Assessment of cost‐effective changes to the current and potential provision of smoking cessation services: an analysis based on the EQUIPTMOD
Bibliographic record
Abstract
BACKGROUND AND AIMS: Increasing the reach of smoking cessation services and/or including new but effective medications to the current provision may provide significant health and economic benefits; the scale of such benefits is currently unknown. The aim of this study was to estimate the cost-effectiveness from a health-care perspective of viable national level changes in smoking cessation provision in the Netherlands and England. METHODS: A Markov-based state transition model [European study on Quantifying Utility of Investment in Protection from Tobacco model (EQUIPTMOD)] was used to estimate costs and benefits [expressed in quality-adjusted life years (QALY)] of changing the current provision of smoking cessation programmes in the Netherlands and England. The changes included: (a) increasing the reach of top-level services to increase potential quitters (e.g. brief physician advice); (b) increasing the reach of behavioural support (group-based therapy and SMS text-messaging support) to increase the success rates; (c) including a new but effective medication (cytisine); and (d) all changes implemented together (combined change). The costs and QALYs generated by those changes over 2, 5, 10 years and a life-time were compared with that of the current practice in each country. Results were expressed as incremental net benefit (INB) and incremental cost-effectiveness ratio (ICER). A sequential analysis from a life-time perspective was conducted to identify the optimal change. RESULTS: The combined change was dominant (cost-saving) over all alternative changes and over the current practice, in both countries. The combined change would generate an incremental net benefit of €11.47 (2 years) to €56.16 (life-time) per smoker in the Netherlands and €9.96 (2 years) to €60.72 (life-time) per smoker in England. The current practice was dominated by all alternative changes. CONCLUSION: Current provision of smoking cessation services in the Netherlands and England can benefit economically from the inclusion of cytisine and increasing the reach of brief physician advice, text-messaging support and group-based therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".