Cost‐effectiveness of increasing the reach of smoking cessation interventions in Germany: results from the EQUIPTMOD
Bibliographic record
Abstract
AIMS: To evaluate costs, effects and cost-effectiveness of increased reach of specific smoking cessation interventions in Germany. DESIGN: A Markov-based state transition return on investment model (EQUIPTMOD) was used to evaluate current smoking cessation interventions as well as two prospective investment scenarios. A health-care perspective (extended to include out-of-pocket payments) with life-time horizon was considered. A probabilistic analysis was used to assess uncertainty concerning predicted estimates. SETTING: Germany. PARTICIPANTS: Cohort of current smoking population (18+ years) in Germany. INTERVENTIONS: Interventions included group-based behavioural support, financial incentive programmes and varenicline. For prospective scenario 1 the reach of group-based behavioral support, financial incentive programme and varenicline was increased by 1% of yearly quit attempts (= 57 915 quit attempts), while prospective scenario 2 represented a higher reach, mirroring the levels observed in England. MEASUREMENTS: EQUIPTMOD considered reach, intervention cost, number of quitters, quality-of-life years (QALYs) gained, cost-effectiveness and return on investment. FINDINGS: The highest returns through reduction in smoking-related health-care costs were seen for the financial incentive programme (€2.71 per €1 invested), followed by that of group-based behavioural support (€1.63 per €1 invested), compared with no interventions. Varenicline had lower returns (€1.02 per €1 invested) than the other two interventions. At the population level, prospective scenario 1 led to 15 034 QALYs gained and €27 million cost-savings, compared with current investment. Intervention effects and reach contributed most to the uncertainty around the return-on-investment estimates. At a hypothetical willingness-to-pay threshold of only €5000, the probability of being cost-effective is approximately 75% for prospective scenario 1. CONCLUSIONS: Increasing the reach of group-based behavioural support, financial incentives and varenicline for smoking cessation by just 1% of current annual quit attempts provides a strategy to German policymakers that improves the population's health outcomes and that may be considered cost-effective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".