Science-informed Policy Making for Protecting People and the Environment from Radiation.

ABSTRACT: The process to arrive at the radiation protection practices of today to protect workers, patients, and the public, including sensitive populations, has been a long and deliberative one. This paper presents an overview of the US Environmental Protection Agency's (US EPA) responsibility in protecting human health and the environment from unnecessary exposure to radiation. The origins of this responsibility can be traced back to early efforts, a century ago, to protect workers from x rays and radium. The system of radiation protection we employ today is robust and informed by the latest scientific consensus. It has helped reduce or eliminate unnecessary exposures to workers, patients, and the public while enabling the safe and beneficial uses of radiation and radioactive material in diverse areas such as energy, medicine, research, and space exploration. Periodic reviews and analyses of research on health effects of radiation by scientific bodies such as the National Academy of Sciences, National Council on Radiation Protection and Measurements, United Nations Scientific Committee on the Effects of Atomic Radiation, and the International Commission on Radiological Protection continue to inform radiation protection practices while new scientific information is gathered. As a public health agency, US EPA is keenly interested in research findings that can better elucidate the effects of exposure to low doses and low dose rates of radiation as applicable to protection of diverse populations from various sources of exposure. Professional organizations such as the Health Physics Society can provide radiation protection practitioners with continuing education programs on the state of the science and describe the key underpinnings of the system of radiological protection. Such efforts will help equip and prepare radiation protection professionals to more effectively communicate radiation health information with their stakeholders.

Methodological improvements to meta-analysis of low dose rate studies and derivation of dose and dose-rate effectiveness factors.

Epidemiological studies of cancer rates associated with external and internal exposure to ionizing radiation have been subject to extensive reviews by various scientific bodies. It has long been assumed that radiation-induced cancer risks at low doses or low-dose rates are lower (per unit dose) than those at higher doses and dose rates. Based on a mixture of experimental and epidemiologic evidence the International Commission on Radiological Protection recommended the use of a dose and dose-rate effectiveness factor for purposes of radiological protection to reduce solid cancer risks obtained from moderate-to-high acute dose studies (e.g. those derived from the Japanese atomic bomb survivors) when applied to low dose or low-dose rate exposures. In the last few years there have been a number of attempts at assessing the effect of extrapolation of dose rate via direct comparison of observed risks in low-dose rate occupational studies and appropriately age/sex-adjusted analyses of the Japanese atomic bomb survivors. The usual approach is to consider the ratio of the excess relative risks in the two studies, a measure of the inverse of the dose rate effectiveness factor. This can be estimated using standard meta-analysis with inverse weighting of ratios of relative risks using variances derived via the delta method. In this paper certain potential statistical problems in the ratio of estimated excess relative risks for low-dose rate studies to the excess relative risk in the Japanese atomic bomb survivors are discussed, specifically the absence of a well-defined mean and the theoretically unbounded variance of this ratio. A slightly different method of meta-analysis for estimating uncertainties of these ratios is proposed, motivated by Fieller's theorem, which leads to slightly different central estimates and confidence intervals for the dose rate effectiveness factor. However, given the uncertainties in the data, the differences in mean values and uncertainties from the dose rate effectiveness factor estimated using delta-method-based meta-analysis are not substantial, generally less than 70%.

U.S. Environmental Protection Agency radiogenic risk projections: uncertainty analysis.

The U.S. Environmental Protection Agency (EPA) has updated its estimates of cancer risks due to low doses of ionizing radiation for the U.S. population, as well as their scientific basis. For the most part, these estimates were calculated using models recommended in the recent National Academy of Sciences' (BEIR VII) report on health effects from low levels of ionizing radiation. The new risk assessment includes uncertainty bounds associated with the projections for gender and cancer site-specific lifetime attributable risks. For most cancer sites, these uncertainty bounds were calculated using probability distributions for BEIR VII model parameter values, derived from a novel Bayesian analysis of cancer incidence data from the atomic bomb survivor lifespan study (LSS) cohort and subjective distributions for other relevant sources of uncertainty. This approach allowed for quantification of uncertainties associated with: 1) the effect of sampling variability on inferences drawn from the LSS cohort about the linear dose response and its dependence on temporal factors such as age-at-exposure, 2) differences in the radiogenic risks in the Japanese LSS cohort versus the U.S. population, 3) dosimetry errors, and 4) several other non-sampling sources. Some of the uncertainty associated with how risk depends on dose and dose rate was also quantified. For uniform whole-body exposures of low-dose gamma radiation to the entire population, EPA's cancer incidence risk coefficients and corresponding 90% uncertainty intervals (Gy) are 9.55 × 10 (4.3 × 10 to 1.8 × 10) for males and 1.35 × 10 (6.5 × 10 to 2.5 × 10) for females, where the numbers in parentheses represent an estimated 90% uncertainty interval. For many individual cancer sites, risk coefficients differ from corresponding uncertainty bounds by factors of about three to five, although uncertainties are larger for cancers of the stomach, prostate, liver, and uterus. Uncertainty intervals for many, but not all, cancer sites are similar to those given in BEIR VII, which were derived using a non-Bayesian approach.

Uncertainty in estimating probability of causation in a cross-sectional study: joint effects of radiation and hepatitis-C virus on chronic liver disease.

Exposure to other risk factors is an important consideration in assessing the role played by radiation in producing disease. A cross-sectional study of atomic-bomb survivors suggested an interaction between whole-body radiation exposure and chronic hepatitis-C viral (HCV) infection in the etiology of chronic liver disease (chronic hepatitis and cirrhosis), but did not allow determination of the joint-effect mechanism. Different estimates of probability of causation (POC) conditional on HCV status resulted from additive and multiplicative models. We therefore estimated the risk for radiation conditional on HCV status using a more general, mixture model that does not require choosing between additivity or multiplicativity, or deciding whether there is interaction, in the face of the large uncertainty. The results support the conclusion that POC increases with radiation dose in persons without HCV infection, but are inconclusive regarding individuals with HCV infection, the lower confidence bound on estimated POC for radiation with HCV infection being zero over the entire dose range. Although the mixture model may not reflect the true joint-effect mechanism, it avoids restrictive model assumptions that cannot be validated using the available data yet have a profound influence on estimated POC. These considerations apply more generally, given that the additive and multiplicative models are often used in POC related work. We therefore consider that an empirical approach may be preferable to assuming a specific mechanistic model for estimating POC in epidemiological studies where the joint-effect mechanism is in doubt.