A Cost-Utility Analysis of Smoking Cessation Programs for Patients with Crohnʼs Disease
Bibliographic record
Abstract
Purpose: Smoking worsens the prognosis of Crohn's disease (CD) by increasing the risk of flaring and surgery; however, smoking cessation programs for CD patients are lacking. Studies evaluating the costs and benefits of introducing a smoking cessation program for CD patients are needed. We assessed the cost-utility of implementing various smoking cessation programs among patients with CD. Methods: TreeAge Pro 2012 was used to create a cost-utility analysis using a Markov model from the perspective of a publicly funded health care system. The base case was a 35-year old smoker with CD in remission on azathioprine. In addition to no program, four smoking cessation strategies were evaluated over a 5-year time horizon: nicotine replacement therapy (NRT), counseling, NRT + counseling, and varenicline (Champix). Health states accounted for disease remission and flares. Patients were on medical therapy (azathioprine or an anti-TNF), had anti-TNF dose escalations, switched to a 2nd anti-TNF, had surgery, or died. The measure of effectiveness was quality-adjusted life-years (QALYs) gained. Direct costs in Canadian dollars were estimated for smoking cessation programs, medications, and surgery. Utilities were derived from the IBD literature. Analyses were performed using Markov cohort simulation with second-order Monte Carlo simulation, used to derive means and 95% confidence intervals (CI). Threshold and probabilistic sensitivity analyses were done. Results: All smoking cessation programs were less costly and more effective than no program. The most cost-effective strategy was varenicline, at $45,653 (95% CI $43,445-$47,862) per patient over 5 years. Prescribing varenicline saved $4,000 per patient when compared to no program. The remaining strategies from most to least cost-effective were NRT + counseling, NRT, and counseling. A threshold analysis demonstrated that no program was the least costly approach only when the other smoking cessation strategies cost over 10 times their modeled cost. Conclusion: Over a 5-year period, all smoking cessation strategies were cost-saving and at least as effective for CD patients compared to the most common current strategy of no program. These cost savings and benefits are in addition to the other positive effects of smoking cessation. Healthcare systems should urgently invest in smoking cessation programs targeted at CD patients.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".