Abstract
Background
Most economic evaluations of smoking cessation interventions have used cohort state-transition models. Discrete event simulations (DESs) have been proposed as a superior approach.
Objective
We developed a state-transition model and a DES using the discretely integrated condition event (DICE) framework and compared the cost-effectiveness results. We performed scenario analysis using the DES to explore the impact of alternative assumptions.
Methods
The models estimated the costs and quality-adjusted life years (QALYs) for the intervention and comparator from the perspective of the UK National Health Service and Personal Social Services over a lifetime horizon. The models considered five comorbidities: chronic obstructive pulmonary disease, myocardial infarction, coronary heart disease, stroke and lung cancer. The state-transition model used prevalence data, and the DES used incidence. The costs and utility inputs were the same between two models and consistent with those used in previous analyses for the National Institute for Health and Care Excellence.
Results
In the state-transition model, the intervention produced an additional 0.16 QALYs at a cost of £540, leading to an incremental cost-effectiveness ratio (ICER) of £3438. The comparable DES scenario produced an ICER of £5577. The ICER for the DES increased to £18,354 when long-term relapse was included.
Conclusions
The model structures themselves did not influence smoking cessation cost-effectiveness results, but long-term assumptions did. When there is variation in long-term predictions between interventions, economic models need a structure that can reflect this.
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