Evaluating County-level Lung Cancer Incidence From Environmental Radiation Exposure, Pm 2.5 , and Other Exposures With Regression and Machine Learning Models

Characterizing the interplay between exposures shaping the human exposome is vital for disease etiology. For example, cancer incidence is attributable to the independent and interactive multifactorial external exposures that shape health. Lung cancer is a perfect example of increased risk linked to environmental, socioeconomic, and lifestyle factors. However, radon epidemiological studies often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM 2.5 , may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End-Results data (2013-17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross activity and indoor radon) and PM 2.5 on lung cancer rates in the United States. The county-level factors (environmental, sociodemographic, lifestyle) were controlled, and Poisson regression and random forest were used to assess associations with lung and bronchus cancer rates. Tree-based ML method improved over traditional regression: Poisson regression: 7.58/7.39 (mean absolute percentage error, MAPE); Poisson random forest regression: 1.21/1.16 (MAPE). Effect of PM 2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM 2.5 on health outcomes. In summary, the results demonstrated (1) a need to include multiple environmental exposures when assessing radon exposure’s association with lung cancer risk, thereby highlighting exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of environmental radiation exposure and lung cancer incidence.