The workers at the Mayak nuclear facility near Ozyorsk,
Russia are a primary source of information about exposure to
radiation at low-dose rates, since they were subject to
protracted exposures to external gamma rays and to internal
exposures from plutonium inhalation. Here we re-examine
lung cancer mortality rates and assess the effects of external
gamma and internal plutonium exposures using recently
developed Monte Carlo dosimetry systems. Using individual
lagged mean annual lung doses computed from the dose
realizations, we fit excess relative risk (ERR) models to the
lung cancer mortality data for the Mayak Workers Cohort
using risk-modeling software. We then used the corrected information matrix (CIM) approach to widen the confidence
intervals of ERR by taking into account the uncertainty in
doses represented by multiple realizations from the Monte
Carlo dosimetry systems. Findings of this work revealed that
there were 930 lung cancer deaths during follow-up.
Plutonium lung doses (but not gamma doses) were generally
higher in the new dosimetry systems than those used in the
previous analysis. This led to a reduction in the risk per unit
dose compared to prior estimates. The estimated ERR/Gy for
external gamma-ray exposure was 0.19 (95% CI: 0.07 to 0.31)
for both sexes combined, while the ERR/Gy for internal
exposures based on mean plutonium doses were 3.5 (95% CI:
2.3 to 4.6) and 8.9 (95% CI: 3.4 to 14) for males and females
at attained age 60. Accounting for uncertainty in dose had
little effect on the confidence intervals for the ERR associated
with gamma-ray exposure, but had a marked impact on
confidence intervals, particularly the upper bounds, for the
effect of plutonium exposure [adjusted 95% CIs: 1.5 to 8.9 for
males and 2.7 to 28 for females]. In conclusion, lung cancer
rates increased significantly with both external gamma-ray
and internal plutonium exposures. Accounting for the effects of dose uncertainty markedly increased the width of the
confidence intervals for the plutonium dose response but had
little impact on the external gamma dose effect estimate.
Adjusting risk estimate confidence intervals using CIM
provides a solution to the important problem of dose
uncertainty. This work demonstrates, for the first time, that
it is possible and practical to use our recently developed CIM
method to make such adjustments in a large cohort
study.
Published: June 22, 2021
Citation
Stram D., M. Sokolnkov, B.A. Napier, V. Vostrotin, A. Efimov, and D.L. Preston. 2021.Lung Cancer in the Mayak Workers Cohort: Risk Estimation and Uncertainty Analysis.Radiation Research 195, no. 4:334-346.PNNL-SA-159210.doi:10.1667/RADE-20-00094.1