Health economic model to evaluate the cost-effectiveness of smoking cessation services integrated within lung cancer screening
Bibliographic record
Abstract
ABSTRACT Introduction Integrating smoking cessation support into lung cancer screening can improve abstinence rates. However, healthcare decision makers need evidence of cost effectiveness to understand the cost/benefit of adopting this approach. Methods To evaluate the cost-effectiveness of different smoking cessation interventions, and service delivery, we used a Markov model, adapted from previous National Institute for Health and Care Excellence guidelines on smoking cessation. This uses long-term epidemiological data to capture the prevalence of the smoking-related illnesses, where prevalence is estimated based on age, sex, and smoking status. Probabilistic sensitivity analysis was conducted to capture joint parameter uncertainty. Results All smoking cessation interventions appeared cost-effective at a threshold of £20,000 per quality-adjusted life year, compared to no intervention or behavioural support alone. Offering immediate smoking cessation as part of lung cancer screening appointments, compared with usual care (onward referral to stop smoking services) was also estimated to be cost-effective with a net monetary benefit of £2,198 per person, and a saving of between £34 and £79 per person in reduced workplace absenteeism among working age attendees. Estimated healthcare cost savings were more than four times greater in the most deprived quintile compared to the least deprived, alongside a fivefold increase in QALYs accrued. Conclusions Smoking cessation interventions within lung cancer screening are cost-effective and should be integrated so that treatment is initiated during screening visits. This is likely to reduce overall costs to the health service, and wider integrated care systems, improve quality and length of life, and may lessen health inequalities. Key messages What is already known on this topic? Smoking cessation interventions are known to be cost-effective in general. However, their cost-effectiveness specifically within lung cancer screening programmes, where they are not routinely commissioned, remains to be established. What this study adds This health economic analysis estimates that offering smoking cessation immediately within a lung cancer screening visits is a cost-effective intervention, with a substantial return on investment for the healthcare service, alongside a reduction in health inequalities and an increase in productivity for the wider economy. How this study might affect research, practice or policy This economic evaluation will provide those commissioning and planning healthcare services with evidence that supports the case for funding smoking cessation services integrated within lung cancer screening programmes as immediate, opt-out services.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".