  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.
