PNNL-13501 - Pacific Northwest National Laboratory

Beyond Dose and Response: Relating Radiation and Detriment
Daniel J. Strom, Bruce A. Napier, Paul S. Stansbury, Sandra F. Snyder, Robert D. Stewart
Study Control Number: PN00013/1420
It is well known that the harmful effects of ionizing radiation cannot be predicted by dose alone. This project describes a
comprehensive model relating radiation and detriment that includes not only dose, but also several other factors known to
affect risk. The project developed software to compute radiation dose rates following intakes of radioactive materials and
demonstrated that plutonium cleanup standards based on current simplistic models are too restrictive.
Project Description
The concept of “Beyond Dose and Response” with regard
to radiation and detriment represents potentially a radical
change in thinking from the traditional use of the linear
nonthreshold dose-response model that currently forms
the basis for radiation protection standards. We outlined a
radiation-detriment model relating risk of stochastic
health endpoints (cancer and heritable ill-health) to
radiation exposure. Adoption by DOE of this
fundamental paradigm shift would change the way
environmental cleanup standards are set, and permit the
full body of scientific knowledge to be brought to bear on
radiation risk assessments and cleanup standards derived
from such assessments.
We developed the capability to input radionuclide
exposure to the GENII environmental pathway code to
predict dose, dose rate, and its distribution in time (dose
rate, dose fractionation, and dose timing) and then to
predict detriment from these and other variables. Using
this tool with human exposure data to radium, thorium,
and plutonium, we demonstrated that cleanup standards
are too restrictive by a factor of about ten.
Introduction
This project has
• enumerated what must be known for a
comprehensive model relating radiation and
detriment
• developed computational tools to permit calculation
of needed input parameters to detriment modeling
• shown the impact of applying human threshold data
directly to plutonium cleanup standards through this
model, which become less restrictive when informed
modeling is used.
278 FY 2000 Laboratory Directed Research and Development Annual Report
Approach
The literature on parameters affecting radiation detriment
(or expectation of harm; ICRP 1991) includes many
factors in addition to dose. We developed a minimum
parameter set needed to predict detriment. The project
then focused on the computational tools needed to predict
dose rate from alpha-emitters to all tissues and organs as a
function of time, as well as to time-dependent tissue and
organ activity content.
Results and Accomplishments
Elements of a Complete Radiation-Detriment Model
The ICRP’s concept of detriment (ICRP 1991) is
“expectation of harm,” which is a comprehensive
expression of risk. Currently, no single model predicts
detriment and deterministic effects for populations and
individuals, prospectively and retrospectively. The
relationship of detriment to radiation is complicated, and
a comprehensive model goes beyond new “Probability of
Causation” software (National Cancer Institute, Bethesda,
Maryland). The goal to this project is to outline a
radiation-detriment model that incorporates all variables
we know are important, that predicts all effects of interest,
and that faithfully carries uncertainty throughout
(especially when knowledge is absent). Such a model
must include thresholds for bone and liver cancers,
hormesis for some endpoints in some irradiation
scenarios, adaptive response with its very complicated
time dependence, sensitive or susceptible subpopulations,
and use a probabilistic approach.
To develop a radiation-detriment model, one must first
list all the organism-level outcomes of interest (various
kinds of cancer, stochastic non-cancer somatic effects,
heritable ill-health, deterministic effects), and for each
outcome, one must then choose a risk measure (such as
relative or absolute risk, severity, or frequency). For each
outcome, one must list all variables known to affect it