Epidemiological studies of natural sources of radiation and childhood cancer: current challenges and future perspectives.

Mazzei-Abba, Antonella; Folly, Christophe L; Coste, Astrid; Wakeford, Richard; Little, Mark P; Raaschou-Nielsen, Ole; Kendall, Gerald M; Hémon, Denis; Nikkilä, Atte; Spix, Claudia; Auvinen, Anssi; Spycher, Ben D (2020). Epidemiological studies of natural sources of radiation and childhood cancer: current challenges and future perspectives. Journal of radiological protection, 40(1), R1-R23. IOP Publishing 10.1088/1361-6498/ab5a38

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Empirical estimation of cancer risks in children associated with low-dose ionizing radiation (<100 mSv) remains a challenge. The main reason is that the required combination of large sample sizes with accurate and comprehensive exposure assessment is difficult to achieve. An international scientific workshop "Childhood cancer and background radiation" organised by the Institute of Social and Preventive Medicine of the University of Bern brought together researchers in this field to evaluate how epidemiological studies on background radiation and childhood cancer can best improve understanding of the effects of low-dose ionising radiation. This review summarises and evaluates the findings of the existing studies in the light of their methodological differences, identifies key limitations and challenges and proposes ways forward. Large childhood cancer registries, such as those in Great Britain, France and Germany, now allow the conducting of studies that should have sufficient statistical power to detect the effects predicted by standard risk models. Nevertheless, larger studies or pooled studies will be needed to investigate disease subgroups. The main challenge is to accurately assess children's individual exposure to radiation from natural sources and from other sources, as well as potentially confounding non-radiation exposures, in such large study populations. For this, the study groups should learn from each other to improve exposure estimation and develop new ways to validate exposure models with personal dosimetry.

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