Department of Mathematical Sciences, Politecnico Di Torino, Corso Duca Degli Abruzzi 24, 10129, Turin, Italy.
Epidemiology and Screening Unit, University Hospital "Città Della Salute E Della Scienza Di Torino", Turin, Italy.
BMC Public Health. 2024 Oct 12;24(1):2792. doi: 10.1186/s12889-024-20212-6.
A large fraction of the disease burden in the Italian population is due to behavioral risk factors. The objective of this work is to provide a tool to estimate the impact of preventive interventions that reduce the exposure to smoking and sedentary lifestyle of the Italian population, with the goal of selecting optimal interventions.
We construct a Markovian model that simulates the state of each subject of the Italian population. The model predicts the distribution of subjects in each health status and risk factor status for every year of the simulation. Based on this distribution, the model provides a rich output summary, such as the number of incident and prevalent cases for each tracing disease and the Disability Adjusted Life Years (DALY), used to assess the impact of preventive interventions, and how this impact is shaped in time.
This paper focuses on the methodological aspects of the model. The proposed model is flexible and can be applied to estimate the impact of complex interventions on the two risk factors and adapted to consider different cohorts. We validate the model by simulating the evolution of the Italian population from 2009 to 2017 and comparing the output with historical data. Furthermore, as a case-study, we simulate a counterfactual scenario where both tobacco and sedentary lifestyle are eradicated from the Italian population in 2019 and estimate the impact of such intervention over the following 20 years.
We propose a Markovian model to estimate how interventions on smoking and sedentary lifestyle can affect the reduction of the disease burden, and validate the model on historical data. The model is flexible and allows to extend the analysis to consider more risk factors in future research. However, we are aware that, given the ever-increasing availability of data, it is necessary in the future to increase the complexity of the model, to be closer to reality and to provide decision-making support to the policy-makers.
意大利人口的大部分疾病负担是由于行为风险因素造成的。这项工作的目的是提供一种工具来估计减少意大利人口吸烟和久坐生活方式暴露的预防干预措施的影响，以选择最佳的干预措施。
我们构建了一个马尔可夫模型，该模型模拟了意大利人口中每个个体的状态。该模型预测了每个健康状况和风险因素状态下的个体在模拟的每一年的分布。基于此分布，该模型提供了丰富的输出总结，例如每种跟踪疾病的发病和现患病例数以及用于评估预防干预措施影响的残疾调整生命年（DALY），以及这种影响如何随时间变化。
本文重点介绍了模型的方法学方面。所提出的模型具有灵活性，可以用于估计复杂干预措施对这两个风险因素的影响，并适应考虑不同队列。我们通过模拟 2009 年至 2017 年意大利人口的演变并将输出结果与历史数据进行比较来验证该模型。此外，作为一个案例研究，我们模拟了一个反事实情景，即在 2019 年从意大利人口中根除烟草和久坐生活方式，并估计这种干预措施在接下来的 20 年中的影响。
我们提出了一个马尔可夫模型来估计干预吸烟和久坐生活方式如何影响疾病负担的减少，并使用历史数据验证了该模型。该模型具有灵活性，可以在未来的研究中扩展分析以考虑更多的风险因素。然而，我们意识到，随着数据的可用性不断增加，未来有必要增加模型的复杂性，使其更接近现实，并为决策者提供决策支持。
