Lung Cancer in the Mayak Workers Cohort: Risk Estimation and Uncertainty Analysis.

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.

Lung Cancer Risk from Plutonium: A Pooled Analysis of the Mayak and Sellafield Worker Cohorts.

In this study, lung cancer risk from occupational plutonium exposure was analyzed in a pooled cohort of Mayak and Sellafield workers, two of the most informative cohorts in the world with detailed plutonium urine monitoring programs. The pooled cohort comprised 45,817 workers: 23,443 Sellafield workers first employed during 1947-2002 with follow-up until the end of 2005 and 22,374 Mayak workers first employed during 1948-1982 with follow-up until the end of 2008. In the pooled cohort 1,195 lung cancer deaths were observed (789 Mayak, 406 Sellafield) but only 893 lung cancer incidences (509 Mayak, 384 Sellafield, due to truncated follow-up in the incidence analysis).

Correction of confidence intervals in excess relative risk models using Monte Carlo dosimetry systems with shared errors.

In epidemiological studies, exposures of interest are often measured with uncertainties, which may be independent or correlated. Independent errors can often be characterized relatively easily while correlated measurement errors have shared and hierarchical components that complicate the description of their structure. For some important studies, Monte Carlo dosimetry systems that provide multiple realizations of exposure estimates have been used to represent such complex error structures.