Modeling the cost of inaction in treating obesity in Canada
Bibliographic record
Abstract
BACKGROUND: Obesity prevalence continues to rise in Canada, highlighting a growing public health concern. This study updates estimates of the societal cost of inaction in treating obesity, emphasizing the significant economic burden stemming from both direct healthcare costs and indirect productivity losses. METHODS: We combined data from national surveys and published literature to estimate the 2023 national economic implications of obesity. Comparing adults with obesity (BMI ≥ 30) to those with healthy weight (25 > BMI ≥ 18.5), we assessed healthcare costs, absenteeism, presenteeism, disability pensions, mortality-related costs, workforce participation, and earnings. Canadian data were used where possible, supplemented by U.S. data, standardized to 2023 CAD$. RESULTS: The cost of inaction in treating obesity in Canada was $27.6 billion in 2023, including $5.9 billion in direct healthcare and $21.7 billion in indirect costs. Excess healthcare costs are driven by higher utilization of medical services. Indirect costs include approximately $8.2 billion from reduced workforce participation, $6.8 billion from presenteeism, $3.8 billion in lower earnings among employed with obesity, $2.0 billion from lost wages due to premature mortality, $682 million from absenteeism, and $268 million from disability pensions. CONCLUSIONS: The economic implications of not addressing obesity effectively are substantial, emphasizing the urgent need for utilizing effective chronic disease management strategies. Our findings highlight the disproportionate impact on women and the broader economic consequences, underscoring the imperative for tailored policy interventions. Investing in comprehensive, evidence-based obesity management not only enhances individual well-being but also yields significant societal and economic benefits.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".