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Environmental Drivers of Respiratory Disease: A District Level Analysis

Rahim Iqbal, Asfi Ahamed, Izzath Nisfer, Shazan Shaheed, Muhammadu Ilham, Nathali Athukorala, Madara Mendis, Nisansa de Silva, Sandareka Wickramanayake

Featured July 23, 2026

Original Paper

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Simply

By combining satellite data and health records, researchers built a district-level risk index to show how air pollution and forest loss impact breathing problems in Sri Lanka, finding air quality is the biggest factor.

In depth
The study addresses the gap in understanding environmental impacts on respiratory health at a granular level by constructing an 11-year harmonized panel dataset for all 25 districts of Sri Lanka. It then employs temporally validated XGBoost models and SHAP analysis to identify the strongest environmental predictors, culminating in the creation of a Forest-Air-Health (FAH) Risk Index to rank districts by their environmental health risk.

Key Takeaways

  • 1
    The authors developed an 11-year harmonized panel dataset for Sri Lanka's 25 districts, integrating diverse environmental and health data to enable granular analysis.
  • 2
    They utilized XGBoost models with temporal validation and SHAP analysis to identify air quality as the dominant environmental driver of respiratory rates (80.1% of SHAP signal).
  • 3
    The study introduced the Forest-Air-Health (FAH) Risk Index, a SHAP-weighted composite score, to rank districts by environmental health risk, highlighting Colombo, Gampaha, and Kalutara as highest risk.

Conceptual Flow

HIGH LEVEL
1
Methodology: How Environmental Risk is Assessed

The researchers gathered many types of environmental and health information, used smart computer programs to find patterns, and then created a simple score to show which areas are most at risk.

Satellite Data
Health Records
Population Info
Combine & Analyze
District Risk Score
2
Results: Key Environmental Drivers and High-Risk Areas

They found that bad air quality is the main reason for breathing problems, and identified specific cities where people face the highest environmental health risks.

Air Quality Data
Forest Health Data
Fire Activity Data
Identify Main Causes
Air Quality Dominates
High Risk Cities