Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
Tobacco Research and Treatment Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
BMJ Open. 2020 May 12;10(5):e032579. doi: 10.1136/bmjopen-2019-032579.
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.
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).
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%).
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.
模拟模型可以预测烟草使用和戒烟的效果，并为烟草控制政策提供信息。大多数现有的烟草模型并未明确纳入复吸这一烟草使用自然史的关键组成部分。我们的目的是开发、校准和验证一种新的个体水平微观模拟模型，该模型将明确纳入吸烟复吸，并预测香烟吸烟行为和相关的死亡风险。
我们开发了模拟烟草和尼古丁结局与政策（STOP）模型，其中个体每月根据起始、戒烟和复吸的速率在烟草使用状态（当前/既往/从不）之间转换。模拟个体面临与烟草使用相关的分层死亡风险。对于美国男性和女性，我们使用癌症干预和监测建模网络（CISNET）模型进行了交叉验证。然后，我们纳入了吸烟复吸并校准了戒烟率，以反映暂时戒烟尝试与持续戒烟之间的差异。我们使用国家健康访谈调查（NHIS）和链接的国家死亡指数进行了外部验证。比较基于均方根误差（RMSE）。
在交叉验证中，STOP 生成的当前/既往/从不吸烟流行率预测与 CISNET 预测数据拟合良好（变异系数（CV）-RMSE≤15%）。在纳入吸烟复吸后，将 CISNET 报告的女性/男性戒烟率乘以 7.75/7.25，以反映戒烟尝试与持续戒烟的比例，这使得与 CISNET 报告的吸烟流行率最接近（CV-RMSE 2%/3%）。使用这些新乘数进行外部验证时，与 NHIS 相比，STOP 生成的 20 岁当前吸烟者和从不吸烟者的累积死亡率曲线的 CV-RMSE 均≤1%。在模拟那些在 1997 年接受 NHIS 调查的人群时，STOP 预测的当前/既往/从不吸烟者的年患病率（1998-2009 年）与 NHIS 报告的结果相似（CV-RMSE 12%）。
纳入复吸的 STOP 模型在验证美国吸烟流行率和死亡率时表现良好。STOP 为烟草和尼古丁产品使用的政策相关分析提供了一个灵活的框架。
