A Bayesian approach to uncover local and temporal determinants of 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 behaviours, risk factors, and relevant socio-demographic information. While the collected data undoubtedly provides valuable information, modelling such data presents several challenges. For instance, in health risk models, it is essential to consider behavioural information, local and temporal dynamics, and disease co-occurrence. In response to these challenges, our work proposes a multivariate temporal logistic model for chronic disease diagnoses at local level. Linear predictors are modelled using individual risk factor covariates and a latent individual propensity to diseases. Leveraging a state space formulation of the model, we construct a framework in which temporal heterogeneity in regression coefficients is informed by exogenous information at local level, correspond ing to different contextual risk factors that may affect the occurrence of chronic diseases in different ways. To explore the utility and the effectiveness of our method, we analyse behavioural and risk factor surveillance data collected in Italy (PASSI), which is well-known as a country characterised by high peculiar administrative, social and territorial diversities reflected on high variability in morbidity among population subgroups.