Modeling the Health Burden of PM2.5: Forecasting Hospital Admissions and Medical Demand in Bangkok and Neighboring Regions

Abstract

Air pollution has emerged as a defining public health challenge of the 21st century, with fine particulate matter (PM2.5) recognized as a major contributor to respiratory and cardiovascular morbidity and mortality. Due to its ultrafine size (<2.5 µm), PM2.5 penetrates deeply into the lungs, aggravating chronic conditions such as asthma, chronic obstructive pulmonary disease (COPD), and ischemic heart disease. In Thailand, especially in Bangkok and its neighboring provinces, PM2.5 levels regularly exceed safety thresholds, exacerbated by rapid urbanization and seasonal agricultural burning. This study investigates the relationship between PM2.5 concentrations and hospital admissions using both regression analysis and machine learning techniques. Based on hospital records from January to February 2025, findings reveal that a 10 µg/m³ increase in PM2.5 is associated with a statistically significant rise in outpatient visits (+258.1 cases, p = 0.0489, R² = 0.573) and inpatient admissions (+13.8 cases, p = 0.0477, R² = 0.577). Predictive models further estimate that weekly outpatient numbers could exceed 2,000 when PM2.5 levels reach 70 µg/m³, placing acute pressure on local healthcare systems. Elderly populations (aged 51 and above) were identified as the most vulnerable cohort. This study contributes to the limited body of predictive public health modeling in Southeast Asia and provides essential data to inform early warning systems, air quality policy, and healthcare capacity planning. The results call for urgent, evidence-based interventions to mitigate the escalating burden of air pollution on healthcare infrastructure.