United States Science Advisory EPA-SAB-RAC-99-008
Environmental Board February 1999
Protection Agency Washington, DC ivww.epa.gov/sab
&EPA AN SAB REPORT:
ESTIMATING UNCERTAINTIES
IN RADIOGENIC CANCER
RISK
REVIEW OF THE OFFICE OF
RADIATION AND INDOOR AIR'S
DRAFT DOCUMENT ESTIMATING
RADIOGENIC CANCER RISKS DRAFT
ADDENDUM: UNCERTAINTY
ANALYSIS BY THE RADIATION
ADVISORY COMMITTEE
-------
February 18, 1999
EPA-SAB-RAC-99-008
Honorable Carol M. Browner
Administrator
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, DC 20460
Re: Review of the Office of Radiation and Indoor Air October, 1997 Draft
Document Estimating Radiogenic Cancer Risks Draft Addendum:
Uncertainty Analysis (October, 1997)
Dear Ms. Browner:
In 1994, EPA published Estimating Cancer Risks, describing the Agency's
methodology for calculating excess cancer morbidity and mortality risks due to ionizing
radiation. Subsequently, the risk projections of EPA's 1994 document were updated
and extended in light of more recent U.S. vital statistics provided in EPA's 1998 Federal
Guidance Report, Number 13 (FGR 13).
The present EPA methodology provides quantitative estimates of uncertainty in
cancer mortality per gray (Gy) of radiation absorbed dose delivered at low doses and
low dose rates by both low-Linear Energy Transfer (LET) and high-LET radiation to the
whole body, the lungs, and the bone marrow. The risk of radiogenic cancer incidence
is based on the quotient of the risk of a radiogenic mortality and a lethality fraction.
The Science Advisory Board (SAB) was asked to by the Office of Radiation and
Indoor Air (ORIA) to review the EPA draft document of October 1997 entitled
"Estimating Radiogenic Cancer Risks Draft Addendum: Uncertainty Analysis," which
presented the methodology developed by EPA to estimate uncertainty in its projections
of radiogenic cancer risk. The Uncertainty in Radiogenic Cancer Risk Subcommittee
(URRS) of the SAB's Radiation Advisory Committee (RAC) subsequently convened in
public meetings in Washington DC, on November 20, 1997 and March 4, 1998 to
receive briefings from the ORIA and interested members of the public, and to discuss
the relevant issues.
-------
In its Charge to the SAB, EPA asked three questions:
a) Are the relevant sources of uncertainty addressed?
b) Is the overall approach to quantifying and combining uncertainties
appropriate?
c) Are the mathematical functions used to characterize the various sources
of uncertainty reasonable in view of available scientific information?
One additional issue identified during the public meeting, was also addressed:
the possibility of "unknown" sources of uncertainty in addition to the seven identified by
EPA.
Overall, the Subcommittee believes that EPA has generated a credible
document, using published techniques in identifying and combining the various sources
of uncertainty. We applaud EPA for recognizing the importance of describing the state
of knowledge of uncertain input variables as subjective probability distributions and
using Monte Carlo simulation to combine these input uncertainties into a subjective
probability distribution of radiogenic cancer risk. We note that in other offices of EPA,
the application of this approach to the evaluation of the dose response of specific
contaminants is still a subject of internal discussion. In issuing its October 1997 Draft
Addendum, the ORIA is demonstrating a leadership position within the Agency. We
encourage the EPA to build on the draft methodology and issue a single, integrated
document that clearly describes the EPA's methodology for estimation of specific
cancer incidence and mortality risks per unit intake of radioactivity, along with their
associated uncertainty. This would be an extension of the work initiated in FGR 13..
As with examining any such complex undertaking, our review found areas in
which improvement was possible. These areas, and our recommendations are:
a) The primary data used for risk estimation should be the Radiation Effects
Research Foundation 1992 data on cancer incidence rather than on
cancer mortality.
(1) Data on cancer incidence are affected less by cancer
misclassification than are data based on mortality.
-------
(2) Use of data based on cancer incidence avoids the uncertainty
introduced when applying lethality fractions to data on cancer
mortality.
(3) Lethality fractions may vary greatly across populations and time,
and may represent an additional source of uncertainty (which
EPA's present approach appears to underestimate).
b) The subjective probability distribution for extrapolation from high to low
dose rates for low-LET radiation should include a greater weight for the
possibility that the low Dose and Dose Rate Effectiveness Factor
(DDREF) could be less than or equal to 1.0, as well as for values higher
than 5.0. In any case, EPA should provide stronger justification to explain
why a subjective weight is not given for values exceeding 5.0. The values
chosen for the DDREF could also vary depending on the type of cancer
and organ affected.
c) EPA should consider the additive, multiplicative, and National Institutes of
Health (NIH) models as alternative modeling approaches for transferring
radiogenic cancer risk from the Japanese Lifespan Survival Study cohort
to the U.S. population. EPA should assign a subjective weight to each
model (additive, multiplicative, and NIH), rather than using a coefficient
based on the geometric mean of the latter two model results.
d) EPA has multiplied the probabilities of uncertain input variables to obtain
a joint probability of the radiogenic cancer risk. This multiplication has
been performed assuming statistical independence among these
quantities. This assumption may be incorrect. For example, statistical
sampling errors in the epidemiological follow-up of exposed cohorts can
be affected by diagnostic misclassification of cancer mortalities; thus, a
dependency between these two variables does exist. Further
investigation should explicitly consider the effect of dependencies among
the inputs used to estimate the radiogenic cancer risk.
e) For those inputs that dominate the overall uncertainty in radiogenic
cancer risk, use of more formal methods of expert elicitation would be
desirable to obtain defensible estimates of subjective distributions that
reflect the current state of knowledge. Formal elicitation of expert
judgment would be preferred to informal estimates made by EPA staff.
Currently, the subjective probability distributions specified by EPA staff
-------
reflect only the state of knowledge of the EPA. A more formal elicitation
would encompass an evaluation of extant data sets by a broader
spectrum of expertise both inside and outside of EPA.
f) As additional information becomes available, the currently specified
subjective probability distributions should be updated objectively using an
intellectually consistent approach (such as a Bayesian process) to reflect
improvements in the state of knowledge. Opportunities for updating
should be encouraged, and incentives provided, when uncertainty levels
are too high to permit confident decision making.
The RAC and its Subcommittee appreciate the opportunity to provide this report
to you and we hope that it will be helpful. We look forward to the response of the
Assistant Administrator for the Office of Air and Radiation to this report in general and
to the comments and recommendations in this letter in particular.
Sincerely,
/signed/
Dr. Joan M. Daisey, Chair
Science Advisory Board
/signed/
Dr. Stephen L. Brown, Chair
Radiation Advisory Committee
Science Advisory Board
/signed/
Dr. F. Owen Hoffman, Chair
Uncertainty in Radiogenic Risk Subcommittee
Radiation Advisory Committee
-------
NOTICE
This report has been written as part of the activities of the Science Advisory
Board (SAB), a public advisory group providing extramural scientific information and
advice to the Administrator and other officials of the Environmental Protection Agency
(EPA). The Board is structured to provide balanced, expert assessment of scientific
matters related to problems facing the Agency. This report has not been reviewed for
approval by the Agency and, hence, the contents of this report do not necessarily
represent the views and policies of the EPA nor of other agencies in the Executive
Branch of the Federal government. In addition, the mention of trade names or
commercial products does not constitute a recommendation for use.
-------
ABSTRACT
The Science Advisory Board (SAB) was asked by EPA's Office of Radiation and
Indoor Air (ORIA) to review the 1997 draft document entitled "Estimating Radiogenic
Cancer Risks Draft Addendum: Uncertainty Analysis," October, 1997. The Charge to
the SAB focused on evaluating sources of uncertainty, methods of quantifying
uncertainties, and the mathematical quantification of sources of uncertainty.
The review of the Uncertainty in Radiogenic Risk Subcommittee (URRS) of the
SAB has concluded that EPA has generated a credible document. The state of
knowledge of uncertain input variables has been properly described by the Agency
staff within the Office of Radiation and Indoor Air (ORIA) as subjective probability
distributions. Monte Carlo simulation is properly employed to combine these input
uncertainties into a subjective probability distribution of radiogenic cancer risk. EPA is
encouraged to build on the draft methodology and issue a single document that clearly
describes its methodology for estimating specific cancer-incidence and mortality risks
per unit intake of radioactivity, along with their associated uncertainty.
URRS recommendations for improving the draft report include (a) use of primary
data based on cancer morbidity rather than mortality; (b) expansion of the subjective
probability distribution for extrapolating from high to low dose and dose rates; (c)
accounting explicitly for alternative modeling approaches used to transfer risk
coefficients from data on the survivors of the atomic bombings of Japan to estimated
risks in the U.S. population; and (d) the use of formal methods of expert elicitation to
quantify uncertainty for the most important input variables, so that subjective probability
distributions reflect the current state of knowledge.
KEYWORDS: uncertainty; radiogenic risk of cancer; subjective probability.
-------
U.S. ENVIRONMENTAL PROTECTION AGENCY
SCIENCE ADVISORY BOARD
RADIATION ADVISORY COMMITTEE
UNCERTAINTY IN RADIOGENIC RISK SUBCOMMITTEE
CHAIR
Dr. F. Owen Hoffman, SENES Oak Ridge, Inc. Oak Ridge, TN
MEMBERS AND CONSULTANTS
Dr. Stephen L. Brown, Risks of Radiation and Chemical Compounds, Oakland, CA
Dr. William Bair, Richland, WA
Dr. Peter G. Groer, University of Tennessee, Dept. Of Nuclear Engineering, Knoxville, TN
Dr. David G. Hoel, Medical University of SC, Charleston, SC (Liaison from SAB
Environmental Health Committee)
Dr. Ellen Mangione, M.D., M.P.H., Colorado Department of Public Health and Environment,
Denver, CO
Dr. Leif E. Peterson, Baylor College of Medicine, Houston, TX
Dr. William J. Schull1, University of Texas, Houston, TX
Dr. Steven L. Simon2, National Academy of Sciences, Washington, DC
Dr. Arthur C. Upton, Robert Wood Johnson Medical School, Piscataway, NJ
Science Advisory Board Staff
Dr. Jack Kooyoomjian, Designated Federal Officer, USEPA, Science Advisory Board, 401 M
Street, SW, Washington, DC
Mr. Samuel Rondberg3, Designated Federal Officer, USEPA, Science Advisory Board, 401 M
Street, SW, Washington, DC
Ms. Diana Pozun, Management Assistant, USEPA, Science Advisory Board, 401 M Street,
SW, Washington, DC
Participated in review of document, but was unable to attend face-to-face public meetings.
Subject matter expert.
Provided editorial support for preparation of this report, but did not participate in the review.
-------
U.S. ENVIRONMENTAL PROTECTION AGENCY
SCIENCE ADVISORY BOARD
RADIATION ADVISORY COMMITTEE
CHAIR
Dr. Stephen L. Brown , R2C2 Risks of Radiation and Chemical Compounds, Oakland, CA
MEMBERS AND CONSULTANTS:
Dr. William Bair, Richland, WA
Dr. Vicki M. Bier, University of Wisconsin, Madison, Wl
Dr. Thomas F. Gesell, Idaho State University, Pocatello, ID
Dr. F. Owen Hoffman , SENES Oak Ridge, Inc, Oak Ridge, TN
Dr. Janet Johnson, Shepherd Miller, Inc., Ft. Collins, CO
Dr. Donald Langmuir, Golden, CO
Dr. Jill Lipoti, New Jersey Department of Environmental Protection, Trenton, NJ
Dr. June Fabryka-Martin4, Los Alamos National Laboratory, Los Alamos, NM
Dr. Ellen Mangione, Colorado Department of Health, Denver, CO
Dr. Paul J. Merges, NY State Department of Environmental Conservation, Albany, NY
Dr. John W. Poston, Sr., Texas A&M University, College Station, TX
Dr. Genevieve S. Roessler, Radiation Consultant, Elysian, MN
PAST CHAIR:
Dr. James E. Watson, Jr., University of North Carolina, Chapel Hill, NC
SCIENCE ADVISORY BOARD STAFF
Dr. Jack Kooyoomjian , Designated Federal Officer, USEPA, Science Advisory Board, 401 M
Street SW, Washington, DC
Ms. Diana Pozun, Management Assistant, USEPA, Science Advisory Board, 401 M Street
SW, Washington, DC
Declined to participate in review.
iv
-------
TABLE OF CONTENTS
1 EXECUTIVE SUMMARY 1
2 INTRODUCTION 5
2.1 Background 5
2.2 Specific Charge 5
3 DETAILED DISCUSSION 6
3.1 Review of the Primary Sources of Uncertainty 6
3.1.1 Statistical Sampling Errors in the LSS Data Set 6
3.1.2 Diagnostic Misclassification in the LSS Data Set 7
3.2 Temporal Projection of Risk 8
3.3 Transfer of Risk from the LSS Cohort to the U.S. Population 11
3.4 Errors in Dosimetry 12
3.5 Low Dose Rate Extrapolation of Low-LET Risk Estimates 14
3.6 Uncertainties in the RBE for Alpha Particle Radiation 17
3.7 Accounting for Additional "Unknown" Sources of Uncertainty 18
4 CONCLUSIONS AND RECOMMENDATIONS 20
4.1 Risk Estimates Based on Cancer Incidence Rather than Cancer Mortality 20
4.2 Alternative Modeling Approaches for Transferring Risk Estimates 20
4.3 Clarification of Uncertainty Distributions 20
4.4 Uncertainty in the DDREF 21
4.5 Future Updates of the Uncertainty Analysis 21
4.6 Subjective Distributions Based on Expert Elicitation 21
4.7 Treatment of Unknown Sources of Uncertainty 22
APPENDIX A - THE COMPARISON OF MORTALITY VERSUS INCIDENCE DATA A-1
APPENDIX B - GENETIC DIVERSITY AND INTERINDIVIDUAL VARIATION IN RADIATION
RESPONSE B-1
APPENDIX C - UNCERTAINTIES IN THE RBE FOR ALPHA RADIATION C-1
APPENDIX D - ACRONYMS D-1
REFERENCES R-1
-------
1 EXECUTIVE SUMMARY
In 1994, EPA published Estimating Cancer Risks (EPA, 1994), which described
the Agency's methodology for calculating excess cancer morbidity and mortality risks
due to ionizing radiation. Subsequently, the risk projections of the 1994 report were
updated in light of more recent U.S. vital statistics which were used to complete EPA's
1998 Federal Guidance Report No. 13 (FGR 13). The most recent attempt by EPA to
quantify the uncertainty in its risk estimates was provided to the SAB as a draft
document "Estimated Radiogenic Cancer Risks Draft Addendum: Uncertainty Analysis"
(EPA, !997).
The present EPA methodology provides quantitative estimates of uncertainty in
cancer mortality per gray (Gy) of absorbed dose delivered at low doses and low dose
rates by both low linear-energy-transfer (LET) and high-LET radiation to the whole
body, the lungs, and the bone marrow. A risk of radiogenic cancer incidence is based
on the quotient of the risk of a radiogenic mortality and a lethality fraction (obtained
from cancer survival statistics).
The EPA methodology includes seven sources of uncertainty:
a) statistical sampling errors of cancer mortality in the survivors of the atomic
bombings of Hiroshima and Nagasaki, as indicated by the Lifespan
Survivor Study (LSS) data,
b) diagnostic misclassification of cancer mortalities in the LSS cohort,
c) projection of risk in the LSS cohort out to the entire lifetime for all exposed
individuals in that cohort,
d) transfer of risk estimates determined from the LSS data to the US
population,
e) errors in dose estimates made for the LSS cohort,
f) extrapolation of risk from high dose rates of low-LET radiation received by
the LSS cohort to low dose rates expected for populations receiving
chronic exposures to man-made and natural radionuclides in the
environment, and
g) the difference in the relative biological effectiveness (RBE) between high-
LET radiation (i.e., alpha particles) and low-LET radiation.
-------
EPA's 1997 document represents each of the first six sources of uncertainty as a
series of bias correction factors that in turn are multiplied by the nominal risk values
used in the earlier report (EPA, 1994) that described EPA's methodology for estimating
radiogenic cancer risks. For a uniform, whole- body, low dose-rate exposure, that
nominal value is 5.75 X 10"2 fatal cancers5 per Gy. This value is intended to represent
the risk to a member of the population who can be defined as an average of relevant
characteristics, e.g., gender and age.
The uncertainty in the bias correction factors is described by subjective
probability distributions representing EPA's current state of knowledge. Some of the
information documenting the need for a bias correction has recently been published in
Federal Guidance Report No. 13 (EPA, 1998). The general approach used by EPA
(1997) to quantify uncertainty is similar, but not entirely identical, to the recent report
No. 126 by the National Council on Radiation Protection and Measurements (NCRP,
1997). The combining of subjective probability distributions for uncertain inputs to
obtain a subjective probability distribution for the excess lifetime risk per gray (Gy) from
whole-body radiation is performed using Monte Carlo simulation. The approach is
similar to that emphasized in NCRP Report 126 (NCRP, 1997) and consistent with the
recommendation in NCRP Commentary No. 14 (NCRP, 1996). Both of these latter
reports were commissioned by EPA.
In the October 1997 Draft Addendum, EPA summarizes the uncertainty in
radiogenic cancer risk upper and lower limits (for 90% subjective confidence intervals)
ranging from 1.0 to 10 X 1Q2 Gy1 for whole-body radiation, from 0.19 to 2.0 X 102 Gy1
for the lungs, and from 0.15 to 0.8 X 102Gy"1 for leukemia.
The Uncertainty in Radiogenic( Cancer) Risk Subcommittee (URRS) of the
SAB's Radiation Advisory Committee subsequently convened public meetings in
Washington DC, on November 20, 1997 and March 4, 1998 to receive briefings from
the Office and Radiation and Indoor Air (ORIA) and other interested members of the
public, and to discuss the relevant issues identified in the Charge (The detailed
Charge is provided in Section 2.2 of this report, below).
In its review of the EPA October 1997 Draft Addendum, the Subcommittee found
a need for improvement in the following areas:
a) The primary data used for risk estimation should be the Radiation Effects
Research Foundation (RERF) data on cancer incidence rather than
cancer mortality.
Updated in the current (1997) EPA report from 5.09 X 1CT
2
-------
(1) Data on cancer incidence are affected less by cancer
misclassification than are data based on mortality.
(2) Use of data based on cancer incidence avoids the uncertainty
introduced when attempting to estimate cancer incidence rates
from cancer mortality data.
(3) Lethality fractions may vary greatly across populations and time
and may represent an additional source of uncertainty which EPA's
present approach appears to underestimate.
b) The subjective probability distribution for extrapolation from high to low
doses and dose rates for low-LET radiation should include a greater
weight for the possibility that the low Dose and Dose Rate Effectiveness
Factor (DDREF) could be less than or equal to 1.0, as well as for values
higher than 5.0. In any case, EPA should provide stronger justification to
explain why a subjective weight is not given for values exceeding 5.0.
The values chosen for the DDREF could also vary depending on the type
of cancer and organ affected.
c) EPA should consider the additive, multiplicative, and National Institutes of
Health (NIH) models as alternative modeling approaches for transferring
radiogenic cancer risk coefficients from the LSS cohort to the U.S.
population. EPA should assign a subjective weight to each model
(additive, multiplicative, and NIH), rather than using a coefficient based
on the geometric mean of the latter two model results.
d) Multiplication of probabilities to obtain the joint probability is appropriate
only for statistically independent quantities. Possible dependence of the
quantities multiplied together should be investigated. For example,
statistical sampling errors can also be affected by diagnostic
misclassification of cancer mortalities.
e) For those inputs that dominate the overall uncertainty in radiogenic
cancer risk, use of more formal methods of expert elicitation would be
desirable to obtain defensible estimates of subjective distributions that
reflect the current state of knowledge. Formal elicitation of expert
judgment would be preferred to informal estimates made by EPA staff.
Currently, the subjective probability distributions specified by EPA staff
reflect only the state of knowledge of the EPA. A more formal elicitation
would encompass an evaluation of extant data sets by a broader
spectrum of expertise both inside and outside of EPA.
-------
f) As additional information becomes available, the currently specified
subjective probability distributions should be updated objectively, using
an intellectually consistent process such as Bayesian updating to reflect
improvements in the state of knowledge. Opportunities for updating
should be encouraged, and incentives provided, when uncertainty levels
are too high to permit confident decision making.
Overall, we believe that EPA's Office of Radiation and Indoor Air (ORIA) has
documented a credible methodology using published techniques to identify and
combine the various sources of uncertainty. We applaud EPA for recognizing the
importance of describing the state of knowledge of uncertain input variables as
subjective probability distributions and using Monte Carlo simulation to combine these
input uncertainties into a subjective probability distribution of radiogenic cancer risk.
We note that in other offices of EPA, the application of this approach to the evaluation
of the dose-response of specific contaminants is still a subject of internal discussion. In
issuing its October 1997 Draft Addendum (EPA, 1997), EPA's ORIA is demonstrating a
leadership position within the agency. We encourage the EPA to build on the 1997
draft methodology (EPA, 1997) and issue a single, integrated document that clearly
describes the EPA's methodology for estimation of specific cancer incidence and
mortality risks per unit intake of radioactivity, along with their associated uncertainty.
This would be an extension of the work initiated in Federal Guidance Report No. 13
(EPA, 1998; See also U.S. EPA/SAB, 1998).
-------
2 INTRODUCTION
2.1 Background
In 1994, EPA published Estimating Cancer Risks (EPA, 1994), which described
the Agency's methodology for calculating excess cancer morbidity and mortality risks
due to ionizing radiation (EPA, 1994). Subsequently, the risk projections of EPA 1994
were updated in light of more recent U.S. vital statistics (Federal Guidance Report No.
13 (EPA, 1998) and were published in the draft documentEsfrmafrng Radiogenic
Cancer Risks Draft Addendum: Uncertainty Analysis (EPA, 1997). The present EPA
methodology provides quantitative estimates of uncertainty in cancer mortality per gray
(Gy) of radiation dose delivered at low doses and low dose rates by both low-linear
energy transfer (LET) and high-LET radiation to the whole body, the lung, and the bone
marrow. A risk of radiogenic cancer incidence is based on the risk of radiogenic
mortality divided by an estimate of the fraction of the population who survive after being
diagnosed with cancer.
The SAB was asked to review the methodology developed by EPA to estimate
uncertainty in its projections of radiogenic cancer risk (EPA, 1997) (The detailed
Charge is provided in Section 2.2 of this report, below). The Uncertainty in Radiogenic
(Cancer) Risk Subcommittee (URRS) of the SAB's Radiation Advisory Committee
subsequently convened public meetings in Washington, DC on November 20, 1997,
and on March 4, 1998, to receive briefings from the Office and Radiation and Indoor Air
(ORIA) and other interested members of the public, and to discuss the relevant issues.
2.2 Specific Charge
In its charge to the SAB, EPA asked three questions:
a) Are the relevant sources of uncertainty addressed?
b) Is the overall approach to quantifying and combining uncertainties
appropriate?
c) Are the mathematical functions used to characterize the various sources
of uncertainty reasonable in view of available scientific information?
The following sections of this report address, in detail, the questions posed by
the Charge for each of the sources of uncertainty. Two additional issues are also
discussed: a) the possibility of "unknown" sources of uncertainty in addition to the
seven identified by EPA; and b) separation of the correction of bias in nominal values
from uncertainty due to lack of knowledge about true values.
-------
3 DETAILED DISCUSSION
3.1 Review of the Primary Sources of Uncertainty
3.1.1 Statistical Sampling Errors in the LSS Data Set
EPA (1997) describes a unitless bias correction factor for statistical sampling
variation in the Lifetime Survival Study (LSS) data as a normal distribution with a mean
of 1.0 and a standard deviation of 0.15 for estimating the risk of low-level, low-LET,
whole body radiation. For lung cancer from low-dose irradiation of the lungs, a normal
distribution is assumed with a mean of 1.05 and a standard deviation of 0.29. For
leukemia from low-dose irradiation of the bone marrow, EPA assumes a normal
distribution with a mean of 1.05 and a standard deviation of 0.18. The Subcommittee
considered the following specific issues posed by the Charge:
a) Are the relevant sources of uncertainty addressed?
The relevant sources are addressed, but it must be understood that a
major philosophical transition has been made from statistical confidence
intervals obtained from classical statistical analysis of LSS data (Shimizu
et a/., 1990) to subjective probability distributions used to extrapolate
beyond the domain of direct observation, based on the state of knowledge
about a dose response.
b) Is the overall approach to quantifying and combining uncertainties
appropriate?
There is a need to make a clear distinction between classical confidence
intervals (CCI) and subjective confidence intervals (SCI). Proper
interpretation of the CCI shows that an unknown true value is not always
contained in the CCI (Neyman, 1977). Adoption of the same numerical
values from the CCI as the upper and lower bound of the SCI (as has
been done in the review document (EPA, 1997)) carries a risk that the
unknown true value of a parameter may lie outside of the SCI.
The calculation of the new "expectations" in the EPA (1997) report using
the numerical limits of the CCI may produce upward or downward bias.
This introduction of bias follows from the properties of the CCI, since CCIs
will, a certain fraction of the time, lie above or below the true value.
c) Are the mathematical functions used to characterize the various sources
of uncertainty reasonable in view of available scientific information?
-------
The distributions assumed by EPA to characterize the state of knowledge
for errors due to statistical sampling appear to be appropriate. It would be
preferable, however, to use previous knowledge to specify prior
distributions and then update these distributions with the data given in
Shimizuefa/. (1990).
3.1.2 Diagnostic Misclassification in the LSS Data Set
EPA recognizes two types of diagnostic misclassification of cancer: classification
of cancer as non-cancers (detection error), and erroneous classification of non-cancers
as cancers (confirmation error). For low-level, low-LET, whole body radiation, the
uncertainty in diagnostic misclassification of cancer mortality is described as an
uncertain multiplicative bias correction factor (unitless) with values being normally
distributed with a mean of 1.2 and a standard deviation of 0.06. For irradiation of the
lungs, a mean of 1.3 and a standard deviation of 0.15 is assumed. For irradiation of
the bone marrow, uncertainty is assumed to be negligible. The Subcommittee
considered the following specific issues posed by the Charge:
a) Are the relevant sources of uncertainty addressed?
The relevant sources are addressed; however, uncertainty could be
substantially reduced by using available data on cancer incidence rather
than data on mortality (see Appendix A).
b) Is the overall approach to quantifying and combining uncertainties
appropriate?
It is appropriate to state uncertainty as a subjective probability distribution
of possibly true values and to use Monte Carlo procedures to combine all
sources of uncertainty into an overall distribution of radiogenic cancer
risk.
c) Are the mathematical functions used to characterize the various sources
of uncertainty reasonable in view of available scientific information?
Lifetime risks of radiation-induced cancer are typically projected on the
basis of mortality or incidence with the Agency choosing mortality-based
data in this case. Mortality projections are determined from direct
application of LSS mortality risk coefficients to baseline cancer mortality
rates in other populations. However, studies in the U.S. and Japan have
evaluated the use of death certificate data as the basis for calculating risk
estimates and found these data to be inaccurate and of limited use
-------
because a) mortality risks as an index of harm underestimate incidence
rates; b) death certificate-based mortality risks do not reveal patterns of
risk and survival by stage of cancer and histological cell type; c) death
certificate-based mortality cannot include risk for benign (non-fatal)
cancers; and d) death certificate-based mortality risks require an
additional bias correction for diagnostic misclassification (Chao and
Devesa, 1996; Gittelsohn and Senning, 1997; Hoelef a/., 1993; Kircheref
a/., 1985; Percy et a/., 1981; Percy et a/., 1990). Incidence-derived risk is
projected two ways: (1) by projecting mortality risks first and then dividing
results by a "lethality" fraction (deaths/new cases) to get incidence or by
(2) direct application of atomic bomb survivor incidence risk coefficients to
baseline cancer incidence rates in other populations. The former
mortality-based incidence method is biased by both diagnostic
misclassification and unknown variation in lethality fractions across time,
across populations, and across cancer sites. The latter incidence method
does not suffer from bias arising from use of death certificate-based
information or lethality fractions.
Because projection methods involving mortality are affected adversely by
the issues noted above, we have significant reservations about these
sources of uncertainty which are unnecessarily introduced by EPA's effort
for projecting (mortality-based) human risks of radiation-induced cancer.
Given the current availability of incidence data upon which radiation
protection standards could appropriately be based, it would seem
reasonable to revise the goal of radiation protection to one of protecting
individuals from exposures that place them at increased risk of developing
cancer, rather than protecting them from exposures leading to an
increased risk of dying from cancer. Further, the use of mortality as an
endpoint of concern is inconsistent with most of EPA's risk carcinogen risk
assessments, which are based on disease incidence. Incidence data
from the LSS are available and should serve as the primary basis for
EPA's estimates of radiogenic cancer risk in order to reduce uncertainty
and increase credibility. The Subcommittee notes that the preceding
discussion notwithstanding, moving to incidence-based projections
constitutes a major policy decision, and should be considered very
carefully before such an action is taken (see Appendix A for further
discussion of this topic).
3.2 Temporal Projection of Risk
This source of uncertainty reflects the fact that many survivors of the bombings
of Hiroshima and Nagasaki who were exposed in childhood are still alive. EPA
describes the uncertainty in temporal dependence to be a unitless bias correction
8
-------
factor that is multiplicative, with the assumption made in the EPA (1994) document that
the relative risk is constant, but that the risk coefficients decrease with age (at time of
exposure), especially for those exposed in childhood.
For cancer mortality from whole-body irradiation, a uniform distribution is
assumed by EPA, with a range from 0.5 to 1.0 for the lung, breast, thyroid, and
remaining sites. For the colon, a uniform distribution of 0.4 to 1.0 is assumed. For the
esophagus, liver, bladder, ovaries, and skin, EPA assumes a uniform distribution
ranging from 0.8 to 1.5 to reflect the fact that the data for these sites are sketchy and
heavily weighted towards adult exposures.
For uniform, whole-body irradiation, the uncertainty in the temporal projections of
risk for all solid tumors is described as a trapezoidal distribution with limits of 0.5 and
1.1, with most likely values ranging from 0.6 to 1.0. This distribution reflects the fact
that the overall effect of temporal projection should result in the tendency to over-
estimate the true risk when using the nominal risk value of 5.75 X 102 Gy"1 (EPA,
1997). For mortality due to low-dose irradiation of the lungs, it is a uniform distribution
ranging from 0.5 to 1.0. For leukemia from irradiation of the bone marrow, EPA
assumes a normal distribution with a mean of 1.1 and a standard deviation of 0.05.
The Subcommittee's specific response to the Charge follows below.
a) Are the relevant sources of uncertainty addressed?
The relevant sources are identified, but these sources may need to be
modified if data on cancer incidence are used instead of data limited to
cancer mortality. It must also be made clear that EPA is only dealing with
average age-at-exposure, since uncertainties in model projections of
childhood exposures far exceed the uncertainty used by EPA.
b) Is the overall approach to quantifying and combining uncertainties
appropriate?
For a particular outcome, lifetime risk is described by estimating the age-
specific changes in excess risk as a function of time since exposure and
then using this information to project patterns for age-time blocks not
adequately covered by current data. As of 1985, 39% of the LSS sample
survivors had died. Although some current information indicates that the
relative risk (RR) is reasonably constant for both total solid cancers and
leukemia, data are not yet available on the level of radiation-related
cancer risk at advanced ages among people exposed at early ages. The
extent to which the relative risk model applies to the LSS population
exposed in childhood has been questioned by UNSCEAR (1994). For
example, the increased risk for lung cancer remains elevated with the A-
-------
bomb survivors, while with spondylitic patients treated with x-rays, there is
no observed increase in risk 25 years after exposure. The proposed
modifications to the constant RR model allow for a decrease with time in
the non-leukemia cancer mortality for the youngest survivors.
The EPA analysis divides the cancers into three groups, by type of
temporal projection used:
(1) follow-up is complete,
(2) constant relative risk with dependence on age at exposure,
and
(3) constant relative risk.
No uncertainty is assigned to category (1), while the multiplicative bias
correction factors (unitless) for categories (2) and (3) are assigned
uniform uncertainty distributions of (0.4, 1) and (0.8, 1.5), respectively.
Leukemia is classified in EPA's group 1 with no uncertainty used on the
temporal projection of risk. This assumption appears too restrictive. The
effects on risk estimates of the choice of temporal projections may be as
much as a factor of two as illustrated on pages 204-205 in BEIR V
(NAS/NRC, 1990). Some recognition is needed of the uncertainty in the
choice of method for modeling the time since exposure for leukemia.
Lung cancer is placed in EPA's group 2. BEIR V assumed that there was
a decreasing relative risk with time for lung cancers. If the risk models
applied do not contain this assumption, then the uniform (0.4, 1)
distribution is appropriate for lung cancer, based on an average age at
exposure. This distribution is quite inadequate, however, if the risk from
exposure during childhood is to be calculated. For example, the lifetime
risk of respiratory cancer drops from 249 to 17 for an exposure to a 5-
year-old male, based on models 0 and 1 in Table 4D-6 of BEIR V, if a
time-since-exposure parameter is added to either model. Therefore, the
probability distribution assumed by EPA to represent uncertainty must be
restricted only to risks based on average age at exposure and not to risks
based on specific ages at exposure.
c) Are the mathematical functions used to characterize the various sources
of uncertainty reasonable in view of available scientific information?
Changes to the subjective probability distributions specified by EPA would
be warranted to reflect the additional uncertainty referred to above.
10
-------
3.3 Transfer of Risk from the LSS Cohort to the U.S. Population
Uncertainty exists over how to apply the results of the analysis of the Japanese
atomic bomb survivors to the estimation of risk in the U.S. population, particularly for
cancer sites which exhibit markedly different baseline rates in the two populations.
EPA has adopted a model for most organ sites in which the age- and sex-specific risk
coefficients are a geometric mean of the corresponding coefficients used in the
multiplicative and National Institutes of Health (NIH) projection models. In transferring
risks across populations, the multiplicative model presumes that the excess risk (i.e.,
the greater risk for those exposed to acute doses, as compared to the risk for those
exposed to the same dose delivered at a low dose rate) will scale with the baseline
cancer rate, whereas the NIH model (NIH, 1985) presumes that the excess risk is
nearly independent of differences in the baseline rate. For the risk of cancer mortality,
EPA assumes an uncertain multiplicative bias correction (unitless) that is described by
a normal distribution with a mean of 1.1 and a standard deviation of 0.12. For lung
cancer mortality, it is a log-uniform distribution ranging from 0.5 to 2.0. For leukemia, it
is a normal distribution with a mean of 1.0 and a standard deviation of 0.1. The specific
issues of the Charge are addressed below:
a) Are the relevant sources of uncertainty addressed?
Cancer mortality risk coefficients in the LSS were developed by using two
Poisson regression models: (1) purely additive and (2) purely
multiplicative. Transfer of risk coefficients from the LSS cohort to other
populations can be done with three models: (1) additive, (2) multiplicative,
and (3) NIH. Although the additive transfer method results in transferred
risk coefficients (for the U.S. population) that are half those obtained with
the multiplicative or NIH methods, EPA based its transfer uncertainty only
on the multiplicative and NIH methods. EPA should use all three transfer
models for assessing transfer uncertainties.
Another factor that EPA might consider in generalizing radiogenic cancer
risk from the survivors of the atomic bombings of Hiroshima and Nagasaki
to the U.S. population are genetic diversity and individual variation in
radiation response. Studies in population genetics now indicate that the
human genome contains "cancer-predisposing genes," which specifically
play an important role in controlling programmed cell death (apoptosis),
cellular proliferation, and DNA repair pathways (Sankaranarayanan and
Chakraborty, 1995) (See Appendix B).
b) Is the overall approach to quantifying and combining uncertainties
appropriate?
11
-------
In an effort to obtain a single parameter to represent both the NIH (1985)
and multiplicative transfer functions (ICRP, 1991), EPA estimated the
geometric mean coefficient (CMC) for risks from both models. In this
fashion, EPA did not assign subjective weights to each transfer model, but
rather combined the two sources of uncertainty into a hybrid of the two,
and then treated the combined uncertainty as a single probability
distribution for sampling. It would be an improvement if EPA instead
assigned a subjective probability weight to each transfer model (additive,
multiplicative, NIH), rather than using a geometric mean coefficient for the
results of just the latter two approaches. The subjective weighting method
assigns a "score" to each model, which is determined as the ratio of each
model's total risk to the sum of risks for all models. The probability
density of each model is then sampled and weighted according to the
model's score, and combined over many iterations (e.g., 5000) into a final
mixture model. As additional information becomes available, the currently
specified subjective probability distributions should be updated
objectively, using a an intellectually consistent approach (such as a
Bayesian process) to reflect improvements in the state of knowledge.
Opportunities for updating should be encouraged, and incentives
provided, when uncertainty levels are too high to permit confident
decision making.
c) Are the mathematical functions used to characterize the various sources of
uncertainty reasonable in view of available scientific information?
New information on transfer of risk based on cancer incidence indicates
much wider variation across sites, gender, and age when compared with
transfer of mortality risks under similar projections (NCRP, 1997).
The NCRP (1997) report concluded that comparisons between
multiplicative and additive transfer for cancer incidence risks varied by
factors of two to three, while for mortality risks the ratio of multiplicative to
additive transfer was on average unity. Therefore, the mortality-based
approach used by EPA does not account for the wider variation (greater
uncertainty) observed when transferring incidence data.
3.4 Errors in Dosimetry
Errors in dosimetry include random errors in the original doses assigned to the
individuals within the LSS cohort, uncertainty in the estimation of neutron doses, bias in
gamma ray estimates, uncertainty in the characterization of radiation shielding by
buildings, and uncertainty in the neutron relative biological effectiveness (RBE). EPA's
estimates of the combination of these five sources of uncertainty are taken directly from
12
-------
NCRP Report No. 126 (NCRP, 1997). For the risk of cancer mortality due to low-level,
low-LET, whole body irradiation the distribution of uncertainty as a result of possible
errors in dosimetry is characterized by a unitless bias correction factor represented by
a normal distribution with a mean of 0.84, and a standard deviation of 0.11. For the risk
of fatal lung cancer from low-dose irradiation of the lungs, a normal distribution is
assumed by EPA with a mean of 0.75 and a standard deviation of 0.15. The latter
assumption is also used for leukemia from low-dose irradiation of the bone marrow.
Whether the error distributions are appropriate is of concern to the Subcommittee and
we commend EPA for itself questioning that point. The Subcommittee's findings on the
relevant aspects of the Charge follow:
a) Are the relevant sources of uncertainty addressed?
The relevant sources of dosimetry error are addressed with respect to the
dosimetry of the LSS. The state of knowledge on this dosimetry,
however, has continued to evolve. Thus, it is recommended that the
newest literature, especially that related to the dosimetric contribution of
fission neutrons, be reviewed. This includes Straume,ef a/. (1992), and
Straume (1996; 1998).
b) Is the overall approach to quantifying and combining uncertainties
appropriate?
Random errors in the Radiation Effects Research Foundation dosimetry
(RERF, 1992) are discussed in Section E of the October 1997 draft EPA
report (EPA, 1997). The text states that such errors result in an
overestimate of doses for the high-dose groups, but neglects to state that
the doses for the low-dose groups may be underestimated. It should be
noted that random errors will affect the entire dose range. In some cases,
e.g., for the A-bomb survivor data, the magnitude of the uncertainty is
dose dependent, resulting in different degrees of distortion over the range
of estimated doses. Random errors will in general increase the range of
estimated doses to be greater than the true range, and result in a bias of
the slope towards lower risk (Pierce and Vaeth, 1991).
The EPA text states that the dose response relationship (risk estimates)
will be biased downward by roughly 10%. This estimate is probably
sufficient, though possibly on the low side. Present views of RERF staff
are that although the random errors for the LSS doses might approach
45%, the downward bias in the risk estimates probably does not exceed
10 to 15% (Donald Pierce, private communication).
13
-------
c) Are the mathematical functions used to characterize the various sources
of uncertainty reasonable in view of available scientific information?
The description of types of possible errors in the RERF dosimetry seems
comprehensive given the state of knowledge today. However, EPA fails
to explain that random errors affect the entire dose range, rather than just
the high dose groups. EPA notes that a true quadratic shape may be
distorted downwards. Similarly, Table 3.3 in NCRP Report 126 (NCRP,
1997; shows that correcting for distortion in a quadratic relationship can
increase the quadratic (high-dose) coefficient several fold, resulting in a
much steeper curve at high doses. The implications of this concept
should be explored in more detail so as to determine whether a large
downward bias of the slope of the quadratic dose-response curve (before
adjustment for random errors) could be operative at dose levels of
concern. In that case, risks could currently be underestimated
significantly.
EPA questions whether the total dosimetry error distribution derived by
Report No. NCRP 126 is adequate, and EPA should be commended for
this. NCRP and EPA assumed that the sub-components of the dosimetry-
related error terms were uncorrelated; thus, it is possible that the
magnitude of the total dosimetric uncertainty is underestimated.
Recognizing the possibility that the dosimetric uncertainty is
underestimated, EPA notes: "...we have adopted the distribution
recommended by the NCRP for each site where the risk model is derived
from the LSS data." EPA should explicitly describe the distribution for
each site and explain how adopting those models eliminates the
likelihood of underestimating dosimetric uncertainty. Table 4 should also
be modified accordingly, as it suggests a single distribution to
characterize dosimetric uncertainty.
3.5 Low Dose Rate Extrapolation of Low-LET Risk Estimates
The extrapolation of observed excess cancer deaths among the atomic bomb
survivors receiving acute doses of 0.1 to 4 Gy to that expected for a similar population
exposed to low doses delivered at low dose rates is usually the most important source
of uncertainty in estimates of risk from environmental exposures to low-LET radiation.
For the risk of cancer mortality due to low level, low-LET, whole body radiation, EPA
assumes a unitless multiplicative bias correction factor used to reduce the effect
observed for acute doses. This factor is referred to as the low dose and dose rate
effectiveness factor (DDREF). The DDREF is applied in the denominator of EPA's
equation for estimating radiogenic cancer risk. The uncertainty in the DDREF is
represented as a trapezoidal distribution with a range of 1.0 to 5 and the most likely
14
-------
values occurring between 1.0 and 2.0. The same distribution is used for the risk of
fatal lung cancer from irradiation of the lungs. For leukemia from irradiation of the bone
marrow, a lognormal distribution is assumed with a geometric mean of 2.5 and a
geometric standard deviation of 1.5. Specific issues of the Charge are:
a) Are the relevant sources of uncertainty addressed?
The URRS concurs with the EPA's decision (for the purpose of
uncertainty analysis) not to address the potential for the occurrence of a
threshold or beneficial effect at very low doses, since the probability of
such effects cannot be quantified, given the data currently available (EPA,
1977; NCRP, 1997).
b) Is the overall approach to quantifying and combining uncertainties
appropriate?
For most biological effects, the effectiveness of low-LET radiation varies
as a function of the dose rate, whereas the effectiveness of high-LET
radiation is relatively dose-rate independent (NCRP, 1990). Thus, a
given dose of low-LET radiation is generally thought to be more effective
if absorbed in a matter of seconds or minutes than if absorbed gradually
over a period of hours or days. In laboratory animals the carcinogenic
effectiveness of a given dose of low-LET radiation may vary by a factor of
two or more, depending on the dose, the dose rate, the type of cancer in
question, the age of the population at risk, and other variables (NCRP,
1980, 1990; UNSCEAR, 1986; 1988; Sinclair, 1993).
Although the influence of the dose rate has been well documented in
experimental model systems using laboratory animals and biological
cultures, there is as yet little quantitative information about its influence
on the carcinogenicity of low-LET radiation for humans. For humans, the
relevant epidemiological data are limited thus far largely to observations
at relatively high doses and high dose rates (Vaethef a/., 1992).
Extrapolation from the existing data to estimate the human cancer risks
attributable to low-level irradiation is, therefore, fraught with considerable
uncertainty.
The various uncertainties that are involved in extrapolating to low doses
and low dose rates have been discussed at length in each of several
recent authoritative reviews of radiation risk assessment (NAS/NRC,
1990; ICRP, 1991; UNSCEAR, 1993; NCRP, 1993, 1997).
15
-------
From review of the available data, the following conclusions appear
to be warranted:
(1) The value of the DDREF may be influenced by a number of
variables, the effects of which cannot be estimated with
confidence from the data that are now available.
(2) The values for the DDREF that have been recommended by
NCRP and other expert bodies represent uncertain
estimates of the average DDREF value and should not be
assumed to apply in all circumstances, to all cancers, or to
all individuals.
(3) Although available data argue strongly against a nominal
DDREF value that is substantially less than 1.0 (UNSCEAR,
1993), for some cancer sites (e.g., breast cancer), the
evidence suggests that the value could be as low as 1.0,
and some probability should be given to the possibility of
values being somewhat less than 1.0.
4) At the upper end of the range of potential DDREF values, on
the other hand, values in excess of 5.0 are not ruled out,
since, as stated in the BEIR V report (NAS/NRC, 1990), "At
low doses, a model dependent interpolation is involved
between the spontaneous incidence and the incidence at
the lowest doses for which data are available." "Moreover,
epidemiological data cannot rigorously exclude the
existence of a threshold in the millisievert dose range."
5) For some sites (e.g., lung cancer), the values of the DDREF
could be large.
c) Are the mathematical functions used to characterize the various
sources of uncertainty reasonable in view of available scientific
information?
The subjective distribution should include the possibility of values
of DDREF being less than 1.0 for certain cancer sites and greater
than 5.0 for very low doses and dose rates. If values less than 1.0
are not plausible, then a subjective weight should be given to the
probability that a DDREF of unity is indeed the true value.
16
-------
3.6 Uncertainties in the RBE for Alpha Particle Radiation
EPA has also addressed the uncertainty in the RBE factor for high-LET radiation
from alpha particle emitting radionuclides. This pertains to the uncertainties in the
application of risk estimates to the intake of alpha-emitting radionuclides, rather than to
the uncertainty in the risk estimates themselves, which was addressed in other sections
of the EPA draft document. For the induction of cancer mortality from solid tumors, a
lognormal distribution of possible values for the RBE is assumed with a geometric
mean of 14.1 and a geometric standard deviation of 1.9, corresponding to a 90%
subjective confidence interval of 5 to 40. These values have no units since the RBE is
a unitless quantity. For leukemia induced from alpha-emitting radionuclides deposited
in the mineral bone, the uncertainty of the RBE is described as a uniform distribution
ranging from 0 to 1.0. Leukemia induced by alpha-emitting radionuclides not deposited
in or on the bone is assigned a lognormal distribution with a geometric mean of 3.0 and
a geometric standard deviation of 1.7, corresponding to a 95% subjective confidence
interval of 1 to 10. For the lungs, the same distribution of the RBE is assumed as for
other solid tumors. Uncertainties in the RBE for liver and bone are considered by EPA
to be negligible since low-LET radiation of these sites should not result in a major
contribution of the total risk induced from the intake of beta or photon emitters.
Findings addressing the Charge follow below:
a) Are the relevant sources of uncertainty addressed?
The uncertainties associated with the concept of RBE, itself, are not
addressed (See Appendix C).
b) Is the overall approach to quantifying and combining uncertainties
appropriate?
Given the many uncertainties in each of the phenomena contributing to
RBE, it is highly appropriate to try to bound the problem. Such an attempt
is synonymous with accounting for lack of knowledge. The EPA attempt
to estimate the bounds of knowledge has been credibly undertaken; there
is no right or wrong answer at this time.
Section G of the EPA draft is a fair statement of the uncertainties
associated with RBE values. That RBE values may vary with the
biological endpoint of interest was recognized and addressed by
assigning RBE values to two endpoints for which there are
epidemiological data, leukemia and lung cancer. This is about as much
as can be done at the present time due to limited epidemiological data. It
might be possible to use the thorotrast (a 25% solution of thorium dioxide)
and radium epidemiological databases from BEIR IV (NAS/NRC, 1988) to
17
-------
derive an RBE for alpha irradiation of liver and bone, respectively.
However, in spite of the presence of low levels of alpha-emitting radio
nuclides in the work-place, the environment, and in medical uses,
relatively few cases of human cancer caused by alpha emitters at low
doses have been recorded, thus severely limiting the epidemiological
database.
c) Are the mathematical functions used to characterize the various sources
of uncertainty reasonable in view of available scientific information?
Adjustments might be warranted to account for the uncertainty of the RBE
concept, itself, which may dwarf other RBE uncertainties addressed in the
EPA draft. Quantifying this would be very difficult and subject to
considerable debate. This might be an area warranting a formal expert
elicitation.
EPA might consider an additional evaluation of model uncertainty for risks
of high-LET radiation. The draft EPA uncertainty document calls attention
to the uncertainty of RBE, particularly with regard to alpha radiation.
Even so, the uncertainties may be understated. An alternative to RBE is
suggested, but it is applied only to radium-induced leukemia and radon-
induced lung cancer. This leaves a very uncertain RBE value of 20 to be
applied to alpha radiation in all other situations. The RBE concept is not
unassailable, because it produces risk estimates that do not always
conform with observed cancer risks in humans exposed to radon or
radium. The usual argument is that the dose from these alpha emitters is
not uniform in the target organ and that proper microdosimetry would
confirm the RBE estimates. However, other models such as ones that
depend on chemical as well as physical properties of the alpha emitters
might be contemplated. Moreover, the DDREF concept is conventionally
stated to be different for alpha radiation than for beta or gamma radiation,
putting further strain on the concept of a universal risk coefficient for RBE-
adjusted dose. The draft document acknowledges the difficulties of RBE
by using non-LSS studies to define risks for radon and radium, but the
reliability of risk estimates for other alpha emitters is not well understood.
3.7 Accounting for Additional "Unknown" Sources of Uncertainty
The task undertaken in the October, 1997 EPA draft document describing
uncertainty in radiogenic cancer risks is similar in scope to the NCRP Report number
126 (NCRP, 1997). The NCRP document includes a multiplicative correction factor to
"...account for unknown uncertainties, some identified but not allowed for and others
assumed to exist but not identified." This Subcommittee examined the method and
18
-------
rationale for the additional factor used by NCRP and whether EPA should include a
similar factor. The following guidance is offered: If significant sources of uncertainty
can be identified, but not quantified, some adjustment might be necessary. This
adjustment could take the form of a multiplicative distribution with a mean of unity and a
variation to be subjectively determined. However, if sources of uncertainty are only
suspected, but cannot be identified (and of course not quantified), no adjustment
should be attempted.
The Subcommittee bases its guidance on the concept that a reasonable degree
of belief regarding a factor must be present to form subjective confidence intervals.
This concept was described by Morgan and Henrion (1990): "Even from the personalist
or subjectivist view, an event or quantity must be well-specified for a meaningful
probability distribution to be assessable." Thus, the difficulty in defining a "catch-all"
parameter to account for uncertainties from unknown sources, is that it cannot be
specified. If it could, then the sources of uncertainty to be accounted for by the "catch-
all" parameter should be treated explicitly.
The decision of EPA not to include such a parameter in its calculations is
sensible. We support such an approach because it constrains subjective judgment to
those situations where evidence, or at the very least, prior experience, is the basis for
parameter estimation.
19
-------
4 CONCLUSIONS AND RECOMMENDATIONS
The Subcommittee commends EPA for producing a generally credible approach
to characterizing the uncertainty in radiogenic cancer risk estimates. References to the
corresponding sections of the detailed discussion in Section 3 appear in parentheses.
4.1 Risk Estimates Based on Cancer Incidence Rather than Cancer Mortality
Given the additional uncertainty introduced from use of mortality data, in future
efforts to improve on its quantification of radiogenic cancer risk and its associated
uncertainty, EPA should consider developing risk estimates for the U.S. population
based primarily on transfer of cancer-incidence based risks from Japanese atomic
bomb survivor studies (Mabuchief a/., 1998; Thompson et a/., 1994; Preston et a/.,
1994); EPA should not rely entirely on mortality risks for this transfer (Shimizuef a/.,
1990). This shift would allow EPA to eliminate transfer uncertainty related to lethality
fractions (cancer survival rates), which vary among countries and over time within any
single country. Incidence-based risks would also allow EPA to (1) account for the
larger variation in transfer when compared with transfer of mortality risks (NCRP,
1997), and (2) transfer absolute and multiplicative risks on a site-, gender-, and age-
specific basis, which is more biologically meaningful. (Section 3.1.2)
4.2 Alternative Modeling Approaches for Transferring Risk Estimates
EPA should consider the additive, multiplicative, and NIH models as alternative
modeling approaches for transferring radiogenic cancer risk coefficients from the LSS
cohort to the U.S. population. EPA should consider assigning a subjective weight to
each model (additive, multiplicative, and NIH), rather than using a coefficient based on
the geometric mean of the latter two approaches. Furthermore, as additional data
become available, the subjective weight initially assigned to the different models should
be updated to get posterior probabilities for the validity of the different models.
4.3 Clarification of Uncertainty Distributions
EPA derives the uncertainty of cancer risk estimates by assigning distributions to
uncertainty bias correction factors and combining these distributions by numerical
techniques. However, it is not made clear that each distribution is defined to represent
the state of knowledge about the value of that parameter that would be relevant to an
average member of the population. The Subcommittee recommends that EPA clarify
the risk assessment objective and carefully define each uncertainty distribution in terms
of that objective and in terms of the current state of knowledge. This clarification would
eliminate any likelihood of readers mistakenly assuming that the distributions represent
empirical variation. (Section 3.2 (b))
20
-------
4.4 Uncertainty in the DDREF
The uncertainty in the DDREF should reflect the current state of knowledge.
The subjective probability distribution for extrapolation from high to low dose rates for
low-LET radiation should include a greater weight to the possibility that the DDREF
could be less than or equal to 1.0. A non-zero weight should also be considered for
DDREF larger than 5.0. In any case, stronger justification is needed to explain why a
subjective weight is not given for values exceeding 5.0. The values chosen for DDREF
could also vary depending on the type of cancer and organ affected.
4.5 Future Updates of the Uncertainty Analysis
Multiplication of probabilities to obtain the joint probability is appropriate only for
statistically independent quantities. Possible dependence of the quantities multiplied
together should be investigated. This issue is especially pertinent for the dependency
of statistical sampling errors on diagnostic misclassification of cancer mortality.
Parts of the EPA approach to uncertainty analysis should be changed in the
future. It is recommended that subjective probability densities be used initially to
describe uncertainty and that these probability densities be updated with available
data. Subjective assessment of the uncertainty of quantities is usually the starting point
of a Bayesian analysis. The next step involves use of available data to update and to
reduce the uncertainty with the likelihood of the probability model under consideration.
At the present time, EPA's subjective assessments do not provide for updating. EPA's
uncertainty assessments should be a living process that allows updating the present
subjective probability distributions as more data become available, and could make the
entire process more objective and amenable to refinement with advances in the state-
of-the-art. This will eventually lead to a consensus.
4.6 Subjective Distributions Based on Expert Elicitation
For those inputs that dominate the overall uncertainty in radiogenic cancer risk,
use of more formal methods of expert elicitation would be desirable to obtain defensible
estimates of subjective distributions that reflect the current state of knowledge. Formal
elicitation of expert judgment would be preferred to informal estimates made by EPA
staff. Currently, the subjective probability distributions specified by EPA staff reflect
only the state of knowledge of the EPA. A more formal elicitation would encompass an
evaluation of extant data sets by a broader spectrum of expertise both inside and
outside of EPA. This is the approach recommended in NCRP Commentary No. 14
(NCRP, 1996). (Section 3.6)
21
-------
4.7 Treatment of Unknown Sources of Uncertainty
The Subcommittee believes that the EPA has used uncertainty analysis methods
in a credible and defensible way to quantify uncertainty and should not attempt to
assign distributions to so-called "unknown" sources of uncertainty which can neither be
identified nor quantified. Such methods were used in NCRP Report No. 126 (NCRP,
1997), but some of the assumptions used in the NCRP document go beyond the weight
of the evidence. The Subcommittee believes that the EPA should examine whether
their calculated range of risk is sufficient to include what are believed to be possible
values of the true dose response, and should provide adequate adjustments or
commentary if such is not the case. (Section 3.7)
22
-------
APPENDIX A - THE COMPARISON OF MORTALITY VERSUS
INCIDENCE DATA
The purpose of the EPA October 1997 Draft Addendum was to describe a
methodology for estimating uncertainties in the EPA risk projections for cancer mortality
estimates. While we understand that this was the Charge given to EPA and the
National Council on Radiation Protection and Measurements, we have significant
reservations about the usefulness of a document that focuses solely on mortality rather
than on incidence data. Given the availability of incidence data since 1958 and the
many problems inherent in using mortality statistics upon which radiation protection
standards could appropriately be based, it would seem reasonable to revise the
approach taken in radiation protection to one of protecting individuals from exposures
that place them at increased risk of developing cancer, rather than protecting them from
exposures leading to an increased risk of dying from cancer. Further, the use of
mortality as the sole endpoint of concern is inconsistent with most of EPA's risk
assessments, which are based on disease incidence. Incidence data from the LSS are
available and should serve as the primary basis for EPA's estimates of radiogenic
cancer risk .
Although mortality data derived from death certificates have served as the basis
for many epidemiologic studies over the years, the accuracy and utility of death
certificate data on underlying causes of death have been called into question when
compared to hospital records, autopsy information, or data from tumor registries
(especially the last). Studies in the U.S. and Japan have evaluated the use of death
certificate data as the basis for calculating risk estimates and found these data to be
inaccurate and of limited use because a) mortality risks as an index of harm
underestimate incidence rates; b) death certificate-based mortality risks do not reveal
patterns of risk and survival by stage of cancer and histological cell type; c) death
certificate-based mortality cannot include risk for benign (non-fatal) cancers; and d)
death certificate-based mortality risks require an additional bias correction for
diagnostic misclassification (Chao and Devesa, 1996; Gittelsohn and Senning, 1997;
Hoelefa/., 1993; Kircheref a/., 1985; Percy eta/., 1981; Percy eta/., 1990). Therefore,
data on the incidence of disease are preferred over mortality statistics when estimating
exposure-response risk coefficients.
It is not surprising that diagnostic misclassification tends to be much less of a
problem with tumor registry than death certificate information. Tumor registries
typically draw information from a variety of sources such as clinical and pathology
records, as well as from death certificates. Diagnoses are usually verified histological
ly and are reportable to the tumor registry within a year of diagnosis. Individuals who
complete registry information forms are specifically trained to abstract accurately
information from all appropriate sources. An incidence-based approach is affected less
by cancer misclassification than when compared with a mortality-based approach. The
A-1
-------
authors of the October 1997 Draft Addendum themselves acknowledge that "...studies
at the level of incidence are inherently more accurate than mortality-based studies
using death certificate diagnosis for classification... " (EPA, 1997; page 18).
Migration of exposed individuals is not a reason to abandon incidence data in
favor of mortality data. It is important to note that in the LSS study, the occurrence of
cancer and the record of this occurrence (i.e., the tumor registry) are really part of a
cohort study rather than part of a geographically constrained plan used in many registry
designs. Further, it is also important to note that death certificates are routinely
included by registries as an important source of information on cases not ascertained
through other means.
Studies in the US have shown that, overall, hospital discharge abstracts, cancer
incidence registries, and autopsy results agree with death certificates only 65% to 77%
of the time (Kircheref a/., 1985; Gittelsohn and Senning, 1979; Percyet a/., 1981,
1990). Accuracy of death certificate data decreases significantly when the certificates
are evaluated for appropriate subtype of cancer within a particular organ system and
with increasing age of the decedent (Chow and Devesa, 1996).
Because of the very large autopsy series among A-bomb survivors, there is a
considerable amount of data on the accuracy of death certificate information during the
period 1961 to 1987 as part of the LSS (Hoelef a/., 1993). The autopsy program
showed that the death certificate detection rate for total cancer was 79%, while the
autopsy confirmation rate was 93%, corresponding to an underestimate of the number
of cancer deaths by 18%. These numbers varied depending on the specific cancer
site. For example, the underestimate for lung cancer was 33%. Among cancers of
interest in radiation carcinogenesis, the detection rate (rate at which true cases of
cancer are detected) for respiratory sites was 54%, for breast 76%, and for
hematopoietic cancers 68%. When death certificates were compared with tumor
registry information, there was considerable underestimation for some cancer sites.
For liver cancer, for example, confirmation (rate at which cases of cancer are classified
accurately) was 34%, while detection was 55%. Further, the quality of death certificate
information has been a problem when deaths are studied over time and over age
groups. Detection and confirmation for a number of sites have improved over the
years, particularly for the older age groups, 75 years and above. The result is a
shifting baseline upon which static conclusions are based. Thus, the very use of
mortality rather than incidence data is an important source of uncertainty.
Migration has been cited as a reason to use mortality rather than incidence data
as the source of information. In the LSS Study, tumor registry data provide an
invaluable source of information. Errors introduced as a result of migration of
individuals from the study area, however, should be readily correctable by including
information from death certificates obtained from across the country and by either
A-2
-------
treating the death as a cancer case incidence as of the year of death or retrospectively
creating a tumor registry record following an investigation of available clinical and
pathology information.
A-3
-------
APPENDIX B - GENETIC DIVERSITY AND INTERINDIVIDUAL
VARIATION IN RADIATION RESPONSE
Justification for transferring radiogenic cancer risks coefficients in Hiroshima and
Nagasaki atomic bomb survivors to other populations is based on strong dose-
response gradients, repeated consistency with results from animal studies (and other
human studies), and findings from molecular biology that have confirmed that radiation
is a known carcinogen. When projecting future risks among other (non-Japanese)
populations, one assumes that risk applies equally to each individual because variation
in response at the individual level is unknown and is currently not well understood. As
more becomes known about the genetic and environmental influences on radiation
dose-response, there will be an increasing tendency to control for such factors when
projecting risks. At present, however, the theoretical framework for assigning
individuals to various subpopulations based on cancer predisposition is only beginning
to be understood.
Studies in population genetics now indicate that the human genome contains
"cancer-predisposing genes," which specifically play an important role in controlling
programmed cell death (apoptosis), cellular proliferation, and DMA repair pathways
(Sankaranarayanan and Chakraborty, 1995). For example, two autosomal recessive
disorders known as xeroderma pigmentosum (incidence of 1/100,000 to 1/250,000) and
ataxia telangiectasia (incidence of 1/90,000 to 1/300,000) confer high radio sensitivity
for UV-related skin cancer and increased reaction to radiation therapy, respectively, as
a result of defects in DMA repair/replication (Kraemeref a/., 1984; Bender et a/., 1985;
Kraemerefa/., 1987; Gattiefa/., 1991; Swift et a/., 1991).
The body of information on increased radio sensitivity for radiation-induced
cancer among carriers of both recessive and dominant mutations is limited. However,
given what is already known about cancer predisposing genes, there is sufficient
evidence to assume that certain mutation carriers are at increased risk of radiation-
induced cancer. To this end, Chakraborty and Sankaranarayanan (1995) recently
introduced a population-based model for Mendelian inheritance of a single-gene
mutation that combines cancer predisposition and radio sensitivity. Their modeling
indicates that, when considering single-gene mutations for which there is likely
increased cancer predisposition and radio sensitivity, an increase in radiation-induced
cancer in the general population is only likely when the proportion of cancers due to
predisposition is large and when the radiation sensitivity differential is considerable.
For small effects of heterogeneity, a large portion of radiation-induced cancers would
likely occur among predisposed individuals. When less than 10% of excess cancers
are due to a susceptibility genotype, as may be the case for the presence of mutations
in the breast cancer genes BRCA1 and BRCA2 among non-Ashkenazi women with
breast cancer, a marked increase in relative and attributable risk is only seen when
B-1
-------
there is a greater than a 1000-fold increase in cancer susceptibility and a greater than
100-fold increase in radio sensitivity. (Chakraborty ef a/., 1997).
Relatives of individuals with susceptibility genotypes are also presumed to be at
increased risk of radiation-induced cancer in comparison with un-related individuals.
Chakraborty, Little, and Sankaranarayanan (1998) modeled proband genotype
frequencies using Hardy-Weinberg expectations and determined that risk of radiation-
induced cancer increases with the degree of relatedness, with higher risk occurring for
close relatives and lower risks occurring for distant relatives. For recessive genotypes,
this relationship drops off very quickly, while for dominant genotypes less quickly. In
addition, they suggested that epidemiologically-based detection of increased radiation-
induced cancer in related individuals is only possible for commonly occurring mutant
alleles and conjointly dramatic predisposition and radio sensitivity. A major observation
that arose from the modeling work of Chakraborty, Sankaranarayanan, and Little was
that in a multi-gene scenario, where more than one susceptibility gene is sufficient for
causing disease, the effects of irradiation could possibly be higher than in a single-
gene framework.
B-2
-------
APPENDIX C - UNCERTAINTIES IN THE RBE FOR ALPHA RADIATION
The discussion in the draft about uncertainties in the RBE (relative biological
effectiveness) for alpha radiation and the need to draw upon epidemiology data to
assign values of RBE for lung cancer and leukemia raises old questions about the
validity of the RBE concept and whether it is possible to achieve the conditions
required to determine RBE values as defined. The RBE for a radiation of interest is
defined as being equal to the dose of reference radiation (x- or gamma radiation)
required to produce a specific level of response divided by the dose of radiation of
interest to produce an equal response. To make a determination of RBE, all physical
and biological variables are to be held constant with the exception of radiation quality
(NCRP, 1990). This conceptual requirement introduces a major uncertainty into any
determination of RBE.
First, there are few circumstances where it can be assured that the distribution of
energy from the radiation of interest to the cells or tissues in which the biological
response occurs is exactly the same as the distribution of energy from the reference
radiation to the same cells or tissues. While the reference radiation generally can be
expected to irradiate all cells and tissues equally, alpha radiation and most beta
radiation will irradiate cells and tissues non-uniformly. This nonuniform distribution
may be enhanced if the radiation originates from nonuniform deposits of radio nuclides
in the body and tissues, even more so if it originates from particulate sources rather
than from molecules of radioactive material distributed uniformly throughout the body.
Lack of knowledge about both the spatial distribution of absorbed energy within the
organ and tissue and the spatial distribution of target cells leads to a serious dosimetry
problem (At one time, prior to ICRP Publications 26 and 30 (1977, 1979), a factor, N,
was incorporated into dose calculations in addition to the quality factor, Q, to account
for nonuniform distribution of deposited radio nuclides, especially in bone). Although
there were considerable uncertainties associated with this practice, they were probably
not lessened when N was dropped. Relating risk directly to exposure, eliminating the
calculation of dose, is another approach that has been considered occasionally as a
way of getting around the uncertainties of RBE as well as those of dosimetry and dose-
response models.)
Second, it is rare that the rate of energy deposition in the cells and tissues in
which the response occurs is the same for the reference radiation and the radiation of
interest. Most alpha emitters are long-lived; this raises questions about the turnover
rates of sensitive cells relative to the radionuclide's physical half-life and suggests that
the same cells may not remain as targets while the radionuclide continues to decay and
that many generations of cells may be irradiated by a given deposition of a
radionuclide. The radionuclides themselves may migrate within the body and within
tissues, further confounding the dosimetry problem. Radon presents a different picture.
Since the half-lives of radon decay products are so very short, delivering energy to
C-1
-------
affected cells at high rates, the RBE for radon might very well differ from those of other
alpha emitters.
Third, the biological response caused by the radiation of interest is not always
the same as that caused by the reference radiation. For example, while alpha and
gamma radiations may both elicit tumors, the tumor types may be different for a variety
of reasons, including the fact that different cells may have been irradiated. Estimates
of cancer risk determined from the LSS and other populations exposed to low-LET
radiations are generally applied to estimate the risks from intakes of internal emitters.
It is assumed that uniform irradiation of a tissue yields the same effects and risks as
nonuniform irradiation by internal emitters. In the case of alpha emitters, in particular,
this assumption results in estimates of the occurrence of health effects from internal
emitters that have never been observed in either experimental animals or exposed
human populations. A case in point is chronic myelogenous leukemia (CML), which
can be caused by exposures to external x- and gamma radiations. It has been
assumed that CML may also result from intakes of radionuclides that deposit in bone.
However, not all radionuclides that deposit in bone, particularly on bone surfaces, emit
radiations that penetrate to the appropriate sensitive cells. An example is plutonium,
an alpha-emitting bone seeker. No case of CML has been observed in thousands of
experimental animals or humans exposed to plutonium (it has been observed only in a
few rodents given highly soluble forms of plutonium, resulting in a high incidence of
bone cancers, including cases in those animals that also developed CML). Of the
cancer sites given in Tables 1 and 2 of the Draft Document (EPA, 1997), only the lung
sites are relevant to plutonium (for soluble forms of plutonium, liver and bone should be
added), yet risks calculated for other tissues which have not really been shown to be at
risk are included in the summation of the total risk, using the Effective Dose
formulation. Total risks calculated in this manner, particularly when doses to non-
responsive tissues are significant, may be less than those calculated by the EPA
approach, which calls for summing the risks calculated separately for each tissue (EPA,
1997). This situation is more likely for radionuclides that do not deposit uniformly in the
body.
The application of an RBE value to a radiation exposure situation in which dose
is either modeled or measured in a way different from that in which the RBE value was
determined contributes further uncertainty to the applicability of RBE. This additional
uncertainty may be relevant to EPA's decision to use a distribution of RBE values for
alpha radiation that is uniform from 0 to 1.0 for leukemia. As noted in the Draft
Document, another explanation for the relative ineffectiveness of alpha radiation in
producing leukemia could be that alpha particles do not reach the sensitive cells, and
because of this limited range, the doses to critical cells are overestimated (EPA, 1997).
The same limitation in range can apply to other tissues including the respiratory tract.
The radiation-insensitive thoracic lymph nodes are at one end of the spectrum; they
receive the highest dose when insoluble plutonium is inhaled. Application of an RBE of
C-2
-------
20 to these doses, which apparently do not reach sensitive cells, would give extremely
high risk estimates, when in fact the risk has been shown to be essentially zero at all
doses. At the other end is the more radiation-sensitive bronchial epithelium, which
receives much lower doses, but application of an RBE of 20 could still lead to
overestimates of the risk of cancer originating in this tissue.
The difficult, if not impossible, task of deriving RBE values for internal emitters is
well known. In ICRP Publication 31, Biological Effects of Inhaled Radionuclides, it was
recognized that derivation of RBE values for alpha emitters that would meet the strict
definition of RBE (see above) was not possible for the primary reason that nonuniform
dose distribution occurs in nearly all cases of alpha emitters in the body (ICRP, 1980).
The quantity RBE is likely a parameter which lumps together several distinct
phenomena. In its place, an Equal Effectiveness Ratio was proposed that included
differences in dose distribution in affected tissues (average tissue doses were
calculated, assuming uniform distribution of energy, for both alpha emitters and
beta/gamma emitters) as well as differences in RBE. This approach gave Equal
Effectiveness Ratios from 6 to 40 (often erroneously quoted as RBE values), depending
upon the dose ranges compared. If the differences in dose distribution within affected
tissues could be removed, then RBE values lower than the nominal value of 20 for
alpha emitters would likely be obtained. The Draft EPA Uncertainty document (EPA,
1997) in effect acknowledges this expectation of lower RBE values for radium-induced
leukemia and radon-induced lung cancer, preferring to use epidemiology data that
suggest RBE values of one for radium-induced leukemia and 10 for radon-induced lung
cancer. This adoption of RBE values less than 20 in itself recognizes the lack of
coherence in the dose-risk approach using the LSS data.
The fact that the basis for our system of radiation protection is not internally
consistent, with risks calculated by applying risk factors to doses rarely in agreement
with risks obtained from epidemiology, is indicative of the uncertainty problem. One
area of uncertainty is clearly the interpretation of the LSS data, but greater
uncertainties may occur in applying the results of the LSS data to other types of
radiation exposures. This issue of transferability has been considered by numerous
committees and individuals with respect to chronic low-LET external radiation but less
so for internal emitters, especially high-LET alpha radiation. Attempts to apply risk
estimates for lung cancer derived from LSS data to radon exposures, using the
accepted values for DDREF and RBE along with current lung dosimetry, have been
discouraged by the ICRP because they lead to estimates that exceed those obtained
from epidemiological studies of miners (ICRP, 1993). This lack of agreement with
epidemiology suggests uncertainties in the process, which could be associated with the
risk factor itself, with the values of DDREF and RBE used in the calculations, and, of
course, with the dose models. This aspect should receive greater attention in the EPA
draft document on Uncertainty Analysis (EPA, 1997).
C-3
-------
Since RBE values for a radiation of interest may vary greatly depending upon
the biological response being measured, the test subject, the magnitude of the dose,
etc., single RBE values are of questionable utility. The current use of a radiation
quality factor, Q, and more recently WR, the radiation weighting factor (a replacement
for Q), based more on linear energy transfer of the radiation than on biology, probably
introduces even more uncertainty, but the practice has merit in not requiring use of
multiple RBEs in calculations of effective dose.
C-4
-------
APPENDIX D -ACRONYMS
ABCC
BEIR
Bq
BRCABRCA1 & BRCA2
CCI
CML
Ci
DDREF
DMA
EPA
CMC
Gy
ICRP
LET
LSS
N
MAS
NCRP
NIH
NRC
ORIA
Q
R
RAC
RBE
RERF
RR
SAB
Atomic Bomb Casualty Commission
Biological Effects of ionizing Radiation
Becguerel (1 disintegration per second)
Breast Cancer Genes
Classical Confidence interval
Chronic Myelogenous Leukemia
cune (3.7x1010 disintegrations per second)
Dose and Dose Rate Effectiveness Factor
Deoxyribo_Nucleic Acid
U.S. Environmental Protection Agency (U.S. EPA)
Geometric Mean Coefficient
Gray (One joule per kilogram of absorbed energy)
international Commission on Radiological Protection
Linear Energy Transfer
Lifespan Survivor Study
A factor incorporated into dose calculations, in addition to
the quality factor, Q, to account for nonuniform distribution
of deposited radionuclides, especially in bone
Rational Academy of Sciences
Rational Council on Radiation Protection and Measurements
National institutes of Health (U.S. NIH)
Muclear Regulatory Commission (U.S. NRC)
Office of Radiation and indoor Air (U.S. EPA/ORIA)
Quality Factor
Uncertainty in CancerRisk
Radiation Advisory Committee (U.S. EPA/SAB/RAC)
Relative Biological Effectiveness
Radiation Effects Research Foundation
Uncertainty in Cancer Risk for theHJroshima/Nagasaki
Population
Uncertainty in Cancer Risk for the United States Population
Relative Risk
Science Advisory Board (U.S. EPA/SAB)
D-1
-------
SCI Subjective Confidence interval
UN United Nations
UNSCEAR United Nations Scientific Committee on the Effects of Atomic
Radiation
URRS Uncertainty in Radiogenic (Cancer) Risk Subcommittee
(U.S. EPA/SAB/RAC/URRS)
U.S. United States
UV Ultra Violet
WR Radiation Weighting Factor
D-2
-------
REFERENCES
Bender, M., Rary, J., and R. Kale. 1985. G2 chromosomal radio sensitivity in
ataxiatelangiectasia lymphocytes. Mutation Res. 152:39-47.
Chakraborty, R., Little, M. P., and K. Sankaranarayanan. 1998. Cancer predisposition,
radio sensitivity and the risk of radiation-induced cancers. IV. Prediction of
radiation cancer risks in relatives of cancer-predisposed individuals. Radial.
s. 149: 493-507.
Chakraborty, R., Little, M. P., and K. Sankaranarayanan. 1997. Cancer predisposition,
radio sensitivity and the risk of radiation-induced cancers. III. Effects of
incomplete penetrance and dose-dependent radio sensitivity on cancer risks in
populations. Radiat. Res. 147; 309-320.
Chakraborty, R., and K. Sankaranarayanan. 1995. Cancer predisposition, radio
sensitivity and the risk of radiation-induced cancers. II. A Mendelian single-
locus model of cancer predisposition and radio sensitivity for predicting cancer
risks in populations. Radiat. Res. 143:293-301.
Chow, W. H., and S. S. Devesa. 1996. Under reporting and misclassification of urinary
tract cancer cases on death certificates. Epidemiology 7:51 7-520.
EPA (Environmental Protection Agency). 1998. Health Risks from Low-Level
Environmental Exposure to Radionuclides. Federal Guidance Report No. 13 -
Part 1. Interim Version. EPA402-R-97-014. U.S. EPA, Washington, DC.
EPA (Environmental Protection Agency). 1997. Estimating Radiogenic Cancer Risks.
Draft Addendum: Uncertainty Analysis. US EPA, Washington, DC (October).
EPA (Environmental Protection Agency). 1994. Estimating Radiogenic Cancer Risks.
EPA Report 402-R-93-076. U.S. EPA, Washington, DC.
Gatti, R., Bode, E., Vinters, H., Sparkes, R., Norman, A., and K. Lange. 1991. Ataxia
telangiectasia: an interdisciplinary approach to pathogenesis. Medicine. 70:99-
117; 1991.
Gittelsohn, A., and J. Senning. 1979. Studies on the reliability of vital and health
records: comparison of cause of death and hospital record diagnoses. Am.
Journ. Public Health 69:680-689
R-1
-------
Hoel, D. G., Ron, E., Carter, R., and K. Mabuchi. 1993. Influence of death certificate
errors on cancer morality trends. Journal of the National Cancer Institute 85:
1063-1068.
ICRP. 1993. International Commission on Radiological Protection. Protection Against
Radon-222 at Home and at Work. ICRP Publication 65. Annals of the ICRP Vol.
23 No. 2
ICRP. 1991. International Commission on Radiological Protection. 1990.
Recommendations of the International Commission on Radiological Protection.
ICRP Publication 60. Annals of the ICRP 21. Pergamon Press, Elmsford, New
York.
ICRP. 1980. International Commission on Radiological Protection. Biological Effects
of Inhaled Radionuclides. ICRP Publication 31. Annals of the ICRP Vol. 4 No.
%. Pergamon Press.
ICRP (International Commission on Radiological Protection). 1979. Limits for Intake of
Radionuclides by Workers, ICRP Publication 30, Part 1. Pergamon Press,
Elsevier Science Ltd., Oxford, UK.
ICRP (International Commission on Radiological Protection). 1977. Recommendations
of the International Commission on Radiological Protection. ICRP Publication
26, Pergamon Press, Oxford, UK.
Kircher, T., Nelson, J., and H. Burdo. 1985. The autopsy as a measure of accuracy of
the death certificate. N. Engl. J. Med. 313:1263-1269.
Kraemer, K., Myung, L., and J. Scotto. 1987. Xeroderma pigmentosum. Arch.
Dermol. 123:241-250.
Kraemer K, Lee M, and J. Scotto. 1984. DNA repair protects against cutaneous and
internal neoplasia: evidence from xeroderma pigmentosum. Carcinogenesis.
5:511-514.
Mabuchi, K. 1998. Tumor Registries and Cancer Incidence Studies. In: Effects of
Ionizing Radiation: Atomic Bomb Survivors and Their Children (1945-1995).
Peterson, L. E., and Abrahamson, S. (eds.). Joseph Henry Press (National
Academy Press), Washington, DC.
Morgan, M. G., and M. Henrion. 1990. Uncertainty: A Guide to Dealing with
Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University
Press, Cambridge.
R-2
-------
NAS/NRC (National Academy of Sciences/National Research Council. 1990. National
Academy of Sciences/National Research Council. Health Effects of Exposure to
Low Levels of Ionizing Radiation, BEIR V. National Academy Press,
Washington, D.C.
NAS/NRC. 1988. (National Academy of Sciences/National Research Council).
Biological Effects of Ionizing Radiation from Alpha Emitters (IV). National
Academy Press, Washington, DC.
NCRP. 1997. National Council on Radiation Protection and Measurements.
Uncertainties in Fatal Cancer Risk Estimates Used in Radiation Protection.
NCRP Report No. 126. National Council on Radiation Protection and
Measurements, Bethesda, MD.
NCRP. 1996. National Council on Radiation Protection and Measurements. A Guide
for Uncertainty Analysis in Dose and Risk Assessments Related to
Environmental Contamination. NCRP Commentary No. 14. NCRP, Bethesda,
MD.
NCRP (National Council on Radiation Protection and Measurements). 1993.
Limitation of Exposure to Ionizing Radiation. NCRP Report No. 116. National
Council on Radiation Protection and Measurements, Bethesda, MD.
NCRP (National Council on Radiation Protection and Measurements). 1990. The
Relative Biological Effectiveness of Radiations of Different Quality. NCRP
Report No. 104. National Council on Radiation Protection and Measurements,
Bethesda, MD.
NCRP (National Council on Radiation Protection and Measurements). 1980. Influence
of Dose and Its Distribution in Time on Dose-Response Relationships for Low-
LET Radiation. NCRP Report No. 64. National Council on Radiation Protection
and Measurements, Bethesda, MD.
Neyman, J. 1977. Frequentist probability and frequentist statistics. Synthese 36:97.
NIH. (National Institutes of Health). 1985. Report of the National Institutes of Health
Ad Hoc Working Group to Develop Radioepidemiological Tables. NIH
Publication 85-2748. U.S. Government Printing Office, Washington, DC.
Percy, C. L., Miller, B. A., Gloeckler, and L. A. Ries. 1990. Effect of changes in cancer
classification and the accuracy of cancer death certificates on trends in cancer
mortality. Annals NY Academy of Sciences. 87-99.
R-3
-------
Percy, C. L, Stanek, E., and L. Gloeckler. 1981. Accuracy of cancer death certificates
and its effect on cancer mortality statistics. Am. J. Public Health 71: 242-250.
Pierce, D. A., and M. Vaeth. 1991. The shape of the cancer mortality dose-response
curve for the A-bomb survivors. Radiat. Res. 126: 36-42.
Preston, D.L., Kusumi, S., Tomonaga, M., Izumi, S., Ron, E., Kuramoto, A., Kamada,
N., Dohy, H., Matsuo, T., Nonaka, H., Thompson, D.E., Soda, M., and K.
Mabuchi. 1994. Cancer incidence in atomic bomb survivors. Part III: Leukemia,
lymphoma, and multiple myeloma, 1950-1987. Radiation Research. 137:868-
S97. [RERF TR 24-92].
Sankaranarayanan, K., and R. Chakraborty. 1995. Cancer predisposition, radio
sensitivity and the risk of radiation-induced cancers.Background. Radiat. Res.
143: 121-143
Shimizu, Y., Kato, H., and W. J. Schull. 1990. Studies of the Mortality of A-Bomb
Survivors. 9. Mortality, 1950-85: Part 2. Cancer Mortality Based on the Recently
Revised Doses (DS86). Radiat. Res. 121,120-141. [RERF TR 5-88].
Sinclair, W. K. 1993. Science, radiation protection and the NCRP, L.S. Taylor Lecture
No. 17, In: Radiation Science and Societal Decision Making. NCRP Annual
Meeting Proceedings No. 15. National Council on Radiation Protection and
Measurements, Bethesda, MD., pp. 209-244.
Straume, T. 1998. Challenges ahead with emphasis on the neutron problem in
Hiroshima. IN: Atomic Bomb Casualty Commission/Radiation Effects Research
Foundation (ABCC/RERF): Commemorating the first 50 years and looking to the
future. National Academy Press.
Straume, T. 1996. Risk implications of the neutron discrepancies in the DS886
Hiroshima dosimetry system. Nuclear Technology Publishing Co. Radiation
Protection Dosimetry. 67(1)9-12.
Straume, T., Egbert, S. D., Woolson, W. A., Finkel, R. C., Kubik, P. W., Gove, H. E.,
Sharma, P., and M. Hoshi. 1992. Neutron discrepancies in the DS886
Hiroshima dosimetry system. Health Physics. 63:421-426.
Swift, M., Morrell, D., Massey, R., and C. Chase. 1991. Incidence of cancer in 161
families affected by ataxia-telangiectasia. N. Eng. J. Med. 325:1831-1836
R-4
-------
Thompson, D. E., Mabuchi, K., Ron, E., Soda, M., Tokunaga, M., Ochikubo, S.,
Sugimoto, S., Ikeda, T., Terasaki, M., Izumi, S., and D. L. Preston. 1994.
Cancer incidence in atomic bomb survivors. Part II: Solid tumors. 1958-87.
Radiation Research. 137:S17-S67. [RERF TR 5-92].
UNSCEAR. 1994. United Nations Scientific Committee on the Effects of Atomic
Radiation. 1994. Sources and Effects of Ionizing Radiation. 1994. Report to the
General Assembly, with Scientific Annexes. United Nations, New York.
UNSCEAR. 1993. United Nations Scientific Committee on the Effects of Atomic
Radiation. Sources and Effects of Ionizing Radiation, No. E 94.IX.2. United
Nations, New York.
UNSCEAR. 1988. United Nations Scientific Committee on the Effects of Atomic
Radiation. Sources, Effects and Risks of Ionizing Radiation, No. E 86.IX.7.
United Nations, New York.
UNSCEAR. 1986. United Nations Scientific Committee on the Effects of Atomic
Radiation. Genetic and Somatic Effects of Ionizing Radiation, No. E 86.IX.1.
United Nations, New York.
U.S. EPA/SAB. 1998. Review of the Office of Radiation and Indoor Air's Federal
Guidance Report 13 - Part 1, Interim Version (FGR-13), Federal Guidance
Report Review Subcommittee of the Radiation Advisory Committee, Science
Advisory Board, U.S. EPA. (EPA-SAB-RAC-99-009) (December).
Vaeth, M., Preston, D. L., and K. Mabuchi. 1992. The shape of the cancer incidence
dose-response curve for the A-bomb survivors. In: Low Dose Irradiation and
Biological Defense Mechanisms. Elsevier Science Publishers, Amsterdam, pp.
75-79.
R-5
-------
DISTRIBUTION LIST
Deputy Administrator
Assistant Administrators
EPA Regional Administrators
EPA Laboratory Directors
Deputy Assistant Administrator for Air and Radiation
Director, Office of Radiation and Indoor Air
Director, Office of Radiation Programs
Deputy Assistant Administrator for Office of Policy, Planning and Evaluation
Director, Office of Strategic Planning and Environmental Data
Director, Office of Policy Analysis
Director, Office of Regulatory Management and Evaluation
EPA Headquarter Libraries
EPA Regional Libraries
EPA Laboratory Libraries
Library of Congress
National Technical Information Service
------- |