Ecological studies on indoor radon exposure and lung cancer in areas with high radon concentrations and where lung cancer incidence is high have emphasized a need for cohort and case–control studies. Furthermore, the following statistical methods for analyzing geographic information systems data were found to be employed: distance calculations, spatial aggregation, clustering, spatial smoothing and interpolation, and spatial regression [8].

Studies in Germany, France, and Switzerland reported indoor radon concentrations similar to those in South Korea. Accordingly, the incidence of lung cancer in South Korea is likely to be affected by indoor radon exposure. One study has reported that about 5 % of all cases of lung cancer in South Korea might be attributable to radon [9]. Another study in Korea analyzed lung cancer-related mortality by region from 2000–2002 in relation to mean radon concentrations for 16 regions from 1989–2009 through a risk modeling method, using exposure, age, and duration, as well as exposure, age, and concentration. Their results were similar to those in other international studies. Most international studies have been designed as case–control studies, although cohort studies on the association between indoor radon and lung cancer have been conducted. These cohort studies have investigated the contribution of radon to lung cancer incidence using modeling methods. Nevertheless, there has been no such cohort study in South Korea so far.

Upon reviewing whether residential radon exposure is associated with an increased risk for developing lung cancer and is regulated by genetic polymorphism in lung cancer in never smokers (LCINS) [10], we suggest that next-generation sequencing (NGS) may enable the detection of lung cancer-related genetic mutations in LCINS patients exposed to radon. Optimizing exom sequencing for use in NGS platforms is needed to analyze LCINS-related genes in normal and lung cancer tissues.

To outline factors affecting indoor radon levels, mathematical models should be designed to apply time-dependent functions for indoor radon concentrations, and stochastic models await development. Applying the dynamic model of Radon Generation, Entry, and Accumulation indoors (RAGENA), which describes all known sources of radon, including soil, building materials, and water, it may be possible to obtain a mathematical model with which to estimate indoor radon concentrations [11].

In reviewing the environmental burden of disease (EBD) attributable to residential radon exposure, we found that smoking status modifies the risk of lung cancer associated with radon; however, EBD among never smokers has not been explored [12]. Furthermore, no study has attempted to estimate the EBD of residential radon among never smokers in South Korea. As well, although residential radon exposure levels vary depending on the age of a building, studies have failed to account for the age of buildings in their assessments. In conclusion, studies that reflect disability weights and the age of buildings in Korea are required to obtain more precise estimates of the EBD of radon exposure indoors.

Investigating and comparing methods of radon measurement, we noted that USA and Canada employ similar methods thereof in primary and secondary testing. In the UK, only long-term radon measurements are taken with seasonal adjustments [13]. In South Korea, both long-term and short-term measurements are taken in only primary testing. Standardization and improvement of domestic radon measurement methods still need to be studied.