Cost‐effectiveness of alternative smoking cessation scenarios in Spain: results from the EQUIPTMOD
Bibliographic record
Abstract
AIMS: To assess the cost-effectiveness of alternative smoking cessation scenarios from the perspective of the Spanish National Health Service (NHS). DESIGN: We used the European study on Quantifying Utility of Investment in Protection from Tobacco model (EQUIPTMOD), a Markov-based state transition economic model, to estimate the return on investment (ROI) of: (a) the current provision of smoking cessation services (brief physician advice and printed self-helped material + smoking ban and tobacco duty at current levels); and (b) four alternative scenarios to complement the current provision: coverage of proactive telephone calls; nicotine replacement therapy (mono and combo) [prescription nicotine replacement therapy (Rx NRT)]; varenicline (standard duration); or bupropion. A rate of 3% was used to discount life-time costs and benefits. SETTING: Spain. PARTICIPANTS: Adult smoking population (16+ years). MEASUREMENTS: Health-care costs associated with treatment of smoking attributable diseases (lung cancer, coronary heart disease, chronic obstructive pulmonary infection and stroke); intervention costs; quality-adjusted life years (QALYs). Costs and outcomes were summarized using various ROI estimates. FINDINGS: The cost of implementing the current provision of smoking cessation services is approximately €61 million in the current year. This translates to 18 quitters per 1000 smokers and a life-time benefit-cost ratio of 5, compared with no such provision. All alternative scenarios were dominant (cost-saving: less expensive to run and generated more QALYs) from the life-time perspective, compared with the current provision. The life-time benefit-cost ratios were: 1.87 (proactive telephone calls); 1.17 (Rx NRT); 2.40 (varenicline-standard duration); and bupropion (2.18). The results remained robust in the sensitivity analysis. CONCLUSIONS: According to the EQUIPTMOD modelling tool it would be cost-effective for the Spanish authorities to expand the reach of existing GP brief interventions for smoking cessation, provide pro-active telephone support, and reimburse smoking cessation medication to smokers trying to stop. Such policies would more than pay for themselves in the long run.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".