ArticleBMC public health2024
Prevention Lab: a predictive model for estimating the impact of prevention interventions in a simulated Italian cohort.
Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA 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.
methodsWe 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.
resultsThis 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.
conclusionsWe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.