Radiation and Cancer: Risks of Leukemia in Nuclear Workers More Than Double Previous Estimate via Counter Punch

In 2013, I discussed several epidemiological studies providing good evidence of radiogenic risks at very low exposure levels.

A powerful new study has been published in Lancet Haematology [1] which adds to this evidence. However the study’s findings are more important than the previous studies, for several reasons.

First, it provides “strong evidence”, as stated by the authors, of a “dose-response relationship between cumulative, external, chronic, low-dose, exposures to radiation and leukaemia”.

Second, it finds radiogenic risks of leukemia among nuclear workers to be more than double the risk found in a previous similar study in 2005. The excess relative risk of leukaemia mortality (excluding workers exposed to neutrons) was 4.19 per Gy.

In 2005, a similar study [2] among nuclear workers (also excluding those exposed to neutrons) in 15 countries by several of the same authors found an ERR of 1·93 per Sv. In other words, the new study’s risk estimates are 117% higher than the older study. The clincher is that the new study’s estimated risks are much more precise than before.

Third, it confirms risks even at very low doses (mean = 1·1 mGy per year). Unlike the Japanese bomb survivors’ study, it observes risks at low dose rates rather than extrapolating them from high levels.

Fourth, it finds risks do not depend on dose rate thus contradicting the ICRP’s use of a Dose Rate Effectiveness Factor (DREF) which acts to reduce (by half) the ICRP’s published radiation risks.

Fifth, it finds radiogenic leukemia risks decline linearly with dose, contradicting earlier studies suggesting a lower, linear-quadratic relationship for leukemia. It strengthens the Linear No Threshold (LNT) model of radiogenic risks, as it now applies to leukemias as well as solid cancers.

Sixth, the study finds no evidence of a threshold below which no effects are seen (apart from zero dose).

Seventh, the study uses 90% confidence intervals and one-sided p-values. In the past, 95% intervals and two-sided p-values were often incorrectly used which had made it harder to establish statistical significance.

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