Long-Term Cost-Effectiveness of Smoking Cessation Interventions in People With Mental Disorders: A Dynamic Decision Analytical Model

OBJECTIVES: People with mental disorders are more likely to smoke than the general population. The objective of this study is to develop a decision analytical model that estimates long-term cost-effectiveness of smoking cessation interventions in this population. METHODS: A series of Markov models were constructed to estimate average lifetime smoking-attributable inpatient cost and expected quality-adjusted life-years. The model parameters were estimated using a variety of data sources. The model incorporated uncertainty through probabilistic sensitivity analysis using Monte Carlo simulations. It also generated tables presenting incremental cost-effectiveness ratios of the proposed interventions with varying incremental costs and incremental quit rates. We used data from 2 published trials to demonstrate the model’s ability to make projections beyond the observational time frame. RESULTS: The average smoker’s smoking-attributable inpatient cost was 3 times higher and health utility was 5% lower than ex-smokers. The intervention in the trial with a statistically insignificant difference in quit rate (19% vs 25%; P=.2) showed a 45% to 49% chance of being cost-effective compared with the control at willingness-to-pay thresholds of £20 000 to £30 000/quality-adjusted life-years. The second trial had a significant outcome (quit rate 35.9% vs 15.6%; P<.001), and the corresponding probability of the intervention being cost-effective was 65%. CONCLUSIONS: This model provides a consistent platform for clinical trials to estimate the potential lifetime cost-effectiveness of smoking cessation interventions for people with mental disorders and could help commissioners direct resources to the most cost-effective programs. However, direct comparisons of results between trials must be interpreted with caution owing to their different designs and settings.