BMC Public Health (Oct 2024)

Prevention Lab: a predictive model for estimating the impact of prevention interventions in a simulated Italian cohort

  • Leonardo Cianfanelli,
  • Carlo Senore,
  • Giacomo Como,
  • Fabio Fagnani,
  • Costanza Catalano,
  • Mariano Tomatis,
  • Eva Pagano,
  • Stefania Vasselli,
  • Giulia Carreras,
  • Nereo Segnan,
  • Cristiano Piccinelli

DOI
https://doi.org/10.1186/s12889-024-20212-6
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 15

Abstract

Read online

Abstract Background 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. Methods 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. Results 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. Conclusions 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.

Keywords