Introduction: Estimates of initiation, cessation, and relapse rates of tobacco cigarette smoking and e-cigarette use can facilitate projections of longer-term impact of their use. We aimed to derive transition rates and apply them to validate a microsimulation model of tobacco that newly incorporated e-cigarettes.
Methods: We fit a Markov multi-state model (MMSM) for participants in Waves 1-4.5 of the Population Assessment of Tobacco and Health (PATH) longitudinal study. The MMSM had nine cigarette smoking and e-cigarette use states (current/former/never use of each), 27 transitions, two sex categories, and four age categories (youth: 12-17y; adults: 18-24y/25-44y/≥45y). We estimated transition hazard rates, including initiation, cessation, and relapse. We then validated the Simulation of Tobacco and Nicotine Outcomes and Policy (STOP) microsimulation model, by: (a) using transition hazard rates derived from PATH Waves 1-4.5 as inputs, and (b) comparing STOP-projected prevalence of smoking and e-cigarette use at 12 and 24 months to empirical data from PATH Waves 3 and 4. We compared the goodness-of-fit of validations with "static relapse" and "time-variant relapse," wherein relapse rates did not or did depend on abstinence duration.
Results: Per the MMSM, youth smoking and e-cigarette use was generally more volatile (lower probability of maintaining the same e-cigarette use status over time) than that of adults. Root-mean-squared error (RMSE) for STOP-projected versus empirical prevalence of smoking and e-cigarette use was <0.7% for both static and time-variant relapse simulations, with similar goodness-of-fit (static relapse: RMSE 0.69%, CI 0.38-0.99%; time-variant relapse: RMSE 0.65%, CI 0.42-0.87%). PATH empirical estimates of prevalence of smoking and e-cigarette use were mostly within the margins of error estimated by both simulations.
Discussion: A microsimulation model incorporating smoking and e-cigarette use transition rates from a MMSM accurately projected downstream prevalence of product use. The microsimulation model structure and parameters provide a foundation for estimating the behavioral and clinical impact of tobacco and e-cigarette policies.
Copyright: © 2023 Schwamm et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.