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
                                   11

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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.
                                    12

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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.
                                    13

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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).
                                    14

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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
                                   15

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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

-------
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

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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

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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 fis*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

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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

-------
      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

-------
      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(18), 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(ls)  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(lB} 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

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      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

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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

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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

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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

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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.
                                    59

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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)
                                    60

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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
                                   61

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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).
                                   62

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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.
                                   63

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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.
                                   64

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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
                                     65

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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
                                    66

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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
                                    67

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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
                                    68

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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).
                                   69

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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.
                                   70

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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.
                                    75

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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.
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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
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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
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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
                                    89

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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.
                                   92

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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
                                   93

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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.
                                   94

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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
                                   96

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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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