Modeling spatio-temporal dynamics of obesity in Italian regions via bayesian beta regression

Abstract In this paper, we investigate the spatio-temporal dynamics of obesity across Italian regions from 2010 to 2022, proposing an integrated Bayesian hierarchical framework tailored to the analysis of aggregated regional obesity data. In particular, we implement a Bayesian hierarchical Beta regression model to analyse regional obesity rates, integrating spatial and temporal random effects, alongside gender and several exogenous predictors. The model leverages the Stochastic Search Variable Selection technique to identify significant predictors supported by the data. The analysis reveals both gender and regional heterogeneity in obesity rates over the study period. Structured spatial and temporal random effects, along with gender, emerge as the primary determinants of obesity prevalence across Italian regions, while the role of exogenous covariates is found to be minimal at the regional level, once structured spatial and temporal effects and gender are accounted for. While socioeconomic and lifestyle factors remain fundamental at a micro-level, the findings suggest that the integration of spatial and temporal structures is crucial for capturing macro-level obesity variations.