Cost-Effectiveness of Smoking Cessation Within a Lung Cancer Screening Program in Canada
Bibliographic record
Abstract
Background: Demonstrated lung cancer mortality reductions through low-dose computed tomography (LDCT) has encouraged some jurisdictions to consider implementing organized LDCT screening. A retrospective analysis of former smokers in the National Lung Screening Trial (NLST) suggested that abstention from smoking coupled with low-dose computed tomography (LDCT) screening realized more mortality benefits than abstinence alone or LDCT alone. Aim: We evaluated the potential costs and cost-effectiveness of lung cancer screening with integrated smoking cessation using OncoSim-Lung (version 2.5), a microsimulation model led by the Canadian Partnership Against Cancer, with model development by Statistics Canada. Methods: We compared organized LDCT screening without smoking cessation to various plausible scenarios of screening with cessation. Assumptions included: annual screening of 55-74 year-old individuals with a 30-pack-yr history; a 42% participation rate reached over 10 years; cessation therapy (nicotine replacement therapy + varenicline + 12 weeks' counseling) at a cost of $490; and up to 10 cessation attempts, with a permanent quit rate of 5% per attempt. Cost-effectiveness was estimated with a lifetime horizon, health system perspective and 1.5% discount rate. Costs are in 2016 CAD. Results: OncoSim-Lung projected that LDCT screening integrated with cessation would cost approximately $76 million annually (undiscounted) from 2017 to 2036 in Canada. About 110 fewer lung cancer (LC) cases and 50 fewer LC deaths would occur annually, compared with screening without cessation. Additionally, many other smoking-related deaths would be prevented. Using a lifetime horizon, smoking cessation would cost $14,000/QALYs gained. In one-way sensitivity analysis, with a 72% participation rate there would be 260 fewer deaths, at $24,000/QALY. With a 10% quit rate, cost-effectiveness would improve to $6,000/QALY. A 50% increase in the cost of the cessation intervention would decrease cost-effectiveness to $22,000/QALY. Conclusion: Robust smoking cessation efforts within a LDCT screening program could save lives and be relatively cost-effective. Cancer control planners should consider integrating smoking cessation when implementing a lung cancer screening initiative.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".