Environmental Drivers of Respiratory Disease: A District Level Analysis

2026-07-05

AI Summary

This study addresses the paradoxical decline in respiratory disease hospitalization rates in Sri Lanka despite worsening forest degradation and air pollution. Leveraging a district-level panel dataset from 2014 to 2024 across 25 administrative districts, the research integrates satellite-derived environmental variables—including PM2.5, NO₂, SO₂, vegetation indices, fire radiative power, and carbon flux—with population-standardized hospitalization rates. Using XGBoost modeling and SHAP interpretability analysis, the work proposes the first subnational Forest–Air–Health (FAH) risk index. Results reveal that cumulative air quality burden accounts for 80.1% of the variation in respiratory disease incidence, with annual and monthly models achieving R² values of 0.937 and 0.976, respectively. Prediction accuracy is high, with 21 districts exhibiting mean absolute percentage errors (MAPE) ≤20%, identifying Colombo, Gampaha, and Kalutara as high-risk areas.

Abstract

Sri Lanka has experienced a decade of progressive forest degradation and rising atmospheric pollution, yet district-level respiratory admissions have paradoxically declined, pointing to the confounding role of healthcare access. This study addresses that gap by constructing an 11-year (2014-2024) panel dataset across all 25 administrative districts, integrating satellite-derived vegetation indices, fire radiative power, pollutant concentrations (particulate matter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2)), carbon flux metrics and population-normalized respiratory admission rates. Two temporally validated XGBoost models were created for annual district-level respiratory rate (R^2 = 0.937) and monthly PM2.5 concentration (R^2 = 0.976) with generalization validated in 21 out of 25 districts (Mean Absolute Percentage Error (MAPE) <= 20%). Shapley Additive Explanations (SHAP) analysis established that cumulative air quality burden is the overwhelming driver of respiratory rate variance (80.1%), ahead of forest degradation (15.6%) and fire activity (4.3%). The Forest-Air-Health (FAH) Risk Index used these SHAP-derived weights to find the districts with the highest risk: Colombo (FAH = 0.802), Gampaha (0.708), and Kalutara (0.682). These findings present the inaugural evidence-based, district-level framework correlating environmental degradation with respiratory health in Sri Lanka, establishing a quantitative basis for focused public health and environmental policy.

Problem

• respiratory disease
• environmental degradation
• air pollution
• forest degradation
• healthcare access

Innovation

• XGBoost modeling
• SHAP analysis
• satellite-derived environmental data
• Forest-Air-Health (FAH) Risk Index
• panel dataset integration