arXiv subjects

Stefano Campostrini

Publications and source records attributed to Stefano Campostrini.

5 recordsLinked to original sources

arXiv2026

The Perceived Impact of Environment on Health in Italy: a Penalized Ordinal Regression Approach

Understanding how individuals perceive their living environment is a complex task, as it reflects both personal and contextual determinants. In this paper, we address this task by analyzing the environmental module of the Italian nationwide health surveillance system PASSI (Progressi delle Aziende Sanitarie per la Salute in Italia), integrating it with contextual information at the municipal level, including socio-economic indicators, pollution exposure, and other geographical characteristics. Methodologically, we adopt a penalized semi-parallel cumulative ordinal regression model to analyze how subjective perceptions are shaped by both personal and territorial determinants. The approach balances flexibility and interpretability by allowing both parallel and non-parallel effects while regularizing estimates to address multicollinearity and separation issues. We use the model as an analytical tool to uncover the determinants of positivity and neutrality in environmental perceptions, defined as factors that contribute the most to improving perception or increasing the sense of neutrality. The results are diverse. First, results reveal significant heterogeneity across Italian territories, indicating that local characteristics strongly shape environmental perception. Second, various individual factors interact with contextual influences to shape perceptions. Third, hazardous environmental factors, such as higher PM2.5 levels, appear to be associated with poorer environmental perception, suggesting a tendency among respondents to recognize specific environmental issues. Overall, the approach demonstrates strong potential for application and provides useful insights for environmental policy planning.

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arXiv2025

Topic-informed dynamic mixture model for occupational heterogeneity in health risk behaviors

Behavioral risk factors, i.e., smoking, poor nutrition, alcohol misuse, and physical inactivity (SNAP), are leading contributors to chronic diseases and healthcare costs worldwide. Their prevalence is shaped %not only by demographic characteristics %but and also by contextual ones such as socioeconomic and occupational environments. In this study, we leverage data from the Italian health and behavioral surveillance system PASSI to model SNAP behaviors through a Bayesian framework that integrates textual information on occupations. We use Structural Topic Modeling (STM) to cluster free-text job descriptions into latent occupational groups, which inform mixture weights in a multivariate ordered probit model. Covariate effects are allowed to vary across occupational clusters and evolve over time. To enhance interpretability and variable selection, we impose non-local spike-and-slab priors on regression coefficients. Finally, an online learning algorithm based on sequential Monte Carlo enables efficient updating as new data become available. This dynamic, scalable, and interpretable approach permits observing how occupational contexts modulate the impact of socio-demographic factors on health behaviors, providing valuable insights for targeted public health interventions.

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arXiv2025

Communicating complex statistical models to a public health audience: translating science into action with the FARSI approach

Background. Effectively communicating complex statistical model outputs is a major challenge in public health. This study introduces the FARSI approach (Fast, Accessible, Reliable, Secure, Informative) as a framework to enhance the translation of intricate statistical findings into actionable insights for policymakers and stakeholders. We apply this framework in a real-world case study on chronic disease monitoring in Italy. Methods. The FARSI framework outlines key principles for developing user-friendly tools that improve the translation of statistical results. We applied these principles to create an open-access web application using R Shiny, designed to communicate chronic disease prevalence estimates from a Bayesian spatio-temporal logistic model. The case study highlights the importance of an intuitive design for fast accessibility, validated data and expert feedback for reliability, aggregated data for security, and insights into prevalence population subgroups, which were previously unobservable, for informativeness. Results. The web application enables stakeholders to explore disease prevalence across populations and geographical area through dynamic visualizations. It facilitates public health monitoring by, for instance, identifying disparities at the local level and assessing risk factors such as smoking. Its user-friendly interface enhances accessibility, making statistical findings more actionable. Conclusions. The FARSI framework provides a structured approach to improving the communication of complex research findings. By making statistical models more accessible and interpretable, it supports evidence-based decision-making in public health and increases the societal impact of research.

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arXiv2024

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.

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arXiv2024

A two-step model to study the inclusivity's distribution of Italian early childhood education and care services

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.

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