Canadian population risk of radon induced lung cancer: a re-assessment based on the recent cross-Canada radon survey

Abstract

Exposure to indoor radon has been determined to be the second leading cause of lung cancer after tobacco smoking. Canadian population risk of radon induced lung cancer was assessed in 2005 with the radon distribution characteristics determined from a radon survey carried out in the late 1970s in 19 cities. In that survey, a grab sampling method was used to measure radon levels. The observed radon concentration in 14 000 Canadian homes surveyed followed a log–normal distribution with a geometric mean (GM) of 11.2 Bq m–3 and a geometric standard deviation (GSD) of 3.9. Based on the information from that survey, it was estimated that ∼10 % of lung cancers in Canada resulted from indoor radon exposure. To gain a better understanding of radon concentrations in homes across the country, a national residential radon survey was launched in April 2009. In the recent survey, long-term (3 month or longer) indoor radon measurements were made in roughly 14 000 homes in 121 health regions across Canada. The observed radon concentrations follow, as expected, a log–normal distribution with a GM of 41.9 Bq m–3 and a GSD of 2.8. Based on the more accurate radon distribution characteristics obtained from the recent cross-Canada radon survey, a re-assessment of Canadian population risk for radon induced lung cancer was undertaken. The theoretical estimates show that 16 % of lung cancer deaths among Canadians are attributable to indoor radon exposure. These results strongly suggest the ongoing need for the Canadian National Radon Program. In particular, there is a need for a focus on education and awareness by all levels of government, and in partnership with key stakeholders, to encourage Canadians to take action to reduce the risk from indoor radon exposure.

Lung cancer incidence attributable to residential radon exposure in Alberta in 2012

“…8 However, although the mean radon level for Canada (41.9 Bq/m 3 ) calculated with more recent data 8 is closer to that observed for Alberta in the current study (71.0 Bq/m 3 ), overall radon levels in Alberta were still higher than comparable values at the national level. Differences in the model used to estimate population attributable risk by Chen and colleagues 8 and in our study likely explain why the estimates of population attributable risk are similar despite higher levels of radon exposure in Alberta.…”

“…She found that the estimated population attributable risks were a bit higher than those observed by Brand and colleagues 6 (ever smokers 7.3%, never smokers 13.5%). More recently, Chen and colleagues 8 applied the Environmental Protection Agency method to data from the CCSRCH and observed a geometric mean radon level of 41.9 Bq/m 3 , which translated to an estimated population attributable risk of about 16%, virtually the same as our estimate. Outside Canada, Gray and colleagues 19 estimated that with a mean indoor radon concentration of 21 Bq/m 3 , the proportion of lung cancer deaths attributable to radon exposure in the United Kingdom in 2006 was 3.3%.…”

“…The population attributable risk for lung cancer due to radon exposure for Canada as a whole was estimated in 3 previous studies. [6][7][8] Brand and colleagues 6 used national survey data from 1978 to characterize radon exposure. They employed the same exposure-age-concentration model recommended by BEIR VI that we used and estimated a population attributable risk of 7.8%.…”

Indoor Radon Characteristics in Canadian Arctic Regions

“…The observed radon concentrations in Canadian homes follow a log-normal distribution (Chen et al 2012). This was also true for the Arctic health regions.…”

“…Long-term radon measurements were performed in all the homes surveyed. The observed radon concentrations in Canadian homes follow a log-normal distribution (Chen et al 2012). Because of the wide distribution in radon concentrations, a central estimate alone, such as arithmetic mean (AM) or geometric mean (GM), will not be able to represent the distribution.…”