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.

翻译：在包括意大利在内的多个国家，人口健康监测的主要方法之一是在短时间内开展重复横截面调查。这些调查收集个体受访者的健康状况信息，包括行为、风险因素及相关社会人口学细节。尽管收集的数据无疑提供了宝贵信息，但对此类数据进行建模仍面临若干挑战。例如，在健康风险模型中，必须考虑行为信息、时空动态及疾病共病性。针对这些挑战，本研究提出一种用于慢性疾病诊断的多变量时空逻辑斯蒂模型。预测变量通过个体风险因子协变量及潜在个体疾病倾向进行建模。借助模型的状态空间形式，我们构建了一个框架，其中回归参数的时空异质性由外生空间信息驱动，这些信息对应可能以不同方式影响健康及慢性疾病发生的各类空间情境风险因素。为验证方法的实用性与有效性，我们分析了在意大利收集的行为与风险因素监测数据（PASSI）。意大利以行政、社会及地域的高度特殊性著称，这些差异显著反映在人口亚群患病率的强变异性上。