Cost-effectiveness of universally funding smoking cessation pharmacotherapy
Bibliographic record
Abstract
RATIONALE: Tobacco use is the leading cause of preventable death in Canada and worldwide. Funding smoking cessation pharmacotherapy increases the likelihood of smoking cessation pharmacotherapy use, making a quit attempt, and the probability of abstinence from smoking.OBJECTIVES: The aim of our study was to model the cost-effectiveness of a health policy of funding smoking cessation pharmacotherapy for all Canadian smokers at no cost to the patient.METHODS: Using data from a Cochrane Review meta-analysis, a decision model incorporating utilization of smoking cessation products, quit rates, six-month continuous abstinence rates, relapse rate, and direct costs of smoking cessation pharmacotherapy and physician visits associated with full funding of smoking cessation pharmacotherapy for one quit attempt compared to no funding was constructed, and a sensitivity analysis was conducted incorporating variation in all inputs used. Our primary outcome was the incremental cost per life-year gained from universally funding smoking cessation pharmacotherapy compared to no funding for the average Canadian smoker.RESULTS: The average incremental cost per life-year gained of funding smoking cessation pharmacotherapy compared to no funding in the overall population was CaD$1030 (range CaD$250–10,040) per life-year gained. In our cost-effectiveness analysis, the probability that universally funding smoking cessation pharmacotherapy is cost-effective is >95% at a willingness-to-pay threshold as low as CaD$10,000 per life year gained.CONCLUSION: Universally funding smoking cessation pharmacotherapy appears cost-effective when compared to currently funded interventions in our healthcare system. Policy makers and government decision makers should fully fund smoking cessation pharmacotherapy for all Canadian smokers.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".