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

2025-10-02

AI Summary

This study investigates how individuals’ subjective perceptions of their living environment are jointly shaped by personal characteristics and contextual geographic factors. Leveraging nationally representative data from Italy’s PASSI health surveillance system, we integrate municipal-level socioeconomic, air quality (e.g., PM₂.₅), and geographic variables. We propose a penalized semiparallel cumulative ordinal regression model that preserves interpretability of covariate effects while flexibly accommodating non-proportional odds, mitigating multicollinearity and data separation. Results reveal substantial geographic heterogeneity in environmental perception; higher PM₂.₅ exposure is significantly associated with more negative perceptions; and person–place interactions are pronounced. This is the first large-scale, nationally representative study to systematically disentangle the multilevel determinants of environmental cognition. It provides empirical evidence and a methodological framework to inform precision environmental health interventions and context-sensitive policy design.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• Analyzing how personal and contextual factors shape environmental health perceptions
• Identifying determinants of positivity and neutrality in environmental perceptions
• Associating hazardous environmental factors with poorer perceived health outcomes

Innovation

• Penalized semi-parallel cumulative ordinal regression model
• Integrating municipal-level contextual and individual data
• Regularizing estimates to address multicollinearity issues