Lung Cancer Risk Estimation from Radon Exposure: A Multiscale Analysis Using Ecological, and Machine Learning Approaches

Lung cancer is one of the major cause of death in both United States and globally, and radon exposure is known for the second-leading cause of lung cancer incidence. The overall objective of this study is to provide a comprehensive multiscale analysis of radon exposure and its association with lung cancer risk, integrating ecological, modeling, and risk estimation methodologies. The research is structured into three primary sections. The first section presents an ecological study across counties in the SEER registry, focusing on lung cancer incidence in relation to environmental radon exposure and other relevant factors. While this analysis highlights significant associations, it also reveals data limitations inherent in the county-level scale, such as geographic variability and the granularity of exposure data.

To address these limitations, the second section shifts focus to estimating and modeling residential radon exposure at the ZCTA level in Pennsylvania. Pennsylvania was chosen due to its notably high levels of both smoking and radon compared to the national average. Using machine learning techniques, residential radon levels are modeled to provide a more detailed spatial understanding of exposure at a finer scale. This approach allows for better resolution in assessing radon-related health risks and aids in overcoming some of the challenges posed by broader geographic scales.

The third section involves review of lung cancer risk estimation models including BEIR models and propose improvements for future modeling. Future models can integrate PM2.5 as a co-exposure factor, incorporate population migration to track dynamic exposure, and replace stepwise functions with continuous risk adjustments.

This work underscores the importance of integrating ecological analysis, fine-scale radon modeling, and migration data to improve risk estimation frameworks, offering a detailed exploration of lung cancer risk attributable to radon and providing recommendations for enhancing current risk models such as BEIR VI.