Predicting residential radon concentrations in Finland: Model development, validation, and application to childhood leukemia.

Abstract

Objectives Inhaled radon gas is a known alpha-emitting carcinogen linked especially to lung cancer. Studies on higher concentrations of indoor radon and childhood leukemia have conflicting but largely negative results. In this study, we aimed to create a sophisticated statistical model to predict indoor radon concentrations and apply it to a Finnish childhood leukemia case-control dataset. Methods Prediction was based on ~80 000 indoor radon measurements, which were linked to national registries for potential indoor radon predictors based on the literature. In modelling, we used classical methods, random forests and deep neural networks. We had 1093 cases and 3279 controls from a nationwide case-control study. We estimated odds ratio (OR) for childhood leukemia using conditional logistic regression adjusted for potential confounders. Results The r 2of the final log-linear model was 0.21 for houses and 0.20 for apartments. Using random forest method, we were able to obtain slightly better fit for both houses (r 2= 0.28) and apartments (r 2= 0.23). In a risk analysis based on the case-control data with log-linear model, we observed a non-significant (P=0.54) increase with predicted radon concentrations [OR for the 2 ndquartile 1.08, 95% confidence interval (CI) 0.77-1.50, OR 1.10 with 95% CI 0.79-1.53 for the 3 rd, and 1.29 with 95% CI 0.93-1.77 for the highest quartile]. Conclusions Our modelling and the previously published models performed similarly but involves major uncertainties, and the results should be interpreted with caution. We observed a slight non-significant increase in risk of childhood leukemia related to higher average indoor radon concentrations.