Funding a Smoking Cessation Program for Crohn’s Disease: An Economic Evaluation
Bibliographic record
Abstract
OBJECTIVES: Patients with Crohn's disease (CD) who smoke are at a higher risk of flaring and requiring surgery. Cost-effectiveness studies of funding smoking cessation programs are lacking. Thus, we performed a cost-utility analysis of funding smoking cessation programs for CD. METHODS: A cost-utility analysis was performed comparing five smoking cessation strategies: No Program, Counseling, Nicotine Replacement Therapy (NRT), NRT+Counseling, and Varenicline. The time horizon for the Markov model was 5 years. The health states included medical remission (azathioprine or antitumor necrosis factor (anti-TNF), dose escalation of an anti-TNF, second anti-TNF, surgery, and death. Probabilities were taken from peer-reviewed literature, and costs (CAN$) for surgery, medications, and smoking cessation programs were estimated locally. The primary outcome was the cost per quality-adjusted life year (QALY) gained associated with each smoking cessation strategy. Threshold, three-way sensitivity, probabilistic sensitivity analysis (PSA), and budget impact analysis (BIA) were carried out. RESULTS: All strategies dominated No Program. Strategies from most to least cost effective were as follows: Varenicline (cost: $55,614, QALY: 3.70), NRT+Counseling (cost: $58,878, QALY: 3.69), NRT (cost: $59,540, QALY: 3.69), Counseling (cost: $61,029, QALY: 3.68), and No Program (cost: $63,601, QALY: 3.67). Three-way sensitivity analysis demonstrated that No Program was only more cost effective when every strategy's cost exceeded approximately 10 times their estimated costs. The PSA showed that No Program was the most cost-effective <1% of the time. The BIA showed that any strategy saved the health-care system money over No Program. CONCLUSIONS: Health-care systems should consider funding smoking cessation programs for CD, as they improve health outcomes and reduce costs.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".