Average uranium bedrock concentration in Swedish municipalities predicts male lung cancer incidence rate when adjusted for smoking prevalence: Indication of a cumulative radon induced detriment.

Bedrock U has been used as a proxy for local indoor radon exposure. A preliminary assessment of cancer incidence rate in a cohort of 809,939 adult males living in 9 different Swedish counties in 1986 has been used to correlate the cumulative lung cancer and total cancer (excluding lung) incidence rates between 1986 and 2020, respectively with the municipality average value of bedrock U concentration obtained from Swedish geological Survey (SGU). To control for regional difference in tobacco smoking, data on county average smoking prevalence, obtained from a survey conducted by the Public Health Agency of Sweden from 2001 to 2004, was used. Regression analysis shows that there is a significant positive correlation between smoking prevalence adjusted lung cancer incidence rate in males and the municipality bedrock U concentration (R2 = 0.273 with a slope 5.0 ±â€¯0.87·10-3 ppm-1). The correlation is even more significant (R2 = 0.759 with a slope = 4.8 ±â€¯0.25·10-3 ppm-1) when assessed on population weighted cancer incidence data binned in nine intervals of municipality average bedrock U concentration (ranging from 0.97 to 4.9 ppm). When assessing the corresponding correlations for total cancer incidence rate (excluding cancer of the lung) with adjustment for smoking prevalence, there appears to be no or little correlation with bedrock U concentration (R2 = 0.031). We conclude that an expanded future study needs age-standardized cancer incidence data to obtain a more consistent exposure-response model. Such model could be used to predict future lung cancer cases based on geological survey maps of bedrock U as an alternative to laborious indoor radon measurements, and to discern what future lung cancer rates can be expected for a population nearing zero smoking prevalence, with and without radon prevention.

How Is the Lung Cancer Incidence Rate Associated with Environmental Risks? Machine-Learning-Based Modeling and Benchmarking.

The lung cancer threat has become a critical issue for public health. Research has been devoted to its clinical study but only a few studies have addressed the issue from a holistic perspective that included social, economic, and environmental dimensions. Therefore, in this study, risk factors or features, such as air pollution, tobacco use, socioeconomic status, employment status, marital status, and environment, were comprehensively considered when constructing a predictive model. These risk factors were analyzed and selected using stepwise regression and the variance inflation factor to eliminate the possibility of multicollinearity. To build efficient and informative prediction models of lung cancer incidence rates, several machine learning algorithms with cross-validation were adopted, namely, linear regression, support vector regression, random forest, K-nearest neighbor, and cubist model tree. A case study in Taiwan showed that the cubist model tree with feature selection was the best model with an RMSE of 3.310 and an R-squared of 0.960. Through these predictive models, we also found that apart from smoking, the average NO2 concentration, employment percentage, and number of factories were also important factors that had significant impacts on the incidence of lung cancer. In addition, the random forest model without feature selection and with feature selection could support the interpretation of the most contributing variables. The predictive model proposed in the present study can help to precisely analyze and estimate lung cancer incidence rates so that effective preventative measures can be developed. Furthermore, the risk factors involved in the predictive model can help with the future analysis of lung cancer incidence rates from a holistic perspective.