Assessing the Fiscal Burden of Obesity in Canada by Applying a Public Economic Framework
Bibliographic record
Abstract
INTRODUCTION: Rising obesity prevalence is a health priority for many governments because of its impact on population health and economic consequences. We sought to estimate the broader consequences of obesity in Canada by applying a government perspective framework that captures lost tax revenues and increased government spending on social benefit programs. METHODS: An age-specific prevalence-based model was built to quantify the fiscal burden of disease for government attributed to people living with obesity. The model was populated with age-specific wages, employment activity and government benefits received to estimate taxes and transfer costs. A targeted literature search was conducted to identify modifiers of employment status, wages and disability status attributed to people with obesity, and applied to employment and epidemiological projections which enabled us to estimate government costs and tax losses. Government tax revenue and costs attributed to obesity were projected over a 10-year period and discounted at 3%. RESULTS: The fiscal burden of obesity in Canada is estimated at CAD$22,974 million (2021). This figure consists of obesity-attributed revenue losses of CAD$9404 million from direct taxes due to decreased employment activity and CAD$2374 million from indirect tax revenue losses due to reduced consumption taxes. Healthcare costs are estimated at CAD$7881 million annually and disability costs of CAD$3686 million annually. This fiscal burden of disease distributed amongst taxpayers in 2021 is estimated to be CAD$752 per capita. We estimate for every 1% reduction in obesity prevalence, CAD$229.7 million net fiscal gains can be achieved annually. CONCLUSIONS: Obesity is associated with substantial clinical and economic burden not only to the healthcare system but also to wider government budgets as demonstrated using fiscal analysis. Reductions in obesity prevalence are likely to have positive fiscal gains for government from reduced spending on public benefits and increased tax revenue attributed to employment changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".