Modeling Spatio-Temporal Dynamics of Obesity in Italian Regions Via Bayesian Beta Regression

2025-08-07

AI Summary

This study investigates the spatiotemporal dynamics and drivers of regional obesity prevalence (a proportion-valued response) across Italian regions from 2010 to 2022. We propose a Bayesian hierarchical Beta regression framework that jointly incorporates spatial and temporal random effects and integrates stochastic search variable selection (SSVS) to identify salient covariates. Results indicate that gender disparities and spatial autocorrelation are the dominant factors explaining regional heterogeneity in obesity, whereas external socioeconomic covariates exert limited influence. Joint spatiotemporal modeling substantially improves trend detection and out-of-sample predictive accuracy compared to static or purely spatial models. This work contributes a robust methodological framework for modeling chronic disease epidemiology at the macro level, underscoring the necessity of simultaneously accounting for spatial dependence and temporal evolution in proportional outcome data.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Analyze spatio-temporal obesity trends in Italian regions
• Identify key predictors using Bayesian Beta regression
• Assess regional heterogeneity and gender impact

Innovation

• Bayesian Beta regression for obesity dynamics
• Spatio-temporal random effects integration
• Stochastic Search Variable Selection technique