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 rates across Italian regions from 2010 to 2022, aiming to identify spatial and temporal trends and assess potential heterogeneities. We implement a Bayesian hierarchical Beta regression model to analyze regional obesity rates, integrating spatial and temporal random effects, alongside gender and various exogenous predictors. The model leverages the Stochastic Search Variable Selection technique to identify significant predictors supported by the data. The analysis reveals both regional heterogeneity and dependence in obesity rates over the study period, emphasizing the importance of considering gender and spatial correlation in explaining its dynamics over time. In fact, the inclusion of structured spatial and temporal random effects captures the complexities of regional variations over time. These 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. While socioeconomic and lifestyle factors remain fundamental at a micro-level, the findings demonstrate that the integration of spatial and temporal structures is critical for capturing macro-level obesity variations.

Figures (12)

• Figure 1: Obesity rate trend from 2010 to 2022 by geographic area and gender.
• Figure 2: Posterior distributions of the gender-specific intercepts: $\xi_1$ for females (in pink) and $\xi_2$ for males (in light blue)
• Figure 3: Posterior density of the space random effects $\psi_i$, coloured by geographical zone (NO=North West, NE=North East, C=Centre, S=South, SI=Islands, see Table \ref{['tab:italian_regions']})
• Figure 4: Posterior mean values of the spatial random effects $\psi_i$ for each Italian region
• Figure 5: Posterior density of the time random effects $\alpha_t$, coloured by year