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This study investigates how to define and measure inclusivity in Italy's early childhood education and care (ECEC) services, bringing to light the gap between legislative principles and local/regional applications. The Italian legislative decree n. 65/2017 prescribes inclusivity in ECEC, defined as being open to all children and indicating it as a top priority. To delve into this concept, we propose a two-step model. First, a latent trait model estimates an inclusivity index as a latent variable. Then, a mixed quantile model examines the distribution of this novel latent inclusivity index across Italian regions. Our findings reveal a substantial variation in inclusivity across Italy. In addition, a proper indicator based on the latent inclusivity index defined in the first step is provided at the NUTS-3 level using the empirical best predictor approach. From our analysis, public facilities demonstrate a higher level of inclusivity compared to their private counterparts. Despite these challenges, we are compelled to identify positive scenarios that can serve as models for regions facing more critical situations. Besides its methodological advancement, this paper provides policymakers and stakeholders with an evident call to action, offering valuable insights into the inclusivity landscape of Italian ECEC services. It underscores the urgent need to standardize the accessibility characteristics of ECEC services throughout Italy to ensure equitable access for all children., Comment: 29 pages, 21 figures

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.