Is it possible to more quickly determine risk estimates for cancer from occupational radiation exposures?
Currently, it can take months, or even more than a year, to perform the dosimetry calculations needed to determine the risk estimates for various jobs and exposures using available data and models. These calculations can depend on many different factors— radiation source, presence or absence of protective clothing or materials, male or female, whole body exposure or just organs or tissues, and so on—which adds a great deal of time and complexity in calculating dose. The federal government uses these risk estimates to set policy regulations for safe, acceptable exposure limits for workers; and it is absolutely critical that this information is regularly updated and accurate, based on the best science available.
Funded by the ORAU-Directed Research and Development program, ORAU researchers are partnering with Texas A&M University to evaluate the feasibility of developing a preliminary set of reference models to address this need. These models could be applied quickly and easily to epidemiologic studies of radiation dose to inform policymaking that sets better limits for protecting workers from adverse health effects, like cancers.
ORAU manages two of the largest data sets that provide millions of records on effective doses received by energy workers: the U.S. Department of Energy’s Radiation Exposure Monitoring System (REMS) and the U.S. Nuclear Regulatory Commission’s Radiation Exposure Information Reporting System (REIRS). Effective dose data are more generalized and provide whole-body averages of dose received by individuals. However, epidemiologic studies need absorbed dose data—or radiation dose absorbed by specific organs or tissues and specific to populations of male or female workers—to estimate risk for specific cancer and non-cancer outcomes. These organ/tissue-specific absorbed doses can then be modelled to account for varying degrees of exposure, but converting effective dose to absorbed dose is one of the most time-limiting factors in generating risk estimates.
The outcome of this research is expected to significantly decrease the time it takes to calculate these conversions. The REMS and REIRS datasets provide a good starting point for this team to create valid and reproducible reference models that can be utilized to save time and effort in this process. The results of this feasibility study are scheduled to be released in 2020.