Bayesian Hierarchical Causal Analysis of Environmental Determinants of Respiratory Function in School-Age Children: A DAG-Based Approach

This study proposes a Bayesian hierarchical causal framework combined with a Directed Acyclic Graph to quantify the impact of indoor environmental quality on respiratory function in school-age children. A four-level hierarchical model with pre-residualization for geographic confounding was applied to spirometric data from 1,868 students nested in classrooms and schools across multiple Italian regions. Variance decomposition revealed that a substantial share of variability lies at the regional and school levels. Modifiable school factors, including ventilation systems, asthma management policies, and classroom conditions, showed consistent positive associations with lung function. Prior sensitivity analysis confirmed the robustness of findings, and comparison across alternative model specifications supported the proposed approach. Results inform the design of school-based environmental interventions to improve children’s respiratory health.