Obesity and overweight in Canada: An updated cost-of-illness study

Centre for Health Evaluation and Outcome Sciences, St Paul's Hospital, Vancouver, BC, Canada V6Z 1Y6.
Obesity Reviews (Impact Factor: 8). 05/2009; 11(1):31-40. DOI: 10.1111/j.1467-789X.2009.00579.x
Source: PubMed


This study is to update the estimates of the economic burden of illness because of overweight and obesity in Canada by incorporating the increase in prevalence of overweight and obesity, findings of new related comorbidities and rise in the national healthcare expenditure. The burden was estimated from a societal perspective using the prevalence-based cost-of-illness methodology. Results from a literature review of the risks of 18 related comorbidities were combined with prevalence of overweight and obesity in Canada to estimate the extent to which each comorbidity is attributable to overweight and obesity. The direct costs were extracted from the National Health Expenditure Database and allocated to each comorbidity using weights principally from the Economic Burden of Illness in Canada. The study showed that the total direct costs attributable to overweight and obesity in Canada were $6.0 billion in 2006, with 66% attributable to obesity. This corresponds to 4.1% of the total health expenditures in Canada in 2006. The inclusion of newly identified comorbidities increased the direct cost estimates of obesity by 25%, while the rise in national healthcare expenditure accounted for a 19% increase. Policies to reduce being overweight and obese could potentially save the Canadian healthcare system millions of dollars.

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    • "Projection of health care cost savings by reduction in the prevalence of overweight and obesity was estimated by multiplying the total direct health care cost for obesity by the proportion of overweight and obese cases prevented by the intervention. An updated estimation by Anis et al showed that the annual direct health care cost of overweight and obesity in Canada was $ 6 billion in 2006 [16]. We assumed that this cost remained unchanged overtime and for every overweight and obese case that we prevented, we avoided the costs for health conditions related to obesity. "
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    ABSTRACT: Background The Alberta Project Promoting active Living and healthy Eating in Schools (APPLE Schools) is a comprehensive school health program that is proven feasible and effective in preventing obesity among school aged children. To support decision making on expanding this program, evidence on its long-term health and economic impacts is particularly critical. In the present study we estimate the life course impact of the APPLE Schools programs in terms of future body weights and avoided health care costs. Method We modeled growth rates of body mass index (BMI) using longitudinal data from the National Population Health Survey collected between 1996–2008. These growth rate characteristics were used to project BMI trajectories for students that attended APPLE Schools and for students who attended control schools (141 randomly selected schools) in the Canadian province of Alberta. Results Throughout the life course, the prevalence of overweight (including obesity) was 1.2% to 2.8% (1.7 on average) less among students attending APPLE Schools relative to their peers attending control schools. The life course prevalence of obesity was 0.4% to 1.4% (0.8% on average) less among APPLE Schools students. If the APPLE Schools program were to be scaled up, the potential cost savings would be $33 to 82 million per year for the province of Alberta, or $150 to 330 million per year for Canada. Conclusions These projected health and economic benefits seem to support broader implementation of school-based health promotion programs.
    PLoS ONE 07/2014; 9(7):e102242. DOI:10.1371/journal.pone.0102242 · 3.23 Impact Factor
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    • "e l s e v i e r . c o m / l o c a t e / y p m e d of overweight and obesity as well as resulting chronic diseases (i.e., cardiovascular disease, hypertension, diabetes, cancers, depression) incurs a significant health and financial burden on individuals and societies (Anis et al., 2010; Tsai et al., 2011). Reductions in workplace, leisuretime , and transportation-related physical activity combined with the consumption of high caloric nutrient poor diets have been implicated as determinants of the obesity epidemic (French et al., 2001; Hill and Peters, 1998). "
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    ABSTRACT: Background: Higher levels of sedentary behavior are associated with adverse health outcomes. Over-reliance on private motor vehicles for transportation is a potential contributor to the obesity epidemic. The objective of this study was to review evidence on the relationship between motor vehicle travel distance and time and weight status among adults. Methods: Keywords associated with driving and weight status were entered into four databases (PubMed Medline Transportation Research Information Database and Web of Science) and retrieved article titles and abstracts screened for relevance. Relevant articles were assessed for their eligibility for inclusion in the review (English-language articles a sample ≥ 16 years of age included a measure of time or distance traveling in a motor vehicle and weight status and estimated the association between driving and weight status). Results: The database search yielded 2781 articles, from which 88 were deemed relevant and 10 studies met the inclusion criteria. Of the 10 studies included in the review, 8 found a statistically significant positive association between time and distance traveled in a motor vehicle and weight status. Conclusions: Multilevel interventions that make alternatives to driving private motor vehicles more convenient, such as walking and cycling, are needed to promote healthy weight in the adult population.
    Preventive Medicine 06/2014; 66. DOI:10.1016/j.ypmed.2014.06.002 · 3.09 Impact Factor
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    • "Obesity’s rise in prevalence over the past 30 years [1], coupled with knowledge of its public health burden [2-6], has opened debate over the best way to measure adiposity in populations. The body mass index - BMI [weight (kg)/height2 (m2)] - is a common measure in population-based surveys: it is relatively inexpensive, simple, and non-intrusive. "
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    ABSTRACT: Background National data on body mass index (BMI), computed from self-reported height and weight, is readily available for many populations including the Canadian population. Because self-reported weight is found to be systematically under-reported, it has been proposed that the bias in self-reported BMI can be corrected using equations derived from data sets which include both self-reported and measured height and weight. Such correction equations have been developed and adopted. We aim to evaluate the usefulness (i.e., distributional similarity; sensitivity and specificity; and predictive utility vis-à-vis disease outcomes) of existing and new correction equations in population-based research. Methods The Canadian Community Health Surveys from 2005 and 2008 include both measured and self-reported values of height and weight, which allows for construction and evaluation of correction equations. We focused on adults age 18–65, and compared three correction equations (two correcting weight only, and one correcting BMI) against self-reported and measured BMI. We first compared population distributions of BMI. Second, we compared the sensitivity and specificity of self-reported BMI and corrected BMI against measured BMI. Third, we compared the self-reported and corrected BMI in terms of association with health outcomes using logistic regression. Results All corrections outperformed self-report when estimating the full BMI distribution; the weight-only correction outperformed the BMI-only correction for females in the 23–28 kg/m2 BMI range. In terms of sensitivity/specificity, when estimating obesity prevalence, corrected values of BMI (from any equation) were superior to self-report. In terms of modelling BMI-disease outcome associations, findings were mixed, with no correction proving consistently superior to self-report. Conclusions If researchers are interested in modelling the full population distribution of BMI, or estimating the prevalence of obesity in a population, then a correction of any kind included in this study is recommended. If the researcher is interested in using BMI as a predictor variable for modelling disease, then both self-reported and corrected BMI result in biased estimates of association.
    BMC Public Health 05/2014; 14(1):430. DOI:10.1186/1471-2458-14-430 · 2.26 Impact Factor
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