report on health expectancy for the obesity subcategories and for the underweight category. When the WHO BMI category for obesity was broken down into subclasses, we observed important variations in HALE by sex. Majer et al.  estimated the hazard rates of various disability states by obesity subcategory in a sensitivity analysis and also found heterogeneity in this weight category: those in the obese class 2+ category were significantly less likely to recover from disability compared to participants of normal weight, while those in the obese class 1 category demonstrated no significant difference for recovery. Other population-based studies examining mortality risk and years of life lost by obesity subcategory [2, 5, 6, 36] demonstrate a similar trend of modest to negative risk for premature mortality in the lower obesity class coupled with more significant risks in the higher classes. This trend has also been observed for men with respect to loss of HRQL, although women tend to have a more important loss of HRQL as BMI increases . With respect to underweight individuals, the observed results are consistent with what was expected based on the evidence regarding both mortality and HRQL among this group. Underweight may be associated with malnutrition, sarcopenia, low-grade inflammation, and frailty, which are each associated with mortality risk and decreased quality of life .
The results we observed for LE by BMI category appear to support those found in the recent meta-analysis of all-cause mortality for overweight and obesity relative to normal weight in the general population . This meta-analysis and individual studies with similar results have generated much discussion about the so-called “obesity paradox” where lower mortality risk is associated with those in the overweight BMI category and where there is no difference in mortality risk associated with the obesity class 1 category compared to those in the normal weight category. Some of the potential reasons proposed for these seemingly counterintuitive results are addressed in the limitations section below: imperfect nature of BMI as a predictor of metabolic risk; BMI being measured solely at baseline and thus not accounting for the effect of body weight changes over time; confounding due to pre-existing illness at baseline; use of self-reported height and weight; and issues around proper control for tobacco use and other potentially modifying factors in the analysis. Other issues of importance not addressed here include: heterogeneity of mortality risk in the BMI normal weight category (i.e., those with a BMI between 18.5 and 22 have been shown to have higher mortality risk); better management of risk factors in overweight and obese clients by the health care community; and the possible benefit of having some adipose reserve during periods of acute catabolic illness .
In the absence of a sufficiently large dataset with a measure of BMI and mortality follow-up, our approach of estimating Canadian HR for BMI mortality and combining this with death data to create age-sex-specific mortality rates by BMI category represents a feasible alternative method. However, the hazard ratios used to produce age-sex-specific BMI mortality are based on height and weight assessments made among the Canadian adult household population in 1994/1995. It is possible that the relationship between BMI and mortality has changed in subsequent cohorts, which would have an impact on estimates of LE and HALE. Future research should be conducted to confirm our results by using national mortality follow-up of early Canadian Community Health Survey cycles in order to determine more recent and stable mortality rates by BMI category.
While our study was based on a representative sample of the Canadian population, certain subpopulations were excluded from the NPHS and CCHS, most notably those in long-term care institutions. It is possible that risks for mortality and loss of HRQL are greater among those living in long-term care institutions and, as such, these individuals may not have the same LE and HALE profile as that of the community-dwelling population used in our study.
We did not assess BMI at multiple intervals during follow-up, which could lead to misclassification of mortality risk for subjects who transition to and from higher-risk BMI categories during follow-up. Indeed, there is evidence to suggest that, among individuals having recently experienced a life-threatening event related to cardiovascular disease (e.g., myocardial infarction or stroke), those with excess weight display healthier trajectories of lifestyle and weight changes than normal weight individuals . Although a recent study determined that repeated measures of BMI did not change mortality estimates , future analyses should incorporate more complex analytical methods and life-course conceptual models to address this potential bias .
We decided to not control for weight loss due to pre-existing illness (or what is commonly referred to in the obesity literature as “reverse causation” or “washout”). Certain studies have found that by excluding deaths that occur in the first few years of follow-up, mortality risks associated with the excess weight BMI categories become much higher. It is hypothesized that this is due to the greater presence of pre-existing illnesses amongst those in the normal weight BMI category at baseline, which in turn lowers their life expectancy compared to those with an excess body mass . A recent mortality study using the same cohort as our study found that results by BMI category were not significantly affected when deaths occurring within the first four years of follow-up were excluded from analysis . Furthermore, studies that actually assess body weight prior to baseline demonstrate no clear association between pre-existing weight loss and subsequent development of cancer or other chronic diseases commonly perceived to be associated with both weight loss and increased mortality risk. Methods to address confounding by illness-related weight loss, such as excluding deaths, may additionally introduce new biases since they will most likely also be excluding large numbers of subjects whose weight loss is not related to illness .
We also combined cycles of the CCHS in order to obtain greater stability in estimates of BMI prevalence and HRQL by BMI category. However, this approach may obscure possible trends occurring over the time period covered by the combined cycles.
Self-reports of height and weight, which are used to calculate HR and to estimate age-sex-BMI category-specific HRQL estimates, systematically underestimate true weight and overestimate true height. The results of this study may not reflect health expectancy estimates according to BMI calculated using measured height and weight. In order to assess the extent of this bias, we conducted sensitivity analyses using a previously published algorithm  that adjusts self-reported BMI values so that they more closely approximate directly-measured values. While estimates calculated using this correction factor did not appreciably differ from uncorrected estimates, the HR for obese class 1 went from being marginally significant at the p < 0.05 threshold to being no longer significant at that threshold. In addition, the LE calculated for obese class 1 was higher than that of normal weight men and women but was not significant at the p < 0.01 level. This leads us to conclude that the association between obese class 1 men and women for decreased mortality risk and increased LE is a weak one and should be interpreted with some caution.
The current study also did not report on health expectancy using other measures of body weight (e.g., waist circumference, skinfold thickness, waist-to-height ratio), as none of these alternatives are available in the National Population Health Survey. These measures can provide more accurate representations of adiposity, and consequently health risk, by distinguishing between lean and fat body mass [37, 43].
Since the goal of this study was to describe HALE by BMI at the population level, we did not adjust for any socioeconomic or behavioral factors. We did however assess the effect of tobacco smoking on our study results by introducing a smoking covariate (“ever” versus “never” tobacco smoker) into the adjusted mortality hazard ratio model. The smoking covariate did not significantly change the estimates and was therefore not included in this report. This finding is consistent with previous recent research on mortality by BMI category using NPHS study data , although reduced sample size in that study may have decreased the power to detect effects. Since our study is based on the same cohort, we may have experienced the same decrease in power. Majer et al., using a much larger sample size (n = 66,331), found that daily smokers had a lower LE compared to never-smokers of the same BMI category. However, their study also observed that patterns of life expectancy between each BMI category did not change appreciably when stratifying by smoking status . Whether factors such as tobacco smoking, alcohol consumption, physical activity level, education, and income moderate observed levels of HALE by BMI group could be examined in future studies. It would also be important to consider that some of these factors commonly treated as confounders are part of the causal web surrounding behavior, BMI, and health, and a strategic approach considering potential mechanisms underpinning the observed relationships should take this into account.