Prevention Lab: a predictive model for estimating the impact of prevention interventions in a simulated Italian cohort
Rattachement africain : it. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Prevention Lab: a predictive model for estimating the impact of prevention interventions in a simulated Italian cohort
- Date Crossref
- 12/10/2024
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Politecnico di Torino pays non établi dans la noticeUniversité ou école supérieure
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Azienda Ospedaliera Citta' della Salute e della Scienza di Torino pays non établi dans la noticeÉtablissement de santé
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Bank of Italy pays non établi dans la noticeOrganisme public
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Ministero della Salute pays non établi dans la noticeOrganisme public
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Department of Mathematical Sciences pays non établi dans la noticeInstitution
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University Hospital "Città Della Salute E Della Scienza Di Torino" Epidemiology and Screening Unit pays non établi dans la noticeUniversité ou école supérieure
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Ministry of Health pays non établi dans la noticeOrganisme public
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Institute for Cancer Research pays non établi dans la noticeStructure de recherche
Politecnico di Torino, Azienda Ospedaliera Citta' della Salute e della Scienza di Torino et Bank of Italy, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.