Blakely Tony, Moss Rob, Collins James, Mizdrak Anja, Singh Ankur, Carvalho Natalie, Wilson Nick, Geard Nicholas, Flaxman Abraham
Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
Institute of Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Int J Epidemiol. 2020 Oct 1;49(5):1624-1636. doi: 10.1093/ije/dyaa132.
Burden of Disease studies-such as the Global Burden of Disease (GBD) Study-quantify health loss in disability-adjusted life-years. However, these studies stop short of quantifying the future impact of interventions that shift risk factor distributions, allowing for trends and time lags. This methodology paper explains how proportional multistate lifetable (PMSLT) modelling quantifies intervention impacts, using comparisons between three tobacco control case studies [eradication of tobacco, tobacco-free generation i.e. the age at which tobacco can be legally purchased is lifted by 1 year of age for each calendar year) and tobacco tax]. We also illustrate the importance of epidemiological specification of business-as-usual in the comparator arm that the intervention acts on, by demonstrating variations in simulated health gains when incorrectly: (i) assuming no decreasing trend in tobacco prevalence; and (ii) not including time lags from quitting tobacco to changing disease incidence. In conjunction with increasing availability of baseline and forecast demographic and epidemiological data, PMSLT modelling is well suited to future multiple country comparisons to better inform national, regional and global prioritization of preventive interventions. To facilitate use of PMSLT, we introduce a Python-based modelling framework and associated tools that facilitate the construction, calibration and analysis of PMSLT models.
疾病负担研究——如全球疾病负担（GBD）研究——以伤残调整生命年为单位对健康损失进行量化。然而，这些研究并未对改变风险因素分布的干预措施的未来影响进行量化，未考虑到趋势和时间滞后因素。本方法学论文解释了比例多状态生命表（PMSLT）建模如何通过对三个烟草控制案例研究（烟草根除、无烟一代，即每年将合法购买烟草的年龄提高1岁）和烟草税）进行比较来量化干预措施的影响。我们还通过展示在以下错误情况下模拟健康收益的变化，说明了在干预措施所作用的对照臂中，如常情况的流行病学规范的重要性：（i）假设烟草流行率没有下降趋势；（ii）未纳入戒烟到疾病发病率变化的时间滞后。随着基线以及预测的人口和流行病学数据的可得性不断提高，PMSLT建模非常适合未来在多个国家进行比较，以便更好地为国家、区域和全球预防性干预措施的优先排序提供依据。为便于使用PMSLT，我们引入了一个基于Python的建模框架及相关工具，以促进PMSLT模型的构建、校准和分析。
