Economic evaluation of a hospital-initiated intervention for smokers with chronic disease, in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Cigarette smoking causes many chronic diseases that are costly and result in frequent hospitalisation. Hospital-initiated smoking cessation interventions increase the likelihood that patients will become smoke-free. We modelled the cost-effectiveness of the Ottawa Model for Smoking Cessation (OMSC), an intervention that includes in-hospital counselling, pharmacotherapy and posthospital follow-up, compared to usual care among smokers hospitalised with acute myocardial infarction (AMI), unstable angina (UA), heart failure (HF), and chronic obstructive pulmonary disease (COPD). METHODS: We completed a cost-effectiveness analysis based on a decision-analytic model to assess smokers hospitalised in Ontario, Canada for AMI, UA, HF, and COPD, their risk of continuing to smoke and the effects of quitting on re-hospitalisation and mortality over a 1-year period. We calculated short-term and long-term cost-effectiveness ratios. Our primary outcome was 1-year cost per quality-adjusted life year (QALY) gained. RESULTS: From the hospital payer's perspective, delivery of the OMSC can be considered cost effective with 1-year cost per QALY gained of $C1386, and lifetime cost per QALY gained of $C68. In the first year, we calculated that provision of the OMSC to 15 326 smokers would generate 4689 quitters, and would prevent 116 rehospitalisations, 923 hospital days, and 119 deaths. Results were robust within numerous sensitivity analyses. DISCUSSION: The OMSC appears to be cost-effective from the hospital payer perspective. Important consideration is the relatively low intervention cost compared to the reduction in costs related to readmissions for illnesses associated with continued smoking.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".