A discrete event simulation model to estimate population level health and economic impacts of smoking cessation interventions

We design and develop a predictive model that estimates health and economic outcomes associated with smoking cessation interventions using discrete-event simulation (DES). Outcomes include estimates of sustained abstinence from smoking, quality of life years gained, cost of treatment, additional health-related morbidity due to long-term effects of smoking (e.g. lung cancer, stroke), and cost-effectiveness of the various smoking cessation options. Interventions assessed include nicotine replacement therapy (patch or gum,), oral medication (bupropion and varenicline), and abstinence without pharmacologic assistance. The DES approach allows us to account for heterogeneity of patients and dynamic changes in disease progression. Results show that even a single quit attempt can be cost-effective over the patients´ lifetime. Furthermore, based on the incremental cost-effectiveness ratios, varenicline dominates other treatments at 10 years, 30 years, and over the lifetime. Understanding the comparative effectiveness and cost of alternative smoking cessation strategies can improve clinical and patient decision-making.