Economic evaluation of smoking cessation in Ontario's regional cancer programs
Bibliographic record
Abstract
Quitting smoking after a diagnosis of cancer results in greater response to treatment and decreased risk of disease recurrence and second primary cancers. The objective of this study was to evaluate the potential cost-effectiveness of two smoking cessation approaches: the current basic smoking cessation program consisting of screening for tobacco use, advice, and referral; and a best practice smoking cessation program that includes the current basic program with the addition of pharmacological therapy, counseling, and follow-up. A Markov model was constructed that followed 65-year-old smokers with cancer over a lifetime horizon. Transition probabilities and mortality estimates were obtained from the published literature. Costs were obtained from standard costing sources in Ontario and reports. Probabilistic and deterministic sensitivity analyses were conducted to address parameter uncertainties. For smokers with cancer, the best practice smoking cessation program was more effective and more costly than the basic smoking cessation program. The incremental cost-effectiveness ratio of the best practice smoking cessation program compared to the basic smoking cessation program was $3367 per QALY gained and $5050 per LY gained for males, and $2050 per QALY gained and $4100 per LY gained for females. Results were most sensitive to the hazard ratio of mortality for former and current smokers, the probability of quitting smoking through participation in the program and smoking-attributable costs. The study results suggested that a best practice smoking cessation program could be a cost-effective option. These findings can support and guide implementation of smoking cessation programs.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".