Obesity and overweight in Canada: an updated cost‐of‐illness study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".