Novel microsimulation model of tobacco use behaviours and outcomes: calibration and validation in a US population. Issue 5 (12th May 2020)

• Record Type: Journal Article
• Authors: Reddy, Krishna P; Bulteel, Alexander J B; Levy, Douglas E; Torola, Pamela; Hyle, Emily P; Hou, Taige; Osher, Benjamin; Yu, Liyang; Shebl, Fatma M; Paltiel, A David; Freedberg, Kenneth A; Weinstein, Milton C; Rigotti, Nancy A; Walensky, Rochelle P
• Is Part Of: BMJ open. Volume 10:Issue 5(2020)
• Publication Date: 2020-05-12
• Subjects: tobacco -- nicotine -- model -- validation -- calibration -- relapse; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2019-032579
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Background and objective: Simulation models can project effects of tobacco use and cessation and inform tobacco control policies. Most existing tobacco models do not explicitly include relapse, a key component of the natural history of tobacco use. Our objective was to develop, calibrate and validate a novel individual-level microsimulation model that would explicitly include smoking relapse and project cigarette smoking behaviours and associated mortality risks. Methods: We developed the Simulation of Tobacco and Nicotine Outcomes and Policy (STOP) model, in which individuals transition monthly between tobacco use states (current/former/never) depending on rates of initiation, cessation and relapse. Simulated individuals face tobacco use-stratified mortality risks. For US women and men, we conducted cross-validation with a Cancer Intervention and Surveillance Modeling Network (CISNET) model. We then incorporated smoking relapse and calibrated cessation rates to reflect the difference between a transient quit attempt and sustained abstinence. We performed external validation with the National Health Interview Survey (NHIS) and the linked National Death Index. Comparisons were based on root-mean-square error (RMSE). Results: In cross-validation, STOP-generated projections of current/former/never smoking prevalence fit CISNET-projected data well (coefficient of variation (CV)-RMSE≤15%). After incorporating smoking relapse, multiplying the CISNET-reported cessation rates for women/men by 7.75/7.25, to reflect the ratio of quit attempts to sustained abstinence, resulted in the best approximation to CISNET-reported smoking prevalence (CV-RMSE 2%/3%). In external validation using these new multipliers, STOP-generated cumulative mortality curves for 20-year-old current smokers and never smokers each had CV-RMSE ≤1% compared with NHIS. In simulating those surveyed by NHIS in 1997, the STOP-projected prevalence of current/former/never smokers annually (1998–2009) was similar to that reported by NHIS (CV-RMSE 12%). Conclusions: The STOP model, with relapse included, performed well when validated to US smoking prevalence and mortality. STOP provides a flexible framework for policy-relevant analysis of tobacco and nicotine product use.