Singh Ankur, Wilson Nick, Blakely Tony
Centre for Health Equity, Melbourne School of Population & Global Health, University of Melbourne, Melbourne, Victoria, Australia.
Public Health, University of Otago, Wellington, New Zealand.
Tob Control. 2020 Jun 25. doi: 10.1136/tobaccocontrol-2019-055425.
To prioritise tobacco control interventions, simulating their health impacts is valuable. We undertook a systematic review of tobacco intervention simulation models to assess model structure and input variations that may render model outputs non-comparable.
We applied a Medline search with keywords intersecting modelling and tobacco. Papers were limited to those modelling health outputs (eg, mortality, health-adjusted life years), and at least two of cancer, cardiovascular and respiratory diseases. Data were extracted for each simulation model with ≥3 arising papers, including: model type, untimed or with time steps and trends in business-as-usual (BAU) tobacco prevalence and epidemiology.
Of 1911 papers, 186 met the inclusion criteria, including 13 eligible simulation models. The SimSmoke model had the largest number of publications (n=46), followed by Benefits of Smoking Cessation on Outcomes (n=12) and Tobacco Policy Model (n=10). Two of 13 models only estimated deaths averted, 1 had no time steps, 5 had no future trends in BAU tobacco prevalence, 9 had no future trends in BAU disease epidemiology and 7 had no time lags from quitting tobacco to reversal of health harm.
Considerable heterogeneity exists in simulation models, making outputs substantively non-comparable between models. Ranking of interventions by one model may be valid. However, this may not be true if, for example, interventions that differentially affect age groups (eg, a tobacco-free generation policy vs increased cessation among adults) do not account for plausible future trends. Greater standardisation of model structures and outputs will allow comparison across models and countries, and for comparisons of the impact of tobacco control interventions with other preventive interventions.
为了确定烟草控制干预措施的优先级，模拟其对健康的影响很有价值。我们对烟草干预模拟模型进行了系统综述，以评估可能导致模型输出不可比的模型结构和输入差异。
我们在Medline数据库中进行搜索，关键词为与建模和烟草相关的交叉词汇。论文仅限于那些对健康产出（如死亡率、健康调整生命年）进行建模的研究，且涉及癌症、心血管疾病和呼吸系统疾病中的至少两种。提取了每个有≥3篇相关论文的模拟模型的数据，包括：模型类型、无时间步长或有时间步长以及常规烟草流行率和流行病学趋势。
在1911篇论文中，186篇符合纳入标准，包括13个合格的模拟模型。SimSmoke模型的出版物数量最多（n = 46），其次是戒烟对结局的益处模型（n = 12）和烟草政策模型（n = 10）。13个模型中有2个仅估计了避免的死亡人数，1个没有时间步长，5个没有常规烟草流行率的未来趋势，9个没有常规疾病流行病学的未来趋势，7个没有从戒烟到健康危害逆转的时间滞后。
模拟模型中存在相当大的异质性，使得不同模型的输出在实质上不可比。由一个模型对干预措施进行排名可能是有效的。然而，如果例如对不同年龄组有不同影响的干预措施（例如无烟草一代政策与成年人戒烟增加）没有考虑到合理的未来趋势，情况可能并非如此。模型结构和输出的更大标准化将允许跨模型和国家进行比较，并将烟草控制干预措施的影响与其他预防性干预措施进行比较。
