Economic evaluation of a hospital-initiated tobacco dependence treatment service
Bibliographic record
Abstract
Abstract The treatment of tobacco dependence in patients admitted to hospital is a priority for the National Health Service in England. We conducted an economic analysis of a pilot intervention adapted from the Ottawa Model of Smoking Cessation, implemented in a major teaching hospital in London, England. The cost-per-patient, cost-per-quit and Incremental Cost Effectiveness Ratio were estimated for 673 patients who smoked and who received the intervention after being admitted to one of 11 acute wards between July 2020 and June 2021. Patient-level readmission costs and bed-days from six months after discharge were compared between the intervention group and a group of benchmark patients who smoked and who did not receive the intervention. The total cost of the intervention was £178,105. On the basis of 104 patients who reported not smoking at six months, the cost-per-quit was £1712.55. Among 611 patients who were successfully matched to a benchmark cohort, re-admissions for patients in the intervention group cost £492k less than their benchmark equivalents over 21 months from January 2021 to September 2022 (£266k vs £758k), incurred 414 fewer bed days (303 vs 717), and re-admitted at a lower rate (5% vs 11%). Lower readmission rates and costs were associated with the intervention regardless of patient smoking status at six months, except among those who had opted out. A pilot tobacco dependence treatment intervention implemented in an acute hospital setting in London demonstrated value for money through reduced readmission rates and costs among all patients who received it.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".