Smoking Cessation Treatment and Outcomes Patterns Simulation: A New Framework for Evaluating the Potential Health and Economic Impact of Smoking Cessation Interventions

Abstract

By allowing for multiple quit attempts over the course of individuals' lives, the simulation can provide more reliable estimates on the health and economic impact of interventions designed to increase abstinence from smoking. Furthermore, the individual nature of the simulation allows for evaluation of outcomes in populations with different baseline profiles. DES provides a framework for comprehensive and appropriate predictions when applied to smoking cessation over smoker lifetimes.

Smoking Cessation: A Comparison of Two Model Structures

“…The population prevalence of each comorbidity and relative risk for a smoker versus a former smoker were taken from various sourcesthe sources are shown in Table 1. We combined the population prevalence, relative risk by smoker status, and proportion of smokers in the population to estimate age-and gender-specific prevalence by smoker status for each comorbidity, consistent with the approach taken in previous models [8,21]. The equation below shows how prevalence in the general population can be decomposed, where C is prevalence, P is the proportion within the population, RR is relative risk, general is general population, never is never smokers, former is former smokers and current is current smokers:…”

“…Research has shown that relative risks of developing comorbidities decreases with time since cessation, so the DES allows this [23]. The equation to model this has previously been used in smoking DES models [8] and considers age and gender in addition to time since cessation. Utilising baseline characteristics and history is a known advantage of DES models over state-transition models and this equation was not considered in the state-transition model (although it may be possible with multiple tunnel states).…”

“…In 2013, Getsios et al reported that models which consider only one smoking cessation attempt lead to biased estimates of cost-effectiveness, and suggest that DES provides a framework for modelling multiple quit attempts in smoking cessation [8]. The majority of economic models in smoking cessation to date have been cohort-level state-transition (Markov) or decision tree models [9,10].…”

Budgetary Impact Analysis of Reimbursement Varenicline for the Smoking-Cessation Treatment in Patients with Cardiovascular Diseases, Chronic Obstructive Pulmonary Disease or Type-2 Diabetes Mellitus: A National Health System Perspective

“…A patient who stops smoking for a full year is considered a smoking-cessation success. The model provides for the possibility of further attempts with the same drug, up to a maximum of 3, during a 5-year period starting from the first attempt to quit, both in case of failure after the initial attempt as well as in case of relapse after a successful attempt [34] . Relapse was estimated at 3% annually [35] .…”

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

“…One DES model (Xenakis et al 2011) measured smoking cessation abstinence and relapse one year after a treatment attempt using clinical trial data comparing varenicline to bupropion or placebo, but changes in lifetime smoking behavior and smoking-related illness were not evaluated. More recently, Getsios et al (2013) use DES to model individual quit attempts and lifetime risk of diseases is determined and quality of life and cost is assigned, rather than being modeled explicitly. We use DES to measure and compare the impact of different pharmacologic and non-pharmacologic smoking cessation interventions (NRT, Varenicline, bupropion, non-pharmacologic-assisted [i.e., cold turkey]) on lifetime smoking-related disease prevalence (chronic obstructive pulmonary disease [COPD], lung cancer, myocardial infarction [MI], stroke), mortality, quality of life, and costs at the US population level.…”