Economic evaluation of a hospital-initiated tobacco dependence treatment service
Bibliographic record
Abstract
OBJECTIVES: The treatment of tobacco dependence in patients admitted to hospital is a priority for the National Health Service in England. We aimed to conduct an economic analysis of a pilot 'opt-out' tobacco dependence treatment intervention adapted from the Ottawa Model of Smoking Cessation. DESIGN: Observational cost analysis of an inpatient tobacco dependence treatment intervention, and matched cohort study comparing readmission costs between patients who received the intervention and benchmarked equivalents who did not. SETTING: 11 acute inpatient wards in a major teaching hospital in London, England. PARTICIPANTS: 673 patients who smoked, admitted between 1 July 2020 and 30 June 2021. INTERVENTIONS: The intervention consisted of the systematic identification of smoking status, automatic referral to tobacco dependence advisors, provision of pharmacotherapy and behavioural support throughout the hospital stay and telephone support for 6 months after discharge. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcomes were cost-per-patient, cost-per-quit and incremental cost effectiveness ratio among patients who received the intervention. The secondary outcomes were patient-level readmission costs and bed-days from 6 months after discharge, compared between the intervention group and a group of matched benchmark patients who smoked but did not receive the intervention. RESULTS: The total cost of the intervention was £178 105. On the basis of 104 patients who reported not smoking at 6 months, the cost-per-quit was £1712.55, equating to an estimated age-adjusted incremental cost per life year gained of £3325. Among 611 patients who were successfully matched to a benchmark cohort, readmissions for patients in the intervention group cost £492 k less than their benchmark equivalents over 21 months from 1 January 2021 to 30 September 2022 (£266 k vs £758 k), incurred 414 fewer bed days (303 vs 717) and readmitted at a lower rate (5% vs 11%). There were reduced readmission rates and costs among all patients who received the intervention compared with their benchmarked equivalents, regardless of smoking status at 6 months, except among those who opted out. CONCLUSIONS: A pilot 'opt-out' 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 | 0.013 | 0.043 |
| 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 | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".