EPA Radiogenic Cancer Risk Models and
Projections for the U.S. Population
Draft
U.S. Environmental Protection Agency
Office of Radiation and Indoor Air
December 2008
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CONTENTS
Acronyms and Abbreviations 6
Executive Summary 7
1. Introduction 10
2. Scientific Basis for Cancer Risk Models 11
2.1 Biological Mechanisms 11
2.1.1 Biophysical Interactions 11
2.1.2 Carcinogenisis 12
2.1.3 Radiogenic Carcinogenisis 13
2.1.4 Extrapolation of Low-LET Risks to Low Doses and
Low Dose Rates 15
2.1.5 Low Dose Phenomena 16
2.2 Epidemiology 18
3. EPA Risk Projections for Low-LET Radiation 21
3.1 Introduction 21
3.2 BEIRVII Risk Models 21
3.3 Residual Site Cancers 29
3.4 Calculating Lifetime Attributable Risk 32
3.5 Dose and Dose Rate Adjustment Factor 34
3.6 EAR and ERR LAR Projections for Cancer Incidence 34
3.7 ERR and EAR Projections for Cancer Mortality 37
3.8 U.S. Baseline and Census Data 39
3.9 Combining Results from ERR and EAR Models 40
3.9.1 BEIRVII Approach 40
3.9.2 EPA Approach 41
3.9.3 Should Risk Models be Combined Using a
Weighted GM? 43
3.10 Calculating Radiogenic Breast Cancer Mortality Risk 47
3.11 LAR by Age at Exposure 50
3.12 Summary of Main Results 54
4. Uncertainties in Projections of LAR for Low-LET Radiation 59
4.1 Introduction 59
4.2 Uncertainty from Sampling Variability 60
4.2.1 Bayesian Approach for Most Solid Cancers 60
4.2.2 Approach for Other Cancers 63
4.3 Non-sampling Sources of Uncertainty 64
4.3.1 Risk Transport 65
4.3.2 DDREF 66
4.3.3 Other Non-sampling Sources of Uncertainty 67
4.4 Results 73
4.5 Comparison with BEIRVII 78
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4.5.1 Quantitative Uncertainty Analysis in BEIR VII 78
4.5.2 Comparison of Results 80
4.6 Conclusions 80
5. Risks from Higher LET Radiation 82
5.1 Alpha Particles 82
5.1.1 Laboratory Studies 82
5.1.2 Human Data 83
5.1.3 Nominal Risk Estimates for Alpha Radiation 91
5.1.4 Uncertainties in Risk Estimates for Alpha Radiation .... 91
5.2 Lower Energy Beta Particles and Photons 92
6. Risks from Prenatal Exposure 96
7. Radionuclide Risk Coefficients 98
References 99
Glossary 112
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LIST OF FIGURES
Figure 2-1 Dose response curves 14
Figure 3-1 Age-time patterns in radiation-associated risks 26
Figure 3-2 ERR for Leukemia for age-at-exposure = 20 and
time-since-exposure = 10 28
Figure 3-3 ERR and EAR by time-since-exposure for three
different ages 29
Figure 3-4 Examples of uniform U(0,1) and trapezoidal distributions 44
Figure 3-5 Sex-averaged LAR for incidence by age at exposure for
selected cancers 51
Figure 3-6 Sex-averaged LAR for cancer mortality by age at
exposure for selected cancers 52
Figure 4-1 Uniform and log-uniform distributions for values of LAR
intermediate between the ERR and EAR projections for
stomach and colon cancer 66
Figure 4-2 Subjective probability density function for DDREF 67
Figure 5-1 Cumulative fraction of total dose as a function of
secondary electron kinetic energies for a variety of
low-LET radiations 94
Figure 5-2 Cumulative fraction of total dose as a function of
secondary electron kinetic energies for a variety of slow
and fast initial electron energies 95
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LIST OF TABLES
Table 3-1 BEIRVII Risk Model Cancer Sites 23
Table 3-2 Summary of BEIRVII Preferred Risk Models 25
Table 3-3 Parameter Values for Preferred Risk Models in BEIRVII 27
Table 3-4 EAR and ERR model projections of LAR Projections
for a stationary population 36
Table 3-5 Age-averaged LAR for solid cancer mortality based on
a stationary population 38
Table 3-6 Baseline lifetime risk estimates of cancer incidence
and mortality 40
Table 3-7 EPA and BEIR VII methods for combining EAR and
ERR LAR incidence projections for selected sites 42
Table 3-8 Comparison of EPA and weighted arithmetic mean
method for combining EAR and ERR LAR projections
for incidence 46
Table 3-9 Female breast cancer cases and 5-y relative survival
rates by age for 12 SEER areas 49
Table 3-10 LAR for Cancer incidence for exposures to a
stationary U.S. population 53
Table 3-11 LAR projections for incidence 54
Table 3-12 LAR projections for mortality 56
Table 3-13 Sex-averaged LAR projections for incidence and mortality 57
Table 3-14 Comparisons of EPA and BEIR VIII LAR calculations 58
Table 4-1 Prior distributions for ERR model parameters 63
Table 4-2 Non-sampling sources of uncertainty 65
Table 4-3a EPA projection and uncertainty distribution for the LAR
for male cancer incidence 73
Table 4-3b EPA projection and uncertainty distribution for the LAR
for female cancer incidence 74
Table 4-3c EPA projection and uncertainty distribution for the
sex-averaged LAR for cancer incidence 75
Table 4-4a EPA projection and uncertainty distributions for male
cancer incidence in a stationary population exposed to
uniform whole-body radiation 76
Table 4-4b EPA projection and uncertainty distributions for female
cancer incidence in a stationary population exposed to
uniform whole-body radiation 77
Table 4-5 95% EPA and BEIR VII 95% uncertainty intervals for LAR
of solid cancer incidence 80
Table 5-1 Lung cancer mortality and RBE 89
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LIST OF ACRONYMS AND ABBREVIATIONS
BCC Basal Cell Carcinoma
BE IR VII Health Risks from Exposure to Low Levels of Ionizing Radiation
BEIR VII Phase 2
Cl Confidence Interval
DDREF Dose and Dose Rate Effectiveness Factor
DEF Dose Effectiveness Factor
DREF Dose Rate Effectiveness Factor
DSB Double Strand Break
EAR Excess Absolute Risk
EPA Environmental Protection Agency
ERR Excess Relative Risk
eV Electron Volt
FGR-13 Federal Guidance Report 13
GM Geometric Mean
GSD Geometric Standard Deviation
Gy Gray
ICRP International Commission on Radiological Protection
IR Ionizing Radiation
IREP Interactive RadioEpidemiological Program
LAR Lifetime Attributable Risk
LET Linear Energy Transfer
LNT Linear No -Threshold
LQ Linear-Quadratic
LSS Life Span Study
MAS National Academy of Sciences
NCHS National Center for Health Statistics
NCI National Cancer Institute
NCRP National Council on Radiation Protection and Measurements
NIOSH National Institute for Occupational Safety and Health
NRC National Research Council
ORIA Office of Radiation and Indoor Air
RBE Relative Biological Effectiveness
REF Radiation Effectiveness Factor
RR Relative Risk
SCC Squamous Cell Carcinoma
SEER Surveillance, Epidemiology, and End Results
Sv Sievert
UNSCEAR United Nations Scientific Committee on the Effects of Atomic
Radiation
WLM Working Level Months
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EXECUTIVE SUMMARY
This document presents new EPA estimates of cancer incidence and
mortality risks due to low doses of ionizing radiation (IR) for the U.S. population,
as well as their scientific basis. For the most part, these estimates are calculated
using models recommended in the National Research Council's BEIR VII Report
(NRC 2006), which was sponsored by EPA and several other federal agencies.
As in BEIR VII, models are provided for estimating risk as a function of
age at exposure, age at risk, gender, and cancer site, but a number of extensions
and modifications to the BEIR VII approach have been implemented. First, BEIR
VII focused on the risk from low-LET radiation only, whereas risks from higher
LET radiations are also addressed here. Second, this document goes beyond
BEIR VII in providing estimates of risk for basal cell carcinomas and bone
sarcomas, and cancers from prenatal exposures. Third, a modified method is
employed for estimating breast cancer mortality risk, which corrects for temporal
changes in breast cancer incidence and survival. Finally, this report provides a
somewhat altered and expanded analysis of the uncertainties in the cancer risk
estimates, focusing especially on estimates of risk for whole-body irradiation and
for some specific target organs.
Underlying the risk models is a large body of epidemiological and
radiobiological data. In general, results from both lines of research are
consistent with a linear, no-threshold dose (LNT) response model in which the
risk of inducing a cancer in an irradiated tissue by low doses of IR is proportional
to the dose to that tissue.
The most important source of epidemiological data is the Life Span Study
(LSS) of the Japanese atomic bomb survivors, who received an acute dose of IR,
mostly in the form of gamma rays, with a small admixture of neutrons. The LSS
study has important strengths, including: a nearly instantaneous exposure, which
can be pin-pointed in time; a large, relatively healthy exposed population
encompassing both genders and all ages; a wide range of radiation doses to all
organs of the body, which can be estimated reasonably accurately; and detailed
epidemiological follow-up for about 50 years. Nevertheless, precision is limited
by errors in dosimetry and sampling errors. The sampling errors are often quite
large for specific cancer types, and the uncertainties are even larger if one
focuses on a specific gender, age at exposure, or time after exposure. Another
important uncertainty is the transfer of site-specific cancer risk estimates to the
U.S. population, based on results obtained on the LSS population, for sites with
substantially different baseline incidence rates.
Summary risk coefficients are calculated for a stationary population
defined by 2000 U.S. Vital Statistics. Numerically, the same coefficients apply for
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a cohort exposed throughout life to a constant dose rate. For uniform whole-
body exposures of low-dose gamma radiation to the entire population, the cancer
incidence risk coefficient is 1.01x10~1 Gy"1 (7.0 x10~2 to 2.4 x10~1), where the
numbers in parentheses represent an estimated 90% confidence interval. The
corresponding coefficient for cancer mortality is about one-half that for incidence:
5.18x10~2 Gy"1 (3.5x10"2 Gy"1 to 1.2 x10"1 Gy"1). For perspective, the average
individual receives about 1 mGy each year from low-LET background radiation,
or (-75 mGy, lifetime). The average cancer incidence and mortality risks from
background radiation are then estimated to be about 0.76% and 0.39%,
respectively. The risks are significantly higher for females than for males:
1.23x10"1 Gy"1 vs. 7.85x10"2 Gy"1 (incidence) and 6.28x10"2 Gy"1 vs. 4.06x10"2 Gy"
1 (mortality), respectively.
Radiogenic risks for childhood exposures are often of special interest.
Doses received from ingestion or inhalation are often larger for children than
adults, and the risks per unit dose are substantially larger for exposures during
childhood (here defined as the time period ending at the 15th birthday) than from
exposures later in life. For children, the estimated risks from uniform whole-
body radiation for cancer incidence are 1.6x10"1 Gy"1 (males) and 3.0x10"1 Gy"1
(females) with 90% uncertainty intervals: 1.0 x10"1 to 4.2x10"1 Gy"1 (males) and
2.0x10"1 to 7.1x10"1 Gy"1 (females). The corresponding estimated risks for
mortality are 7.2x10"2 Gy"1 (males) and 1.4 x10"1 Gy"1 (females). There is
generally much more uncertainty about the estimated risks from childhood
exposures than for risks for the entire population. One oft-cited reason for this
(EPA 1994, 1999) is that A-bomb survivors who were children at the time of the
bombings (ATB) still have substantial years of life remaining in which cancers are
to be expressed. At the present, there are too few cancer cases for precise
estimate of risks from childhood exposures.
For ingestion or inhalation of many radionuclides that concentrate in
individual organs risks for specific sites are important. For most cancer sites, the
new EPA risk projections for incidence are not very different from the risk
projections in the current version of FGR 13. Exceptions include female lung,
female bladder, thyroid, and kidney (increased); and female colon cancer
(decreased). For both males and females, the LAR for all cancer combined
increased by about 20%. For mortality, there was a notable decrease in
estimated risk for cancers of the stomach and female colon. Estimated mortality
risks increased for cancers of the female lung, female thyroid, and female kidney.
In general, the new EPA mortality estimates are remarkably consistent with those
in FGR 13; e.g., for all sites combined, the estimates decreased by about 10%
for both males and females. Not surprisingly, uncertainties for site-specific
cancer risks are greater than for uniform whole-body radiation. This is largely
due to the smaller number of cancers for specific sites in the LSS and to
uncertainties in how radiogenic risks for specific cancer sites in the U.S. might
differ from those in a Japanese population of A-bomb survivors.
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The most contentious issue in radiation risk assessment is the
extrapolation of risk estimates derived from relatively high acute exposures in
case of the LSS cohort to low dose, or chronic exposure situations, which are of
greatest interest to EPA. Many subjects in the LSS cohort did receive very low
doses, but there is inadequate statistical power to quantify risk below about 0.1
Gy. This is about 100 times the annual whole-body, low-LET dose to an average
individual from natural background. Thus, the question is how to extrapolate
from an observed risk due to an instantaneous dose of 0.1 Gy or more to an
extrapolated risk from a chronic exposure of« 1 mGy/y.
Efforts have been made to integrate information gathered from radiation
biology and epidemiology into a theoretical framework that would allow reliable
risk projections at dose rates approaching natural background. IR is known to
induce mutagenic damage to the cell's DMA. Due to clustering of ionizations
produced by low-LET as well as high-LET radiation, this damage is often
complex, involving two or more breaks with concomitant base damage all within
a few nanometers in the DMA molecule. This argues against a threshold for
radiation-induced carcinogenesis and in favor of a linear dose-response
relationship at low doses. However, experimental studies have uncovered novel
low-dose phenomena, raising doubts about the reliability of the LNT model. In
view of these findings, some have contended that very low doses of IR may be
much less harmful than estimated based on LNT, and may even be beneficial.
But the relevance of these findings to human carcinogenesis remains unclear,
and epidemiological studies of cancer induction in cohorts receiving fractionated
or chronic exposures have so far been broadly consistent with LNT predictions.
The BEIR VII Committee unequivocally recommended continuing adherence to
the LNT approach. EPA also finds strong scientific support for LNT, while
acknowledging that new research might conceivably force a revision to this
approach in the future.
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1. Introduction
The 1994 report, Estimating Radiogenic Cancer Risks, presented EPA
estimates of site-specific risks cancer incidence and mortality associated with low
doses of ionizing radiation (IR) (EPA 1994). Primarily, the calculated risks were
derived from models recommended by the International Commission on
Radiological Protection (Land and Sinclair 1991), based on analysis of
epidemiological data on Japanese atomic bomb survivors. While focusing mainly
on a quantitative assessment of uncertainties in these estimates, a subsequent
report also made minor adjustments in EPA's cancer risk estimates, reflecting
changes in U.S. vital statistics (EPA 1999a). Finally, the methodology developed
in the above reports was used in Federal Guidance Report No. 13 (FGR-13) to
derive cancer risk coefficients for low level internal and external exposures to a
set of over 800 radionuclides (EPA 1999b).
In 2006, the National Research Council of the National Academy of
Sciences (NAS) released the BEIR VII report (NRC 2006), which reviewed recent
evidence pertaining to the health risks from low-level, low linear energy transfer
(LET) ionizing radiation (IR). The BEIR VII Committee developed models for
calculating the risks of radiogenic cancers, based on updated information on the
A-bomb survivors, as well as other data. In this report, we employ the BEIR VII
models to arrive at revised estimates of radiogenic risks for most cancer sites.
BEIR VII risk estimates were derived for low doses of gamma rays with typical
energies between about 0.1 and 10 MeV, with a brief discussion of possible
enhancement of risk for more densely ionizing electrons and photons. Although
the main focus here is, like BEIR VII, on low-LET risks, we extend the evaluation
of cancer risks to high-LET radiation (alpha-particles) and to lower energy
photons and electrons, which may convey a higher risk than the higher energy
gamma rays irradiating the LSS cohort. We also present risk models and
estimates for bone cancers and non-melanoma skin cancers, which are not
covered in BEIR VII. Finally, we derive uncertainty bounds on our risk estimates,
based on information on BEIR VII and other relevant sources.
This report is not intended to provide an exhaustive review of the scientific
basis for our risk models. For the most part, the reader is referred to BEIR VII
and other sources in the literature. We have attempted to highlight major
sources of uncertainty and, where pertinent, to include recently published
information not considered by the BEIR VII Committee.
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2. Scientific Basis for Cancer Risk Models
2.1 Biological Mechanisms
2.1.1 Biophysical Interactions. By definition, IR passing through matter
has sufficient energy to break chemical bonds and to remove electrons from
molecules. When this chemical damage occurs in the DMA of a somatic cell, a
mutation in the genetic material can result, ultimately leading to a malignancy.
The damage can be produced directly, when an ionizing particle impacts the
DMA, or indirectly, through the creation of free radicals in the cellular medium,
which diffuse and interact with the genetic material.
Only a tiny fraction of the free radicals produced in cells each day arise
from IR; nevertheless, DMA damage by low-level IR is not negligible. This is
because energy deposition events are often produced in clusters, which can, in
turn, produce double strand breaks (DSBs) and more complex damage in DMA,
involving multiple breaks and chemical modifications within a very restricted
portion of the double helix. Cellular repair processes are less capable of
repairing DSBs and complex damage than the simpler types of damage almost
always induced by isolated free radicals. This makes IR unique among
environmental carcinogens. Even a single track of IR is capable of producing
complex damage sites, which, if misrepaired, can leave the cell with a mutated
gene that can be passed on to the cell's progeny. Depending on the nature of
the mutation, this may be one step in the formation of a malignancy. At
reasonably low doses of IR the number of DSBs and sites of complex damage is
expected to be strictly proportional to dose (UNSCEAR 2000b, NCRP 2001, NRC
2006); this is the primary basis for the linear no-threshold (LNT) theory in which
the probability of inducing a cancer by IR is proportional to dose with no
threshold below which there is no risk.
Some recent research has cast doubt on the LNT assumption, but the
BEIR VII Report concluded that these results in no way constituted compelling
evidence against LNT. Additional discussion of the issue will be found in
sections below.
The degree of clustering of ionizations, and therefore of the DNA damage,
depends on the type of radiation and its energy. This is reflected in the linear
energy transfer of charged particle radiation (LET), which is a measure of the
amount of energy deposited, per unit path length, as the particle passes through
a medium. Alpha-particles emitted by the decay of unstable atomic nuclei have a
relatively high LET («100-200 keV/um) in aqueous media, producing a high
density of ionizations, leading to a high frequency of DSBs and clustered damage
sites in the DNA. Since this type of damage is more likely to be misrepaired,
high-LET radiation is more effective at causing mutations, cell transformation,
and cell death (NCRP 2001). This higher effectiveness per unit dose, relative to
some standard radiation (e.g., 60Co gamma rays), is expressed in terms of a
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factor called the Relative Biological Effectiveness1 (RBE) (see Section 5).
Initially, 200 kVp x rays were used as the reference; however, since current
radiogenic cancer risk estimates largely rest on studies of the Japanese atomic
bomb survivors, whose predominant exposure was from gamma rays, it is now
common to use 60Co gamma rays as the reference radiation. That convention
will be adopted here.
Compared to alpha-particles, beta-particles and the secondary electrons
produced by incident gamma rays or medical x rays typically have much lower
linear energy transfer (0.1 -10 keV/um). The ionizations produced by energetic
electrons are more widely spaced, on average, but their production is a
stochastic process in which several ionizations can be created separated by a
distance no greater than the characteristic distance between adjacent DMA
bases or between DMA strands. Moreover, as electrons lose energy, the LET
increases and closely spaced ionizations become more frequent. Hence,
clustered DMA damage is more likely to be produced near the ends of the
electron tracks.
X rays and gamma rays can travel appreciable distances through matter
without producing ionizations; however, they interact with atoms to produce
energetic secondary electrons, which behave identically to incident electrons of
the same energy. In aqueous media, over the incident photon energy range 0.1-
10 MeV, the predominant photon interaction is Compton scattering, a process in
which an incident photon transfers part of its energy to an atomic electron,
creating a free electron and a lower energy photon. The energy of a Compton
electron is positively correlated with the incident photon energy. Consequently,
as the incident photon energy is reduced within this energy range, a higher
fraction of the energy is dissipated in the form of lower energy (higher LET)
electrons, resulting in more complex DMA damage and, therefore, perhaps an
increased RBE. As the incident photon energy is reduced further, below 0.1
MeV, photoelectric absorption becomes increasingly important compared to
Compton scattering, and the variation of LET with the photon energy is no longer
monotonic.
2.1.2 Carcinogenesis. Carcinogenesis is thought to be a multi-staged
process "initiated" by a mutation in a single cell. Before a malignancy can result,
however, additional mutations must accumulate. This process may be enhanced
by enlarging the pool of initiated cells (clonal expansion), which might be
Kocher et al. (2005) have introduced a quantity called the "radiation effectiveness factor" (REF)
to compare the cancer causing potency in humans of a specified type of radiation relative to
some standard. According to their definition, the REF is to be distinguished from measured RBEs
that may be used as a basis for estimating the REF, although the RBEs themselves may have
been measured for a different end-point or in a different species. Although it is important to keep
in mind that RBEs used for human risk estimation are generally extrapolated, and not directly
measured, we follow common practice here in applying the term RBE more broadly to include the
estimation of human radiogenic cancer risk.
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triggered by the presence of a "promoter". After clonal expansion, more initiated
cells are available to undergo additional mutations, a process referred to as
"cancer progression". Particularly important may be those mutations that
increase the probability of further mutations - e.g., those impairing DMA repair
processes. Eventually, a set of mutations may remove the essential controls
over cell division, resulting in a malignancy.
2.1.3 Radiogenic Carcinogenesis. Over a period of decades, a
conceptual model of radiation carcinogenesis was built up from numerous
studies conducted at the molecular, cellular, tissue, and whole organism levels.
In this picture an ionizing track produces DMA damage through direct interaction
with the double helix or through the interaction of free radicals diffusing to the
DMA damage site, after being produced nearby. Misrepair of the DMA damage
can then lead an initiated cell and, eventually, to a malignancy as outlined above.
The dose response for radiation carcinogenesis is then expected to have the
same mathematical form as that for radiation-induced mutations.
As shown in Figure 2-1, the dose response for the induction of mutations,
cell transformation, or carcinogenesis by low-LET IR appeared to be linear at low
doses, curvilinear upward at higher doses until eventually becoming concave
downward at still higher doses. Mathematically, the initial portions of the curve is
expressed as a "linear-quadratic" (LQ) function of effect (E) vs dose (D).
E = a!D + a2D2 (2-1)
At low dose rates, the effect was found to increase linearly, with the same slope,
ai, observed initially at high dose rates. The expected response at high doses is
therefore reduced by lowering the dose rate, which effectively removes the
quadratic term in Eq. 2-1.
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120
AJphft particles
Neutrons
, ABSORBED DOSE (Oy)
Figure 2-1: Solid curves depict the classical dose-response curves for low-LET
gamma rays and high-LET neutrons or alpha-particles. The dashed lines show
the expected response at low dose rates for each type of radiation. From
UNSCEAR 1993, p. 698.
As also shown in Figure 2-1, the dose-response for high-LET radiation,
appeared to be linear and independent of dose rate, except at rather high doses,
where the function flattens or even turns over. At the high doses, moreover, an
"inverse dose rate effect" may be observed in which the response is increased
when the dose rate is reduced.
Thus, at low doses and dose rates the dose-response for either low- or
high-LET radiation appears to be linear with no evidence of a threshold.
In the case of low-LET radiation, it was inferred that the passage of two
tracks close together in space and time increases the probability of misrepaired
damage, either because the damage produced is more complex or because the
repair machinery becomes partially saturated, reducing its effectiveness. It was
presumed that, at either low doses or low dose rates, only the damage produced
by single tracks is significant, and the response is simply proportional to dose. At
high dose rates, however, repair efficiency will decrease with increasing dose,
leading to the quadratic term in Eq. 2-1.
At low or moderate doses of high-LET radiation, the production of multiply-
damaged sites in DMA is dominated by single track events. The flattening or
downturn observed at high acute doses may reflect cell killing (NCRP 1980). An
alternative explanation has been proposed in which at any given time a
subpopulation of cells exists in a sensitive time window; spreading the dose out
more in time allows more cells to be hit while they are in that time window,
resulting in an enhanced response (Rossi and Kellerer 1986, Elkind 1994).
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Downward curvature and an inverse dose rate effect can also result from the
"bystander effect" (Brenner and Sachs 2003), which will be discussed below.
Conclusions: Traversal of a cell nucleus by IR can induce damage to the
cell's DMA, initiating the carcinogenic process. Since the damage produced by
even a single track of IR can sometimes be misrepaired, a threshold for cancer
induction would appear improbable unless there is a mechanism for eliminating
essentially all dividing cells with damaged DMA (e.g., through some kind of
immune surveillance). A nearly foolproof screening mechanism of this sort would
seem to be ruled out, however, by the significant rate of cancer incidence among
people not exposed to high levels of IR.
Under conditions of low doses or low dose rates, the effect of multiple
tracks is expected to be negligible, so the probability of a cell becoming initiated
is simply proportional to dose. This provides a mechanistic basis for the linear
no-threshold (LNT) model of carcinogenesis in which the probability of IR causing
a cancer is proportional to dose, even at very low doses for which there is
insufficient statistical power to detect any excess incidence of the disease in a
human population.
2.1.4 Extrapolation of Low-LET Risks to Low Doses and Dose Rates.
As discussed above, radiobiological data suggest that the probability of
mutational damage in a cell's DMA from an acute exposure to low-LET IR can be
expressed as a linear-quadratic (LQ) function of dose (D)\
E = oclD + a2D2 (2-1)
The linear term is assumed to reflect the effect of single tracks, the quadratic
term the added effect of two tracks traversing the cell close together in space and
time, or perhaps the saturation of repair mechanisms at higher doses. If doses
are delivered in a widely space temporal series of acute dose fractions, it is
expected that each dose fraction, £>/, will produce an incremental effect,
Ef = alDf + a2 D2f (2-2)
If each fraction is made very small, the quadratic terms will be negligible, and the
overall summed effect will be linear with dose; i.e., E=diD, where D=LDf. A
chronic exposure can be thought of as a sequence of very small fractionated
exposures. It follows that if the dose rate from a chronic exposure is low enough
so that the interaction of multiple tracks can be neglected, then the effect will
again be simply given by E=aiD, where/) is the total dose.
The effect per unit dose will be reduced in going from a large acute dose,
D, where the quadratic term is significant, to a low dose, where only the linear
term contributes. Overall the effect will be reduced by a Dose Effectiveness
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Factor (DEF) = (a1+a2D)/a1 = l+QD, where 6= 0.2/0.1. Likewise the estimated
effect per unit dose will be reduced by a Dose Rate Effectiveness Factor (DREF),
when a large acute dose is delivered chronically. Since the slope is the same
(a?) at low doses or dose rates, the DREF and the DEF are equal. Thus,
according to the LQ model, the extrapolation from a high acute dose to either a
low dose or to a low dose rate can be embodied into a single correction factor,
the Dose/Dose Rate Effectiveness Factor (DDREF).
It is presumed that the probability of carcinogenesis induced in an
organism from an exposure to IR is proportional to the number of induced
mutations remaining after repair is complete. This has led scientists to model the
excess risk as a LQ function of dose for a relatively high acute dose, with a
reduction by a DDREF factor for low doses and dose rates. The DDREF for
carcinogenesis would be equal to that for the underlying process of radiation-
induced mutagenesis.
Based on its review of radiobiological and epidemiological data, the
UNSCEAR Committee (UNSCEAR 1993; 2000b) concluded that any dose below
200 mGy, or any dose rate below 0.1 mGy/min (when averaged over about an
hour), should be regarded as low. Thus, according to the linear-quadratic model,
for these doses and dose rates, the risk per unit dose would be approximately
equal to the linear coefficient, aj.
2.1.5 Low Dose Phenomena. Much recent research in radiobiology has
focused on several new phenomena relating to the effects of low dose IR,
including: (1) the adaptive response, (2) genomic instability, and (3) bystander
effects. These phenomena have raised questions about the reliability of the LNT
model for radiation carcinogenesis. They indicate that, at least under some
conditions, IR may induce DMA damage, indirectly, by affecting non-targeted
cells, and that the processing of DMA damage by cells may be strongly
dependent on dose, even at very low doses.
Adaptive Response. Under some conditions, it has been found that pre-
irradiating cells with an "adapting dose" of low-LET radiation (~10 mGy) reduces
the effects (e.g., chromosome damage, mutations, or cell transformation) of a
subsequent "challenge dose" of ~1 Gy. This has provided some support for the
suggestion that low-dose radiation may stimulate defense mechanisms, which
could be beneficial in preventing cancer or other diseases. Supporting this view
also have been studies in which the spontaneous transformation rates of certain
cells in culture have been reduced by exposure to very low level IR (Azzam et al.
1996, Redpath and Antoniono 1998). A subsequent study, however, has shown
a threshold for this "beneficial effect"; suppression of transformation disappeared
when the dose rate was reduced below 1 mGy/day (Elmore et al. 2008). Thus,
even if this phenomenon occurs in vivo, it may not be operative at environmental
exposure levels.
16
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Genomic Instability. It has been found that irradiation of a cell can
produce some kind of change in that cell, not yet characterized, which increases
the probability of a mutation one or more cell divisions later (Morgan et al. 1996).
The relatively high frequency of inducing genomic instability implies that the
relevant target is much larger than a single gene, and there is evidence that, at
least in some cases, the phenomenon is mediated by IR-induced epigenetic
changes rather than DMA damage (Kadhim et al. 1992, Morgan et al. 1996). The
delayed mutations are typically simple point mutations, unlike other mutations
caused by IR, which are typically deletions or other types of chromosomal
changes resulting from DSBs and more complex DMA damage (Little et al.
1997).
Bystander Effects. Contrary to the conventional picture, DMA damage in
a (bystander) cell can be induced by passage of an ionizing track through a
neighboring cell. The bystander effect can apparently be triggered by passage of
a signal through gap junctions (Azzam et al. 1998). Media transfer experiments
have demonstrated that it can also be induced - although probably less
effectively (Mitchell et al. 2004) - by molecules leaking out into the extracellular
fluid (Mothersill and Seymour 1998, Lehnert and Goodwin 1998). It also appears
that the adaptive response and genomic instability may be induced in bystander
cells under some conditions (Coates et al. 2004, Kadhim et al. 2004, Tapio and
Jacob 2007). Recent evidence has also been found of bystander signals from
irradiated cells inducing apoptosis in neighboring transformed cells (Portess et al.
2007).
The preponderance of data regarding these effects has been obtained
from experiments on isolated cells. There is limited information on the
occurrence of these effects in vivo, and no understanding of how they might
modulate risks at low doses. At first sight, it would appear that the adaptive
response should be protective, whereas bystander effects and genomic instability
might increase risk. Interpretation may be complicated, however, by the
possibility for triggering protective mechanisms in bystander cells, such as an
adaptive response or apoptosis of precancerous cells (Lyng et al. 2000, Portess
et al. 2007, Tapio and Jacob 2007).
The BEIR VII Committee was not convinced that these effects would
operate in vivo in such a way as to significantly modify risks at low doses. It was
a consensus of the Committee that:
the balance of evidence from epidemiologic, animal and mechanistic
studies tend to favor a simple proportionate relationship at low doses
between radiation dose and cancer risk. (BEIR VII, p. 14)
A similar conclusion was reached by another group of experts assembled by the
International Commission on Radiological Protection (ICRP 2005).
17
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In contrast, the French Academy of Sciences issued a report that strongly
questioned the validity of the LNT hypothesis (Tubiana et al. 2005). The French
Academy report cited a paper by Rothkamm and Lobrich (2003) showing that
repair of DSBs, as measured by the disappearance of y-H2AX foci, was absent
or minimal at low doses, presumably leading to apoptosis of cells with DSBs.
The French Academy report claimed that this finding indicated that risks were
greatly overestimated at low doses. Recent studies have cast doubt on the
significance of this finding, however (Lobrich et al. 2005, Markova et al. 2007).
Conclusion. EPA accepts the recommendations in the BEIR VII and
ICRP Reports to the effect that there is strong scientific support for LNT and that
there is no plausible alternative at this point. However, research on low dose
effects continues and the issue of low dose extrapolation remains unsettled.
2.2 Epidemiology
There is overwhelming evidence from epidemiological studies of irradiated
human populations that IR increases the risk of cancer. Most important from the
standpoint of quantifying radiation risks is the Lifespan Study (LSS) of atomic
bomb survivors in Hiroshima and Nagasaki, Japan. The survivors constitute a
relatively healthy population at the time of exposure, including both genders and
all ages, with detailed medical follow-up for about half a century. Extremely
significant, also, is the wide range of fairly accurately known individual radiation
doses.
The LSS cohort shows an excess in various types of cancer, with the rates
increasing with increasing dose to the target organ. The data from the LSS are
adequate to serve as a basis for developing detailed mathematical models for
estimating risk as a function of cancer site, dose, age, and gender. However,
due to limitations in statistical power, it has not been possible to demonstrate and
quantify risk in the LSS at doses below about 100 mGy.
Epidemiological studies of medically irradiated cohorts provide strong
confirmation for the carcinogenic effects of IR and some additional information for
generating risk estimates - in particular, for the bone, thyroid, liver, and breast.
Radiation risks have also been extensively studied in occupationally exposed
cohorts, but so far such studies - aside from those on radon-induced lung
cancers in underground miners - have not proved very useful for actually
quantifying risk. Major reasons for this failure have been: poor dosimetry; low
doses, leading to low statistical power; and potential confounding by life-style
factors or other occupational exposures. As discussed in a later section,
however, recent data on workers at the Mayak plutonium production plant in the
former Soviet Union may provide an improved basis for estimating risks from
inhaled alpha-emitters.
18
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Although the epidemiological data on radiation-induced carcinogenesis
are extensive, calculated risks to members of the U.S. population from doses of
IR typically received environmentally, occupationally, or from diagnostic medical
procedures suffer from significant sources of uncertainty. Among these sources
are: (1) errors in the epidemiological data underlying the risk models, including
sampling errors, errors in dosimetry, and errors in disease ascertainment; (2)
uncertainties in how risks vary over times longer than the period of
epidemiological follow-up; (3) uncertainties in "transporting" risk estimates to the
U.S. population from a study population (e.g., the LSS cohort), which may differ
in its sensitivity to IR; (4) differences in the type of radiation or its energy between
the epidemiological cohort and the target U.S. population; and (5) uncertainty in
how to extrapolate from moderate doses (>0.1 (By), for which there are good data
upon which to quantify risk, to lower doses, and from acute to chronic exposure
conditions.
Especially contentious is the extrapolation to low doses and dose rates.
Generally speaking, epidemiology cannot be used to detect and quantify the
carcinogenic effects of radiation at doses below about 100 mGy of low-LET
radiation because of limitations on statistical power (Land 1980, Brenner et al.
2003). Most cells in the body receive a radiation dose of about 1 mGy/y -
predominantly gamma rays from cosmic, terrestrial and internal sources. Given
the typical energies of these background gamma rays (0.1-3 MeV) this
corresponds to roughly 1 ionizing track traversing each cell nucleus, on average,
annually. Thus, during the estimated typical time for DMA repair to be completed
(a few hours), roughly 1 out of 1,000 cell nuclei will be hit, and the probability of
multiple hits to the same nucleus will be very low. By way of comparison, at the
lowest doses for which risk can be quantified in the A-bomb survivors, each
nucleus was instantaneously impacted by ~100 tracks.
A notable exception to this 100 mGy limit on the sensitivity of
epidemiological studies appears to be for studies of childhood cancers induced
by prenatal exposure to diagnostic x rays, where an excess risk has been
observed at a dose level of about 6-10 mGy (see Section 6). In this case,
statistical power is magnified by the apparent heightened sensitivity of the fetus,
combined with a low background rate of childhood cancers. Typically, the x rays
employed in these examinations were 80 kVp, and the estimated mean dose was
6 mGy; this corresponds to only about 1 incident photon per cell nucleus
(Brenner and Sachs 2006). Thus, this finding argues against a threshold for
radiation carcinogenesis.
Although epidemiology otherwise lacks the power to detect risks from
acute doses of radiation below about 100 mGy, it can provide information on
risks from smaller doses through studies of populations receiving fractionated or
chronic IR doses that cumulatively add up to about 100 mGy or more. For
example, it was found that multiple fluoroscopic examinations, each delivering an
average dose of approximately 8 mGy, produced a similar increase in breast
19
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cancer, per unit dose, as a single acute dose to the breast (Howe and
Mclaughlin 1996). Likewise, female scoliosis patients under 20 years of age,
who received repeated x-ray examinations, each with a mean breast dose of
approximately 4 mGy, had a higher breast cancer mortality compared to controls
and an increasing mortality with an increasing number of examinations (Doody et
al. 2000). In both these studies, breast cell nuclei received at most a few nuclear
hits from each dose fraction. Finally, children irradiated for ringworm (mean total
thyroid dose 84 mGy in 5 fractions had a statistically significant increase in
thyroid cancer compared to unirradiated controls (Ron et al. 1989)
In addition, epidemiological studies have been conducted on cohorts of
individuals who received cumulative doses of 100 mGy or more, but where the
dose is spread out over months or years. Radiologists (Lewis 1963, Smith and
Doll 1981) and radiological technicians (Wang et al. 1988, Doody et al. 2006),
working before modern radiation protection standards had been implemented,
show increased risks of leukemia and breast cancer, respectively. However,
individual dose estimates are generally lacking in these studies, and they are not
very useful for obtaining quantitative risk estimates. A number of cohort studies
are underway, however, which may better demonstrate and quantify risks from
protracted doses of low-LET IR.
Among the most important of these studies are: nuclear workers in various
countries (Cardis et al. 2005, 2007); Chernobyl cleanup workers ("liquidators")
(Hatch et al. 2005); residents downriver from the Mayak nuclear plant in Russia
(Ostroumova et al. 2006, Krestinina et al. 2005); residents downwind from the
Semipalatinsk nuclear test site in Kazakhstan (Bauer et al. 2005); and inhabitants
of Taiwanese apartments constructed with steel beams contaminated with 60Co
(Hwang et al. 2008). Studies on these populations are ongoing and suffer from
various shortcomings, including incomplete follow-up and dosimetric
uncertainties. Nevertheless, results from several of them suggest that radiation
risks can be detected and quantified, even in cases where the average dose rate
is well below 1 mGy/day, corresponding to less than 1 ionizing track per cell
nucleus per day (Puskin 2008).
20
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3. EPA Risk Projections for Low-LET Radiation
3.1 Introduction
For cancer sites other than bone and skin cancer, the new EPA risk
projections for low-LET radiation are based on the risk models recommended in
BEIR VII and are described in the next section. As in BEIR VII, the risk models
form the basis for calculating estimates of lifetime attributable risk (LAR), which
approximate the premature probability of a cancer or cancer death which can be
attributed to radiation exposure. Relatively minor modifications were made to
the approach used in BEIR VII to the methodology for calculating LAR; details
are given in Section 3.2 and subsequent sections. Although the main results are
the new EPA estimates of LAR associated with a constant lifetime dose rate, we
also provide estimates to indicate how radiogenic risks might depend on age at
exposure. A detailed discussion of the uncertainties associated with these risks
is given in Section 4.
The main focus of the BEIR VII Report was to develop estimates of risk for
low-dose, low-LET radiation. However, the BEIR VII models are predominantly
based on analyses of the A-bomb survivor data, where the exposure included
high-LET neutrons, as well as gamma rays. A recently completed reappraisal of
the A-bomb dosimetry, referred to as DS02, was used as a basis for the BEIR VII
analysis. In BEIR VII, it was assumed that neutrons had a constant RBE of 10
compared to gamma rays, implying a "dose equivalent", d, to each survivor (in
Sv) given by:
d=dy+ Wdn,
where dy and dn are, respectively, the gamma ray and neutron absorbed doses
(in Gy). The BEIR VII approach then yields models for calculating the risk per
Sv, which can be directly applied to estimate the risk per Gy from a gamma-ray
exposure.
With a constant RBE of 10, the estimated contribution of neutrons is
relatively minor, although not negligible. A recent publication (Sasaki et al. 2008)
presented radiobiological data supporting an RBE for neutrons that was highly
dose dependent, approaching a value of nearly 100 in the limit of low doses. The
authors found that applying their estimates for the RBE brought about better
agreement between Hiroshima and Nagasaki chromosome aberration data and
reduced the estimate of gamma-ray risk by about 30%.
3.2 BEIR VII Risk Models
The BEIR VII Committee used excess relative risk (ERR) and excess
absolute risk (EAR) to project radiogenic cancer risks to the U.S. population for
each of the cancer sites given in Table 3-1. ERR represents the ratio of the age-
21
-------
specific increase in cancer rate attributable to a radiation dose divided by the
baseline rate, i.e. the rate associated with the background radiation level,
whereas EAR is simply the difference in rates attributable to radiation. In the
models preferred by the BEIR VII Committee for solid cancer sites, ERR and
EAR are functions of age-at-exposure, attained age (the age at which a cancer
might occur), and sex. For leukemia, the "BEIR VII models" also explicitly allow
for dependence of ERR or EAR on time-since-exposure.
For all cancer sites, the BEIR VII risk models were based, at least
partially, on analyses of data from atomic bomb survivors. ERR and EAR
models of the form given in Eq. 3-1 and 3-2 were fit to LSS data on incidence
and mortality:
ERR model: A(c, s, a, M) = 4, (c, s, a, b}[\ + ERR(s, e, a, d}]
~ (3-1)
EAR model: A(c, s, a, b,d) = A^ (c, s, a, b) + EAR(s, e, a, d)
= 4, (c, s, a,b} + d ~EAR(s, e, a, d} (3-2)
Here, ERR(s,e,a,d)and EAR(s,e,a,d)are, respectively, the ERR and EAR for a
given sex (s), age at exposure (e), attained age (a), and absorbed dose (d).
ERR(s,e,a,d)and EAR(s,e,a,d) denote the ERR and EAR per unit of dose
expressed in Gy (for low-LET radiation), and ^(c^s^a^b} is the baseline rate,
which depends on city (c, Hiroshima or Nagasaki), sex, attained age, and year of
birth (b). For all solid cancer sites, an LNT model was fit to the LSS data. In
other words, increases in solid cancer rates were assumed to be approximately
equal to the product of a linear-dose parameter that depends on sex, the
absorbed dose, and a function that depends on age-at-exposure and attained-
age, so that ERR and EAR does not depend on dose.
22
-------
Table 3-1: BEIR VII risk model cancer sites
Cancer site(s)
ICD-O-2 codes
Stomach
Colon
Liver
Lung
Breast (female only)
Prostate
Uterus
Ovary
Bladder
Thyroid
"Remainder category".
Solid cancers of the oral cavity, esophagus,
small intestine, rectum, gall bladder, pancreas,
digestive system*, nasal cavity, larynx, other
respiratory system*, thymus, kidney, and
central nervous system. Also includes renal
pelvis, ureter cancers, melanoma, bone,
connective tissue, other genital cancers*, and
other solid cancers*
Leukemia
C16/3
C18/3
C22/3
C33, 34/3
C50/3
C61 /3
C53-54, C559 / 3
C 56, C57 (0,1,2,3,4,8)7 3
C67/3
C739 7 3
COO-C15 7 3, C17 7 3, C19-21 73, C 23-25 7 3,
C26 73, C422 7 3, C37-39 7 3, C379 7 3, C649 7
3, C70-727 (2,3), C40 7 3, C41 73, C47 7 3, C49
73, C44 7 3, M8270-8279, C659 7 3, C 669 73,
C51/3, C52/3, C57 (7,8,9)73, C58 7 3, C60 73,
C63 73, C42 (0,1,3,4) 7 3, C69 7 3, C74-76 7 3,
C77/3, C809/3.
Revised ICD 9: 204-208
* Refers to sites not specified elsewhere in this table.
The BEIR VII committee used very similar models to project risks to the
U.S. population. Their ERR and EAR preferred risk models are of the form,
= ^ (s, a)[\ + d ERR(s, e, a, d)]
a, d) = AQ (s, a) + d EAR(s, e,a,d)
(3-3)
(3-4)
The only difference in the BEIR VII models for projecting risk to the U.S.
compared to the models fit to the LSS data is that in Eq. 3-3 and 3-4, A0(s,a)
represents the baseline rate for the U.S. population, which depends only on sex
and attained age. Otherwise, the two set of models are identical, i.e.,
ERR(s,e,a,d} and EAR(s,e,a,d} represent the same function in Eq. 3-3 and 3-4
as in Eq. 3-1 and 3-2. For example, the BEIR VII committee found that the ERR
decreased by about 25% per decade in the model that "best" fit the LSS data for
23
-------
most cancer sites; consequently, the ERR decreases by the same 25% per
decade in their models used to project risk to the U.S.
Of the two types of risk models, ERR models are more appropriate for
cancer sites for which age-specific excess in cancer incidence rates attributable
to radiation might be roughly proportional to the baseline rate - independent of
the population. In contrast, EAR models are appropriate when the excess in
cancer rates is independent of the baseline risks. The BEIR VII Committee used
each type of risk model (EAR and ERR) to calculate site-specific risk projections
for a U.S. population. For cancers for which the baseline rates are higher in the
U.S. than in the LSS, the ERR models tend to yield larger projections of
radiogenic risk than the projections from EAR models. For other cancer sites,
the projections from EAR models tend to be larger.
A compromise between the two approaches was used for most cancer
sites. If, as seems likely, radiogenic risks for most cancer sites for the U.S.
population are within the ranges defined by the ERR and EAR projections, a
reasonable approach would be to calculate an "average" the projections based
on the two types of risk models, e.g., a weighted arithmetic or geometric mean.
This is the approach used by BEIR VII and other comprehensive reports on
radiation risks and is described in more detail in Section 3.9.
Table 3-2 provides a summary of the BEIR VII ERR and EAR risk models.
For all solid cancer sites except breast and thyroid, the BEIR VII models were
based exclusively on analyses of the A-bomb survivor incidence data. This
differs from EPA's current risk models (EPA 1994), which for most cancer sites
were derived from LSS mortality data. In general, the LSS incidence data is
preferred as a basis for the risk models because "site-specific cancer incidence
data are based on diagnostic information that is more detailed and accurate than
death certificate data and because, for several sites, the number of incident
cases is larger than the number of deaths (NRC 2006)." For breast and thyroid
cancers, the BEIR VII models were based on pooled analyses of both A-bomb
survivor and medical cohort data. The risk model for leukemia was based on an
analysis of mortality within the LSS cohort. In contrast to some other cancer
types, "the quality of diagnostic information for the non-type-specific leukemia
mortality used in these analyses is thought to be high (NRC 2006)."
24
-------
Table 3-2: Summary of BEIR VII preferred risk models
Cancer site
Description
Data sources
Solid cancers ERR and EAR increase linearly with
except breast, dose; depends also on sex(s), age at
thyroid exposure (e), attained age (a)
Breast EAR increases linearly with dose.
ERR model not used. Effect
modifiers: (e, a).
1958-1998 LSS cancer incidence
1958-1993 LSS breast cancer
incidence; Massachusetts TB
fluoroscopy cohorts (Boice et al. 1991);
Rochester infant thymic irradiation
cohort (Hildreth et al. 1989)
Thyroid
ERR increases linearly with dose.
EAR model not used. Effect
modifiers (s ,e ,a).
Leukemia
ERR and EAR are quadratic functions
of dose. Effect modifiers: (s ,e ,a)
and time since exposure (t).
1958-1987 LSS thyroid cancer
incidence (Thompson et al. 1994);
Medical cohort studies: Rochester
thymus (Shore et al. 1993), Israel tinea
capitis (Ron et al. 1989), Chicago
tonsils (Schneider et al. 1993), Boston
tonsils (Pottern et al. 1990).
Medical case-control studies: Cervical
cancer (Boice et al. 1988), Childhood
cancer (Tucker et al. 1991).
1950-2000 LSS cancer mortality
(Preston et al. 2004).
25
-------
Solid cancer sites other than breast and thyroid. For most solid
cancer sites, the preferred BEIR VII EAR and ERR models are functions of sex,
age at exposure, and attained age, and are of the following form:
EAR(d,s,e,a)or ERR(d,s,e,a) = /3sdexp(>e*}(a160)*,
min(e,30)-30
where e* =
10
(3-5)
(3-6)
As seen in Table 3-3, the values for the parameters J3s,y, and 77 depend on the
type of model (EAR or ERR). For ERR models for most sites:
/?, the ERR per Sv at age-at-exposure 30 and attained age 60,
tends to be larger for females than males;
Y = -0.3 implies the radiogenic risk of cancer at age e falls by about
25% for every decade increase in age-at-exposure up to age 30;
and
77 = -1.4 implies the ERR is almost 20% smaller at attained age 70
than at age 60.
As a consequence, ERR decreases with age-at-exposure (up to age 30) and
attained age. In contrast, for EAR models, y = -0.41 and 77 = 2.8 for most sites.
Thus EAR decreases with age-at-exposure, but increases with attained age.
These patterns are illustrated in Figure 3-1.
J.4
3-2
_ J.O
* 1,6
I '•*
1 "
*'•»
| 1.6
I OJ
*" M
ft*
Afl* at f»is«*j(* 10
m m
Attained oga
W
s
8 20
I 10
0
» W 70
Figure 3-1: Age-time patterns in radiation-associated risks for solid cancer incidence
excluding thyroid and nonmelanoma skin cancer. Curves are sex-averaged estimates of
the risk at 1 Svfor people exposed at age 10 (solid lines), age 20 (dashed lines), and age
30 or more (dotted lines). (BEIR VII: Figure 12-1A, p. 270).
Thyroid. For thyroid cancer, the BEIR VII Committee used only an ERR
model to quantify risk. It was of slightly different form than for other solid cancers
26
-------
in that ERR continues to decreases exponentially with age-at-exposure for ages
greater than 30 y, and ERR is independent of attained age. ERR for thyroid
cancer is given in Eq. 3-7:
ERR(d,s,e) =
(3-7)
Table 3-3: Parameter values for preferred risk models in BEIR VII
Cancer
Stomach
Colon
Liver
Lung
Breast
Prostate
Uterus
Ovary
Bladder
Other solid
Thyroid2
Leukemia
PM
0.21
0.63
0.32
0.32
0.12
0.5
0.27
0.53
1.1
5 = -0
ERR
PF
0.48
0.43
0.32
1.4
Not
0.055
0.38
1.65
0.45
1.05
1.2
48
0 = 0.87SV1, <
model
Y
-0.3
-0.3
-0.3
-0.3
used
-0.3
-0.3
-0.3
-0.3
-0.3
-0.83
-0.4
^ = 0.42
n
-1.4
-1.4
-1.4
-1.4
-1.4
-1.4
-1.4
-1.4
-2.8
0
None
PM
4.9
3.2
2.2
2.3
EAR
PF
4.9
1.6
1
3.4
model
Y
-0.41
-0.41
-0.41
-0.41
H
2.8
2.8
4.1
5.2
See text
0.11
1.2
6.2
1.62
6 =
1.2
0.7
0.75
4.8
Not
0.93
0.88Sv"1, $
-0.41
-0.41
-0.41
-0.41
-0.41
used
0.29
£ = 0.56
2.8
2.8
2.8
6
2.8
None
1 Adapted from Tables 12-2 and 12-3 of BEIR VII.
2 Unlike for other sites, the dependence of ERR on age-at-exposure is not limited to ages<30.
Breast. For breast cancer, the BEIR VII Committee used only an EAR
model to quantify risk. In the BEIR VII model, EAR depends on both age at
exposure and attained age (Eq. 3.8). Unlike other cancers, the EAR continues to
decrease exponentially with age-at-exposure throughout one's lifetime, and the
EAR increases with attained age less rapidly after age 50 (about the time of
menopause).
(3-8)
where 77 = 3.5 for a < 50 and 1 for a > 50.
Leukemia. BEIR VII provided both EAR and ERR risk models for
leukemia (see Eq. 3-9). These differ from models for most other cancer sites. In
the leukemia models, both ERR and EAR depend on time since exposure (t), and
27
-------
risk is a linear-quadratic function of dose. As shown in Figure 3-2, the EAR and
ERR per unit dose both increase with dose (the fitted value for d in Eq. 3-9 is
positive).
for t > 5 , and
EAR(d, e, t) = EAR(d, e, 5), for 2 < t < 5 ,
, for 2 < t < 5 , and
(3-9a,b)
EAR(d, e, t) = ERR(d, e, t) = 0 for t < 2.
The dependence of EAR and ERR on age and time-since-exposure is
illustrated in Figure 3-3. Both EAR and ERR decrease with time-since-exposure
for t > 5, and the rate of decrease is larger for younger ages at exposure. For the
time period 2 to 5 y after exposure, the EAR is constant. The EAR that would be
calculated using the ERR model (note that excess absolute risk is equal to the
product of the ERR and the baseline cancer rate) is also constant for this time
period (2 10
o
X
LIJ 6
linear-quadratic
linear component
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Dose(Gy)
Figure 3-2: ERR for leukemia for age-at-exposure = 20 and time-since-exposure
= 10. The linear component of the dose-response is also shown.
28
-------
Males
a:
a:
25
20
15
10
5
0
Females
0
x 10"
20
40
60
80
25
20
15
10
5
0
0
20
40
60
80
x 10
-4
o:
<
LU
20 40 60
Time since exposure
80
20 40 60
Time since exposure
80
Figure 3-3: ERR and EAR by time-since-exposure for three different ages at
exposure: 10 (solid), 20 (dashed), and 30 (dotted).
3.3. Residual Sites and Skin Cancer
BEIR Vll's risk model for what are often termed "residual site" cancers
deserves special mention. The residual category generally includes cancers for
which there were insufficient data from the LSS cohort or other epidemiological
studies to reliably quantify radiogenic site-specific risks. For these sites, results
from the LSS cohort were pooled to obtain stable estimates of risk. With five
exceptions (cancers of the esophagus, bone, kidney, prostate and uterus) the
BEIR VII Report included the same cancers in this category as EPA did in its
previous risk assessment (EPA 1994, 1999).
Esophagus. EPA (1999) employed a separate risk model for esophageal
cancer, whereas in BEIR VII the esophagus is one of the "residual" sites. In part,
this is because the risk models for the previous assessment were based on LSS
mortality data, for which there was a significant dose-response for esophageal
cancer. In contrast, the BEIR VII models are based on LSS incidence data, for
which there was insufficient evidence of a dose-response. Consistent with BEIR
VII, we include esophageal cancer as one of the residual sites. This decision is
expected to have only a minor impact on EPA's risk coefficients for intake of
radionuclides.
29
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Kidney. EPA (1999) uses a separate model for cancer of the kidney, but
BEIR VII includes kidney as one of the residual sites. In contrast to esophageal
cancer, a separate risk model is needed for this cancer site because the kidney
is an important target for several radionuclides, including isotopes of uranium.
There is little direct evidence upon which to base an estimate for kidney cancer
LAR. In a recent analysis of LSS incidence data (Preston et al. 2007), there
were only 115 kidney cancers, 70% of which were renal cell cancers. The
authors estimated only 6 excess renal cell cancers from radiation exposure.
Furthermore, whatever the association might be between kidney cancer and
radiation, it is complicated by the fact that the etiology for the various kidney
cancer types differ. The estimated dose-response in the LSS appears to be
sensitive to the type of model being fit. Within the LSS cohort, no indication of a
positive dose response was found (p > 0.5) when a constant ERR model was fit,
but results were significant when fit to a constant EAR model. Confidence
intervals for linear dose response parameters are wide for both models, and
there is insufficient evidence to conclude that the dose response in LSS is
substantially different for kidney cancers than other residual site cancers. It was
therefore concluded that a reasonable approach would be to use the BEIR VII
residual site ERR model for kidney cancers. For the kidney EAR model, an
adjustment factor was applied, equal to the ratio of the age-specific kidney
cancer baseline rates divided by the rates for the residual site cancers. EPA's
new kidney cancer EAR model is given in Eq. 3-10:
EARkidney (s, C, O) = 1-mmy - EARresidual (s, 6, O) (3-1 0)
Bone. A new EPA model for alpha-particle-induced bone cancer risks is
based on an analysis of data on radium dial painters exposed to 226Ra and 228Ra
and patients injected with the shorter-lived isotope 224Ra (Nekolla et al. 2000).
The risk per Gy for low-LET radiation is assumed to be 1/10 that estimated for
alpha-particle radiation. Details about the EPA bone cancer risk model and its
derivation are provided in Section 5.1.2 (on human data on risks from higher-LET
radiation).
The new risk projections for bone cancer incidence from low-LET radiation
are 2.04x10'4 Gy1 (males), 1.95x10'4 Gy1 (females), and 1.99x10'4 Gy1 (sex-
averaged). About 35% of all bone cancers are fatal, and it is assumed here that
the same lethality holds for radiogenic cases. The mortality risk projections are
7.13x10'5 Gy1 (males), 6.82x10'5 Gy1 (females), and 6.96x10'fe Gy1 (sex-
averaged).
Prostate and Uterus In contrast to EPA (1999), BEIR VII provides
separate risk models for these two cancer sites, and these BEIR VII models form
the basis for new EPA projections. This is in contrast to EPA (1999), in which
30
-------
these two cancer sites were included in the residual category. The A-bomb
survivor data now provides sufficient information for radiogenic uterine cancer to
formulate a risk projection of reasonable precision. BEIR VII cited the vastly
differing baseline rates for the U.S. compared to Japan as a reason for providing
a separate prostate estimate.
Skin. Previously, EPA risk estimates for radiation-induced skin cancer
mortality (EPA 1994) were taken from ICRP Publication 59 (ICRP 1991). The
one modification made by EPA was to apply a DDREF of 2 at low doses and
dose rates. Recognizing that the great majority of nonmelanoma skin cancers
are not life threatening or seriously disfiguring, EPA included only the fatal cases
in its estimates of radiogenic skin cancer incidence. The contribution of skin
cancers to the risk from whole-body irradiation was then minor: about 0.2% and
0.13% of the total mortality and incidence, respectively.
ICRP's calculation of skin cancer incidence risk employed an ERR of 55%
per Sv, along with U.S. baseline skin cancer incidence rates from the 1970's.
The ICRP mortality estimate was also based on conservative assumptions that:
(1) 1/6 of radiogenic skin cancers would be squamous cell carcinomas (SCC),
the remainder basal cell carcinomas (BCC); and (2) essentially all of the BCC
would be curable, whereas about 1% of SCC would be fatal. Predicated on
these considerations, ICRP Publication 59 estimated that 0.2% of the cases
would be fatal.
The ICRP risk estimates closely mirror those previously published by
Shore (1990), who also served as a member of the committee that drafted ICRP
Publication 59. Shore (2001) reviewed the subject again in light of additional
information and concluded that essentially all of the radiation-induced skin
cancers at low to moderate doses would be BCC. He maintained that the fatality
rate for BCC is "virtually nil" but cites a study indicating a rate of 0.05%
Weinstock (1994). Shore also notes that there is no persuasive evidence that
radiation-induced BCC would be more fatal than sporadic cases.
At the same time, there is evidence that the baseline rates for BCC have
increased dramatically since the 1970's, which might also result in a higher
(absolute) risk per unit dose of inducing a radiogenic skin cancer.
For our new skin risk model, we applied results from a recent analysis of
LSS incidence data (Preston et al. 2007). Of these, the most appropriate for our
purposes is the estimated ERR of 48% per Gy (90% Cl 0.12 to 1.3) for BCC
among survivors with doses < 1 Gy. We note that for doses above 1 Gy, the
ERR per Gy was significantly greater: 2.64, Cl = (2.2, 3). The authors found no
evidence for an association between dose and SCC. As for most other cancer
sites, we employ a DDREF of 1.5.
31
-------
For lifetable calculations, baseline incidence rates are needed, but SEER
does not include nonmelanoma skin cancers in its database. BCC incidence
rates have increased dramatically over the last 3 decades (Karagas et al. 1999),
and it has been estimated that there are 900,000 incident cases of BCC annually
in the U.S. (550,000 in men, 350,000 in women), the great majority of these in
whites (Ramsey 2006). The estimated lifetime risk of BCC in the white
population is very high: 33-39% in men and 23-28% in women. Overall, the age-
adjusted incidence per 100,000 white individuals is 475 cases in men and 250
cases in women. To calculate age-specific baseline incidence rates, we applied
these age-adjusted numbers and assumed that the rates increase with age to the
power of 4.5, which is the roughly the pattern observed for many cancers
(Breslow and Day 1987).
The age-adjusted fatality rate has recently been estimated to be 0.08 per
100,000 individuals, based on only 12 BCC deaths in the state of Rhode Island
between 1988 and 2000 (Lewis and Weinstock 2004). The case fatality rate for
BCC can then be roughly estimated to be: 0.08 / 0.5(475+250) « 0.03%, which is
what we used for our mortality projections.
The new risk projections for skin cancer incidence are 1.10x10"1 Gy"1
(males), 6.37x10"2 Gy"1 (females), and 8.67x10"2 Gy"1 (sex-averaged). The
mortality risk projections are 3.31x10"5 Gy"1 (males), 1.91x10"5 Gy"1 (females),
and 2.60x10"5 Gy"1 (sex-averaged).
3.4 Calculating Lifetime Attributable Risk
As in BEIR VII, lifetime attributable risk (LAR) is our primary risk measure.
As discussed in Section 3.2, separate evaluations of LAR were made for most
cancer sites using both an excess absolute risk (EAR) model and an excess
relative risk (ERR) model. For a person exposed to dose (d) at age (e), the
LAR is:
110
LAR(d,e) = \M(d,e,a)-S(a)IS(e)da, (3-11)
e+L
where M(d,e,d) is the excess absolute risk at attained age a from an exposure at
age e., S(a) is the probability of surviving to age a, and L is the latency period (2 y
for leukemia, 5 y for solid cancers). (Note: In Eq. 3-11 and subsequent
equations, dependence of these quantities on gender is to be understood). The
LAR approximates the probability of a premature cancer death from radiation
exposure and can be most easily thought of as weighted sums (over attained
ages a up to 110) of the age specific excess probabilities of radiation-induced
cancer incidence or death, M(d,e,d).
32
-------
For any set of LAR calculations (Eq. 3-11), the quantities M(d,e,a)were
obtained using either an EAR or ERR model. For cancer incidence, these were
calculated using either:
Mj(d,e,d) = EARj(d,e,d) (EAR model) (3-12)
or MI(d,e,d) = ERRI(d,e,d)-AI(d) (ERR model) (3-13)
where A7(a)is the U.S. baseline cancer incidence rate at age a. Datasets used
for the baseline incidence rates are described in Section 3.8.
For mortality, the approach is very similar, but adjustments needed to be
made to the equations since both ERR and EAR models were derived using
incidence data. In BEIR VII, it was assumed that the age-specific ERR is the
same for both incidence and mortality, and the ERR model-based excess risks
were calculated using:
MM (d, e, a) = ERR, (d, e,a)-^ (a) . (3-1 4)
Here, the subscripts M and / denote mortality and incidence. For EAR models,
BEIR VII used essentially the same approach by assuming:
(3-15)
(or)
Note that in Eq. 3-15, the ratio of the age-specific EAR to the incidence rate is
the ERR for incidence that would be derived from the EAR model. Eq. 3-14 was
used for all cancer sites and Eq. 3-15 for all sites except breast cancer. A
description of the approach for estimating breast cancer mortality risk, and its
rationale, is given in Section 3.10.
The LAR for a population is calculated as a weighted average of the age-
at-exposure specific LAR. The weights are proportional to the number of people,
N(e), who would be exposed at age e. The population-averaged LAR is given by:
UO-L
~t 11U —L
LAR(d,pop} = \ N(e)-LAR(d,e)-de. (3-16)
N* {
For the BEIR VII approach, N(e) is the number of people, based on
census data, in the U.S. population at age e for a reference year (1999 in BEIR
VII), and TV* is the total number summed over all ages. In contrast, for our
primary projection, we used a hypothetical stationary population for which N(e) is
proportional to S(e), based on observed 2000 mortality rates. In this case,
33
-------
110-L
J S(e)-LAR(d,e)-de
LAR(d, stationary) = — ^^ . (3-17)
j S(e)de
0
Eq. 3-17 represents the radiogenic risk per person-Gy from a lifetime chronic
exposure. For stationary populations, Eq. 3-17 is equivalent to Eq. 3-16, so it
also represents the (average) radiogenic risk for a stationary population for an
acute exposure. Equation 3-16 is only valid for projecting risks from chronic
exposures if one can assume no appreciable changes in future mortality rates.
3.5 Dose and Dose Rate Adjustment Factor
To project risk at low or chronic doses of low-LET IR, the BEIR VII
Committee recommended the application of a Dose and Dose Rate Effectiveness
Factor (DDREF), as described in Section 2.1.4. Effectively, this assumes that at
high acute doses, the risk is given by a linear-quadratic (LQ) expression, a}D+
a2D2, whereas at low doses and dose rates, the risk is simply a}D.
In the case of leukemia, LSS data shows upward curvature with increasing
dose. The BEIR VII fit to the LQ model yielded a value of 6 = a2/a} = 0.88 Sv"1.
For solid tumors, the upward curvature in the LSS data appears to be
lower and is not statistically significant (i.e., 0 is not significantly different from 0).
While BEIR VII did not explicitly recommend a LQ model for solid cancer risk, it
nevertheless concluded that some reduction in risk at low doses and dose rates
was warranted. It adopted a Bayesian approach, developing separate estimates
of the DDREF from radiobiological data and a statistical analysis of the LSS data.
The estimate for the DDREF obtained in this way was 1.5, somewhat lower than
values that had been commonly cited in the past. The BEIR VII Report notes
that the discrepancy can largely be attributed to the fact that the DDREF is
dependent on the reference acute dose from which one is extrapolating.
According to BEIR VII, the appropriate dose should be about 1 Sv because data
centered at about this value drives the LSS analysis. In contrast, much of the
radiobiological data refers to effects observed at somewhat higher doses, for
which the DDREF would be higher. Assuming that the extrapolation is indeed
from an acute dose of 1 Sv, the DDREF of 1.5 corresponds to a LQ model in
which 0 = 0.5 Sv1.
3.6 EAR and ERR LAR Projections for Cancer Incidence
EAR and ERR model-based LAR projections for a stationary population
based on 2000 mortality data are given in bold typeface in Table 3-4. These
are compared to EAR and ERR projections based on census data, with weights
34
-------
proportional to the number of people of each age in the year 2000. The results
indicate that our primary risk projections are about 5-10% lower than they would
be if based on a census population. Results in Table 3-4 reflect the DDREF
adjustment of 1.5 for all cancer sites except leukemia.
35
-------
Table 3-4: EAR and ERR model projections of LAR1 for a stationary
population derived from 2000 decennial lifetables (Arias 2008) or a
population based on 2000 census data (NCHS 2004)
Risk Model
Population Weighting
Cancer site
Stomach
Colon
Liver
Lung
Breast
Prostate
Uterus
Ovary
Bladder
Thyroid
Residual
Kidney
Bone
Leukemia
Skin
Sex
M
F
M
F
M
F
M
F
F
M
F
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
ERR Projection
Stationary Census
15
20
160
104
17
7
154
482
Not used
125
11
34
107
105
22
110
229
252
26
24
2
2
109
87
1100
637
16
21
171
110
19
8
165
517
Not used
135
12
37
114
111
24
120
250
272
28
26
2
2
109
88
1150
663
EAR Projection
Stationary Census
171
204
112
67
92
53
120
233
281
4
50
29
75
63
No model
No model
191
181
26
19
2
2
53
32
No model
No model
184
217
120
71
98
56
126
244
308
4
53
31
79
66
No model
No model
205
193
28
20
2
2
57
34
No model
No model
1 Number of cases per 10,000 person-Gy.
36
-------
3.7 ERR and EAR Projections for Cancer Mortality
We adopt the BEIR VII approach for ERR and EAR projections of LAR for
mortality for all cancer sites except breast cancer. As noted previously, for its
ERR model-based projection, BEIR VII used:
MM (d, e, a) = ERR, (d, e,d)-^ (a) , (3-14)
and for its EAR based projections,
~ EARj(d,e,d)
MM(d,e,d) =
(3-15)
In Eq. 3-15, the ratio in square brackets is equal to the ERR for incidence
that would be calculated using the EAR model. In both Eq. 3-14 and 3-15, the
BEIR VII approach assumes that the ERR for incidence and mortality are equal.
However, this ignores the "lag" between incidence and mortality, which could
lead to bias in the estimate of mortality risk in at least two different ways.
First, there would be a corresponding lag between the ERR for incidence
and mortality, which might result in an underestimate of mortality risk. For
purposes of illustration, suppose that (a) a particular cancer is either cured
without any potential life-shortening effects or results in death exactly 10 y after
diagnosis and (b) survival does not depend on whether or not it was radiation-
induced. Then,
ERRM (e, a) = ERR, (e,a-10)> ERR, (e, a).
The relationship would also hold for the EAR if the baseline cancer rate has the
same age-dependence for A-bomb survivors as for the U.S. population.
Second, since current cancer deaths often occur because of cancers that
developed years ago, application of the EAR-based ERR for incidence can result
in a substantial bias due to birth cohort effects. If age-specific incidence rates
increase (decrease) over time, the denominator in Eq. 3-15 would be too large
(small). This could result in an underestimate (overestimate) of the LAR.
The BEIR VII approach is reasonable for most cancers, because the time
between diagnosis and a resulting cancer death is typically short. An exception
is breast cancer, for which our approach is presented in Section 3.10.
Results of LAR calculations using the BEIR VII approach are given in
Table 3-5. Although not shown, LAR for mortality tends to be about 5% larger
for census-based weights than for weights based on a stationary population.
37
-------
Mortality and incidence data used for the calculations are described in the next
section.
Table 3-5: Age-averaged LAR1 for solid cancer mortality based on
a stationary population (Arias 2008). Except for skin and bone
cancers, projections are based on BEIR VII risk models.
Cancer Site
Stomach
Colon
Liver
Lung
Breast
Prostate
Uterus
Ovary
Bladder
Thyroid
Residual
Kidney
Bone
Leukemia
Skin
Sex
M
F
M
F
M
F
M
F
F
M
F
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
Risk Model
ERR
8
11
75
46
13
7
142
385
Not used
20
2
22
21
28
3
8
94
106
8
7
No model
No model
80
64
0.3
0.2
EAR
87
111
52
30
75
47
113
199
1212
0.8
15
22
19
22
No model
No model
104
105
10
7
0.7
0.7
32
20
Not used
Not used
Cases per 10,000 person-Gy
See Section 3.10
38
-------
3.8 U.S. Baseline and Census Data
Cancer specific incidence and mortality rates are based on the
Surveillance, Epidemiology, and End Results (SEER) program of the National
Cancer Institute (NCI). Begun in the early 1970s, SEER collects data from
several, mostly statewide and metropolitan, cancer registries within the U.S.
Rates for this report are calculated using SEER-Stat and the 1975-2005 SEER
public-use data (SEER 2007a,b) available from the SEER website
(http://seer.cancer.gov). The dataset is structured to represent two notable
expansions in the SEER program: from 9 registries to 13 registries (SEER 13) in
the early 1990's and most recently to 17 registries (SEER 17). For this report,
incidence rates are averages of SEER 13 data for the years 1998-2000 and
SEER 17 data for the years 2000-2002. This contrasts with BEIR VII, which
used (a previous version) of public-use SEER 13 data for the years 1995-99.
SEER regularly revises its statistics on baseline rates, and the baseline
rates used for our final risk assessment will likely be based on SEER statistics for
the year 2000 that are not yet available. For example, it is anticipated that the
denominator (person years at risk) for future versions of the SEER cancer data
will be derived using 2000 decennial census results.
SEER areas currently comprise about 26% of the U.S. population and are
not a random sample of areas within the U.S. Nevertheless the cancer rates
observed in the combined SEER areas are thought to be reasonably similar to
rates for the U.S. population.
Finally, 2000 decennial lifetables (Arias 2008) were used instead of 1999
tables as in BEIR VII. Baseline lifetime risk estimates of cancer incidence and
mortality for a stationary population based on these data are given in Table 3-6.
39
-------
Table 3-6: Baseline lifetime risk estimates of cancer incidence
and mortality1
Cancer
Site
Stomach
Colon
Liver
Lung
Breast
Prostate
Uterus
Ovary
Bladder
Thyroid
Residual
Kidney
Solid
Leukemia
Skin
All
Male
1160
4220
852
7910
0
17100
0
0
3560
323
11500
1550
48200
974
(38300)2
49200
Incidence
Female
731
4360
416
6080
13800
0
3350
1500
1150
909
8580
916
41800
752
(22700)2
42600
Male
600
2000
634
7340
0
2980
0
0
738
43
6060
576
21000
732
12
21700
Mortality
Female
406
1970
368
4900
2990
0
772
1040
319
60
4760
340
17900
568
7
18500
1
Estimated cancer cases or deaths in population of 100,000
Not included in all
3.9 Combining Results from ERR and EAR Models
3.9.1 BEIR VII Approach. BEIR VII calculates LAR values separately
based on preferred EAR and ERR models and then combines results using a
weighted geometric mean. More specifically,
LAR(B7) =
(3-16)
with weight (w*) - based on results from the ERR model - depending on cancer
site. If the weight (w*) equals 0.5, a simple GM would be calculated. Instead for
most cancer sites, BEIR VII recommended a weight (w*) equal to 0.7 - placing
somewhat more emphasis on results from ERR models. (A notable exception is
lung cancer, for which the EAR model is given more weight, reflecting near
additivity between smoking and gamma radiation in the A-bomb survivor data.)
A problem with the BEIR VII method for averaging the EAR and ERR
projections is that the GM is not additive in the sense that the GM of two risk
projections for the combined effect of separate exposures is generally not equal
to the sum of the GM projections for the exposures. We circumvent this problem
by first calculating the weighted GM of the EAR for the two projection models, for
40
-------
each age at exposure and attained age. Then, results can be integrated to
obtain the risk from chronic lifetime exposure.
3.92 EPA Approach. We calculate the combined age-specific risk (at
high dose rates) according to:
M(-EPA\d,e,d) = [Mw(d,e,a)Y*[M™(d,e,aJ\1-*', (3-17)
with the LAR at exposure age e calculated as before:
110
LAR(d, e} = J M(EPA} (d, e, a) • S(a) I S(e}da. (3-18)
e+L
In Eq. 3-17, Jvt~A} and Jvt~R} represent the age-specific EAR derived from the EAR
and ERR models, respectively; e.g. for incidence: M(IA\d,e,a) = EARI(e,a)d, and
M(IR\d,e,a) = ERRI(e,a)d-hI(a}. The difference from the BEIR VII approach is
that the risk models are combined before integrating the expression in Eq. 3-18
to obtain the LAR.
Results from the two methods of combining results from EAR and ERR
models, BEIR VII and the EPA approach, are compared for selected sites in
Table 3-7. Of the two methods, the BEIR VII approach yields larger LAR
projections for cancer incidence. However, for all sites except for those in the
residual category, results from the two methods differ by less than 10%. For all
sites combined other than skin cancer, the difference is 5% for males, and 3% for
females.
41
-------
Table 3-7: EPA and BEIR VII Methods for Combining EAR and ERR LAR
incidence projections1 for selected sites
Cancer Site
Stomach
Colon
Lung
Residual
Leukemia
Total3
ERR EAR EPA BEIR VII
Projection Projection Projection Approach2 Ratio:
Sex (A) (B) (C) (D) D/C
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
15
20
160
104
154
482
228
252
109
87
171
204
112
67
120
233
191
181
53
32
31
40
142
90
125
272
194
201
81
60
785
1230
31
40
144
91
129
290
216
228
88
64
826
1280
1.00
1.00
1.01
1.01
1.03
1.07
1.11
1.14
1.08
1.06
1.05
1.04
1 Cases per 10,000 person-Gy.
2 Weighted geometric mean of the ERR and EAR projections
3 Sum of projections for all cancer sites. Excludes non-fatal skin cancers.
42
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3.9.3 Should Risk Models Be Combined Using a Weighted GM?
EAR and ERR model excess rates were combined here using a weighted
GM. An alternative approach would be to use a weighted arithmetic mean.
Under the arithmetic mean approach, the (nominal) combined age specific risk
projections are calculated using:
M(Antk} ^ e^ = w* [M w ^ e^ a)j + (l_ W*)[A/W (d, e, a)] . (3-1 9)
(In subsequent equations, the notation (d,e,d) is dropped). This approach would
be appropriate, if for example, the age-specific excess risks for the U.S. can be
approximated as a weighted arithmetic average of the relative risk and absolute
risk models, a subjective probability distribution might be assigned to the weight
(w), and the expected value of the probability distribution is the BEIR VII nominal
value (E[w] = w*). The remainder of this section describes how subjective
probability distributions might be assigned, and compares our results to what the
results would have been using the weighted arithmetic mean approach.
Let us assume there is an (unknown) parameter (w), such that the (true)
excess riskA/fr"e) in the U.S. population is given by:
) = w J) + (1 - w) J) . (3-20)
It follows from Eq. 3-20 that:
and if, 0
-------
say, some type of average of the two risk models, then other choices, such as a
trapezoidal distribution, Tr(a,b,c,d), might be more appropriate (see Figure 3-4).
Uniform
1.5
0.5
1.5
Trapezoidal
0.5
0.5
weight parameter
0.5
weight parameter
Figure 3-4: Examples of uniform, U(0,1) and trapezoidal distributions, Tr(0,
0.25, .75, 1.0), which might be used for the risk transport weight parameter.
Probabilities for the weight parameter are equal to areas under the curve.
A fundamental problem with assigning subjective distributions is that there
is very rarely a unique subjective distribution which best describes what might be
agreed upon for a parameter. In particular, there is no unique way to define what
is meant by statements such as "any value in the interval (A/A\ A/R)) is equally
likely". For example, if h^A) < A^R, two possibilities are:
a) M(tme) is uniformly distributed:
b) \og(A/^true)) is uniformly distributed:
log(A/fr"e)) ~
(3-22)
(3-23)
In the latter case, one might re-parameterize Eq. 3-20 as:
M(true} = exp[M/los) log(M(fl) ) + (1 - M/IOS) ) log(M
(3-24)
For this parameterization, note that:
w:
(3-25)
To illustrate the difference between the two parameterizations, assume
that for a hypothetical site A/^ = 20, A/70 = 80, and t^tme} = 40. From Eq. 3-21, w
44
-------
= 1/3, whereas from Eq. 3-25, w(log)= 0.5. The interpretation is that on the original
(non-transformed scale, the risk (M(tme)) lies one-third of the way between the
EAR and ERR model risk projections, whereas on the logarithmic scale, A^true) is
halfway between the two "extremes".
The arithmetic mean approach for combining results from the two
projection methods (Eq. 3-19) is most easily understood using the
parameterization in Eq. 3-20. Note that for any subjective probability distribution
for the parameter w,
It follows that, under this parameterization, if the nominal weight parameter is
equal to its expected value, E[w], the resulting arithmetic mean is unbiased with
respect to the subjective distribution assigned to w. However, this argument
does not work for the parameterization in Eq. 3-24.
It is somewhat more difficult to make the same type of argument for the
GM approach used in BEIR VII. Even if one accepts the parameterization given
in Eq. 3-24 and assigns a subjective distribution to w(1°8), it can be easily shown
that:
E\M(tme}\ > exp[E(M/log))log(M(fl)) + (1 -E(w0og)))log(.Mw)] (3-27)
Thus, if the nominal weights are unbiased "estimates" of the parameters (w(108)),
the weighted GM approach will result in projections that tend to underestimate
risks to the U.S. population. On the other hand, the weighted GM approach
would result in very reasonable projections with respect to subjective distributions
for M/IOS), for which probabilities are concentrated around the nominal (BEIR VII)
weights (w*). With respect to some of these distributions, the weighted
arithmetic mean can result in substantial bias.
In general, the weighted arithmetic mean approach (Eq. 3-19) will always
result in larger LAR projections than our approach based on the GM. However,
as can be seen in Table 3-8, the difference is substantial only for sites such as
stomach, liver, prostate, and uterine cancers, for which the LAR projection is
sensitive to the model type (ERR vs. EAR). For all cancers combined (excluding
non-fatal skin cancers), use of the weighted arithmetic mean would result in a
LAR projection about 1 1 % (females) or 17% (males) greater than our projection.
45
-------
Table 3-8: Comparison of EPA and weighted arithmetic mean method for
combining EAR and ERR LAR projections for incidence1
Cancer Site
Stomach
Colon
Liver
Lung
Prostate
Uterus
Ovary
Bladder
Residual
Kidney
Leukemia
Total
(excluding skin)
Sex
M
F
M
F
M
F
M
F
M
F
F
M
F
M
F
M
F
M
F
M
F
ERR
Projection
(A)
15
20
160
104
17
7
154
482
125
11
34
107
105
228
252
26
24
109
87
EAR
Projection
(B)
171
204
112
67
92
53
120
233
4
50
29
75
63
191
181
26
19
53
32
EPA
Projection
(C)
31
40
142
90
28
13
125
272
42
17
32
94
87
194
201
24
20
81
60
785
1230
Weighted
Arithmetic
Mean of
A and B:
(D)
62
75
146
93
40
21
130
308
89
23
33
97
93
217
231
26
22
92
70
921
1361
Ratio:
DIG
2.01
1.90
1.03
1.03
1.45
1.60
1.04
1.13
2.10
1.36
1.03
1.04
1.06
1.12
1.15
1.05
1.10
1.13
1.16
1.17
1.11
1 Cases per 10,000 person-Gy.
It is unclear which of the two commonly used projection methods would be
more appropriate. The weighted GM (BEIR VII) approach yields projections
which might be substantially biased with respect to "preferred" subjective
probability distribution for either w(l°s) or w for sites such as stomach cancer, yet it
is difficult to ascertain how large the bias might be. A crude indication is given by
the results in Table 3-8, which suggest that the absolute relative bias of the
weighted GM might be as great as 50% for stomach cancer, but much smaller for
most other sites. The arithmetic mean approach might also be biased,
depending on what type of subjective probability distribution might be
appropriate: e.g., whether the distribution is defined with respect to w(l°B} orw.
46
-------
There is no obvious scientific basis for choosing an appropriate
parameterization and/or a distribution for the weight parameter. For example,
BEIR VII applied the Moolgavkar-Knudson two-stage clonal expansion model to
argue that, for many types of cancer, a transportation model should place greater
"weight" on the ERR model over one based on absolute risk. However, BEIR VII
did not provide any rationale for the type of parameterization to be used or any
explicit guidance as to how the weight parameter might be defined or interpreted.
3.10 Calculating Radiogenic Breast Cancer Mortality Risk
This section details our method for calculating radiogenic breast cancer
mortality risk and compares results with calculations based on the BEIR VII
method.
Let Mj(d,e,aj} denote the EAR for incidence at attained age a} from an
exposure at age e. The density function for a radiogenic cancer at a: would be:
fdje (a, )=M(d, e, a1)S(a1}l S(e) . (3-28)
For the cancer to result in a death at age aM >a}, the patient would have
to survive the interval (a/5aM), and then die from the cancer at age a:. This and
the concept of the relative survival rate form the basis for the method. The
relative survival rate for a breast cancer patient would be the ratio of the survival
rate for the patient divided by the expected survival rate (without breast cancer).
Assume the relative survival depends only on the length of the time interval and
and the age of diagnosis. Let t = aM-a} andR(t,a}) be the relative survival
function. Then the probability of survival with breast cancer for the interval
(aj,aM) is:
S(a)IS(aI}R(t,aI). (3-29)
Suppose the breast cancer mortality rate (h) among those with breast
cancer depends on the age of diagnosis, but does not depend on other factors
such as a) whether the cancer is radiogenic, or b) attained age. Then the density
function for age of a radiogenic breast cancer death can be shown to equal:
aM
fd.e (aM) = j h(ai Wi (d, e, a:) S(d) I S(aI )R(t, a:) da1. (3-30)
e+L
The LAR for breast cancer mortality for an exposure at age e is:
47
-------
110
LAR(d,e} = \fd,(aM}daM, (3-31)
e+L
and Eq. 3-17 is applied as before to calculate the LAR for the U.S. population.
110-L
J S(e}-LAR(d,e)-de
LAR(d, stationary} = -2 — (3-17)
J
For these calculations, we used the 5-y relative survival rates given in
Table 3-9 (Ries and Eisner, 2003) and assumed that breast cancer mortality
rates (for those with breast cancer) depend only on age at diagnosis and are
equal to:
h(ai) = -(0.2)108^(5,^) (3-32)
It should be noted that results from several studies indicate that, for most stages,
breast cancer mortality rates are not highly dependent on time since diagnosis -
at least for the first 10 years (Bland etal. 1998, Cronin etal., 2003).
Based on the method just outlined, the LAR for breast cancer mortality is
1.21x10"2 Gy"1. This is about 30% larger than what would be calculated using the
methods in BEIR VII (see Section 3.7).
Much of the discrepancy between the two sets of results seems to be a
consequence of observed increases in breast cancer incidence rates and
declines in mortality rates. From 1980 to 2000, age-averaged breast cancer
incidence rates (per 100,000 women) increased by about 35% (102.2 to 136.0),
whereas the mortality rates declined by about 15% (31.7 to 26.6), (Ries, et al.
2008).
48
-------
Table 3-9: Female breast cancer cases and 5-y relative
survival rates by age for 12 SEER areas, 1988-2001,
adapted from Table 13.2 in Ries and Eisner (2003)
5-y RSR
Age (y) Cases
20-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
Total
6,802
12,827
24,914
33,784
34,868
32,701
32,680
34,435
32,686
27,134
17,475
12,457
302,763
77.8
83.5
88.0
89.5
89.5
89.6
90.1
91.0
91.8
91.4
90.7
86.6
89.3
To understand the effect these trends in incidence and mortality have on
the BEIR VII LAR projection for mortality, recall the BEIR VII formula:
M(d,e,a)=
(or)
The underlying assumptions are that a) the absolute risk of radiogenic cancer
death from an exposure at age e is equal to the absolute risk of a radiation-
induced cancer multiplied by a lethality ratio (that depends on attained age), and
b) lethality ratios can be approximated by current mortality to incidence rate
ratios. However, since the time between breast cancer diagnosis and death is
relatively long, lethality rates might be better approximated by comparing current
mortality rates to incidence rates observed for (much) earlier time periods. If, as
data indicate, current incidence rates are considerably higher than past incidence
rates, the BEIR VII denominator is too large, and the estimated lethality ratio is
too small. This would result in a downward bias in the BEIR VII projection for
mortality.
Our projection has limitations which must be noted. First, its validity
depends on the extent to which estimates of relative survival functions can be
used to approximate mortality rates from breast cancer for people with breast
cancer. Long-term survival rates for breast cancer patients are desirable for
constructing valid estimates for this approach, but since these survival rates can
49
-------
change rapidly, there is considerable uncertainty for extrapolation of rates for
periods beyond 5 to 10 y. Finally, reduced expected survival among breast
cancer patients may be partly attributable to causes other than breast cancer.
For example, if some breast cancers are smoking-related, breast cancer patients
as a group may be at greater risk of dying from lung cancer.
3.11 LAR by Age at Exposure
Sex-averaged LAR for incidence and mortality by age-at-exposure are
plotted in Figures 3-6 and 3-7 for selected cancer sites. More specifically, for
both males and females, LAR are calculated as described in Section 3.9
according to:
110
LAR(d, e) = J M(EPA} (d, e, a) • S(a) I S(e)da, (3-18)
e+L
where
M(EPA} (d, e, a) = [M(A) (d, e, a)f* [M(R'> (d, e, a)]1""*, (3-17)
and sex-averaged LAR were calculated using Eq. 3-33:
1.048^ (e^LAR^ (d, e) + SFEMALE (e)LARFEMALE(d,e)
LARAVG(d,e) = -
(3-33)
Figures 3-6 and 3-7 show that, for most cancer sites, the probability of premature
cancer (or cancer death) attributable to an acute exposure decreases with age-
at-exposure. The notable exception is leukemia mortality, for which the
projected LAR increases slightly from birth to about age 60.
For most cancers, the decrease in LAR with age-at-exposure is assumed
to be similar to the pattern shown for colon, lung, and bladder cancers: the LAR
decreases by a factor of about 2 or more from birth to age 30; it then levels off
until about age 50 and then gradually decreases towards 0. During the first 30 y,
the decrease in LAR is almost entirely attributable to the exponential decline in
modeled age-specific ERR and EAR (in the risk models y < -0.3), whereas the
decrease in LAR after age 50 is largely attributable to competing risks - as
people age, they have an ever-decreasing chance of living long enough to
contract a radiation-induced cancer. For breast and thyroid cancers, the
modeled age-specific ERR or EAR continue to decrease after age 30, and the
LARs do not level off after age 30. In general, the LAR decreases more rapidly
for breast, bone, thyroid, and residual cancers than for other sites.
50
-------
Colon Cancer
Lung Cancer
(9 0.02
o>
Q.
<= 0.01
0
(
\
\
-\;
3 20 40 60 80
(^ 0.04
o>
Q.
< 0.02
0
(
\
\
\
V~^\"
3 20 40 60 80
age at exposure age at exposure
0.03
0 0.02
8.
<= 0.01
0
(
Bladder Cancer
\
V \
3 20 40 60 80
0.08
>, °-06
CD
8. 0.04
a:
^ 0.02
0
(
Residual Cancers
\
\
\
3 20 40 60 80
age at exposure age at exposure
0.2
>, °-15
CD
8. 0.1
a:
^ 0.05
0
(
Breast Cancer
\
3 20 40 60 80
0.06
0 0.04
8.
§ 0.02
0
(
Thyroid Cancer
\
\
\,
\
3 20 40 60 80
age at exposure age at exposure
1
CD
8. 0.5
QL
n
x -jQ"3 Bone Cancer
\
\
\
\
\
\
\
0.02
>, 0.015
CD
8. 0.01
QL
^ 0.005
n
Leukemia
\
\
\
V___
"^X
20 40 60 80
age at exposure
20 40 60 80
age at exposure
Figure 3-5: Sex-averaged LAR for incidence by age at exposure for selected cancers.
A DDREF of 1.5 is used for all solid cancer sites.
51
-------
Colon Cancer
Lung Cancer
0 0.01
0)
Q.
<= 0.005
0
\
\
-\
0 20 40 60 80
0 0.04
0)
Q.
^ 0.02
0
\
• V^
^^-^
0 20 40 60 80
age at exposure age at exposure
6
0 4
8.
5'
0
x 103 Bladder Cancer
\
\
\^
~^\
0 20 40 60 80
0.03
0 0.02
8.
^ 0.01
0
Residual Cancers
\
\
\
V—\._
0 20 40 60 80
age at exposure age at exposure
0.03,
0 0.02
8.
<= 0.01
0
c
Breast Cancer
\
\
\
^~~---_____^
20 40 60 80
)
4r
>, 3
CD
8. 2
a:
^ 1
0
C
< 10 3 Thyroid Cancer
\
\
\
\
\
20 40 60 80
age at exposure age at exposure
4r
>, 3
CD
8. 2
QL
-" 1
n
( -jQ"4 Bone Cancer
\
\
\
^--^_^^
6r
CD^ 4
8.
< 2
n
( -jQ"3 Leukemia
^ x
\ -
\
\
\
20 40 60 80
age at exposure
20 40 60 80
age at exposure
Figure 3-6: Sex-averaged LAR for mortality by age at exposure for selected cancers.
A DDREF of 1.5 is used for all solid cancer sites.
52
-------
Radiogenic risks for childhood exposures are often of special interest. As
shown in Figures 3-5 and 3-6, the LAR per unit dose is substantially larger for
exposures during childhood (here defined as the time period ending at the 15th
birthday) than later on in life. In addition, doses received from ingestion or
inhalation are often larger for children than adults. Table 3-10 shows the
contribution to the LAR for cancer incidence for exposures before age 15 and
compares it to LAR for the entire population (all ages). For uniform, whole body
radiation, about 785 radiogenic cancers are expected to occur among U.S. males
from a cumulative radiation dose of 10,000 person-Gy. About 313 of the 785
cancers (about 40% of the radiogenic cancers) would occur in males exposed
before age 15. For females, about 565 of 1230 cancers (about 45%) would
occur among those exposed before age 15. An estimated 145 of 406 cancer
deaths (males) and 256 of 628 cancer deaths (females) would be attributable to
childhood exposures.
Table 3-10: LAR for cancer incidence1 for exposures to a stationary U.S.
population
LAR
Contribution from exposures for
ages <15
Cancer site
Males
Females
1 Cases per 10,000 person-Gy.
2 Excludes non-fatal skin cancers.
Males
Females
Stomach
Colon
Liver
Lung
Breast
Prostate
Uterus
Ovary
Bladder
Thyroid
Residual
Kidney
Bone
Solid2
Leukemia
Skin
Total2
31
142
28
125
0
42
0
0
94
22
194
24
2
703
81
1100
785
40
90
13
272
281
0
17
32
87
110
201
20
2
1170
60
637
1230
12
54
11
48
0
16
0
0
34
16
89
10
1
289
24
248
313
15
33
5
102
160
0
7
13
31
83
89
8
1
547
18
137
565
53
-------
3.12 Summary of Main Results
New EPA LAR projections for incidence are given in Table 3-11. The
table also provides 90% uncertainty intervals for the LAR, and - for purposes of
comparison - the EPA projections in the current version of FGR 13 (EPA 1999).
These intervals were calculated using Bayesian methods, which involved a
somewhat complex (Markov Chain) Monte Carlo method for generating site-
specific LAR values. This approach allowed for the quantification of uncertainties
associated with sources such as: 1) sampling variability, 2) transport of risk
estimates from the Japanese A-bomb survivor population, 3) uncertainty
associated with the DDREF, and 4) dosimetry errors.
Table 3-11: LAR projections for incidence1
New EPA
Cancer Site
Stomach
Colon
Liver
Lung
Breast
Prostate
Uterus
Ovary
Bladder
Thyroid
Residual
Kidney
Esophagus
Bone
Solid3
Leukemia
Skin
Total3
LAR
31
142
28
125
42
94
22
194
24
None
2
703
81
(1100)3
785
Males
90% Ul
(9, 280)
(52, 300)
(9, 150)
(47,310)
None
(0, 520)
(18,220)
(6, 73)
(100,600)2
None
(420, 1910)
(32, 200)
(510,2000)
FGR
13(1999)
Females
LAR
40
90
13
272
281
17
32
87
110
201
20
2
1170
60
(637)3
1230
90% Ul
(1 1 , 300)
(35, 230)
(5, 120)
(120,710)
(160,490)
(0, 320)
(12,110)
(14, 160)
(32, 370)
(120,670)2
None
(770, 2760)
(24, 150)
(830, 2830)
Males
36.1
152
19.4
81.2
None
None
65.5
20.5
191
9.9
7.7
1.3
586
65.4
651
Females
54.0
225
12.3
126
198
None
41.7
30.4
43.8
229
6.0
16.8
1.4
983
47.5
1030
1 Cases per 10,000 person-Gy.
2 Interval for residual and kidney cancer cases. Residual includes esophageal cancers.
3 Excludes non-fatal skin cancers
54
-------
For most of the cancer sites, BEIR VII derived parameter estimates for
ERR and EAR models based on a statistical analysis of LSS data that was cross-
classified by city, sex, dose, and intervals based on age-at-exposure, attained
age, and follow-up time. Sampling variability refers to the uncertainty in
parameter estimates associated with the variation in the observed numbers of
cancer cases or deaths within each of these subgroups. In contrast to BEIR VII,
our uncertainty analysis at least partially accounted for the sampling variability
associated with risk model parameters for age-at-exposure and attained age.
Transport of risk estimate uncertainty refers of uncertainty associated with how to
apply the results from the analysis of the Japanese LSS cohort data to the U.S.
The ratio of LAR projections based on the EAR model divided by the projection
based on the ERR model is a crude indicator of the magnitude of this uncertainty.
It follows that "transport" uncertainty is greatest for sites such as stomach and
prostate cancer, for which Japanese and U.S. baseline rates are vastly different.
A dominant source of uncertainty for all cancers combined is the uncertainty
associated with the DDREF. This includes some of the uncertainty associated
with the shape of the dose-response function at very low doses. As discussed in
Section 4, it does not incorporate uncertainty associated with the validity of the
assumption that the linear portion of the dose-response function fitted to the LSS
data can be equated to the response that would be observed at lower doses or
for chronic exposures. Additional sources of uncertainty, including what is often
called model uncertainty, were incorporated by multiplying the randomly
generated site-specific LAR values by a random (lognormal) variable. More
details are provided in Section 4.
The new EPA risk projection is 785 cancer cases per 10,000 person-Gy
for males, and 1230 cancer cases for females. The 90% uncertainty intervals
suggest these projections are accurate to within a factor of about 2 or 3.
Uncertainties, as measured by the ratio of the upper to lower uncertainty bounds,
are greatest for stomach, prostate, uterine, bladder, liver, and thyroid cancers.
For most cancer sites, the new EPA risk projections for incidence are not
very different from the risk projections in the current version of FGR 13. Cancer
sites for which the relative change from the projected LAR in FGR 13 is greatest
include: female colon (|), female lung (|), female bladder (|), female thyroid (|),
and kidney (|). For both males and females, the LAR for all cancers combined
increased by about 20%. The overall increase in LAR is not due to changes in
the basic risk models, which in many cases would yield smaller LAR projections
than the FGR 13 models if they were applied to comparable mortality and
incidence data. Instead the increase is largely attributable to the use of the more
recent SEER incidence data as a primary basis for the calculating baseline
incidence rates. For FGR 13, probabilities for radiation-induced incidence were
derived by applying the inverse of cancer-specific lethality fractions to excess
mortality rates. The lifetime probability of getting cancer calculated from the
more recent SEER rates is about 40% larger for males and 55% for females than
55
-------
rates implicit in the FGR 13 calculations. The increase in LAR is also due to a
reduction in the nominal DDREF for most cancer sites from 2 to 1.5.
Table 3-12 gives the LAR projections for mortality. The largest relative
changes in LAR compared to the projections in FGR 13 were for stomach (|),
female colon (|), female lung(t), female thyroid (|), and female kidney (|)
cancers. In general, the projections were remarkably consistent; e.g., the LAR for
all sites combined decreased by about 15% (males) and 10% (females).
Table 3-12: LAR projections for mortality1
Cancer Site
Stomach
Colon
Liver
Lung
Breast
Prostate
Uterus
Ovary
Bladder
Thyroid
Residual
Kidney
Esophagus
Bone
Solid
Leukemia
Skin
Total
New
Males
16
66
21
117
None
8
20
3
91
8
None
0.7
350
56
0.3
406
EPA
Females
22
40
12
227
121
4
22
25
8
98
7
None
0.7
585
42
0.2
628
Males
32.5
83.8
18.4
77.1
None
None
32.8
2.1
135
6.4
7.3
0.9
397
64.8
0.9
462
FGR 13
Females
48.6
124
11.7
119
99.0
None
29.2
15.2
4.4
163
3.9
15.9
1.0
636
47.1
1.0
683
1 Deaths per 10,000 person-Gy
Table 3-13 summarizes the sex-averaged LAR projections for cancer
incidence and mortality. Finally, Table 3-14 compares the new EPA LAR
projections with projections in BEIR VII. For all cancers combined, the EPA
projections are 12% less than the projections in BEIR VII for incidence, 14% less
for mortality in males, and 5% less for mortality in females. The difference is
56
-------
primarily attributable to 1) our use of a stationary population (see Section 3.6)
and 2) the age-specific method of combining projections from the ERR and EAR
models (see Section 3.9). The decrease in the mortality projection for all female
cancer sites is only about 5% because of the larger EPA LAR projection for
breast cancer mortality.
Table 3-13: Sex-averaged LAR projections for incidence and mortality1
Incidence
Cancer Site
Stomach
Colon
Liver
Lung
Breast2
Prostate2
Uterus2
Ovary2
Bladder
Thyroid
Residual
Kidney
Bone
Solid
Leukemia
Skin
Total3
Projection
35
116
20
199
142
21
9
16
90
66
197
22
2
936
71
867
1010
90% Ul
(1 1 , 290)
(50, 250)
(8, 130)
(93, 490)
(80, 250)
(0, 250)
(0, 160)
(6, 54)
(21, 180)
(25, 200)
(130,610)
(620, 2270)
(39, 150)
—
(700, 2360)
Mortality
Projection
19
53
16
172
61
4
2
11
23
5
94
8
1
468
49
0.3
518
90% Ul
(6, 150)
(22, 110)
(6, 100)
(78, 430)
(34, 110)
(0, 45)
(0, 37)
(4, 38)
(5, 44)
(2,16)
(57, 280)
(290, 1160)
(27, 100)
—
(350, 1220)
1 Deaths per 10,000 person-Gy.
2 Sex-averaged results for these cancers are about one-half as large as in Tables 3-11,3-12.
2 Excludes nonfatal skin cancers
57
-------
Table 3-14: Comparison of EPA and BEIR VII LAR calculations
Site
Stomach
Colon
Liver
Lung
Breast
Prostate
Uterus
Ovary
Bladder
Thyroid
Residual
Kidney
Bone
Solid
cancers
Leukemia
Total
Sex
M
F
M
F
M
F
M
F
F
M
F
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
EPA
31
40
142
90
28
13
125
272
281
42
17
32
94
87
22
110
194
201
24
20
2
2
703
1170
81
60
785
1230
Incidence1
BEIR VII
34
43
160
96
27
12
140
300
310
44
20
40
98
94
21
100
290
290
None
None
None
None
800
1310
100
72
900
1382
EPA
16
22
66
40
21
12
117
227
121
8
4
22
20
25
3
8
91
98
8
7
1
1
350
585
56
42
406
628
Mortality1
BEIR VII
19
25
76
46
20
11
140
270
73
9
5
24
22
28
None
None
120
132
None
None
None
None
410
610
69
52
479
662
1 Cases or deaths per 10,000 person-Gy.
58
-------
4. Uncertainties in Projections of LAR for Low-LET Radiation
4.1 Introduction
This chapter describes a quantitative uncertainty analysis for the LAR
projections given in Section 3. As described elsewhere (e.g., Sinclair 1993, EPA
1999), the uncertainty for each site-specific risk estimate can be treated
mathematically as the product of several independent sources of uncertainty. A
novel feature of this uncertainty analysis is a Bayesian analysis of site-specific
risks using the LSS incidence data which - after application of the life-table
calculations described in Chapter 3 - results in simulated values for LAR
applicable to the Japanese A-bomb survivor cohort. The Bayesian analysis
accounts for sampling variability, but does not account for many other important
sources of uncertainty such as those associated with DDREF, risk transport, and
dosimetry errors. For each of these other sources, a distribution is assigned to
an "uncertainty factor" (EPA 1999, Kocher 2008), which defines "the probability
that the assumption employed in the model pertaining to the source of
uncertainty either underestimates or overestimates the risk by any specified
amount" (EPA 1999). Finally, a joint probability distribution for the combined
uncertainty due to all sources is obtained through Monte Carlo techniques.
The Bayesian analysis of the LSS data is described in detail in Section
4.2. For most cancer sites, the risk models used for this analysis are the same
ERR risk models which BEIR VII fit to the LSS data. That is, we used the same
parameters to describe the dependence of ERR on dose, age-at-exposure and
attained age as in BEIR VII. However, there were two important differences
between the two approaches. First, BEIR VII used classical statistical methods
to derive "best" estimates for these parameters, whereas we assigned (prior)
probability distributions to these parameters and then applied information
gleaned from the LSS to update these distributions. Second, for most sites our
Bayesian analysis placed fewer restrictions on parameters, e.g., the parameters
for the dependence on age-at-exposure or attained age.
The Bayesian approach allowed for a relatively straightforward method to
generate distributions of values for LAR, which account for sampling variability
associated with dose, age-at-exposure, and attained age. Rationale for
distributions assigned to uncertainty factors for other sources are described in
Section 4.3. The next section (4.4) presents the main results of the quantitative
uncertainty analysis; distributions for LAR which reflect the combined
uncertainties from these sources are summarized.
A comparison with BEIR Vll's uncertainty analysis is given in Section 4.5.
Section 4.5 first outlines the BEIR VII uncertainty analysis and some of its more
important limitations. We then compare the BEIR VII distributions for site-specific
LAR values to ours.
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Finally, conclusions are given in Section 4.6. Foremost among them is
that results from the EPA uncertainty analysis should not be over-interpreted.
The results presented in Section 4.4 are meant solely as guidance as to the
(relative) extent to which "true" site-specific risks for a hypothetical stationary
U.S. population might differ from the central estimates derived in Section 3. This
is because, it was not always possible to satisfactorily evaluate "biases"
associated with sources of uncertainty such as risk transport.
4.2 Uncertainty from Sampling Variability
4.2.1 Bayesian Approach for Most Solid Cancers
For most cancer sites, BEIR VII derived parameter estimates for ERR and
EAR models based on a statistical analysis of LSS cancer cases and deaths,
which were cross-classified by city, sex, dose, and intervals based on age-at-
exposure, attained age, and follow-up time. Sampling variability refers to the
uncertainty in parameter estimates associated with the variation in the observed
numbers of cancer cases or deaths for each of the sub-categories.
This section describes in detail our Bayesian approach for deriving
distributions for LAR for uncertainties associated with sampling variability. Such
distributions were derived for all solid cancer sites except breast, thyroid, and
bone. (Our treatment of sampling variability for the latter three sites and leukemia
is given in Section 4.2.2). Section 4.3 describes how results from the Bayesian
analysis were then combined to derive the 90% uncertainty intervals for LAR of
cancer incidence presented in Section 4.5.
The approach is based on a Bayesian analysis of LSS incidence data for
the follow-up period 1958-1998, which in many ways parallels analyses of LSS
incidence data by Preston et al. (2007) and the BEIR VII Committee. In
particular, essentially the same data and risk models were used.
Data. The dataset we used is a subset of the incidence data analyzed
by Preston et al. (2007). This data can be downloaded from the RERF website
at http://www.rerf.or.ip/library/dle/lssinc07.html (file Issinc07.csv). The dataset
incorporates the latest (DS02) dosimetry and is otherwise essentially the same
as the one used for the BEIR VII analysis, in that it excludes the "not-in-city"
group (see Preston et al. 2007 for details).
Risk models. For most solid cancer sites, we used the same ERR
models BEIR VII used in its analysis of the LSS data, which were described in
Section 3.2. That is, for a specific cancer site, the ERR for an atomic bomb
survivor at attained age attained age (a) who was exposed at age (e) is:
ERR(d,s,e,a) = j3sdexp(ye*)(a/60y , (4-1)
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where a and e denote attained age and age-at-exposure, and e* is the age-at-
exposure function, which is set to 0 for ages > 30. The corresponding cancer
rate is:
A(d, s, a, b, c) = A0 (s, a, b, c)[\ + ERR(d, s, e, a)] (4.2)
Here, ^ (5, a, 6, c) denotes the baseline rate, which depends on sex (s), attained
age (a), year of birth (b), and city (c).
An important feature of our uncertainty analysis is that the age-at-
exposure and attained-age parameter values are allowed to depend on site.
Separate sets of these two parameters were used for almost all cancer sites; the
only exceptions are cancers of the prostate, ovary, and uterus, for which the ERR
was assumed to be independent of both age-at-exposure and attained age. This
is the approach adopted by Preston et al. because there were insufficient data on
these cancers to provide stable estimates for these parameters or their
uncertainties. It should be noted that the uncertainty intervals for these three
sites are not meant to adequately account for (all) uncertainties relating to age
and temporal dependence in risk.
Baseline cancer rate models. For each cancer site, the same sex-
specific parametric models as in Preston et al. are used for the baseline rates
4/-J: "In the most general models, for each sex, the log rate was described
using city and exposure status effects together with piecewise quadratic
functions of log age joining smoothly at ages 40 and 70 and piecewise quadratic
functions of birth year joining smoothly at 1915 (age at exposure 30) and 1895
(age at exposure 50). A smooth piecewise quadratic function of x with join points
aixi and x2 can be written as/?0+/?1x+/?2x2+/?3max(x-x1,0)2+/?4max(x-.r2,0)2. This
parameterization provides flexible but relatively parsimonious descriptions of the
rates."
fiayes method for simulating LAR. The essential difference between
our Bayesian analysis and the analyses by Preston et al. and the BEIR VII
Committee, is that prior distributions are assigned to each of the unknown
parameters used to define ERR (fis,y , and 77) and to the baseline cancer rates.
In theory, these prior distributions would describe what our state of knowledge
about baseline rates and ERR would be without the information from the LSS.
We then applied a complex Markov Chain Monte Carlo (MCMC) technique using
the software WinBUGS (Lunn et al., 2000) to the LSS data to simulate the ERR
parameters. This simulation was done with respect to a (posterior) distribution
which reflects information implicit in the prior distribution and information that can
be gleaned about these parameters from the LSS. The formulas of Section 3.4
were then applied to the sets of ERR parameters to calculate equally likely
values for the population LAR. Each of these LAR values were obtained under
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the assumption that the ERR method for risk transport is valid. Section 4.2
describes how we quantified uncertainty relating to risk transport.
Prior distributions. For baseline cancer rate parameters, the priors
were normal distributions with mean 0 and very large variances (variance =
1000). This is an example of what are sometimes referred to as non-informative
priors. Use of non-informative priors will often yield results similar to what would
be obtained from more traditional statistical methods, e.g., maximum likelihood.
The priors we assigned to the ERR parameters are discussed next and
summarized in Table 4-1.
First, independent U(-1,1) distributions were assigned to site-specific, age-
at-exposure parameters for most cancers. That is, in Eq. 3-1, y is defined
separately for most cancer sites, and each of these is assumed to follow an
independent uniform distribution. This allows the ERR to be up to 20 times larger
(or smaller) at birth than at age 30. As we believe is appropriate, the range of
possible values (-1, 1) for the site-specific parameters is considerably wider than
BEIR Vll's 95% confidence interval for the age-at-exposure parameter for all
solid cancers: (-0.51, -0.10). The LSS data are insufficient to evaluate
uncertainties associated with age-at-exposure or attained age for prostate,
uterine, and ovarian cancers (see Preston et al. 2007). Thus, a constant ERR
model was assumed for these three cancers, i.e., y = 0 and 77= 0.
Second, for cancers other than prostate, uterine or ovarian, an
independent normal distribution with mean -1.4 and variance 2 was assigned to
the attained age parameter (77). The distribution has a central value equal to the
BEIR VII nominal value and assigns a 95% probability to the interval (-4.2, 1.4).
For many cancers, the lower limit (-4.2) corresponds roughly to a constant EAR
model.
Third, lognormal distributions were assigned to each of the linear dose-
response parameters. For males,
log(/?M)~7V(//M,r2). (4-3)
That is, the log-transformed parameters for each cancer site were assumed to
have prior distributions with a common (unknown) mean (//M) and variance (r2).
For females, the same type of distribution was used but with a different mean
(jup). The essentially non-informative priors in Eq. 4-4a-c were then assigned to
these unknown means and variances. Although this may seem complicated to
some, it is a convenient method for allowing the sharing of information on
radiogenic risks among cancer sites. The rationale for this type of approach is
given in Pawel et al. (2008).
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#,-#(-1.0,10) (4-4a)
#.-#(-0.7,10) (4-4b)
1/r2 ~Gawwa(0.001,0.001) (4-4c)
Table 4-1: Prior distribution for ERR model parameters
Parameter
Cancer Site
log(/?M) log(/?M) r r,
Stomach
Colon
Liver #(/4f>r2) #(#?>r2) U(-1,1) N(-1.4,2)
Lung
Bladder
Prostate #(//M,r2) — 00
Uterus
Ovary
Breast
Other solid
#(//F,r2)
- N(vF,T2)
7V(-1,10) 7V(-0.7,10)
0
0
U(-1,1)
U(-1,1)
0
0
N(-1 42)
N(-1 42)
#,-#(-1.0,10), #,-#(-0.7,10)
All sites
1/r2 ~Gamma(0.001,0.001)
4.2.2 Approach for Other Cancers
Cancer sites included here are leukemia, breast, thyroid, and bone. EPA
risk models for the latter three are not based exclusively on analyses of the LSS
data. We also discuss the approach for uniform whole-body radiation.
Leukemia. We applied, with little modification, the BEIR VII uncertainty
intervals for LAR from lifetime exposures at 1 mGy per year (Table 12-7). The
95% Cl were (33, 300) x10"5 excess cases for males and (21, 250) x10"5 cases
for females. We assumed lognormal uncertainty distributions for the LAR with
GMs equal to the new nominal EPA estimates of 81 x10"4 person-Gy (males) and
60 x10"4 person-Gy (females). The GSDs, derived from the 95% Cl in BEIR VII
are 1.76 (males) and 1.88 (females). Unlike for other cancer sites, we did not
treat uncertainties from other sources separately. This lognormal distribution for
leukemia accounts for both sampling and risk transport uncertainties. The BEIR
VII intervals account for uncertainties relating to a possible quadratic component
in the dose response.
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Breast and thyroid cancers. The EPA nominal estimates for these two
cancer sites were based on risk models derived from a pooled analysis of data
from medical cohorts as well as the LSS. It would thus be inappropriate to base
uncertainties on sampling variability for estimates derived solely from the LSS (as
we did for almost all other cancer sites). For these two sites, the uncertainty from
sampling variability was assumed to be lognormal with GMs equal to nominal
EPA estimates presented in Section 3. GSDs were derived from the 95% Cl in
Table 12-2 of BEIR VII for linear dose response parameters. For breast cancer,
the 95% Cl for EAR is (6.7, 13.3) per 104 PY-Sv (GSD = 1.2). For thyroid cancer,
the 95% Cl for ERR/Gy is (0.14, 2) for males and (0.28, 3.9) for females, and the
corresponding GSDs are both about 2.0.
Bone cancer. The nominal EPA risk model was derived from data on
radium dial painters exposed to 226Ra and 228Ra and patients injected with the
shorter-lived isotope 224Ra. The risk of bone cancer is a relatively small
component of the risk for all cancers from uniform whole-body radiation.
Uncertainties for bone cancer are not quantified here, but EPA plans to address
this issue when it revises FGR 13.
Uniform whole-body radiation. To quantify uncertainties for the LAR for
all cancers from uniform whole-body radiation the simulated site-specific LAR
values were summed (over all cancer sites) at each iteration.
4.3 Non-sampling Sources of Uncertainty
A combined non-sampling uncertainty factor was obtained as the product
of uncertainty factors generated separately for risk transport, DDREF, and
several sources of uncertainty not quantified in BEIR VII. The latter include
uncertainties about age and temporal dependencies unrelated to sampling
variation, dosimetry errors, diagnostic misclassification, and selection bias. It
was concluded that some other sources of uncertainty, such as model mis-
specification for the dose-response, could not be credibly quantified. A summary
of how each source of uncertainty was treated is given in Table 4-2, followed by
more detailed discussions in Sections 4.3.1-4.3.3.
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Table 4-2: Non-sampling sources of uncertainty
Source Distribution
Risk Transport See Section 4.3.1
DDREF LN(1.0,1.35)
Age and temporal dependence LN(1.0, 1.2)
Errors in dosimetry LN(0.9, 1.16)
Random: linear dose response LN(1.0, 1.05)
Random: DDREF LN(0.95, 1.1)
Systematic LN(1.0,1.1)
Nominal neutron RBE LN(0.95, 1.05)
Errors in disease detection/diagnosis LN(1.1, 1.06)
Selection bias LN(1.1,1.1)
Relative effectiveness of X-rays Not quantified
Model misspecification for dose response Not quantified
Total for all sources not quantified in BEIR VII LN(1.09, 1.3)
4.3.1 Risk Transport
For sites other than thyroid, breast, bone, lung, and leukemia independent
subjective probability distributions were assigned to LAP^true) as follows:
P[LAR(tme} = LAR(R)} = 0.35 ; P[LAR(tme} = LAR(A}} = 0.15 ;
LAR~U(mm(LAR(R\LAR(A)\m^(LAR(R\LAR(A)}) with probability 0.25
LAR ~ LU(mm(LAR(R\LAR(A}\m?K(LAR(R\LAR(A))) with probability 0.25
If the only source of uncertainty is risk transport, then from this distribution either
a) the true value for LAR is equal to the ERR or EAR projection, each with
probability 0.5, or b) the distribution is uniform or log-uniform for intermediate
values. These uniform and log-uniform distributions and the EPA distribution for
intermediate values are illustrated in Figure 4-1 for both stomach and colon
cancer. For lung cancer, the only difference is that P[LAR(tme} =LARm] = 0.\5
and P[LAR(tme} = LAR(A)] = 0.35. These distributions appear reasonable, in that it
is arguably equally plausible that, for any site, either the ERR or EAR model
would yield a much better approximation to the true LAR than the other, or the
LAR "could be anywhere in between the two extremes." Admittedly, for some
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sites, the LAR may not be bounded by the ERR and EAR projections. However,
there seems to be no good way to determine how far the probability distribution
should be extended to account for this.
For bone, thyroid, and breast cancer, no risk transport uncertainty was
assumed. For the latter two cancer sites, note that the BEIR VII projections were
based on analyses of data from non-Japanese populations, as well as from the
LSS cohort.
For leukemia, we applied, with little modification, uncertainty intervals
given in BEIR VII (see Sections 4.2.2 and 4.5.1). Probabilities of 0.7 and 0.3
were assigned to ERR and EAR models, respectively.
Since, the Bayesian analysis for sampling variability generated values of
LAR(true}
LAR from the ERR model, the uncertainty factor is:
LAR
(K)
Stomach
Colon
0.03
0.025
0.02
0.015
0.01
0.005
0
50
100
LAR
150 200
0.03
0.025
0.02
0.015
0.01
0.005
0
100 120
140
LAR
160 180
Figure 4-1: Uniform (dash-dot) and log-uniform (dash) distributions for
values of LAR intermediate between the ERR and EAR projections for
stomach and colon cancer. The EPA distribution for these intermediate
values is the average of these two (solid).
4.3.2 DDREF
A lognormal uncertainty factor with GM=1 and GSD=1.35 was assigned to
the DDREF for solid cancers (Figure 4-2). This is essentially the same
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distribution for DDREF which BEIR VII used for its quantitative uncertainty
analysis.
BEIR Vll's distribution for uncertainty in DDREF was based, in part, on a
Bayesian analysis of the LSS data and animal carcinogenesis studies. The main
objective of this analysis was to estimate the curvature of the dose-response,
which, as described in Section 2.1.4, translates directly into an estimate for
DDREF. The Bayesian analysis resulted in a posterior distribution for the
DDREF with GM=1.5 and GSD=1.28. The latter is equivalent to Var(\og(DDREF)
= 0.06. However, the BEIR VII Committee opined that: "the [Bayesian] DDREF
analysis is necessarily rough and the variance of the uncertainty distribution is
..., if anything, misleadingly small." Accordingly, they inflated the variance
representing the log(DDREF) by 50% and set its variance equal to 0.09.
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
2 2.5 3 3.5 4 4.5 5
DDREF
0 0.5 1 1.5
Figure 4-2: Subjective probability density function for DDREF
4.3.3 Other Non-sampling Sources of Uncertainty
Sources of uncertainty considered here include uncertainties from: age
and temporal dependence unrelated to sampling variation, dosimetry errors, and
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diagnostic misclassification. We assigned a single (encompassing) log normal
uncertainty factor with GM=1.09 and GSD = 1.3.
Age and temporal dependence. About 40% to 45% of the projected
cancer incidence radiation risk is associated with childhood exposures (see
Section 3.11), and there is considerable uncertainty for the estimated risk for
children. An oft-cited reason for this (EPA 1994, 1999) is that A-bomb survivors
who were children at the time of the bombings (ATB) still have substantial years
of life remaining in which cancers are to be expressed. For a crude indication of
the relative precision of the LAR for childhood exposures, we note that, for the
BEIR VII analysis of the LSS cohort, fewer than 2100 survivors with cancers
were exposed at age < 15 compared to more than 3400 for age-at-exposure 15-
30. Furthermore, approximately 90% of children < 10 ATB were still alive in the
year 2000 (Little et al. 2008). More generally, about 45% of all survivors in the
LSS were still alive in 2000, so that uncertainties in LAR projections from the
incomplete follow-up, especially for cancers that tend to develop relatively late in
life, merit careful consideration.
Both sampling error and modeling uncertainties can lead to uncertainties
relating to temporal and age dependence. Here, sampling error uncertainty
refers to uncertainty in, say, an LAR associated with the age-at-exposure and
attained age parameter (y, rj) sampling errors. Modeling uncertainties refer to
possible effects of model misspecification. For example, in models described by
Little et al. (2008), ERR and EAR for solid cancer mortality depend on time-since-
exposure. The uncertainty of projections based upon the parametric
representations in BEIR VII depend on the extent to which ERR and EAR for
incidence and mortality depend on time-since-exposure and other factors not
accounted for in their risk models.
For EPA's previous assessment of radiogenic cancer risks, based
primarily on analysis of the LSS mortality data for follow-up up to 1985, site-
specific uniform distributions were assigned to "uncertainty factors" to account for
sampling errors and possible model misspecification associated with temporal
dependence (1999). For stomach, colon, lung, breast, thyroid and residual site
cancers, it was thought that these uncertainties might lead to an overestimate of
population risk. For these sites, a relative risk model was used that depended
on age-at-exposure but not attained age, and most of the projected risk was
associated with exposures before age 20. It was thought that "the contribution
of childhood exposures was highly uncertain in view of statistical limitations [i.e.,
sampling error] and possible decreases in relative risk with time after exposure
[i.e, modeling misspecification]". For most of these sites, the distribution, U(0.5,
1), was assigned to the uncertainty factor. In other words, the ratio of the "true"
population risks to the EPA projection was thought to range between 0.5 and 1.
For other solid cancer sites (except bone), the distribution for the uncertainty
factor was 0.8 to 1.5. Due to the longer follow-up period and more flexible and
appropriate modeling of age dependence in BEIR VII, uncertainties associated
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with both sampling error and modeling misspecification should be greatly
reduced. In addition, uncertainties associated with sampling error relating to both
the age-at-exposure and attained-age parameters are now explicitly accounted
for in the Bayesian analysis already described in Section 4.2.
To update the uncertainty analysis to account for modeling uncertainties,
the new EPA risk models (see Section 3) were used to calculate the LAR for
time-since-exposure restricted to between 13 and 53 y: the period of follow-up for
the LSS incidence data. Slightly more than one-half of the projected LAR is
associated with this time period. Thus, unless the temporal dependence differs
substantially for time-since-exposure from what has been observed for the period
of follow-up in the LSS, it is unlikely to be a major source of uncertainty, with the
possible exception of childhood exposures. A common lognormal uncertainty
factor with GM = 1 and GSD = 1.2 was used for solid cancers. Leukemia
deserves special mention. To paraphrase Little et al. (2008), uncertainties in risk
projections for leukemia would have more to do with risks for times soon after
exposure than for times extending beyond the current LSS follow-up. This is
because the mortality follow-up in the LSS began in October, 1950, about 5
years after the bombings in Hiroshima and Nagasaki, and there is evidence of
risk for time-since-exposures < 5 y from other studies. In particular, a substantial
number of leukemia cases reportedly occurred in the LSS before 1951, with an
apparent subsequent decline; a significant increase in leukemia within 5 y of
radiotherapy was observed in the International Cervical Cancer study; and in an
analysis of the Mayak worker study (Shilnikova et al. 2003), the ERR/Gy for
leukemia mortality was significantly higher for external doses received 3-5 y prior
to death than for doses received more than 5 y earlier. Although the uncertainty
associated with time-since-exposures < 5 y might be larger than modeling
uncertainties associated with most solid cancers, it is our subjective judgment
that it is small compared to the sampling uncertainties described in Section 4.2.2,
and we did not quantify this source of uncertainty.
Errors in dosimetry. In 2003, RERF implemented a revised dosimetry
system called DS02, which is the culmination of efforts stemming from concerns
about the previous (DS86) system for assigning doses to the A-bomb survivors.
Chief among these concerns were discrepancies between DS86 calculations and
measured thermal neutron activation values (Roesch 1987). These
measurements indicated that DS86 might have seriously underestimated neutron
doses for Hiroshima survivors, and, as a result, gamma ray risk estimates for
solid cancers could possibly be underestimated by more than 20% (Preston et al.
1993, EPA 1999). However, the magnitude of this bias would depend on factors
such as the RBE for neutrons. Other factors motivating development of the new
system included improved computer models for radiation transport and
biodosimetric and cancer data indicating overestimation of doses for Nagasaki
factory workers (Preston et al. 2004).
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A comprehensive review adequately resolved issues relating to the
discrepancies with neutron activation measurements (Preston et al. 2004). As
summarized in Preston et al. 2004 and detailed elsewhere (Cullings et al. 2006,
Young et al. 2005), major changes in DS02 include: 1) changes in the height
burst and yield for the Hiroshima bomb; 2) changes in the gamma radiation
released by the Nagasaki bomb; 3) use of new data on neutron scattering, etc.,
to improve calculations for radiation transport; 4) more detailed information and
better methods to account for in-home and terrain shielding; 5) more detailed
information for computing free-in-air fluences; and 6) new weighting factors for
fluence-to-kerma and fluence-to-dose calculations.
The RERF report on DS02 (Young et al., Chapter 13) divides uncertainties
associated with the dosimetry system into two broad categories: systematic and
random. "Systematic" refers to "the likelihood that doses to all individuals at a
given city will increase or decrease together [from imperfectly or unknown
effects]", whereas "random" refers to effects on individual survivor doses that act
more or less independently. Examples of systematic uncertainties are those
relating to the yields, neutron outputs and burst heights, and the air transport
calculation method. Examples of random uncertainties are those relating to
survivor location and inputs needed to estimate shielding for individual survivors.
In Young et al. (pp. 989, 991), a coefficient of variation (CV) of 12-13%
(corresponding to a GSD of about 1.12) was associated with systematic
uncertainties.
For assessing the effects of random dose errors on risk projections, we
refer to the recent contribution by Pierce et al. (2008). As they note, "RERF has
for more than 15 years made adjustments to individual (DS86 and DS02) dose
estimates to reduce the effects of imprecision" on estimates of risk. Without
adjustment, it is well-established that random dose errors would cause a
downward bias in risk estimates if a linear dose-response is assumed. They may
also introduce a bias in the estimate of curvature, which is used for evaluating
the DDREF. RERF adjustments are currently based on the assumption that the
random errors are independent and lognormal with CV = 0.35 (GSD = 1.42).
Pierce et al. argue for adjustments based on more sophisticated treatment of the
random errors that account for effects of "the use of smoothing formulae in the
DS02 treatment of location and shielding." Results in Pierce et al. (Table 1, p.
123) indicate that the more realistic and sophisticated modeling of random dose
errors would result in a change of about 2% in the estimated linear dose-
response estimate of ERR and about a 15-20% change in the estimate of
curvature, compared to estimates based on current methods and assumptions.
The effect on the estimate of DDREF would be somewhat less than this, in part
because it depends also on data from animal carcinogenesis studies. Perhaps
somewhat conservatively, we assign lognormal uncertainty factors with a GSD
equal to 1.05 (effects of random errors on the linear dose parameter estimate)
and 1.1 (effects on the estimate for the DDREF). The GM for these factors are
set to 1 and 0.95, respectively.
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Finally, we quantify uncertainties relating to the use of a nominal neutron
RBE of 10. The use of this nominal weight assigned to the neutron component of
dose has already been discussed in Section 3.1. Calculations in Preston et al.
(2004) indicate that the use of an RBE of 20 would result in a relative decrease in
ERR estimates for solid cancers by about 5%. Radiobiological data (Sasaki et
al.) indicate an RBE of 20 or greater cannot be ruled out. A lognormal
uncertainty factor with GM = 0.95 and GSD of 1.05 is assigned to this source.
Errors in disease detection and diagnosis. The BE IR VII Committee
concluded that "this is unlikely a serious source of bias in risk estimates." Types
of diagnostic misclassification that can occur include classification of cancers as
non-cancers (detection error) and erroneous classification of non-cancer cases
as cancer (confirmation error). The former leads to an underestimate of the
EAR, but does not affect the estimated ERR. Conversely, the latter leads to an
underestimate of the ERR but does not affect the EAR (EPA 1999).
Analyses of LSS mortality data formed the basis for EPA's previous risk
assessment. For that assessment, results from studies of Sposto et al. (1992)
and Pierce et al. (1996) were used to estimate the bias in risk estimates due to
diagnostic misclassification in the LSS mortality data. Conclusions from these
studies were that the ERR estimate for solid cancers in the LSS should be
adjusted upward by about 12% and the EAR estimate should be adjusted upward
by about 16%. Based on these results and results from the uncertainty analysis
by the NCRP 126 Committee (NCRP 1997), EPA assigned a N(1.15, 0.06) to the
uncertainty factor for diagnostic misclassification.
Misdiagnosis is likely to lead to a somewhat smaller bias in the BEIR VII
projections than in EPA's 1994 projections because the former were based on
the LSS incidence data. As noted in the BEIR VII report, "cancer incidence data
are probably much less subject to bias from underascertainment or from
misclassification, and this was an important reason for the committee's decision
to base models for site-specific cancers on incidence data. However, incidence
data are not available for survivors who migrated from Hiroshima to Nagasaki.
Adjustments are likely to account for this (Sposto et al. 1992), but there is likely
to be some uncertainty in the adequacy of these adjustments." We assign a
lognormal uncertainty factor with a GM=1.1 and a GSD = 1.06 for diagnostic
misclassification. Admittedly, this understates the uncertainty for some cancers,
since the uncertainty factor does not account for misclassification among
different cancer types.
Relative effectiveness of medical x rays. For breast and thyroid
cancers, the BEIR VII risk models were based on pooled analyses of data from
the LSS and several medical studies. Most of the medical studies were based
on data from patients who had received X-ray therapy. If the RBE for lower
71
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energy photons is greater than 1, the medical x rays may have been more
carcinogenic, per unit dose, than gamma rays. In that case, there may be an
upward bias in risk estimates derived from the pooled studies, because the
higher-energy gamma dose (that would result in the same risk) would be larger
for these patients.
However, in many of the medical studies the doses were fractionated.
The possibility for an upward (RBE) bias is countered by the possibility that a
smaller DDREF than 1.5 should be applied to results derived (in part) from
studies involving fractionated doses. If, as seems likely given the evidence
presented in Section 4.2, the RBE is typically about 1.5 for x rays at high dose
and dose rates, then there would be only a small bias associated with the relative
effectiveness of medical x rays.
We did not incorporate any uncertainty associated with the RBE for
medical x rays. It should be relatively small compared to the uncertainties
associated with sampling variability - especially for thyroid cancer.
Selection bias in the LSS cohort. Here, selection bias refers to the
possibility that risk estimates derived from the LSS are biased downward
because members of the cohort, by being able to survive the bombings,
demonstrated a relative insensitivity to radiation. The question as to whether
there is a serious selection bias has been a subject of considerable controversy.
For example, Little (2002) cited several papers by Stewart and Kneale from 1973
to 2000 which argued that the selection bias may be substantial. In a recent
analysis, Pierce et al. (2007) argue that the magnitude of the bias on the ERR
estimate for solid cancer is unlikely to be greater than 15-20%. (The bias might
be greater for non-cancer effects). We assign a lognormal distribution with GM
1.1 and GSD 1.1 to the uncertainty factor for selection bias.
Shape of the dose response. As described in Section 3.5, BEIR VII
models explicitly (leukemia) or implicitly (solid cancers) assume a linear-
quadratic (LQ) dose response for cancer induction by IR. Although
epidemiological data are generally consistent with linearity at low doses (Section
2.2), recent mechanistic studies have revealed complex phenomena (Section
2.1) that could conceivably modulate risks at very low doses and dose rates,
either up or down, from what would be projected based on a LQ model. The
BEIR VII Committee did not attempt a quantification of this source of uncertainty.
Attempting to assign a probability distribution to the dose-response model would
necessarily be highly speculative and subjective; consequently, EPA does not
deem it appropriate to include this source of uncertainty in its quantitative
uncertainty analysis. However, it is acknowledged that a breakdown in the model
at low doses, leading to substantial errors in our risk projections, cannot be ruled
out.
72
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4.4 Results
The mean, median, and 90% uncertainty intervals for male and female
cancer incidence LAR are given in Tables 4-3(a, b). Except for prostate and
uterine cancers, these were generated using the Monte Carlo methods described
above (Section 4.1-4.3). Lower bounds for these two cancers were set to 0,
since the analyses of LSS incidence data provide insufficient evidence to indicate
a positive dose-response (Preston et al. 2007, NRC 2006). The tables also
include the EPA nominal projections described in Section 3. Sex-averaged
uncertainty intervals are given in Table 4-3c.
Table 4-3a: EPA projection and uncertainty distribution for the LAR for
male cancer incidence1
Uncertainty Distribution
Cancer Site
Stomach
Colon
Liver
Lung
Prostate
Bladder
Thyroid
Residual3
All solid4
Leukemia
Total4
EPA
Projection
31
142
28
125
42
94
22
220
703
81
785
Lower (5%)
Mean Limit (L)
86
140
51
150
160
90
28
290
990
95
1090
9
52
9
47
O2
18
6
100
420
32
510
Median
44
130
34
130
99
70
22
250
890
82
990
Upper (95%)
Limit (U)
280
300
150
310
520
220
73
600
1910
200
2000
U/L
32
5.8
16
6.5
00
13
11
5.7
4.5
6.3
3.9
1 Cases per 10,000 person-Gy
2 Set to zero. Dose response for prostate cancer is not significant at 0.05 level.
3 Includes kidney and other cancers not here specified.
4 Excludes skin cancer
73
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Table 4-3b: EPA projection and uncertainty distribution for the LAR
for female cancer incidence1
Uncertainty Distribution
Cancer Site
Stomach
Colon
Liver
Lung
Breast
Uterus
Ovary
Bladder
Thyroid
Residual3
All solid4
Leukemia
Total4
EPA
Projection
40
90
13
272
281
17
32
87
110
223
1170
60
1230
Lower (5%)
Mean Limit (L)
96
110
37
340
300
110
47
64
140
330
1570
70
1640
11
35
5
120
160
O2
12
14
32
120
770
24
830
Median
51
94
22
290
280
76
40
50
110
280
1450
61
1520
Upper (95%)
Limit (U)
300
230
120
710
490
320
110
160
370
670
2760
150
2830
U/L
27
6.7
25
6.0
3.1
oo
8.6
12
11
5.4
3.6
6.4
3.4
1 Cases per 10,000 person-Gy
2 Set to zero. Dose response for uterine cancer is not significant at 0.05 level.
3 Includes kidney and other cancers not specified here.
4 Excludes skin cancer
74
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Table 4-3c: EPA projection and uncertainty distribution for the sex-
averaged LAR for cancer incidence1
Uncertainty Distribution
Cancer EPA
Site Projection
Stomach
Colon
Liver
Lung
Breast
Prostate
Uterus
Ovary
Bladder
Thyroid
Residual3
All solid4
Leukemia
Total4
35
116
20
199
142
21
9
16
90
66
221
936
71
1010
Mean
91
130
44
240
150
78
56
24
77
86
310
1280
83
1365
Lower (5%)
Limit (L)
11
50
8
93
80
O2
O2
6
21
25
130
620
39
700
Upper (95%)
Median Limit (U)
49
110
28
210
140
49
39
20
64
69
270
1180
75
1270
290
250
130
490
250
250
160
54
180
200
610
2270
150
2360
U/L
27
5.0
16
5.3
3.1
00
oo
8.6
8.2
8.2
4.8
3.7
3.9
3.4
1 Cases per 10,000 person-Gy
2 Set to zero. Dose response for these cancers are not significant at 0.05 level.
3 Includes kidney and other cancers not specified here.
4 Excludes skin cancer
The last column of each of these tables gives the ratio of the upper to
lower uncertainty bounds by cancer site. These ratios range from about 25 to °°
for stomach, liver, prostate and uterine cancers (largest uncertainty) to about 3
for breast cancer (smallest uncertainty). For many sites, the ratio is about 10.
For liver and prostate cancers, both risk transport and sampling variability are
important sources of uncertainty, whereas for many other sites, the uncertainty
for DDREF is most important. For uniform whole-body radiation, the upper-to-
lower bound ratio is about 4, and the most important contributor to the
uncertainty appears to be DDREF. The sex-averaged 90% uncertainty interval
for uniform whole-body radiation is (700, 2360).
Results in Tables 4-3(a-c) were used to calculate uncertainty intervals for
radiation-induced cancer mortality. This was accomplished by applying crude
estimates of radiogenic cancer fatality rates, equal to the ratio of the nominal
EPA projection for mortality divided by the corresponding projection for incidence
to the lower and upper bounds for cancer incidence. For uniform whole-body
v2
v2
radiation, 90% Uls for cancer mortality are 2.5x10 to 9.8x10 for males,
4.1x10"2 to 1.5x10"1 for females, and 3.5x10"2 to 1.2x10"1 Gy"1 when sex-
averaged. These intervals do not account for uncertainty associated with the
cancer fatality ratios.
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Tables 4-4(a, b) presents results for childhood exposures. The 90% Ul
indicate, for a stationary population, ranges for the LAR associated with
exposures before age 15. For example, in a stationary population exposed to
10,000 person-Gy of uniform whole-body radiation, the 90% Ul for the LAR
associated with childhood exposures is (200, 840) for males and (380, 1340) for
females. For specific cancer sites, the ratio of upper to lower uncertainty bounds
are about 1.5 times larger for childhood exposures than for all ages-at-exposure.
The largest uncertainties for childhood exposures are for stomach, bladder, and
liver cancers.
Table 4-4a: EPA projection and uncertainty distributions for male cancer
incidence in a stationary population exposed to uniform whole-body
radiation: LAR associated with exposures < age 15 for selected sites1
Uncertainty Distribution
Cancer
Site
Stomach
Colon
Liver
Lung
Bladder
Residual3
All4
EPA
Projection
12
54
11
48
34
100
313
Lower (5%)
Mean Limit (L)
30
58
20
48
31
170
420
3
18
3
9
2
55
200
Upper (95%)
Median Limit (U)
15
49
12
37
19
140
370
100
130
62
120
100
380
840
U/L
41
7.1
23
14
47
6.9
4.2
1 Cases per 10,000 person-Gy
2 Set to zero. Dose response for prostate cancer is not significant at 0.05 level.
3 Includes kidney and other cancers not specified here.
4 Excludes skin cancer
76
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Table 4-4b: EPA projection and uncertainty distributions for female cancer
incidence in a stationary population exposed to uniform whole-body
radiation: LAR associated with exposures < 15 for selected sites1
Uncertainty Distribution
Cancer
Site
Stomach
Colon
Liver
Lung
Bladder
Thyroid
Residual3
All4
EPA
Projection
15
33
5
102
31
83
99
565
Mean
33
44
14
110
21
110
190
740
Lower (5%)
Limit (L)
3
11
1
24
2
24
63
380
Upper (95%)
Median Limit (U)
17
36
8
85
12
82
160
680
110
110
48
270
72
280
440
1340
U/L
35
9.3
39
11
40
11
6.9
3.5
Cases per 10,000 person-Gy
: Set to zero. Dose response for uterine cancer is not significant at 0.05 level
! Includes kidney and other cancers not specified here
1 Excludes skin cancer
Results suggest that the EPA risk projections for uniform whole-body
radiation (total for all cancer sites) are likely to be well within a factor of 3 of the
"true" risk for the U.S. population. For individual sites, the projections and actual
risks might differ by a factor of about 3 to 5 for most sites to almost 10 for
stomach cancer. An important caveat is that the analysis did not account for
important uncertainties associated with the shape of the dose response at low
doses and dose rates. Another caveat is that it is very difficult to quantify the
bias of these risk projections.
If one defines bias as the difference between the means of the uncertainty
distributions summarized in this section and the EPA projections presented in
Section 3, then bias is dependent on the subjective distributions assigned to
sources of uncertainty such as risk transport. For most sites, the means of the
uncertainty distributions, based on the subjective distributions EPA assigned to
sources of uncertainty, are greater than the nominal EPA projections given in
Section 3. (The same is true for the medians, although arguably for most cancer
sites, the median and the EPA projection are consistent). For most sites for
which there appears to be a large discrepancy. It stems from how the problem of
risk transport is handled under the two approaches. For prostate and uterine
cancers, the larger mean values also relate to features of the Bayesian analysis
outlined in Section 4.2.1. These features include: 1) the use of a lognormal prior
distribution for the linear dose response parameter and 2) the sharing of
information on radiogenic risks among cancer sites. Details about these
(technical) points are given next.
77
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Risk transport. For sites such as stomach, liver, uterine and prostate
cancer, the baseline cancer rates are very different in the U.S. compared to
Japan, and, as a result, the ERR and EAR model-based projections are also very
different. For the uncertainty analysis, we adopted a distribution which assigns
with 50% probability one of the two EAR or ERR model-based projections and
with 50% probability either a uniform or log-uniform distribution for possibilities
between the two "extremes". The net result is a mean value which for most sites
is not much different from a nominal estimate based on a weighted arithmetic
mean - with a weight equal to 0.6 for the ERR model. As indicated in Section 3,
projections based on arithmetic means would be twice as large as the EPA
projection for sites such as stomach cancer, so a much larger mean for the
uncertainty distribution is not surprising. If different distributions had been
assigned for risk transport, the means for the uncertainty distributions for sites
such as stomach cancer could be quite different.
Linear dose response parameter. The prior distribution for the linear
dose response parameter was assumed to be lognormal. Taken literally, this
rules out the possibility that there is no effect of radiation, which is not
appropriate for sites such as prostate or uterine cancer. As already mentioned,
the lower bound for these sites is set to 0.
Sharing of information on radiogenic risks. The Bayesian analysis
provided a convenient method to share information on radiogenic risks among
cancer sites. In essence, the final uncertainty distributions for ERR for a specific
solid cancer site represents a compromise between a distribution of ERRs which
would have been derived only from data for the specific cancer and a distribution
of ERRs derived from data for all solid cancer sites. A consequence is that
central values for the uncertainty distributions for the LAR for cancers with small
estimated ERRs, such as of the prostate and uterus, are larger than the
corresponding ERR estimates given in BEIR VII.
4.5 Comparison with BEIR VII
4.5.1 Quantitative Uncertainty Analysis in BEIR VII
The BEIR VII Report includes a quantitative uncertainty analysis with 95%
subjective CIs for each site-specific risk estimate of LAR for low LET radiation.
The analysis focused on three sources of uncertainty thought to be most
important: sampling variability in the LSS data, the uncertainty in transporting risk
from the LSS to the U.S. population, and the uncertainty in the appropriate value
of a DDREF for projecting risk at low doses and dose rates from the LSS data.
Their treatment of these and other sources of uncertainty are outlined next.
Sampling variability. For most cancer sites, BEIR VII derived parameter
estimates for ERR and EAR models based on a statistical analysis of LSS cancer
78
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cases and deaths, which were cross-classified by city, sex, dose, and intervals
based on age-at-exposure, attained age, and follow-up time. For all solid cancer
sites except breast and thyroid, the BEIR VII uncertainty analysis accounted for
only the sampling variability associated with the linear dose parameter (ft). The
uncertainty analysis made use of an approximation for the variance of the
log(LAR) associated with sampling variability:
VarSAMPL1NO [\og(LAR(d, e))] * Var[\og(jB)]. (4-5)
Risk transport. To quantify uncertainties from risk transport, BEIR VII
essentially assumed that either the EAR or ERR model is "correct" for risk
transport, and a weight parameter (w) equals the probability the ERR model is
correct. BEIR VII approximated Var[\og(LAR)] as follows:
VarTRANSPORT[\og(LAR)] « \Og[LARm(Sm)l LAR(A\S(A))}2W(l-W) . (4-8)
Here, S(R) denotes the vector of estimated and nominal parameter values for/?,
y, rj, and DDREF for the ERR model, and LAR(R\&R}) represents the
corresponding nominal LAR estimate. Likewise, $(A} and LAR(A}($(A}} represent
the estimated parameter values and nominal LAR values for the EAR model.
DDREF. BEIR Vll's distribution for uncertainty in DDREF has been given
in Section 4.3.2.
Combining sources of uncertainties. To calculate the var(log(LAR)),
the BEIR VII Committee simply summed the variances for \og(LAR) associated
with sampling error, risk transport, and DDREF. To calculate 95% subjective
confidence intervals, they further assumed that the combined uncertainty for LAR
follows a lognormal distribution.
Unquantified sources of uncertainty. BEIR VII noted several other
sources of uncertainty but did not quantify them, arguing instead that
uncertainties for many of these other sources are relatively small. These other
sources of uncertainty include: 1) uncertainty in the age and temporal pattern of
risk, especially for individual sites, which was usually taken to be the same as
that derived for all solid tumors; 2) errors in dosimetry; 3) errors in disease
detection and diagnosis; and 4) unmeasured factors in epidemiological
experiments.
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4.5.2 Comparison of Results
Results from EPA's quantitative uncertainty analysis are compared with
BEIR VII uncertainty intervals for LAR cancer incidence (Table 4-5). For
purposes of comparison, 95% uncertainty intervals were calculated which
account for only those uncertainties associated with sampling variability, risk
transport, and DDREF. That is, uncertainty factors for other sources of
uncertainty, other than those quantified in BEIR VII, were not applied to generate
these results. For most sites, results are reasonably consistent. Notable
exceptions are prostate cancer, for which the BEIR VII intervals appear to be too
wide, and uterine cancer, for which the EPA upper bound is about 2% times
larger (330 compared to 131).
Table 4-5: 95% EPA and BEIR VII 95% uncertainty intervals for LAR of solid
cancer Incidence, which account for sampling variability, risk transport,
and DDREF
Cancer Site
Stomach
Colon
Liver
Lung
Prostate
Breast
Uterus
Ovary
Bladder
Remainder
Thyroid
Solid cancers
EPA
(7, 290)
(45, 280)
(7, 150)
(37, 290)
(0, 540)
None
(12,230)
(89, 570)
(5,91)
(390, 1700)
Males
BEIR VII
(3, 350)
(66, 360)
(4, 180)
(50, 380)
(<0, 1860)
None
(29, 330)
(120,680)
(5, 90)
(490, 1920)
Females
EPA BEIR VII
(10, 300)
(29, 220)
(3, 120)
(100,670)
(140,550)
(0, 330)
(10, 100)
(11, 160)
(110,630)
(26, 450)
(690, 2600)
(5, 390)
(34, 270)
(1, 130)
(120,780)
(160,610)
(<0, 131)
(9, 170)
(30, 290)
(120,680)
(25, 440)
(740, 2690)
1 Cases per 10,000 person-Gy
4.6 Conclusions
The main results given in Section 4.4 suggest that the EPA risk
projections for uniform whole-body radiation (total for all cancer sites) are likely to
be well within a factor of 3 of the "true" risk for the U.S. population. For individual
sites, the projections and actual risks might differ by a factor of about 3 to 5 for
most sites to about 10 for stomach cancer. For childhood exposures, the
uncertainties are somewhat larger. An important caveat is that the analysis did
not account for important uncertainties associated with the shape of the dose
response at low doses and dose rates.
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The quantitative uncertainty analysis did allow for some sources of
uncertainty, such as dosimetry errors and cancer misdiagnosis, which were not
quantified in BEIR VII. For sources of uncertainty quantified in BEIR VII, results
from this analysis and BEIR VII are consistent for most sites.
Results from the EPA uncertainty analysis should not be over-interpreted.
The results presented in Section 4.4 are meant solely as guidance as to the
(relative) extent to which "true" site-specific risks for a hypothetical stationary
U.S. population might differ from the central estimates derived in Section 3. This
is because it was not always possible to satisfactorily evaluate "biases"
associated with sources of uncertainty such as risk transport.
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5. Risks from Higher LET Radiation
5.1 Alpha Particles
Assessing the risks from ingested or inhaled alpha-emitting radionuclides
is problematic from two standpoints. First, it is often difficult to accurately
estimate the dose to target cells, given the short range of alpha-particles in
aqueous media (typically < 100 urn) and what is often a highly non-uniform
distribution of a deposited radionuclide within an organ or tissue. Second, for
most cancer sites, there are very limited human data on risk from alpha particles.
For most tissues, the risk from a given dose of alpha radiation must be calculated
based on the estimated risk from an equal absorbed dose of y rays multiplied by
an "RBE" factor that accounts for different carcinogenic potencies of the two
types of radiation, derived from what are thought to be relevant comparisons in
experimental systems
The high density of ionizations associated with tracks of alpha radiation
produces DMA damage which is less likely to be faithfully repaired than damage
produced by low-LET tracks. Consequently, for a given absorbed dose, the
probability of inducing a mutation is higher for alpha radiation, but so is the
probability of cell killing. The effectiveness of alpha radiation relative to some
reference low-LET radiation (e.g., 250 kVp x rays or 60Co y rays) in producing a
particular biological end-point is referred to as the alpha-particle relative
biological effectiveness (RBE). The RBE may depend not only on the observed
end-point (induction of chromosome aberrations, cancer, etc.), but on the species
and type of tissue or cell being irradiated, as well as on the dose and dose rate.
In most experimental systems, the RBE increases with decreasing dose
and dose rate, apparently approaching a limiting value. This mainly reflects
reduced effectiveness of low-LET radiation as dose and dose rate are decreased
— presumably because of more effective repair. In contrast, the effectiveness of
high-LET radiation in producing residual DMA damage, transformations, cancer,
etc. may actually decrease at high doses and dose rates, at least in part due to
the competing effects of cell killing. For both low- and high-LET radiations, it is
posited that at low enough doses, the probability of a stochastic effect is
proportional to dose, and independent of dose rate. Under these conditions, the
RBE is maximal and equal to a constant RBEM. In order to estimate site-specific
cancer risks for low dose alpha radiation, we need a low-dose, low-LET risk
estimate for that site and an estimate of the RBEM.
5.1.1. Laboratory Studies
The experimental data on the RBE for alpha particles and other types of
high-LET radiation have been reviewed by the NCRP (NCRP 1990) and the
ICRP (ICRP 2003). From laboratory studies, the NCRP concluded that: "The
effectiveness of alpha emitters is high, relative to beta emitters, being in the
82
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range of 15 to 50 times as effective for the induction of bone sarcomas, liver
chromosome aberrations, and lung cancers." The NCRP made no specific
recommendations on a radiation weighting factor for alpha radiation.
The ICRP has reiterated its general recommendation of a radiation
weighting factor of 20 for alpha-particles (ICRP 2003, 2005). However, ICRP
Publication 92 further states (ICRP 2003):
Internal emitters must be treated as a separate case because their RBE depends
not merely on radiation quality, but also, and particularly for a-rays with their
short ranges, on their distribution within the tissues or organs. It is, accordingly,
unlikely that a single WR should adequately represent the RBEM for different a
emitters and for different organs...The current WR of 20 for a-rays can thus serve
as a guideline, while for specific situations, such as the exposure to radon and its
progeny, or the incorporation of 224Ra, 226Ra, thorium, and uranium, more
meaningful weighting factors need to be derived.
Another set of recommendations for a-particle RBE is contained in the
NIOSH-lnteractive RadioEpidemiological Program (NIOSH-IREP) Technical
Documentation meant for use in adjudicating claims for compensation of
radiogenic cancers (NIOSH 2002, Kocher et al. 2005). For alpha-particle caused
solid cancers (other than radon-progeny-induced lung cancer), IREP posits a
lognormal uncertainty distribution for its radiation effectiveness factor (REF,
equivalent to RBEM) with a median of 18 and a 95% Cl [3.4, 101]. For leukemia,
IREP employs a hybrid distribution: REF=1.0 (25%); =LN(1,15) (50%); =LN(2,60)
(25%) where LN(a,b) represents a lognormal distribution with a 95% Cl of [a,b].
Studies comparing groups of animals inhaling insoluble particles to which
are attached alpha or beta emitters have been performed to assess RBE for lung
cancer. In a large long-term study of beagle dogs, Hahn et al. (1999) reported
that the RBE was at least 20. In contrast, from an analogous study of lung
cancer induction in CBA/Ca mice, Kellington et al. (1997) estimated the RBE to
be only 1.9.
5.1.2 Human Data
Results from epidemiological studies of groups exposed to alpha radiation
can be used directly to develop risk estimates for alpha radiation; they can also
be used in conjunction with low-LET studies to estimate RBE; finally, it is
possible to use results from these studies in combination with estimates of RBE
to derive low-LET risk estimates where none can be obtained from low-LET
studies.
There are four cancer sites for which there are direct epidemiological data
on the risks from alpha irradiation: bone, bone marrow, liver, and lung. Not
coincidentally, these are sites for which we are particularly interested in obtaining
high-LET risk estimates because they are ones which tend to receive higher than
average doses of alpha radiation from certain classes of internally deposited
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radionuclides. For each of these sites except bone, we also have risk estimates
for low-LET radiation derived from the LSS.
Bone cancer. Although there is some new information coming from
research on Mayak plutonium workers (Koshurnikova et al. 2000), the most
definitive information on bone cancer risk continues to be radium dial painters
exposed to 226Ra and 228Ra and patients injected with the shorter-lived isotope
224Ra. The usefulness of the dial painter data for low dose risk estimation suffers
from lack of complete epidemiological follow-up and from the possible
complicating effects of extensive tissue damage associated with very high doses
of radiation in the bone. For this reason, EPA has taken its estimates of risk of
alpha-particle-induced bone sarcoma from the BEIR IV analysis of the 224Ra
data, which is consistent with a linear, no-threshold dose response (NRC 1988,
EPA 1994). The corresponding low-LET risk estimate (per Gy) was assumed to
be a factor of 20 lower based on the assumed alpha-particle RBEM of 20.
Subsequent to BEIR IV, improvements have been made in the dosimetry
for the 224Ra patients, especially those treated as children. Some additional
epidemiological data have also become available. The updated data set has
been analyzed by Nekolla et al. (2000) and found to be well-described by an
absolute risk model, which for small acute doses reduces to the form:
Ar = aDg(e)h(t),
where Ar is the increment in bone cancer incidence from an endosteal dose, D,
of alpha-radiation; g(e) reflects the variation in risk with age at exposure, e; and
h(t), the variation in with time after exposure, t. A good statistical fit was found for
g(e) as an exponentially decreasing function of age at exposure, and h(t) as a
lognormal function of time after exposure.
Normalizing the time integral of h(t) to unity, a maximum likelihood
calculation yielded:
a= 1.782x lO^Gy'1,
g(e) = exp[-0.0532(e - 30)],
h(f) = (lira) 1/2 x exp
(ln(0-ln(O)2
where t0 is 12.72 y and o is 0.612. Thus, the temporal response, h(t), has a
GM=12.72 y and a GSD = ea = 1.844.
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For estimates of bone cancer risk from alpha radiation, we adopt the
model and calculational methods of Nekolla et al., with one modification. For
simplicity, those authors assumed a fixed life-span of 75 y; our lifetime estimates
are derived using their derived mathematical models, but, as with our other risk
estimates, applied in conjunction with gender-specific survival functions
determined from U.S. Vital Statistics. In this way, it is calculated that the
average lifetime risk of bone cancer incidence is 2.04x10"3 Gy ~1 for males and
1.95x10~3 Gy"1 for females. The population average of 1.99x10~3 Gy"1 is close to
the FGR-13 estimate of 2.72x10"3 Gy"1 (EPA 1999b). About 35% of all bone
cancers are fatal (SEER Fast Stats), and it is assumed here that the same
lethality holds for radiogenic cases - half that previously assigned (EPA 1994).
Thus the mortality risk projections for alpha-particle-induced bone cancer are
7.13x10"4 Gy"1 (males), 6.82x10"4 Gy"1 (females), and 6.96x10"4 Gy"1 (sex-
averaged).
Human data on bone cancer risk from low-LET radiation are very sparse,
but an estimate of the RBE for bone cancer induction can be derived from a
comparative analysis of data on beagles injected with the alpha-emitter 226Ra or
the beta-emitter 90Sr, both of which are distributed fairly uniformly throughout the
volume of calcified bone. Employing a two-mutation model for bone cancer
induction, Bijwaard et al. (2004), found that the dose-response relationship for
both these radionuclides was approximately linear-quadratic at low doses, and
that the linear coefficient was approximately 9.4 times higher for radium than for
strontium. Based on this finding, EPA is adopting a revised RBE value of 10 for
bone cancer; i.e., the risk per Gy for low-LET radiation is assumed to be 1/10 that
estimated for alpha-particle radiation.
There has been a great deal of discussion in the scientific literature
concerning a possible threshold for induction of bone sarcoma (NRC 1988).
Often cited is a plot of bone cancer risk versus dose in radium dial painter data,
which appears to show a rather abrupt threshold at about 10 Gy. However, the
apparent threshold may simply be an artifact of presenting the data on a semi-log
plot (incidence vs. log dose); a conventional plot of incidence vs. dose is
consistent with linearity (Mays and Lloyd 1972, NRC 1988). In laboratory
studies, Raabe et al. (1983) found that the mean time to tumor increases with
decreasing dose rate, suggestive of a "practical threshold" in dose rate below
which the latency period would exceed the lifespan of the animal. However,
interpretation of this finding remains controversial (NRC 1988). A postulated
mechanism for producing a sub-linear dose response relationship, resulting in a
practical threshold below which the risk is negligible, is: 1) a requirement for two
radiation-induced initiation steps (NRC 1988) or 2) the need for radiation-induced
stimulation of cell division (Brenner et al. 2003). It may be hard, however, to
reconcile these mechanisms with analyses of the 224Ra injection studies
discussed above, which seem indicative of a linear or linear-quadratic dose-
response relationship.
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Leukemia. Excess leukemia cases have not been observed in studies of
radium dial painters or patients injected with high levels of 224Ra, although in
some cases there was evidence of blood disorders that may have been
undiagnosed leukemias (NRC 1988). It appears from these studies, however,
that bone sarcoma is a more common result of internally deposited radium, and
that the radium leukemia risk is much lower than that calculated using ICRP
dosimetry models together with a leukemia risk coefficient derived from the LSS
weighted by an RBE of 20 (Mays et al. 1985, NRC 1988, Harrison and Muirhead
2003, Cerrie 2004).
In part, the anomalously low risk of leukemia from alpha-particles might be
attributed to microdosimetry: i.e., target cells may be non-uniformly distributed in
the bone marrow in such a way that the dose to these cells is considerably lower
than the average marrow dose. Evidence suggests, however, that
microdosimetric considerations do not fully account for the lower risk, and that
high-LET radiation is only weakly leukemogenic. Thorotrast patients, who are
expected to have a more even distribution of alpha-particle energy, do show an
excess of leukemia, but only about twice the risk per Gy as seen in the LSS
(ICRP 2003). Moreover, an RBE of only about 2.5 has been found for neutron-
induced leukemia in mice (Ullrich and Preston 1987), a situation in which the
high-LET radiation dose would have been nearly uniform throughout the marrow.
The BEIR VII low-LET risk estimate for leukemia incidence is roughly 50% higher
than that of UNSCEAR (2000b) or EPA (1994). Using a Bayesian approach,
Grogan et al. (2001) estimated the alpha-particle leukemia risk to be 2.3x10"2 per
Gy. If one adopts the BEIR VII low-LET leukemia (incidence) risk estimate, this
would correspond to an RBE of approximately 2.9. Through a comparison of
Thorotrast and A-bomb survivor data, Harrison and Muirhead (2003) also
estimated the RBE to be 2-3. However, the authors noted that the Thorotrast
doses were likely to be underestimated by a factor of 2-3 (Ishikawa et al. 1999),
and that the RBE was perhaps very close to 1.
Ankylosing spondylitis patients (mostly young adult males) injected with
relatively low amounts of 224Ra had a higher rate of leukemia than that projected
from the general population or that observed in a group of unirradiated control
patients (Wick et al. 1999, 2008). After 26 y of average follow up, the exposed
group of 1471 patients had 19 leukemias compared to 6.8 expected based on
age- and gender-specific population rates; after 25 y of average follow up, the
1324 control patients had 12 leukemias (7.5 expected). The average dose to
bone surface was estimated at 5 Gy in these patients. According to ICRP
dosimetry models, the average marrow dose is about 10% of the bone surface
dose for internally deposited 224Ra (ICRP 1993). Thus, the estimated average
marrow dose is « 0.5 Gy, and the excess risk, calculated using the population
projected rate is « 1.7x10"2 Gy"1. This is about twice the leukemia risk projection
for 30-y old males derived in BEIR VII from the LSS data (NRC 2006, p. 281).
Thus, these radium-injection data are also roughly consistent with an RBE of
about 2. Alternatively, if the unirradiated control patients are used as the
comparison group, the estimated risk per Gy and RBE are roughly halved.
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Hence, these data also support an RBE for leukemia induction of about 1-2. It
should be noted, however, that the temporal variation of excess leukemias
appeared different in this study from that observed in the LSS (Wick et al. 1999).
EPA has been employing an RBE of 1 for alpha-particle induced leukemia
(EPA 1994). Based on the information discussed above, the RBE is being
adjusted upward to a value of 2, with a confidence interval of 1-3.
Liver cancer. The LSS shows a statistically significant excess of liver
cancer. The uncertainty bounds derived by BEIR VII are wide, both because of
the large sampling error and the uncertainty in the population transport (liver
cancer rates are about an order of magnitude lower here than in the LSS cohort).
The BEIR VII central estimate for gamma radiation is « 2x10"3 Gy"1. For
comparison, updated analyses of data on Thorotrast patients from Denmark
(Andersson et al.1994) and Germany (van Kaick et al. 1999) yielded estimates of
7x10"2 and 8x10"2 excess liver cancers per Gy, respectively. Assuming an RBE
of 20 for the alpha-particle RBE, these values are about a factor of 2 higher than
what would be projected from the BEIR VII liver cancer model - quite reasonable
agreement given the large uncertainties and difference in age and temporal
distribution. However, Leenhouts et al. (2002) has reanalyzed the Danish
Thorotrast data, employing a biologically based, two-mutation model of
carcinogenesis, and derived a lifetime liver cancer risk estimate of 2x10"2 Sv"1 (4
x10"1 Gy"1), an order of magnitude higher than the BEIR VII central estimate, but
consistent with the BEIR VII upper bound. One reason given by Leenhouts et al.
for the higher risk estimate is that the model projects risk over a whole lifetime,
whereas the original analysis by Andersson et al. addressed only the risk over
the period of epidemiological follow-up. The increase may also partly stem from
a correction for downward curvature in the dose-response function at high doses.
An excess of liver cancer has been found among workers at the Mayak
nuclear facility in the Russian Federation, especially among workers with
Plutonium body burdens and among female workers (Gilbert et al. 2000).
Averaged over attained age, the ERR per Gy for plutonium exposures was 2.6
for males and 29 for females. (Sokolnikov et al. 2008). For comparison, the
BEIR VII risk model for y-ray induced liver cancer derived from the LSS yields an
ERR per Gy of 0.32 for males and females, calculated for exposure age 30 and
attained age 60. Thus, the RBEs that would be derived from the LSS and Mayak
worker study would be roughly 8 for males and 90 for females.
In conclusion, the Danish and German Thorotrast results are in good
agreement with one another, and the risk projections derived from them are in
quite reasonable agreement with what would be projected from the LSS,
assuming a plausible RBE of about 40. There is considerable uncertainty in the
estimates, relating to uncertainty in the dose estimates, the fraction of the dose
"wasted" because it was delivered after the cancer was initiated, and the
extrapolation from high doses (several Gy) to low environmental doses. In
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addition, as seen from the Leenhouts et al. modeling exercise, there is
considerable uncertainty in projecting risk over a whole lifetime, especially the
contribution from childhood exposures. The results from the Mayak worker study
appear to be in only fair agreement with those from the Thorotrast studies.
Based on its review of the available information, EPA adopts a model for
calculating a-particle induced liver cancer, which is a scaled version of the BEIR
VII model, equivalent to multiplying the corresponding BEIR VII low-LET risk
estimates, on an age- and gender-specific basis, by an RBE of 40. The
population average risk is then 8x10~2Gy~1.
Lung. Excess lung cancers have been associated with the inhalation of
alpha-emitting radionuclides in numerous epidemiological studies. Cohort
studies of underground miners have shown a strong association between lung
cancer and exposure to airborne radon progeny. This association has also now
been found in residential case-control studies. In addition, a cohort study of
workers at the Mayak nuclear plant has also shown an association with inhaled
Plutonium (Gilbert et al. 2004). The miner studies serve as the primary basis for
BEIR VI and EPA estimates of risk from radon exposure (NRC 1999, EPA 2003),
and results from the residential studies are in reasonable agreement with those
risk estimates (Darby et al. 2005, Krewski et al. 2005). The Agency has no plans
at this time to reassess its estimates of risk from exposure to radon progeny, but
it is the intent here to reassess estimates of risk from inhaled plutonium and other
alpha-emitters, along with the lung cancer risk associated with low-LET
exposures.
Table 5-1 compares summary measures of risk per unit dose for the U.S.
population derived from the LSS in BEIR VII and from the pooled underground
miner studies in BEIR VI. For radon, the estimation of lung dose requires a
conversion from radon progeny exposure, measured in working level months
(WLM). Estimating this conversion factor involves a model calculation of the
deposition of radon progeny in the airways, the distribution of alpha particle
energy on a microdosimetric scale, and the relative weights attached to different
tissues in the lung (NRC 1999, EPA 2003, James et al. 2004). Results are
presented for the dose conversion factor of« 12 mGy/WLM derived by James et
al. (2004), or the lower estimate of 6 mGy/WLM recommended in UNSCEAR
2000a, respectively.
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Table 5-1: Lung cancer mortality and RBE
Data
Source
A-bomb
mortality
EPA radon
risk model
Gender
Male
Female
Combined
Male
Female
Combined
Risk per 106
Person-WLM
—
—
640
440
540
Risk per 104
Person-Gy
140
270
210
8001 16002
5501 11002
6751 13502
RBE
1.0
1.0
1.0
5.71 11.42
2.01 4.12
3.21 6.42
1 Risk per Gy to the whole lung or RBE calculated assuming: (1) 12 mGy/WLM, on average, to
sensitive cells in the bronchial epithelium (James et al. 2004) and (2) lung risk partitioned 1/3
(bronchi): 1/3 (bronchioles): 1/3 (alveoli).
2 Calculated assuming 6 mGy/WLM, on average, to sensitive cells in the bronchial epithelium
(UNSCEAR2000a).
When compared to results from animal studies, the inferred alpha-particle
RBEs in Table 5-1 may appear to be unreasonably low - especially for females.
It should be recognized, however, that the risk model used to derive risk
estimates for radon are in certain ways incompatible with the models for low-LET
lung cancer risk in BEIR VII. They differ not only with respect to their functional
dependence on age, gender, and temporal factors, but also with respect to the
interaction with smoking. In contrast to the BEIR VII models, the radon risk
models do not incorporate a higher risk coefficient for females or for children.
The miner cohorts from the radon models were derived consisted essentially
entirely of adult males, and it is possible that radon risks are being
underestimated for children and females. The radon risk appears to be almost
multiplicative with smoking risk (or the baseline lung cancer rate), whereas the
LSS data suggests an additive interaction. It is unclear whether these apparent
differences with respect to gender and smoking reflect a real mechanistic
difference in carcinogenesis by the two types of radiation exposure (chronic
alpha versus acute gamma) or unexplained errors inherent in the various studies.
Lung cancer results from the LSS cohort can also be compared with those
on Mayak workers, whose lungs were irradiated by alpha particles emitted by
inhaled plutonium (Gilbert et al. 2004), but the results of such an analysis must
be viewed critically. The dose from inhaled Pu is highly uncertain, as is the
relative sensitivity of different target cells to radiation. Information on smoking in
both cohorts is limited. The populations are quite different with respect to gender
and age profile. Males account for about 75% of the PY and over 90% of the
lung cancers among the internally exposed Mayak workers, but for only about
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30% and 55% of the PY and lung cancers, respectively, among the LSS cohort.
Another issue is that the dependence of the risk on attained age appears to be
quite different in the two studies - a monotonically increasing EAR for the LSS
but a sharp decrease in the EAR above age 75 for the Mayak workers. There
are, however, very few data on these older Mayak workers. Focusing just on
lung cancers appearing between ages 55 and 75, one finds that the central
estimates of risk per Sv in the two studies are comparable, consistent with an
RBE for alpha particles of 10 or more.
A more recent analysis of the Mayak plutonium worker data, based on
improved dosimetry, has been published (Sokolnikov et al. 2008). From a
statistical modeling of the lung cancer data, it was estimated that the ERRs per
Gy at age 60 were 7.1 for males and 15 for females. For comparison, the LSS
study yielded an ERR per Gy of 0.32 and 1.4, respectively, for males and
females for exposure age 30 and attained age 60. Thus, the two sets of data
together would suggest an RBE of roughly 20 for males and 10 for females.
The risk per unit dose estimate from the plutonium exposed Mayak
workers appears to be considerably higher than that from the radon studies
despite the fact that the lung dose from radon progeny is projected to be almost
entirely to the epithelial lining of the airways, whereas the inhaled plutonium dose
is expected to be concentrated in the alveoli, which is generally thought to be a
much less sensitive region for cancer induction.
There seems to be no fully satisfactory way to reconcile all the results
from the LSS, miner and Mayak worker studies with what one would expect from
the dosimetry and experimental determinations of a-particle RBE, even taking
into account the sampling errors in the various epidemiological studies. The
Mayak study is ongoing, with possible improvements in the dosimetry still to be
made; the LSS risk estimates are also somewhat suspect, especially their
dependence on gender and age at exposure (see Section 3.2). In particular, it is
odd that the risk is higher in females than males among the A-bomb survivors,
despite the much lower lung cancer incidence among Japanese women than
men. Also, the BEIR VII lung cancer model reflects the negative trend with age
at exposure obtained from the analysis of all solid tumors, but there are still
very little data to directly support a higher lung cancer risk for childhood
exposures.
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5.1.3 Nominal Risk Estimates for Alpha Radiation
Information on alpha-particle RBEM (relative to y-irradiation) for induction
of cancer is sketchy, especially in humans. Laboratory studies are mostly
indicative of a value of about 20, but with likely variability depending on cancer
site and animal species or strain. There is also evidence in both animals and
humans that the RBEM is much lower for induction of leukemia. Comparisons of
data on lung cancer induction by inhaled radon progeny or plutonium with data
on the A-bomb survivors yields somewhat conflicting results, suggesting possible
errors in the data or in underlying assumptions regarding the form of the models,
internal dosimetry, or the sensitivity of different parts of the lung. At this point,
comparisons between the radon data and the LSS data suggest an RBE much
lower than 20 for lung cancer induction, but the Mayak results so far fail to
substantiate this. Further follow-up of the LSS cohort and additional information
on the Mayak workers may help to resolve this issue.
EPA's site-specific a-particle risk estimates will be obtained by applying an
RBE of 20 to our y-ray risk estimates, with three exceptions: 1) an RBE = 2 for
leukemia, 2) an RBE = 40 for liver cancer, and 3) continued use of models
derived from BEIR VI to estimate lung cancer risk from inhaled radon progeny
(MAS 1999, EPA 2002). The low dose, y-ray risk estimate for bone cancer is
obtained by dividing the risk per Gy for a-particles - estimated from patients
injected with 224Ra - by an RBE of 10.
Aside from those revisions pertaining to leukemia, liver cancer, and bone
cancer described above, this approach is consistent with previous EPA practice
except in the case of breast cancer, where previously an RBE of 10 was
employed rather than 20 (EPA 1994). The justification for the lower RBE was
that the estimated (y ray) DDREF was 1 for breast cancer but 2 for other solid
tumors. The evidence for such a difference in DDREF appears weaker now,
and, for simplicity, we are now applying the same nominal DDREF (1.5) and RBE
(20) for most solid tumors, including breast.
5.1.4 Uncertainties in Risk Estimates for Alpha Radiation
For most cancer sites, the uncertainty in a-particle risk can be calculated
from the combined uncertainties in y-ray risk, as presented in Section 4, and in a-
particle RBE. For solid cancers, EPA previously assigned a lognormal
uncertainty distribution to the alpha-particle RBE, with a 90% Cl from 5 to 40.
The median value is thus « 14.1 and the GSD « 1.88 (EPA 1999a). This
distribution still appears reasonable for solid tumors other than liver and bone
cancers. The uncertainty distribution for leukemia induced by alpha-emitters
deposited in the bone was previously taken to be uniform over the interval [0,1]
(EPA 1999a). Based on the more current information discussed above, a
lognormal distribution is now assumed, with GM=2 and GSD=1.4.
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In the case of a-particle induced liver cancer, EPA is basing its 95% upper
confidence limit on the risk estimate derived from the modeling approach of
Leenhouts et al. (4x10"1 Gy"1). This upper bound value is consistent with a
lognormal distribution with a GM equal to EPA's nominal central estimate of
8x10"2 Gy"1 and a GSD of 2.66. The lower 95% confidence limit on the
distribution is then 1.6x10~2/Gy, which corresponds to what would be inferred
from the LSS liver cancer risk estimate in conjunction with an assumed a-particle
RBEofS.
Risk projections for bone cancer are only important when considering
internally deposited "bone-seekers." Given the difficulties in estimating the dose
to target cells in bone, EPA is deferring the quantification of uncertainty in bone
cancer risks until the Agency reevaluates the risks from specific internal emitters.
5.2 Lower Energy Beta Particles and Photons
As energetic electrons lose energy in passing through matter, they
become more densely ionizing: i.e., the average distance between ionization
events shrinks, and more energy is deposited in ionization clusters. As
discussed earlier, such clusters produce DSBs and complex DMA damage that
are more difficult for the cell to repair. Indeed it has been suggested that a large
fraction of the residual damage caused by low-LET radiation may stem from such
clusters produced at the ends of electron tracks (Nikjoo and Goodhead 1991).
For this reason, it might also be expected that lower energy beta particles would
be more biologically damaging than higher energy betas. Furthermore, since the
energy distribution of secondary (Compton) electrons is shifted downward as
incident photon energy is reduced, the biological effectiveness of photons might
also be expected to rise with decreasing energy, implying that lower energy
photons, including medical x rays, which typically have energies below 250 keV,
might be more damaging than the gamma rays to which the LSS cohort was
exposed.
Results from many studies tend to confirm these predictions for low-LET
radiations, including measurements of chromosome aberrations, mutations, cell
transformation and cancer induction. The most direct source of data on the
subject consists of comparative studies of x- and gamma-ray induction of
dicentrics in human lymphocytes. In these studies, 220-250 kVp x rays, which
are often used for diagnostic purposes in medicine, generally produced 2-3 times
as many dicentrics as 60Co gamma rays (NCRP 1990). The relevance of such
findings for cancer induction is unclear, however, since a dicentric will render a
cell incapable of cell division. Other laboratory studies directed at ascertaining
the RBE for various types of radiation relative to x rays or gamma rays provide
additional indirect information, suggesting again that orthovoltage x rays may be
a factor of 2-3 times more hazardous than gamma rays with energies above
about 250 keV (Kocher et al. 2005, NCRP 1990, NRC 2006). Kocher et al.
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further conclude that x rays below 30 keV, such as those used in mammography
may have a slightly higher RBE than those in the range 30-250 keV.
Kocher et al. also consider what RBEs should be applied to beta particles.
Noting that the average energy of a Compton electron produced by an incident
250 keV photon is 60 keV, they conclude that beta particles above about 60 keV
should have about the same RBE as those [photons??] above 250 keV - i.e.,
=1.0. One important radionuclide that emits a substantial fraction of its decay
energy in the form of a lower energy beta is 3H, for which the mean beta energy
is 5.7 keV and the maximum is 18.6 keV. For 3H and other betas with average
energy below 15 keV, the authors recommend a lognormal uncertainty
distribution with GM=2.4 and a GSD=1.4, corresponding to a 95% Cl of (1.2,
5.0). The reference radiation is again chronic gamma rays. In addition, they
assign the same probability distribution to the RBE for internal conversion or
Auger electrons with energy < 15 keV as for 3H. This uncertainty range is
comparable to what was recommended for <30 keV photons and is generally
consistent with experiments in which investigators compared 3H with gamma rays
in the induction of various end-points.
Kocher et al. also state that electrons of energy 15-60 keV would be
expected to have about the same RBE as 30-250 keV photons but that direct
biological data are lacking.
A review of tritium risks has recently been conducted by an independent
advisory group for the Health Protection Agency of the UK (HPA 2007). The
authors found that, in a wide variety of cellular and genetic studies, the RBE
values for tritiated water were 1-2 when compared with low dose-rate
orthovoltage x rays and 2-3 when compared with chronic gamma rays. It was
concluded that "an RBE of two compared with high energy gamma radiation
would be a sensible value to assume". Although much of the experimental
evidence suggested a value between two and three, fractional values were "not
considered appropriate."
The conclusions of the HPA report were supported by experimental and
theoretical evidence (Nikjoo and Goodhead 1991, Goodhead 2006) that the
biological effects of low-dose, low-LET radiation predominantly reflect complex
DMA damage generated by ionization and excitation events produced by low
energy electrons near the ends of their tracks with energies above 100 eV but no
more than about 5 keV. Figure 5-1 shows a plot, for various incident radiations,
of F, the cumulative fraction of the total dose deposited in an aqueous medium
by electrons of energy T (>100 eV). These fractions were estimated by Nikjoo &
Goodhead (1991) using track-structure simulation codes and results were found
to be similar to those of a numerical approximation method developed by Burch
(1957). Assuming that the amount of critical damage is proportional to F(5 keV),
the estimated RBE is =2.3 for 3H beta particles and =1.4 for 220 kV x rays, both
relative to 60Co gamma rays or 1 MeV electrons. Alternatively, if the critical
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damage is taken to be proportional to F(1 keV), the estimated RBEs would be
=1.6 for 3H and =1.2 for the x rays.
1.0
0.8
Cumulative
fraction
of
total
F
0.4
0.2
0
220KV X-ray
103 104 105 1
Electron Energy, T(§VJ
10'
Figure 5-1: Cumulative fraction of the total dose, F, plotted against secondary electron
kinetic energies, T, for a variety of low-LET radiations calculated by Nikjoo & Goodhead
(1991) using the method of Burch (1957).
By means of a more accurate Monte Carlo procedure, Nikjoo and
Goodhead calculated, for each of several initial electron energies, the cumulative
fraction of the total dose deposited by electrons with energies between 100 eV
and a specified energy. Those results are shown in Figure 5-2. From the figure,
it is estimated that the contribution of low-energy (0.1 to 5 keV) electrons to the
total dose from an electron with initial energy 10 keV would be =63%, compared
to =51% for an incident 100 keV electron. The authors did not calculate the
distribution for higher energy incident electrons, but assuming that the fractional
increase in F obtained in applying the Monte Carlo method in place of the Burch
approximation is about the same as for 100 keV electrons (=10%), the result
would be =37% for the higher energy electrons or 60Co gamma rays. Using this
approach, it should be possible to estimate average RBEs for a whole range of
low-energy beta emitters. Furthermore, from spectral information on the
secondary electrons produced by a photon source of a given energy, RBEs could
also be estimated for photon emitters.
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1.0
0.8
Cumulative
fraction Q.6
of
total dose
F 0.4,
0,2
103 104
Electron Energy, T(eV)
10s
Figure 5-2: Cumulative fraction of total dose, F, plotted against secondary electron
kinetic energies, T, for a variety of slow and fast initial electron energies calculated by
the Monte Carlo track structure method (Nikjoo and Goodhead, 1991).
No firm conclusions can be drawn from human epidemiological data on
the RBE for lower energy photons and electrons. Risk coefficients derived from
studies of cohorts medically irradiated with x rays are in some cases lower than
what has been observed for the A-bomb survivors. Nevertheless, given the
various uncertainties, such as those relating to dosimetry, sampling error,
population differences, and possible confounders, it is still possible that medical x
rays are significantly more carcinogenic, per unit dose, than gamma rays. This
issue can only be resolved through experiment and a better understanding of the
dependence of DNA damage and carcinogenesis on microdosimetric
parameters.
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6. Risks from Prenatal Exposures
First carried out by Stewart and coworkers (Stewart et al. 1958, Bithell and
Stewart 1975), case-control studies of childhood cancer have shown about a
40% increase in risk associated with exposure to diagnostic x rays in utero.
Typically, the x rays employed in Stewart's "Oxford series" were 80 kVp and the
mean dose was 6-10 mGy; this corresponds to about 1 photon per cell nucleus.
Hence this finding argues against the likelihood of a threshold for radiation
carcinogenesis.
The estimate of risk for childhood cancer derived from the Oxford survey
is about 0.06 per Gy (95% Cl 0.01-0.126) for all cancers and about 0.025 per Gy
for leukemia (Mole 1990, Doll and Wakeford 1997). Although numerous other
case-control studies have shown a similar radiation-related risk as the Oxford
survey (Doll and Wakeford 1997), the evidence from cohort studies is equivocal
(Boice and Miller 1999). Children exposed in utero to radiation from the atomic
bomb explosions have not experienced any detectable increase in cancer, and
the derived upper bound is lower than the estimate derived from the case-control
studies (Doll and Wakeford 1997). Results from a large cohort study did show
an increase in leukemia of about the same magnitude as the Oxford series, but
the observed increase in childhood solid tumors was much lower and not
statistically significant (Monson and MacMahon 1984). Another question
regarding the risk of solid tumors has been that the excess relative risk seen in
the case-control studies is about the same, regardless of the type of tumor. This
may suggest that the increase is due to some unaccounted for source of
confounding (Boice and Miller 1999).
On balance, the evidence from the epidemiological studies indicates that
the fetus is at risk of childhood cancer from ionizing radiation (Doll and Wakeford
1997). Following the recommendations of Doll and Wakeford (1997) and the
ICRP (2000), EPA adopts the estimate of 0.06 Gy"1 for prenatal exposures to
diagnostic x rays. Survival rates for childhood cancer are approximately 70-80%
for childhood cancer for both leukemia and solid tumors (SEER 2006c, Tables
XXVIII-1- and XXIX-6), but this does not include any delayed mortality due to
second cancers resulting from the treatment. NCRP cites a value of 5x10"2 Sv"1
for fetal exposure to internally deposited radionuclides (NCRP 1998). However,
as discussed in Section 5.2, an RBE of about 1.4 for cancer induction should
perhaps be assigned to x rays commonly used in medicine. Therefore, in the
case of most internally deposited p/y-emitters or external gamma radiation, a
lower risk estimate of = 4x10"2 Gy"1 should be applied for childhood cancer
incidence.
The studies of medically irradiated fetuses only address the induction of
childhood cancers. Epidemiological follow-up of the A-bomb survivors has
indicated that individuals irradiated in utero may have a lower risk of adult
cancers than those irradiated as young children, but the difference is not
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statistically significant (Preston et al. 2008). Based on this finding, we adopt the
same set of models employed for calculating risk for exposure to young children
to assess the risk of adult cancers caused by in utero exposure. More
specifically, we directly applied the risk models of Section 3 with age-at-exposure
set to 0. The sex-averaged projected risk for adult cancers (attained age > 15) is
0.29 per Gy for incidence and 0.12 per Gy for mortality. This risk is a factor of 2-
3 times higher than that for the general U.S. population. It is also about a factor
of 5 times the estimated risk of a radiogenic childhood cancer from prenatal
exposures. Nevertheless it constitutes only a small fraction (<3%) of the risk
from a uniform whole-body exposure to the U.S. population.
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7. Radionuclide Risk Coefficients
Subsequent to publication of this report, EPA will use its revised radiation
risk models and ICRP's latest dosimetric models to update the radionuclide risk
coefficients in Federal Guidance Report 13 (EPA 1999b). Radionuclide risk
coefficients are EPA's best estimates of the lifetime excess mortality or morbidity
risk per unit intake of a given radionuclide by ingestion or inhalation, or per unit
exposure for external irradiation. The current version of FGR 13 contains risk
coefficients for environmental exposure to over 800 radionuclides.
Based on the values in Table 3-12, EPA expects that updated mortality
risk coefficients for those radionuclides that irradiate the body uniformly will be
similar to currently published values, whereas corresponding morbidity risk
coefficients will likely increase by about 20%. For radionuclides irradiating the
body nonuniformly, EPA anticipates both increases and decreases, depending
on the target organ. For example, updated risk coefficients for inhaled
radionuclides retained in the lung may be larger than present estimates because
the population-averaged lung cancer risk has increased substantially over time.
Conversely, updated risk coefficients for radionuclides that are poorly absorbed
from the intestines into the bloodstream and that emit short-range radiation,
especially alpha particles, should be smaller than current values because of
reduction in colon cancer risk and adoption of new ICRP alimentary tract models
(ICRP 2009) that place the location of target cells in the intestinal wall out of
range of alpha particles emitted from the contents of the colon.
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GLOSSARY
Absorbed dose: The energy deposited by ionizing radiation per unit mass of
tissue irradiated. It can be expressed in units of gray (Gy) or milligray
(mGy) where 1 Gy = 1000 mGy.
Adaptive response: A reduced response to IR radiation induced by a prior dose.
Alpha particle: A particle consisting of two protons and two neutrons emitted
from a decay of certain heavy atomic nuclei. A type of high-LET IR.
Apoptosis: Programmed cell death.
BCC: Basal cell carcinoma.
Baseline cancer rate: The cancer mortality or incidence rate in a population in
the absence of the specific exposure being studied.
Bayesian: A statistical approach in which probability reflects the state of
knowledge about a variable, often incorporating subjective judgment.
BEIR VII: A National Research Council Report, Health Risks from Exposure to
Low Levels of Ionizing Radiation. BEIR VII. Phase 2.
Beta particle: An electron emitted from a decay of an atomic nucleus. A type of
low-LET IR.
Bystander effect: A change in a cell due to irradiation of a nearby cell.
Confidence Interval (Cl): A range of values calculated from sample
observations that are believed, with a particular probability to contain the
true parameter value. Upper and lower values of a Cl are called
confidence limits. A 90% Cl implies that if the estimation process were
repeated many times, about 90% of the intervals would contain the true
value. The 90% probability refers to the properties of the interval and not
the parameter itself.
Confounder: In an epidemiological study, a factor that is associated with both
the exposure and outcome of interest and thereby distorts or masks the
true effect of the exposure.
Dose and dose-rate effectiveness factor (DDREF): A factor used to account
for an apparent decrease in the effectiveness of low-LET radiation in
causing a biological end-point (e.g., cancer) at low doses and dose rates
compared with observations made at high, acutely delivered doses.
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Dose effectiveness factor (DEF): A factor estimated from the LQ model to
account for a decrease in the effectiveness of low-LET radiation in causing
a biological end-point (e.g., cancer) at low doses compared with that at
high acute doses.
Dose equivalent: A weighted sum of absorbed doses of different types of IR,
measured in units of sieverts (Sv). The ICRP recommended values for
the weighting factors wr are: 1.0 for photons and electrons, 10 for fission
neutrons, and 20 for alpha particles. Thus, for low-LET radiation, the dose
equivalent in Sv is numerically equal to the absorbed dose in Gy, whereas
for alpha-particles an absorbed dose of 1 Gy corresponds to 20 Sv.
Dose rate effectiveness factor (DREF): A factor used to account for an
apparent decrease in the effectiveness of low-LET radiation in causing a
biological end-point (e.g., cancer) at low dose rates compared with high
dose rates.
Double strand break (DSB): DMA damage in which a break extends over both
strands of the double helix.
Electron volt (eV): The customary unit of energy for all ionizing radiations'. 1 eV
is equivalent to the energy gained by an electron passing through a
potential difference of 1 volt. 1 keV = 1000 eV; 1 MeV = 1,000,000 eV.
EPA: Environmental Protection Agency.
Excess absolute risk (EAR): The rate of disease in an exposed population
minus that in an unexposed population. Also termed "attributable risk."
Excess relative risk (ERR): The rate of disease in an exposed population
divided by that in an unexposed population minus 1.
Gamma rays (or gamma radiation): Photons of nuclear origin similar to x rays
but usually of higher energy. A type of low-LET IR.
Genomic instability: An enhanced rate of spontaneous genetic change in a cell
population..
Geometric mean (GM): The GM of a set of positive numbers is the exponential
of the arithmetic mean of their logarithms.
Geometric standard deviation (GSD): The GSD of a lognormal distribution is
the exponential of the standard deviation of the associated normal
distribution.
Gray (Gy): Unit of absorbed dose.
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High-LET radiation: IR such as neutrons or alpha particles that produce
ionizing events densely spaced on a molecular scale (e.g., LET > 10
keV/um).
HPA: Health Protection Agency of the United Kingdom
ICRP: International Commission on Radiological Protection. An independent
international organization that provides recommendations and guidance
on radiation protection against ionizing radiation.
Ionizing Radiation (IR): Any radiation capable of removing electrons from atoms
or molecules as it passes through matter, thereby producing ions.
kVp (kV): Kilovolt potential - refers to the potential difference between the
electrodes of an x ray tube. For example, the output of a 200 kVp x-ray
tube will consist of photons with a range of energies up to 200 keV.
LET: Average amount of energy lost per unit track length of an ionizing charged
particle.
Life table: A table showing the number of persons who, of a given number born
or living at a specified age, live to attain successively higher ages,
together with the number who die in each interval.
Linear no-threshold (LNT) model: Dose-response for which any dose greater
than zero has a positive probability of producing an effect. The probability
is calculated from the slope of a linear (L) model or from the limiting slope,
as the dose approaches zero, of a linear-quadratic (LQ) model.
Linear (L) model: A model in which the probability of an effect (e.g., cancer) is
expressed as being proportional to the dose.
Linear-quadratic (LQ) model: A model in which the probability of an effect (e.g.,
cancer) is expressed as the sum of two terms - one proportional to the
dose, the other to the square of the dose. In the limit of low doses and low
dose rates, the quadratic term can be ignored.
Low-LET radiation: IR such as x rays, gamma rays, or electrons that produce
sparse ionizing events on a molecular scale (e.g., LET < 10 keV/um).
Lognormal distribution: A distribution in which the logarithm of a randomly
distributed quantity has a normal distribution.
Life Span Study (LSS): Long term study of health effects in the Hiroshima and
Nagasaki atomic bomb survivors.
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Mortality (rate): the frequency at which people die from a specific cause (e.g.,
lung cancer), often expressed as the number of deaths per 100,000
population per year.
NCRP: National Council on Radiation Protection and Measurements. A Council
commissioned to formulate and disseminate information, guidance, and
recommendations on radiation protection and measurements.
NIOSH: National institute for Occupational Safety and Health.
Photon: A quantum of electromagnetic energy. Energetic photons in the form of
x rays or gamma rays can ionize atoms or molecules in a medium upon
which they are incident.
Radiation Effectiveness Factor (REF): An estimate of the RBE for estimating
human cancer risk. The estimated value at low doses is denoted as REFL.
Relative Biological Effectiveness (RBE): The relative effectiveness of a given
type of radiation in producing a specified biological effect compared to
some reference radiation. For purposes of this document, the reference
radiation is generally taken to be low dose gamma rays.
RBEM: The maximal limiting value of the RBE for a high-LET radiation attained in
the limit of low doses.
Relative Risk (RR): The rate of disease in an exposed population divided by that
in an unexposed population.
Risk coefficient: the increase in the annual incidence or mortality rate per unit
dose: (1) absolute risk coefficient is the increase in the incidence or
mortality rate per unit dose; (2) relative risk coefficient is the fractional
increase above the baseline incidence or mortality rate per unit dose.
SCC: Squamous cell carcinoma.
SEER: Surveillance, Epidemiology, and End Results.
Sievert (Sv): Unit of dose equivalent. In the BEIR VII analysis of the A-bomb
survivor data, the dose equivalent was calculated from the absorbed
gamma ray and neutron doses, assuming a radiation weighting factor of
10 for neutrons.
Stationary population: A hypothetical population in which the relative number of
people of a given age and gender is proportional to the probability of
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surviving to that age. At age 0, the number of males is taken to be 1.051
times the number of females to reflect males' higher birth rate.
Uncertainty: A term used to describe the lack of precision and accuracy of a
given estimate.
Uncertainty distribution: A mathematical expression defining the relative
probabilities of different values for an estimated quantity.
UNSCEAR: United Nations Scientific Committee on the Effects of Atomic
Radiation. A UN committee that publishes reports on sources and effects
of ionizing radiation.
WLM: Working level months, a measure of radon decay product exposure.
X radiation or x rays: Energetic photons usually produced by bombarding a
metallic target with fast electrons in a high vacuum.
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