Low-level radon exposure and lung cancer mortality

Abstract

Background: It is agreed that high level radon exposure is harmful to humans. However, some published literature suggests that low levels of radon show no adverse effects or may even be protective. Claims made using traditional methods of analysis on observational data often fail to replicate. Here, we use a simple, alternative data-analytic strategy for examining effects of low-level indoor radon exposure on lung cancer mortality. One objective will be to demonstrate that local population characteristics can alter expected effects.

Methods: Observational data on indoor radon exposure levels and lung cancer mortality for 2881 U.S. counties were obtained from federal and state governmental agencies. A new "statistical thinking" step-by-step analysis strategy called Local Control (LC) allows us to perform analyses of observational data that are more objective and "fair" than regression-like methods. LC analytical strategy makes as few and as realistic assumptions as possible. As a result, key LC inferences are nonparametric, and estimates of potentially heterogeneous treatment effect-sizes are robust.

Results: Our LC analyses suggest that lung cancer mortality usually tends to decrease as background radon exposure increases. Local rank correlation (LRC) effect-sizes are shown to be predictable from confounding local characteristics like percentage of residents over 65, percentage of residents who currently smoke and percentage of obese residents.

Conclusions: At low indoor radon exposure levels, reverse (negative) LRCs between radon exposure level and lung cancer mortality predominate. The strengths of these associations vary with local demographics.

Keywords: Causal inference; Fair comparisons; Local control strategy; Observational data.