There are many places on the earth, where natural background radiation exposures are elevated significantly above about 2.5 mSv/year. The studies of health effects on populations living in such places are crucially important for understanding the impact of low doses of ionizing radiation. This article critically reviews some recent representative literature that addresses the likelihood of radiation-induced cancer and early childhood death in regions with high natural background radiation. The comparative and Bayesian analysis of the published data shows that the linear no-threshold hypothesis does not likely explain the results of these recent studies, whereas they favor the model of threshold or hormesis. Neither cancers nor early childhood deaths positively correlate with dose rates in regions with elevated natural background radiation.

A re-analysis has been carried out of thirty-two case–control and two ecological studies concerning the influence of radon, a radioactive gas, on the risk of lung cancer. Three mathematically simplest dose–response relationships (models) were tested: constant (zero health effect), linear, and parabolic (linear–quadratic). Health effect end-points reported in the analysed studies are odds ratios or relative risk ratios, related either to morbidity or mortality. In our preliminary analysis, we show that the results of dose–response fitting are qualitatively (within uncertainties, given as error bars) the same, whichever of these health effect end-points are applied. Therefore, we deemed it reasonable to aggregate all response data into the so-called Relative Health Factor and jointly analysed such mixed data, to obtain better statistical power. In the second part of our analysis, robust Bayesian and classical methods of analysis were applied to this combined dataset. In this part of our analysis, we selected different subranges of radon concentrations. In view of substantial differences between the methodology used by the authors of case–control and ecological studies, the mathematical relationships (models) were applied mainly to the thirty-two case–control studies. The degree to which the two ecological studies, analysed separately, affect the overall results when combined with the thirty-two case–control studies, has also been evaluated. In all, as a result of our meta-analysis of the combined cohort, we conclude that the analysed data concerning radon concentrations below ~1000 Bq/m³ (~20 mSv/year of effective dose to the whole body) do not support the thesis that radon may be a cause of any statistically significant increase in lung cancer incidence.

The priming dose effect, called also the Raper–Yonezawa effect or simply the Yonezawa effect, is a special case of the radiation adaptive response phenomenon (radioadaptation), which refers to: (a) faster repair of direct DNA lesions (damage), and (b) DNA mutation frequency reduction after irradiation, by applying a small priming (conditioning) dose prior to the high detrimental (challenging) one. This effect is observed in many (but not all) radiobiological experiments which present the reduction of lesion, mutation or even mortality frequency of the irradiated cells or species. Additionally, the multi-parameter model created by Dr. Yonezawa and collaborators tried to explain it theoretically based on experimental data on the mortality of mice with chronic internal irradiation. The presented paper proposes a new theoretical approach to understanding and explaining the priming dose effect: it starts from the radiation adaptive response theory and moves to the three-parameter model, separately for two previously mentioned situations: creation of fast (lesions) and delayed damage (mutations). The proposed biophysical model was applied to experimental data—lesions in human lymphocytes and chromosomal inversions in mice—and was shown to be able to predict the Yonezawa effect for future investigations. It was also found that the strongest radioadaptation is correlated with the weakest cellular radiosensitivity. Additional discussions were focussed on more general situations where many small priming doses are used.

The linear no-threshold (LNT) dose-effect relationship has been consistently used by most radiation epidemiologists to estimate cancer mortality risk. The large scattering of data by International Agency for Research on Cancer, IARC ( Vrijheid et al. 2007 ; Therry-Chef et al. 2007 ; Cardis et al. 2007 ), interpreted in accordance with LNT, has been previously demonstrated ( Fornalski and Dobrzyński 2009 ). Using conventional and Bayesian methods the present paper demonstrates that the standard mortality ratios (SMRs), lower in the IARC cohort of exposed nuclear workers than in the non exposed group, should be considered as a hormetic effect, rather than a healthy worker effect (HWE) as claimed by the IARC group.

The influence of ionizing radiation of (222)Rn and its progeny on lung cancer risks that were published in 28 papers was re-analyzed using seven alternative dose-response models. The risks of incidence and mortality were studied in two ranges of low annual radiation dose: 0-70 mSv per year (391 Bq m(-3)) and 0-150 mSv per year (838 Bq m(-3)). Assumption-free Bayesian statistical methods were used. The analytical results demonstrate that the published incidence and mortality data do not show that radiation dose is associated with increased risk in this range of doses. This conclusion is based on the observation that the model assuming no dependence of the lung cancer induction on the radiation doses is at least ∼90 times more likely to be true than the other models tested, including the linear no-threshold (LNT) model.