Radiation risk estimation

Boice, J.D., Jr., Beebe, G.M. and Land, C.E. (1985a). Absolute and relative time-response models in radiation risk estimation, page 22 in the Proceedings of the Tluentieth Annual Meeting of the NatioruU Council on Radiation Protection and Measurements, NCRP Proceedings, No. 6, (National Council on Radiation Protection and Measurements, Bethesda, Maryland). 

Beardsley, T, Fallout New Radiation Risk Estimates Prompt Calls for Tighter Controls. Sci. Amer., 35 (March 1990). 

Sir Edward Pochin (1978) Why be Quantitative about Radiation Risk Estimates Hymer L. Friedell (1979) Radiation Protection-Concepts and Trade Offs Harold O. Wyckoff (1980) From Quantity of Radiation and Dose to Exposure and Absorbed Dose -An Historical Review James F. Crow (1981) How Well Can We Assess Genetic Risk Not Very Eugene L. Saenger (1982) Ethics, Trade-offs and Medical Radiation Merril Eisenbud (1983) The Human Environment-Past, Present and Future Harald H. Rossi (1984) Limitation and Assessment in Radiation Protection John H. Harley (1985) Truth (and Beauty) in Radiation Measurement Herman P. Schwan (1986) Biological Effects of Non-ionizing Radiations ... 

Seymour Jablon (1987) How to be Quantitative about Radiation Risk Estimates Bo Lindell (1988) How Safe is Safe Enough ... 

The only estimate of dominant effects in humans comes from mouse data. BEIR III and UNSCEAR both used skeletal anomalies and cataracts as a basis for human radiation-risk estimates. With chemicals, there are greater uncertainties in extrapolating from mouse to man. The skeletal and cataract systems have not been used widely enough for their validity to be assessed, but at present there is little choice but to use these if an estimate must be made. We suggest that the human impact in the first 5-10 generations be estimated as 4 times the first-generation estimate, as explained in Chapter 7. As is also discussed in Chapter 7, there is no feasible way to estimate the total genetic impact. 

However, radiation risk estimates are not merely based on the follow-up of the atomic bomb survivors. 

NCRP115 = National Council on Radiation Protection and Measurements. 1993. Risk Estimates for Radiation Protection, Report 115. Bethesda, Maryland NRC = Nuclear Regulatory Commission. 

Van Bekkum, D.W. and P. Bentvelzen, The Concept of Gene Transfer-Misrepair Mechanism of Radiation Carcinogenesis May Challange the Risk Estimation For Low Radiation Doses, Health Physics 43 231-237 (1982). 

Aarkrog, A. 1990. Environmental radiation and radiation releases. Inter. Jour. Radiation Biol. 57 619-631. Abrahamson, S. 1990. Risk estimates past, present, and future. Health Physics 59 99-102. 

Radiation-induced genomic instability and bystander effects are now well-established consequences of exposure of living cells to ionizing radiation. Cells not directly traversed by radiation may still exhibit radiation effects. This phenomenon, known as bystander effect, has become a major activity in radiation biology and in some cases has challenged the conventional wisdom. An example is the currently accepted models used for low-dose extrapolation of radiation risks. The currently used models assume that cells in an irradiated population respond individually rather than collectively. If bystander effects have implications for health risks estimates from exposure to ionizing radiation, then the question of whether this is a general phenomenon or solely a characteristic of a particular type of cell and the radiation under test becomes an important issue. 

National Research Council, Comparative Dosimetry of Radon in Mines and Homes, Panel on Dosimetric Assumption Affecting the Applications of Radon Risk Estimates, Board on Radiation Effects Research, Commission on Life Sciences, National Academy Press, Washington, D.C., 1991. 

The health risks associated with ozone depletion will principally be those due to increased ultraviolet-B (UV-B) radiation in the environment, that is, increased damage to the eyes, the immune system, and the skin. Some new risks may also be introduced with the increased use of alternatives to the ozone-depleting substances (ODS). Quantitative risk estimates are available for some of the UV-B-associated... 

SC 1-5 Uncertainty in Risk Estimates SC 1-6 Basis for the Linearity Assumption SC 1-7 Information Needed to Make Radiation Protection Recommendations for Travel Beyond Low-Earth Orbit SC 9 Structural Shielding Design and Evaluation for Medical Use of X Rays and Gamma Rays of Energies Up to 10 MeV SC 46 Operational Radiation Safety... 

The accumulation of human data on a wider scale, and in quantitative form, has enabled radiation epidemiology to advance from an essentially descriptive stage to an analytic one in which numerical, dose-spedfic, risk estimates have begun to be treated statistically in order to identify determinants of risk. As presently conceived, these determinants include characteristics of the radiation exposure, underlying dose-response relationships, host factors, other environmental factors, differential tissue sensitivity, time after exposure, and natural levels of incidence. 

The BEIR III risk estimates formulated under several dose-response models demonstrate that the choice of the model can affect the estimated excess more than can the choice of the data to which the model is applied. BEIR III estimates of lifetime excess cancer deaths among a million males exposed to 0.1 Gy (10 rad) of low-LET radiation, derived with the three dose-response functions employed in that report, vary by a factor of 15, as shown in Ikble 6.1 (NAS/NRC, 1980). In animal experiments with high-LET radiation, the most appropriate dose-response function for carcinogenesis is often found to be linear at least in the low to intermediate dose range (e.g., Ullrich and Storer, 1978), but the data on bone sarcomas among radium dial workers are not well fitted by either a linear or a quadratic form. A good fit for these data is obtained only with a quadratic to which a negative exponential term has been added (Rowland et al., 1978). 

Sensitivity of specific target tissues We are confronted with the fact that risk estimates per unit of tissue dose (as currently measured) vary widely among spedfic organs, whether measured in terms of absolute or relative risk, and that these estimates bear no obvious relation to natural incidence. Until we know the basis for these differences, radiation carcinogenesis will not be understood. 

For assessing the risks of chemicals, the approach is similar to that used with radiation in those cases where human data are available, but the data are rarely as complete as with radiation. Furthermore, estimation of the dose is usually more difficult with chemicals because of the lack of good monitoring data and other sources of uncertainty (see Section 5). For example, the dose is not usually well quantified even at levels of exposure where carcinogenic effects are conspicuous. 

Tb a considerable extent, these risk estimates and risk comparisons are merely exposure comparisons. Their interpretation is aided by comparing them with natural background radiation exposure and its variations or comparison to the other risks of a particular activity or to the risks associated with safe industry . 

If the exposure had been much smaller, the risk calculation would have been less direct and less certain. For purposes of risk reduction in public health, we may choose to err on the pessimistic side in risk estimations. For purposes of attribution, however, we want to make best estimates. Most of the numbers in Ikble 8.4 are overestimates of the risks. For radiation-induced leukemia, as described in Section 6.1.2, the best dose-incidence model might be lineai>quadratic and not linear. Thus, someone exposed to 50 mSv (5 rem) might be considered, on a linear extrapolation basis, to have a radiation related lifetime risk of cancer mortality of 10 (2 x 10 Sv 2 x 10 rem ), or a lifetime risk of mortality from leukemia of approximately 1.5 x 10 (0.3 x 10" Sv 0.3 X 10 rem ). The natural lifetime risk of mortality from leukemia other than chronic lymphocytic leukemia is approximately 56 x 10 . Therefore, the percent attribution to radiation according to the linear model would be ... 

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