Spatio-Temporal Variability and Environmental Associations of Emergency Department Demand: A Longitudinal Analysis in Zaragoza, Spain (2011–2024)

Emergency department (ED) overcrowding has become a critical public health issue worldwide, driven by increasing demand and limited healthcare resources. This study analyzes the spatio-temporal variability of ED visits at Royo Villanova Hospital (Zaragoza, Spain) from 2011 to 2024, integrating clinical, demographic, environmental, and socioeconomic data. Using geospatial tools and machine learning models (XGBoost with SHAP interpretation), we identify key patterns in ED demand across time and space. Results show that the hour of the day is the most influential variable across all diagnoses, while temperature, humidity, and air pollutants (NO2, SO2, O3) significantly affect respiratory and injury-related visits. Spatial analysis reveals persistent high-demand clusters in specific health zones, with proximity to the hospital playing a major role. The COVID-19 pandemic caused structural shifts in demand, particularly in pediatric care. Our findings highlight the need for tailored, diagnosis-specific predictive models and support the use of geospatial and environmental data for proactive ED resource planning. This approach enhances the capacity of health systems to anticipate demand surges and allocate resources efficiently.