A Bayesian approach to model local and temporal heterogeneity in repeated cross-sectional health surveys

In several countries, including Italy, a prominent approach to population health surveillance involves conducting repeated cross-sectional surveys at short intervals of time. These surveys gather information on the health status of individual respondents, including details on their behaviors, risk factors, and relevant socio-demographic information. While the collected data undoubtedly provides valuable information, modeling such data presents several challenges. For instance, in health risk models, it is essential to consider behavioral information, spatio-temporal dynamics, and disease co-occurrence. In response to these challenges, our work proposes a multivariate spatio-temporal logistic model for chronic disease diagnoses. Predictors are modeled using individual risk factor covariates and a latent individual propensity to the disease. Leveraging a state space formulation of the model, we construct a framework in which spatio-temporal heterogeneity in regression parameters is informed by exogenous spatial information, corresponding to different spatial contextual risk factors that may affect health and the occurrence of chronic diseases in different ways. To explore the utility and the effectiveness of our method, we analyze behavioral and risk factor surveillance data collected in Italy (PASSI), which is well-known as a country characterized by high peculiar administrative, social and territorial diversities reflected on high variability in morbidity among population subgroups.