Machine learning and dose response effect models: an integrated approach to analyze the association between environmental variables and Cuneo Emergency Department admissions for Acute Otitis Media (2007-2015)

Clinical Snapshot

Key Findings

• P Value: Not explicitly reported but CIs exclude null value

• Effect Size: Temperature: RR > 1.20 across all lag periods; NO₂ and PM10: RR 1.02-1.04; O₃: RR 1.01-1.02

• Primary Outcome: Daily Emergency Department admissions for acute otitis media

• Nnt Or Sensitivity: Model sensitivity: Cross-validated R² = 0.987; Temperature contributed 44% to model predictions

• Confidence Interval: All relative risks reported with 95% CI > 1 for significant associations

Clinical Application

Environmental monitoring data readily available in most Australian cities; early warning systems technically feasible Relevant to Australian pediatric emergency departments; could inform RACGP guidelines on environmental health risks; may support public health interventions during high pollution episodes Pediatric patients under 5 years at risk of acute otitis media, particularly during adverse environmental conditions

Abstract

Acute otitis media (AOM) is a leading cause of pediatric Emergency Department visits, particularly among children under five years of age. Although its seasonal pattern is well established, the role of air pollution and meteorological factors remains unclear. This study aims to investigate their impact on daily AOM visits by integrating machine learning and epidemiological approaches. We conducted a retrospective analysis of pediatric AOM diagnoses (2007-2015) at S. Croce and Carle Hospital (Cuneo, Italy). Predictors included PM10, NO₂, O₃, and eleven meteorological variables. Ensemble machine learning models (Random Forest, XGBoost, and AdaBoost) were trained and validated using 10-fold cross-validation. Model interpretability was assessed through SHAP values. Distributed Lag Nonlinear Models (DLNM) were applied to estimate delayed exposure-response relationships over lag periods of 0-1, 0-3, 0-5, and 0-10 days, with results expressed as Relative Risks (RRs) and 95% Confidence Intervals (CIs). AdaBoost showed the best performance (R² = 0.974; MAE = 0.019 cases/day; cross-validated R² = 0.987). SHAP analysis identified mean temperature as the most influential predictor (44%), while PM10, NO₂, and O₃ each contributed approximately 10%. DLNM analysis confirmed a strong and consistent effect of temperature across all lag periods (RR > 1.20, CI > 1). Moderate associations were observed for NO₂ and PM10 (RR: 1.02-1.04). O₃ exhibited smaller but significant effects at shorter lags (RR = 1.01 at 0-1 days; RR = 1.02 at 0-3 days; CI > 1). Environmental factors, particularly temperature, play a significant role in pediatric AOM incidence. The integration of machine learning and DLNM enhances predictive accuracy and improves the understanding of exposure timing. These findings support the development of early warning systems and targeted preventive strategies under adverse environmental conditions. Further validation in larger urban settings is needed.

References

1. 1.Rondinone, M., Condemi, V., Gestro, M., & Telesca, V. (2026). Machine learning and dose response effect models: an integrated approach to analyze the association between environmental variables and Cuneo Emergency Department admissions for Acute Otitis Media (2007-2015). International Journal of Biometeorology. https://doi.org/10.1289/ehp.0901483