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For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies\u00a0of radon aimed at\u00a0identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM\n                    <jats:sub>2.5<\/jats:sub>\n                    , may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute\u2019s Surveillance, Epidemiology, and End-Results data (2013\u201317) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\gamma$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u03b3<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    activity and indoor radon) and PM\n                    <jats:sub>2.5<\/jats:sub>\n                    on lung cancer incidence\u00a0rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled\u00a0for, and Poisson regression and random forest models\u00a0were used to assess the association between radon\u00a0exposure and lung and bronchus cancer incidence\u00a0rates. Tree-based machine learning (ML) method\u00a0perform better than traditional regression: Poisson regression: 6.29\/7.13 (mean absolute percentage error, MAPE), 12.70\/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22\/1.16 (MAPE), 8.01\/8.15 (RMSE). The effect of PM\n                    <jats:sub>2.5<\/jats:sub>\n                    increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM\n                    <jats:sub>2.5<\/jats:sub>\n                    on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure\u2019s association with lung cancer risk, thereby highlighting (1)\u00a0the importance of an\u00a0exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon\u00a0exposure and lung cancer incidence.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1007\/s10653-023-01820-4","type":"journal-article","created":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T10:02:20Z","timestamp":1708164140000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Evaluating county-level lung cancer incidence from environmental radiation exposure, PM2.5, and other exposures with regression and machine learning models"],"prefix":"10.1007","volume":"46","author":[{"given":"Heechan","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heidi A.","family":"Hanson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremy","family":"Logan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dakotah","family":"Maguire","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anuj","family":"Kapadia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaheen","family":"Dewji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Greeshma","family":"Agasthya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,17]]},"reference":[{"issue":"3","key":"1820_CR1","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1080\/09553002.2022.2027542","volume":"98","author":"R Abergel","year":"2022","unstructured":"Abergel, R., Aris, J., Bolch, W. 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