Return on investment of Canadian tobacco control policies implemented between 2001 and 2016
Bibliographic record
Abstract
OBJECTIVES: To determine the return on investment (ROI) associated with tobacco control policies implemented between 2001 and 2016 in Canada. METHODS: Canadian expenditures on tobacco policies were collected from government sources. The economic benefits considered in our analyses (decrease in healthcare costs, productivity costs and monetised life years lost, as well as tax revenues) were based on the changes in smoking prevalence and attributable deaths derived from the SimSmoke simulation model for the period 2001-2016. The net economic benefit (monetised benefits minus expenditures) and ROI associated with these policies were determined from the government and societal perspectives. Sensitivity analyses were conducted to check the robustness of the result. Costs were expressed in 2019 Canadian dollars. RESULTS: The total of provincial and federal expenditures associated with the implementation of tobacco control policies in Canada from 2001 through 2016 was estimated at $2.4 billion. Total economic benefits from these policies during that time were calculated at $49.2 billion from the government perspective and at $54.2 billion from the societal perspective. The corresponding ROIs were $19.8 and $21.9 for every dollar invested. Sensitivity analyses yielded ROI values ranging from $16.3 to $28.3 for every dollar invested depending on the analyses and perspective. CONCLUSIONS: This analysis has found that the costs to implement the Canadian tobacco policies between 2001 and 2016 were far outweighed by the monetised value associated with the benefits of these policies, making a powerful case for the investment in tobacco control policies.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".