Medical Practice Evaluation Center, Mongan Institute, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
Harvard Medical School, Boston, Massachusetts, United States of America.
PLoS One. 2023 Apr 14;18(4):e0284426. doi: 10.1371/journal.pone.0284426. eCollection 2023.
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.
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.
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.
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.
估算香烟和电子烟的起始、停止和复发率可以帮助预测其使用的长期影响。我们旨在得出转移率,并将其应用于验证新纳入电子烟的烟草微观模拟模型。
我们对人口烟草与健康评估(PATH)纵向研究的第 1 至 4.5 波参与者进行了马尔可夫多状态模型(MMSM)拟合。MMSM 有九个香烟吸烟和电子烟使用状态(每种的当前/以前/从不使用),27 个转换,两个性别类别和四个年龄类别(青少年:12-17 岁;成年人:18-24 岁/25-44 岁/≥45 岁)。我们估计了转移风险率,包括起始、停止和复发。然后,我们通过以下两种方法验证了模拟烟草和尼古丁结果与政策(STOP)微观模拟模型:(a)将来自 PATH 第 1-4.5 波的转移风险率作为输入,(b)将 STOP 预测的吸烟和电子烟使用流行率与 PATH 第 3 和 4 波的实证数据进行比较。我们比较了使用“静态复发”和“时变复发”的验证的拟合优度,其中复发率与禁欲时间无关或依赖于禁欲时间。
根据 MMSM,青少年吸烟和电子烟使用通常更不稳定(随着时间的推移,维持相同电子烟使用状态的可能性较低),而成年人则较为稳定。STOP 预测的吸烟和电子烟使用流行率与实证数据的均方根误差(RMSE)<0.7%,对于静态和时变复发模拟均具有相似的拟合优度(静态复发:RMSE 0.69%,CI 0.38-0.99%;时变复发:RMSE 0.65%,CI 0.42-0.87%)。PATH 实证估计的吸烟和电子烟使用流行率大多在两种模拟估计的误差范围内。
纳入 MMSM 中吸烟和电子烟使用转移率的微观模拟模型准确预测了产品使用的下游流行率。微观模拟模型结构和参数为评估烟草和电子烟政策的行为和临床影响提供了基础。