(13) Other issues

Comments: Several reviewers commented on the need for better documentation of the
assumptions and uncertainty analysis used in NIOSH-IREP, and specific suggestions were made
for improvement of the technical background document. One reviewer suggested the
incorporation of adjustments to the risk transfer function for cancers (in addition to skin), based
on race/ethnicity or other factors. One reviewer (D. Richardson) requested more information
about how doses would be estimated to organs in practice.

Response: NIOSH will implement the specific suggestions made for improving the technical
background document. We concur that better documentation of the models and assumptions is
needed within NIOSH-IREP. However, the primary basis of NIOSH-IREP is NCI-IREP, the
documentation of which is awaiting finalization at NCI and HHS (and is not yet available to be
published). As soon as it is feasible, the final NCI-IREP will be made available along with the
NIOSH-IREP documentation. The primary means of communicating the underlying assumptions
of the models will be to embed them in the “View Model Details” area of the NIOSH-IREP
website.

We concur that differences in background cancer rates should be incorporated into NIOSH-IREP,
if feasible, if differences are substantial, and if underlying reasons for these differences are
understood. The only cancer that met these criteria at this time was skin cancer, as discussed in
the technical documentation for NIOSH-IREP. That is, skin cancer has the most highly-variable
incidence rates (by racial/ethnic group), and variation is due to the variability in the presence of
melanin in the skin. Since the primary causal factor is known for skin cancer, its expected
interaction with ionizing radiation exposure can be modeled within NIOSH-IREP, and can be
determined fairly simply on an individual basis for estimating a claimant’s probability of
causation. As information on etiologic factors underlying racial and ethnic differences in cancer
incidence rates improves, it may be possible to include this variability in NIOSH-IREP.

Finally, the dose estimation procedures are detailed more completely in the dose
reconstruction regulation, and in the technical documentation for the dose reconstruction process.
Citations for these methods have been added to the NIOSH-IREP technical documentation.

20


(7) The distribution used in NIOSH-IREP for the “dose and dose-rate effectiveness factor”
(DDREF)

Comments: There were several comments on the approach used to adjust for low dose-rate
exposures in NIOSH-IREP. D. Stram remarked that the approach is not adequately justified, and
that it reflects the judgment of a small group, which could be highly criticized. D. Richardson
commented that the DDREF was too high, on average, and that it does not reflect the latest
research from the low-dose ranges of the Japanese A-bomb survivors. D. Hoel argued against
the assumption of linearity at low dose for all solid tumors, suggesting that analyses based on
cancer mortality among the A-bomb cohort are subject to bias. R. Hornung questioned why no
adjustment was made for a possible inverse dose-rate effect for radon and lung cancers at
exposures above 50 working level months.

Response: We concur with D. Richardson that the A-bomb survivor study (which is the
epidemiological data source deemed most relevant for adjustment by other risk modifiers such as
gender and age at exposure) does not support the use of a DDREF of much larger than one, for
low-dose acute exposures. [The DDREF in NCI-IREP is phased-in at acute doses lower than 0.2
Sv-well above levels found to be linear in studies of incidence (Pierce and Preston 2000) and
mortality (Pierce et al. 1996) in the A-bomb survivor cohort.]

The recent strong evidence for a linear (or, more weakly, for a supra-linear) dose-
response relationship for all solid incident cancers in the dose range of 0.05 to 0.1 Sv in the A-
bomb study is made more compelling because it avoids the potential biases for which the finding
in the mortality series has been criticized (e.g., by D. Hoel).

Despite the evidence from the A-bomb survivor study supporting the use of a DDREF of
one, there is much evidence from animal studies supporting a DDREF of greater than one, for a
variety of endpoints, including cancer (summarized on pp. 60-66 of NCRP 1997), from low-LET
exposures. Most expert committees, including the NCRP, the ICRP, and UNSCEAR, advocate
the use of an expected value for the DDREF of about 2 (NCRP 1997, p 66; ICRP 1991;
UNSCEAR 2000, p 358); however, these recommendations preceded the new analysis of cancer
incidence in low-dose ranges of the Japanese A-bomb study (Pierce and Preston 2000).

Therefore, the NCI has shifted the DDREF distribution for all solid cancers in its
proposed version of IREP to more heavily weight a DDREF of one, and to include a small
probability for a DDREF of less than one (i.c., a supralinear effect at low doses). This
distribution, more similar to that used by the U.S. EPA (USEPA 1999), and the recent report by
Grogan and colleagues (Grogan et al. 2000), is also the basis for the revised NIOSH-IREP (Fig.
4a). To make the DDREF distribution consistent for breast and thyroid cancers, NCI has added a
small probability of supralinear effects at low doses (i.e., a DDREF of less than one; Fig. 4b).
This has also been adopted for use in NIOSH-IREP.

In response to the comment about the need to incorporate a possible inverse dose-rate
effect for radon and lung cancer above 50 WLM, such a separate adjustment is unnecessary. The
model for radon exposures developed by NCI researchers incorporates a power-law function in
the dose-response relationship that accounts for a possible inverse dose rate effect (Charles Land,
personal communication).

It should be noted that at present IREP (both NCI and NIOSH versions) assumes the
quadratic term in the leukemia dose-response relationship is fixed. Ideally, this term should have

14


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Figure 2. ERR/Sv estimates (at the upper 99" percentile of the credibility distribution) for
female breast cancer (FBC, triangle) and for male miscellaneous cancer (M Misc, X)
models, from NCI (2001).

100.0

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Age at diagnosis


References:

Bernstein L, Ross RK, Pike MC, Casagrande JT, Henderson BE. Urinary estrogens and male-to-
female breast cancer ratios, Int. J. Cancer 44:954-956, 1989,

Boice JD Jr, Engholm G, Kleinerman RA, et al. Frequent chest x-ray fluoroscopy and breast
cancer incidence among tuberculosis patients in Massachusetts. Radiat. Res, 125:214-222, 1991.

Boice Jr JD, Land CE, Preston DL. Ionizing Radiation. Ch 16 in Cancer Epidemiology and
Prevention, 2" Ed. Schottenfeld D, Fraumeni Jr JF, eds. New York: Oxford University Press.
1996, 1521 pp.

Cardis E, Gilbert ES, Carpenter L, Howe G, Kato I, Fix J, Salmon L, Cowper G, Armstrong BK,
Beral V, Douglas A, Fry SA, Kaldor J, Lave C, Smith PG, Voelz GL, Wiggs LD. Combined
Analysis of Cancer Mortality among Nuclear Industry Workers in Canada, the United Kingdom
and the United States of America. IARC Technical Report No. 25, International Agency for
Research on Cancer, Lyon, 1995a, 147 pp.

Cardis E, Gilbert ES, Carpenter L, Howe G, Kato I, Armstrong BK, Beral V, Cowper G, Douglas
A, Fix J, Fry SA, Kaldor J, Lave C, Salmon L, Smith PG, Voelz GL, Wiggs LD. Effects of low
doses and low dose rates of external ionizing radiation: cancer mortality among nuclear industry
workers in three countries. Radiat. Res. 142:117-132, 1995b.

Casagrande JT, Hanisch R, Pike MC, Ross RK, Brown JB, Henderson BE. A case-control study
of male breast cancer. Cancer Res. 48:1326-1330, 1988.

Gilbert ES. Discussion: Temporal effects in radiation epidemiology-II. p. 734-735 of Proceedings
of the American Statistical Association 2000 Conference on Radiation and Health. Radiat. Res.
154:714-741, 2000.

Grogan HA, Sinclair WK, Voilleque PG, Till JE. Final Report. Assessing Risks of Exposures to
Plutonium, Revision 2. Radiological Assessments Corporation Report No. 5-CDPHE-RFP-
1998-FINAL(Rev.2), February 2000.

Henderson BE, Pike MC, Bernstein L, Ross RK. Breast Cancer. Ch 47 in Cancer Epidemiology
and Prevention, 2"° Ed. Schottenfeld D, Fraumeni Jr JF, eds. New York: Oxford University
Press. 1996, 1521 pp.

International Commission on Radiological Protection (ICRP). 1990 recommendations of the
International Commission on Radiological Protection, ICRP Publication 60, Ann. ICRP 21, No.
1-3, 1991.

Land C, Gilbert E, Smith J. Draft Report of the NCI-CDC Working Group to Revise the 1985
NIH Radioepidemiological Tables. December 18, 2001. 119 pp.

21



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Health Phys. 58:729-735, 1990.

Stewart AM, Kneale GW. A-bomb survivors: factors that may lead to a re-assessment of the
radiation hazard. Int. J. Epidemiol. 29:708-714, 2000.

Thompson DE, Mabuchi K, Ron E, Soda M, Tokunaga M, Ochikubo S, Sugimoto S, Ikeda T,
Terasaki M, Izumi S, Preston DL. Cancer incidence in atomic bomb survivors. Part II: Solid
Tumors, 1958-1987. Radiat. Res. 137:S17-S67, 1994,

United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). Sources,
Effects and Risks of Ionizing Radiation, No. E.94.IX.2, United Nations, New York, 1993.

United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR).
Combined effects of radiation and other agents. Annex H in: Sources and Effects of Ionizing
Radiation. Vol. II. Effects. 2000.

United States Environmental Protection Agency (USEPA). Estimating Radiogenic Cancer Risks.
EPA Report 402-R-00-003, Washington, DC, May 1999.

Wing S. The influence of age at exposure to radiation on cancer risks in humans. p. 732-733 of
Proceedings of the American Statistical Association 2000 Conference on Radiation and Health.
Radiat. Res. 154:714-741, 2000.

23



(10) ‘Interaction of radiation with other carcinogenic exposures

Comments: Several subject matter experts commented about the assumptions in NIOSH-IREP
regarding radiation interactions with other carcinogens. R. Shore stated that “most hormonal
factors do not interact (with radiation).” D. Richardson suggested the inclusion of an uncertainty
factor to account for workplace chemical interactions with radiation. R. Hornung questioned the
lack of adjustment for tobacco use in cancers other than lung.

Response: The current (default) assumption in NIOSH-IREP is that radiation interacts in a
multiplicative way with chemical exposures and cigarette smoking. That is, the proportional
increase in cancer risk from radiation exposure is assumed to be the same for individuals with
different levels of exposure to chemical carcinogens. The one exception is for cigarette smoking
and trachea, bronchus or lung cancer. In this case, the distribution of the interaction term
between smoking and ionizing radiation exposure is centered on a sub-multiplicative interaction,
with small probability of the interaction being super-multiplicative or additive. No adjustment
exists for other tobacco-related cancers because of a lack of information about the form of
interaction between these cancers and radiation exposure. In the absence of such information, the
interaction is assumed to be multiplicative, and no adjustment to the risk models is needed.

Because EEOICPA does not allow consideration of compensation claims for chemical
exposure, the use of an adjustment for DOE workplace-related chemical exposures could have
the effect of reducing a claimant's chance of compensation for a cancer that may have been
caused by joint exposure to radiation and chemicals. The default assumption, of a multiplicative
interaction, is likely to be favorable to the claimant, since exposures that have the same mode of
action are likely to interact in a sub-multiplicative manner (Land et al. 2001, p. 39).

In addition, it is not scientifically supportable nor practically feasible to adjust
NIOSH-IREP risk models for the multitude of occupational and community exposures that may
modify risk from radiation exposures. The risks associated with most chemical exposures, and
the appropriate form of their interaction with radiation, have not been adequately quantified.
Moreover, it would be very difficult to obtain data on the individual claimant's exposure to
chemicals or radiation in the community. Access to data on past occupational exposures to
chemicals is also infeasible at this time.

The adjustment for smoking history in NIOSH-IREP has been adopted from the approach
developed by NCI. The NCI’s review of relevant literature, and a scientific consensus panel
opinion (UNSCEAR 2000, pp. 201-203), concluded that the best-supported models to evaluate
the form of interaction between smoking and radiation are based on meta-analyses of radon-
exposed workers. Combined analyses of these studies suggests that the most appropriate form of
interaction is sub-multiplicative, but greater than additive (Lubin and Steindorf 1995). This
information was used to develop an uncertainty distribution for the form of interaction between
smoking and radiation in the lung cancer risk models that is centered on a sub-multiplicative
model (i.e., assuming that the excess relative risk per unit of radiation dose is lower for
individuals who smoke more), but includes the possibility of either a multiplicative model (i.e.,
that excess relative risk per unit of radiation dose is the same for various levels of smoking) or a
super-multiplicative model (i.e., that excess relative risk per unit dose is higher for individuals
who smoke more). As with all assumptions, this uncertainty distribution is subject to
modification in future revisions of NIOSH-IREP, pending the availability of new scientific
information.

18



(5) Use of Japanese atomic bomb data as source of radiation risk coefficients.

Comments: D. Hoel commented on quality differences between the A-bomb survivor cancer
incidence and mortality that recommend the use of the former, D. Richardson argued that the A-
bomb survivor study is inadequately adapted as a source of risk coefficients for DOE workers. A
number of limitations were enumerated, including the lack of adjustments for a “healthy survivor
effect”, for some types of random and systematic error in dose estimation from the study, and for
other sources of bias described in the NCRP Report 126 (NCRP 1997).

Response: Cancer incidence data from the Japanese A-bomb survivor study are the basis for the
risk estimates for all cancers except lung cancer from exposures to radon, and thyroid cancer.
The bias and random error sources considered in NCRP Report include selection bias,
misspecification of the true dose-response function, errors in the classification of cancers, and
dosimetry errors. Many of these biases were considered by NCI in establishing the risk models
used in IREP. The potential for bias in risk coefficients resulting from a “healthy survivor
effect” in the Japanese A-bomb cohort has been described (Stewart and Kneale 1990, 2000,
NCRP 1997). In a formal evaluation (Little and Charles 1990), its magnitude appeared relatively
weak, appeared negligible beyond a few years after exposure, and may be attributable to errors in
the (T65D) dosimetry system employed. While it is not clear at this time how a potential
selection bias should be incorporated into NIOSH-IREP, this issue should be addressed
systematically in future versions of IREP.

No direct adjustment is made in IREP for dose-response misspecification. However, the
use of an uncertainty distribution for the dose and dose-rate effectiveness factor (DDREF) for
solid cancers indirectly modifies the dose-response relationship. The use of a distribution
encompassing values above and below unity ensures that the possibility of supra-linear, linear, or
sub-linear dose-response relationships for these cancers are represented in the risk models. For
non-CLL leukemia, the dose-response relationship is fixed as linear-quadratic. The need to adapt
this approach for leukemia is discussed further below (See Item #7).

Some of the epidemiological biases discussed in NCRP Report 126 are more pertinent to
the use of cancer mortality than cancer incidence data (NCRP 1997, p. 16 Charles Land, personal
communication). For example, bias or error resulting from the use of cancer mortality data to
estimate dose-related increases in cancer incidence would not be relevant if cancer incidence data
were modeled directly. Biases due to misclassification of cancer cases are not as serious a
problem for cancer incidence as for mortality data (NCRP 1997, p. 16-19). Underascertainment
of incident cancers due to migration would be expected to affect absolute, not relative, risk
coefficients, since it appears independent of radiation exposures (NCRP 1997, p. 19). The
NCRP Report 126 declined to adjust risk coefficients based on cancer incidence data for
epidemiologic bias and random error (NCRP 1997, p. 22). We believe that these potential
epidemiologic biases are not of substantial concern for IREP, since cancer incidence data are the
direct source of risk coefficients in NIOSH-IREP.

Dr. Richardson criticized IREP for failing to account for random errors in the A-bomb
dosimetry. This criticism appears to stem from a misunderstanding about the way the random
dosimetry error coefficient is applied. For solid cancers and non-CLL leukemias, the ERR/Sv
coefficient is multiplied (not divided) by a value selected by the uncertainty distribution shown in
in equations (1) and (2), respectively. This serves to increase, not decrease, the risk coefficients

10


Both the basal cell carcinoma and the miscellaneous cancer models have higher ERR/Sv
estimates than the squamous cell carcinoma model (NCI 2001). Of these two, the basal cell
carcinoma model produces higher ERR/Sv estimates for men at all combinations of age at
exposure and attained age, and for women at younger ages of exposure (Fig. 3). At ages of
exposure above about 35, the miscellaneous cancer model produces slightly greater ERR/Sv
estimates (but these are both quite low, considering typical exposure patterns at DOE facilities).
Therefore, it would in general be most claimant-favorable to use the basal cell carcinoma model
to provide excess relative risk estimates for malignant melanoma. These estimates should be
applied to the background incidence rates for malignant melanoma in Japan and the U.S., and the
same risk transfer model as for other skin cancers, as discussed below (i.e., the distribution
favoring neither the additive nor the multiplicative interaction model).

In summary, NIOSH-IREP incorporates three different skin cancer models. For non-
melanoma skin cancer, basal cell and squamous cell carcinoma models are based on different sets
of risk coefficients, In claims where the non-melanoma skin cancer type is indeterminate, the
model for basal cell carcinoma will be used. Both basal cell and squamous cell carcinoma
models are modified by race-specific background incidence rates for all non-melanoma skin
cancers. A third skin cancer model, for malignant melanoma, uses basal cell carcinoma risk
coefficients, modified by background incidence rates for malignant melanoma. All three skin
cancer models use the “general” risk transfer uncertainty distribution (developed for all solid
cancers save breast and thyroid).


(3) Selection of risk coefficients for squamous cell carcinoma and malignant melanoma.

Criticism: One reviewer (R. Shore) argued against the use of the basal cell carcinoma risk
coefficients for malignant melanoma. This reviewer suggested that malignant melanoma is only
inducible at high doses. Another reviewer (D. Hoel) asserted that evidence is weak for dose-
related increases in malignant melanoma among nuclear workers, and that the basal cell
carcinoma (BCC) risk coefficients should not be used for squamous cell carcinoma or other non-
melanoma skin cancers (SCC), which showed much lower risk estimates. The NCI also
discouraged the use of BCC risk coefficients for SCC, and developed a separate model for the
two forms of non-melanoma skin cancer.

Response:

Upon further review of the arguments for the use of separate SCC risk coefficients, we
determined that it is both appropriate and feasible to employ separate models for BCC and SCC
within NIOSH-IREP. The development of risk coefficients for these separate models for the
finalized NCI-IREP is quite consistent with that for other cancers. The results of this modeling
effort support the use of substantially different risk coefficients for the two non-melanoma skin
cancer types. However, it should be noted that ICD-9 (and its revision, ICD-10) do not
distinguish between SCC and BCC within the non-melanoma skin cancer category. In cases
where it is not possible to determine which cancer cell type applies to a given claimant, DOL will
be instructed to use the risk models for basal cell carcinoma. Background incidence rates for non-
melanoma skin cancer remain based on the combined rates of these and other non-melanoma
skin cancers, as separate Japanese rates for BCC and SCC are not available.

The argument that malignant melanoma may be inducible only at high doses, and that,
therefore, no risk models should be developed, is inconsistent with the approach used for other
cancers for which such arguments have been made (e.g., bone cancer, squamous cell carcinoma,
cancer of the uterus). The approach used in IREP for these types of cancers takes into account
the full uncertainty distribution—including the possibility of great attenuation of effect at low
doses~of the risk coefficient. We acknowledge that the evidence for radiogenicity of malignant
melanoma is weak. In the Japanese A-bomb survivor study, only 13 cases of malignant
melanoma were observed, which is far below the level at which risk estimates were derived
independently by NCI for the IREP program. Point estimates for risk coefficients in this study
are consistent with the risks observed for basal cell carcinoma. There is also (weak) evidence
from nuclear worker studies that malignant melanoma may be related to ionizing radiation dose
(although, as indicated by D. Hoel, the confidence intervals on the risk coefficients are wide;
Muirhead et al. 1999),

There is great need for future studies of malignant melanoma in radiation-exposed
populations, in order to better estimate risk coefficients for this cancer. However, in the absence
of direct information, three reasonable potential sources of risk coefficients are those developed
for non-melanoma skin cancer (one model each for basal cell and squamous cell carcinoma) and
the miscellaneous site cancer model. As discussed for male breast cancer, above, using the
model producing the highest ERR/Sv risk coefficients would be consistent with HHS policy ©
decisions about selecting among equally-valid alternatives in the Probability of Causation rule.


(11) Dose rate assumptions for badge readings.

Comment: One subject matter expert (R. Shore) commented that it is unrealistic to assume that
worker doses are acute, since there is much data that suggests that most doses are protracted. He
noted that this is inconsistently handled for neutron and gamma exposures.

Response: In determining the dose rate (i.¢., acute or chronic) that should be assumed for use in
NCI-IREP, the NCI working group referenced the definition used by the UNSCEAR (UNSCEAR
1993), which is that an acute dose is delivered at a dose rate of 6 mGy per hour or greater (NCI
2001, p. 37), averaged over several hours. This is equivalent to a dose rate of 0.1 mGy (10 mrem)
per minute. Lower exposure rates should be considered chronic.

By this definition, work-related medical x-ray exposures are clearly acute. Neutron and
gamma exposures in the workplace are indeterminate as to the dose rate. For neutron and gamma
exposures, as specified in NIOSH-IREP, the doses will be entered at the level of the badge
reading. Although likely to have been chronic, the true dose rate of these gamma and neutron
exposures is unknown for any given worker within the badging period. Therefore, the approach
used is to make an assumption about duration of exposure that is most favorable to the claimant.
This is consistent with the approach used elsewhere in NIOSH-IREP, of providing the benefit of
doubt to the claimant where two or more plausible alternatives for input exist. This approach
was, additionally, recommended by the NAS panel reviewing NCI-IREP: “... the officials
responsible for adjudicating claims might routinely assume that the exposure aggregated over the
smallest unit of time available (such as a quarter-year) was received as a single acute dose to
provide a liberal estimate of risk” (NAS/NRC 2000, p. 13). In the case of gamma exposures, such
a liberal assumption would be that the dose was acute. For neutron exposures, an assumption of
chronic dose rate would produce the most liberal estimate of risk.

(12) Consideration of carcinoma in situ in NIOSH-IREP

Comment: One subject matter expert (D. Hoel) asked for clarification of the decision to consider
carcinomas in situ (CIS), for purposes of estimating probability of causation.

Response: With the improvement in cancer screening technology, many cancers of epithelial
origin are now being detected before they have spread to the basement membrane of the affected
tissue. As discussed in the NIOSH-IREP documentation, diagnosis and treatment for many of
these neoplasms is the same as for cancer in later stages. It is uncertain what proportion of these
would proceed to invasive malignant neoplasms without intervention. Because many of the
conventional treatments for these neoplasms are the same as for cancer in later stages, it is
impossible to determine this at the level of the individual claimant. A policy consistent with
others used in NIOSH-IREP is to provide the benefit of doubt to the claimant, and to assume that
a carcinoma in situ is a malignant neoplasm. The same risk model is employed for a CIS as for
the corresponding malignant neoplasm. No distinction is made among the sites at which the CIS
might develop with regard to this policy.

19


(4) Appropriate risk transfer method (from Japanese to U.S. population) for skin cancers

Comment: Two reviewers (R. Shore and D. Hoel) asserted that the evidence of the tinea capitis
study, in which relative risks were similar for white and black subjects, supports the use of a
multiplicative over an additive risk transfer model.

Response: There is substantial scientific uncertainty regarding the appropriate risk transfer
model to be used for skin cancer. As acknowledged in the NIOSH-IREP technical
documentation, the tinea capitis study referenced by the reviewers tends to support the use of a
multiplicative interaction; however, observations of greater relative risks from ionizing radiation
in sun-protected, compared to sun-exposed, skin among the Japanese A-bomb survivors strongly
suggests that an additive (or other sub-multiplicative) interaction with ultraviolet (UV) radiation
sensitivity is warranted. The UNSCEAR report (UNSCEAR 2000, pp. 177-295) states that a
sub-multiplicative model should be employed for risk modeling, based on the totality of
evidence, including the studies mentioned above.

The uncertainty distribution for the generic risk transfer model in NIOSH-IREP is
designed to incorporate this sort of scientific uncertainty. It assigns a uniform distribution to a
multiplicative vs. an additive interaction model (with small probabilities associated with sub-
additive and super-multiplicative interactions). Based on consideration of the opinions of the
expert reviewers, we concur with NCI that there is insufficient information to favor an additive
over a multiplicative interaction between ionizing and UV radiation exposure for skin cancers.
Thus, the revised version of NIOSH-IREP reverts to the default risk transfer function used for all
cancers other than stomach and female breast.


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Response to Subject Matter Expert Review Comments on NIOSH-IREP

(1) Use of the upper 99% subjective confidence interval estimate for the probability of
causation as an adjudication level.

Comments: Several reviewers (R. Shore, D, Stram, R. Hornung) questioned whether this
practice would be more likely to result in a claim award for “less-radiogenic” cancers, which
presumably have higher error estimates about the ERR/Sv, than for cancers acknowledged to be
strongly associated with cancer. D. Stram recommended the use of random effects models to
reduce the tendency for rarer cancers to be compensated.

Response: The relevance of these comments depends on several factors, including the method
by which radiogenicity is defined, and the intent of the EEOICPA in addressing the role of
uncertainty in awarding claims. While the latter issue results from a mandate of Congress, the
former issue may be addressed explicitly, by considering the uncertainties in risk coefficients
from the Japanese A-bomb cohort for various cancers. The radiogenicity of many cancers has
been classified (somewhat informally) by Boice et al. (1996), into four groupings, based on the
totality of the epidemiologic evidence regarding their association with ionizing radiation
exposure. The four cancer groupings are:

1. “Cancers frequently associated with radiation with authoritative risk estimates”
Leukemia (except CLL), thyroid, female breast

2. “Cancers occasionally associated with radiation with valid risk estimates”
Lung, stomach, colon, esophagus, bladder, ovary, multiple myeloma

3. “Cancers rarely associated with radiation with uncertain risk estimates”
Brain and nervous system, kidney, liver, salivary glands, non-Hodgkin’s lymphoma, skin,
rectum, uterus, bone, connective tissues

4. “Cancers never or sporadically associated with radiation with no risk estimates”
Chronic lymphocytic leukemia, pancreas, Hodgkin’s disease, prostate, testis, cervix,
retinoblastoma, Wilms’ tumor, and others of embryonic origin, muscles, tendons, and
synovial membranes of joints

It can be seen that upper tail estimates of the distribution of the ERR/Sv for “less inducible”
cancers classified by the method of Boice et al. (1996) are not in general higher than those of
“more inducible” cancers (Table 1, Fig. 1). This tendency is strengthened, in particular, when
salivary cancer (a rare cancer with high risk coefficients in the A-bomb cohort analysis) is
excluded from the comparison (Fig. 1).

The pooling of biologically-similar cancers by NCI for the derivation of radiation risk estimates
(NCI 2001) should further reduce concern about this issue.


(6) Modeling the influence of age at exposure on radiation risk estimates

Comments: One reviewer (D. Richardson) argued that the dose-response models used in
NIOSH-IREP should be changed in several ways: first, the models should based on adult (not
childhood) exposures, and second, the models should include a possibility of higher cancer risks
(per unit dose) at older ages, based upon findings among some DOE populations.

Response: _—_ There is substantial evidence from several key studies in addition to those of the
A-bomb cohort that suggests radiation risk for many cancers decreases with increasing age at
exposure. These include studies of breast cancer among x-ray tuberculosis patients (Boice et al.
1991), of thyroid cancer among medically- and occupationally-exposed populations (summarized
in UNSCEAR 2000, pp. 338-343), and of skin cancer (UNSCEAR 2000, p. 402).

The NCI approach to adjusting radiation risk estimates for different exposure ages was
adopted for NIOSH-IREP. The models used in the revised NCI-IREP were developed using a
somewhat novel procedure (Land et al. 2001). Effects of age at exposure and attained age were
modeled for all solid tumors as a group. The risk coefficients from these adjustments were then
applied to the site-specific models, unless there was a statistically-significant difference between
the age-at-exposure terms for the site-specific and general model. These general coefficients
show reductions in risk per unit dose between ages-at-exposure of 15 and 30 (if attained age is
held constant). The ERR per Sv estimates do not change after age-at-exposure of 30, This
approach was recommended by an international expert committee (Pierce and Preston 1993;
UNSCEAR 2000, p. 208). Thus, for most cancers (except as noted below) NIOSH-IREP relies
on direct evidence from the A-bomb survivors exposed as adults rather than as young children.
This modulates the strong decline in risk with increasing age that is observed among those
exposed as children in that study, and is, we believe, more appropriate for modeling risks among
those exposed as adults.

It may be questioned why exposures as young as age 15 were modeled for IREP. This is
a result of the Poisson regression modeling procedure used to analyze the atomic bomb survivor
cohort. This is a grouped data method; data are available for modeling within 5-year age-at-
exposure categories; therefore, the modeled exposure classes for adults must begin at either age
15 or age 20, Because DOE employees may have begun working and accrued workplace
radiation exposures before age 20, we believe, for EEOICPA, that it is necessary to model
exposure categories beginning at age 15.

No age-at-exposure effect has been incorporated for acute myeloid leukemia, chronic
myeloid leukemia, lung cancer (non-radon exposures), and female genital cancers other than
ovary. The NCI models incorporate a trend of decreasing risk per unit dose with increasing age
at exposure for acute lymphocytic leukemia, all leukemia other than chronic lymphocytic, and
basal cell carcinoma, and for all other solid cancers. For thyroid and non-melanoma skin cancer
models, the age at exposure effects were modeled using site-specific estimates, and generally
decrease with increasing age at exposure. For the skin cancer model, a log-linear term is used to
decrease risks for increasing ages at exposure between 10 and 40. Ideally, this model would
include only adult exposures, as for other cancers; however, the data were not modeled in this
way for NCI-IREP, and the data were not available to NIOSH researchers. For radon exposures
and lung cancer, there is no direct adjustment for exposure age: risks are dependent on time since
last exposure and on age at diagnosis. The effect of this adjustment is that, at a constant “time
since last exposure”, the risk decreases for increasing age at last exposure; however, for constant

12


applied to the models in IREP. For solid cancers, the multiplier has an expected value of 1.088,
and a 95% confidence interval of 1.060-1.130. For leukemia, the expected value is 1.056, and
the 95% confidence interval is 1.038-1.082.

Rggsoiay ~ 1+ lognormal(GMg, GSD.) qd)
where GMg = geometric mean for solid tumors = 0.088

GSD, = geometric standard deviation for solid tumors = 1.22

Ry ~ 1+ lognormal(GM,, GSD,) (2)

(leukemia)

where GM, = geometric mean for leukemia = 0.0556

GSD, = geometric standard deviation for leukemia = 1.22

In summary, we concur that more evaluation is needed of additional sources of bias and
random error in the epidemiologic factors relating to the risk coefficients developed for NIOSH-
IREP. However, it is not clear what magnitude or uncertainty distributions would be appropriate
for incorporating this error. Moreover, as mentioned by Dr, Richardson, it is likely that the dose
estimates for the A-bomb survivors may change in the near future; hence, it seems prudent to
consider these issues more fully in future revisions of IREP. This issue is equally germane to the
NCI’s IREP program, which is used by the Department of Veterans’ Affairs for determining
assigned share from radiation exposures. Thus, the uncertainty distributions for NIOSH-IREP
should be developed in conjunction with those used for NCI-IREP. More important, we
believe, is the need to incorporate information from other epidemiological studies, particularly
those of DOE workers, into the risk models developed for NIOSH-IREP.


Figure 3. ERR/Sv estimates (at the upper 99" percentile of the credibility distribution) for
basal cell carcinoma (triangle) and for miscellaneous cancer (X) models, from NCI (2001),
for (a) males and (b) females.

(a) Males

100.0 —k— BCC, Exposure age=20 —— Misc, Exposure age=20
a ~8BCC, Exposure age=30 — — Misc, Exposure age=30
“ & - BCC, Exposure age=40 — — Misc, Exp. age=40

>

“

md

i-4

Ww

3 oe oe bh

3

8 10.0

5

&

Fa i te

5

=

= a Se ee Se
—~e ee TMK

1.0 t t . . . . , . 1

25 30 35 40 45 50 55 60 65 70
Age at diagnosis

(b) Females

—k— BCC, Exposure age=20 —<-— Misc, Exposure age=20
100.0 — &- ‘BCC, Exposure age=30 — >< ‘Misc, Exposure age>=30
= m= BCC, Exposure age=35 9 9k = BCC, Exposure age=40

& he he she $k $e a

Upper 93th percentile of ERR/Sv

100
7 * =
Em
PE a oY
10 + + + + t + + + + 4

25 30 35 40 45 50 55 60 65 70
Age at diagnosis


Little MP, Charles MW. Bomb survivor selection and consequences for estimates of population
cancer risks, Health Phys. 59:765-775, 1990.

Lubin JH, Boice JD Jr, Edling C, et al. Lung cancer risk in radon-exposed miners and estimation
of risk from indoor exposure. J. Natl. Canc. Inst. 87:817-827, 1995.

Lubin JH and Steindorf K.. Cigarette use and the estimation of lung cancer attributable to radon
in the United States. Radiat. Res. 141:79-85, 1995.

Muirhead CR, Goodill AA, Haylock RGE, Vokes J, Little MP, Jackson DA, O’Hagan JA,
Thomas JM, Kendall GM, Silk TJ, Bingham D, and Berridge GLC. Occupational radiation
exposure and mortality second analysis of the National Registry for Radiation Workers. J Radiol
Prot 19:3-26, 1999.

National Academy of Sciences/National Research Council (NAS/NRC). A Review of the Draft
Report of the NCI-CDC Working Group to Revise the “1985 Radioepidemiological Tables”,
2000.

National Cancer Institute (NCI). Draft Report of the NCI-CDC Working Group to Revise the
1985 NIH Radioepidemiological Tables, December 18, 2001. 119 pp.

National Council on Radiation Protection and Measurements (NCRP) . Uncertainties in fatal
cancer risk estimates used in radiation protection. Recommendations of the National Council on
Radiation Protection and Measurement. NCRP Report No. 126, 1997, 112 pp.

Pierce DA, Mendelsohn ML. A model for radiation-related cancer suggested by atomic bomb
survivor data. Radiat. Res. 152:642-654, 1996.

Pierce DA, Preston DL. Joint analysis of site-specific cancer risks for the A-bomb survivors.
Radiat. Res. 137:134-142, 1993.

Pierce DA, Preston DL Radiation-related cancer risks at low doses among atomic bomb
survivors. Radiat. Res. 154:178-186, 2000.

Pierce DA, Shimizu Y, Preston DL, Vaeth M, Mabuchi K. Studies of the mortality of atomic
bomb survivors, Report 12, Part I. Cancer: 1950-1990. Radiat. Res. 146:1-27, 1996.

Preston DL, Kusumi S, Tomonaga M, Izumi S, Ron E, Kuramoto A, Kamada N, Dohy H, Matsui
T, Nonaka H, Thompson DE, Soda M, Mabuchi K. Cancer incidence in atomic bomb survivors.
Part III: Leukemia, lymphoma and multiple myeloma, 1950-1987. Radiat. Res. 137:S68-S97,
1994.

Richardson DB, Wing S, Hoffmann W. Cancer risk from low-level ionizing radiation: the role of
age at exposure, Occupat. Med.: State of the Art Reviews 16:191-218, 2001.

Stewart AM, Kneale GW. A-bomb radiation and evidence of late effects other than cancer.

22


(2) Use of female breast cancer ERR/Sv coefficients for male breast cancer model

Comment: One reviewer (D. Hoel) questioned whether sufficient scientific evidence supports
the assumption that male breast cancer is hormonally-related, which underlies the decision to use
risk coefficients from female breast cancer for male breast cancer models within NIOSH-IREP.
This reviewer suggests that the residual cancer model be used to derive risk estimates.

Response: As acknowledged by the NIOSH-IREP technical documentation, little
epidemiological evidence exists about the etiology of male breast cancer. After further review of
the relevant literature, particularly in an authoritative review of the etiology of breast cancer
(Henderson et al. 1996), it appears the “mainstream” scientific view is that male breast cancers
are hormonally-mediated. The evidence for this hormonal linkage is manifold: 1) Breast cancer
in males, as in females, increases greatly with age 2) Male breast cancer is associated with
overweight in early adulthood (Casagrande et al. 1988), a finding that is true for post-menopausal
women as well, 3) Gynecomastia (a factor related to excess estrogen) is a risk factor for breast
cancer in men 4) Evidence from mathematical modeling of breast tissue aging in men and
women suggests that differences in predicted tissue concentrations of estrogen are sufficient to
explain the differences in breast cancer incidence among the sexes (Bernstein et al. 1989). A
further question, then, is whether excess relative risk (ERR) from radiation exposures is higher
among young than older males, as is the case for females. No quantitative studies have estimated
such risks directly. Two reasonable surrogate approaches to estimate the radiation-related risks
for male breast cancer would be to use either the female breast cancer or the residual cancer
ERR/Sy coefficients, applied to the male background cancer risks for Japan and the U.S.
However, no information exists in the literature to choose between these two models. This
determination has been confirmed by discussion with experts at NCI (Charles Land, personal
communication).

In the absence of scientific information to determine which of two or more alternative
methods should be used, a consistent policy throughout the development of the HHS rule on
Probability of Causation has been to use the approach that is most favorable to the claimant. The
female breast is considered among the most radiosensitive tissues in the body (Boice et al. 1996);
however, sensitivity of the breast decreases greatly with increasing age at exposure. Therefore, it
is not immediately clear which source of risk coefficients provides the most claimant-favorable
estimate of probability of causation. Examination of the upper 99" percentile ERR/Sv estimates
for both models (Fig. 2) shows that the use of the female breast cancer model provides the most
claimant-favorable estimates, at most combinations of exposure and diagnosis ages. The female
breast cancer model risk coefficients, therefore, will be employed in the NIOSH-IREP models.

There is, similarly, no data to support the use of a particular risk transfer model between
the Japanese and U.S, populations. In the absence of such information, the approach developed
for all cancers other than female breast and stomach will be employed in NIOSH-IREP (NCI
2001). This transfer function is trapezoidal, with no preference for an additive over a
multiplicative model.


some variability associated with it (this was also mentioned by the NAS panel reviewing the draft
NCI-IREP); however, it is not clear what that uncertainty distribution should be. Modification of
risk by dose and dose rate for both solid and hematopoietic cancers is another area of active
research that will be re-evaluated in future revisions of NIOSH-IREP.

(8) Risk coefficients used in NIOSH-IREP for bone cancer

Comments: The NCI and one of the subject matter expert reviewers (D. Hoel) argued that the
bone cancer incidence and mortality data are too sparse to support the development of a unique
bone cancer model from the Japanese A-bomb survivor study. Both the NCI and D. Hoel
recommend the use of the “miscellaneous cancer” risk coefficients for bone cancer claims under
EEOICPA.

Response: The risk coefficients used in the draft NIOSH-IREP, taken from the mortality data
from Pierce and colleagues (Pierce et al. 1996), were used in a recent risk assessment of
plutonium at the Rocky Flats facility (Grogan et al. 2000). However, in that risk assessment,
these estimates were supplemented with risk coefficients from animal and human studies of
internal exposures, which were not considered consistent with the approach used to develop risk
coefficients for use in NCI-IREP (which also form the basis of NIOSH-IREP).

The primary argument against the use of the miscellaneous cancer risk coefficients for
bone cancer claims is that the model inchides many disparate types of cancers, including those of
bone, connective tissue, eye, male breast cancer, non-thyroid endocrine glands, and ill-defined
sites, The alternative, however, of using highly uncertain risk coefficients (from the bone cancer
mortality series) without modification from other information sources, seems less defensible, and
inconsistent with the approach used for other cancers in this class. Therefore, we concur with the
NCI and subject matter expert reviewer that the most appropriate source of risk coefficients for
use at this time in the bone cancer models is the miscellaneous cancer model from NCI-IREP,
which is the same set of risk coefficients used in NCI-IREP. As is the case for other cancers in
this category, the risk coefficients are modified by the background incidence rates for the specific
cancer,

15


(9) Models for chronic lymphocytic leukemia in NIOSH-IREP

Comments: One of the subject matter expert reviewers (D. Richardson) criticized the lack of a
tisk model for chronic lymphocytic leukemia (CLL) in NIOSH-IREP, stating that the assumption
that CLL is non-radiogenic for purposes of compensation under EEOICPA is inconsistent with
the approach used for other cancers. He argued that the upper bound on the confidence interval
of risk coefficients for CLL in the international nuclear workers’ study is higher than that for
acute lymphocytic leukemia (ALL).

Response: The latest version of the NCI-IREP program does not include a recommended model
for CLL. CLL is specifically excluded from the type-specific leukemias, as well as from the
general leukemia model. The NIOSH-IREP technical documentation summarizes the evidence
regarding an association between CLL and ionizing radiation exposure in many studies. No
reviewed studies demonstrated such an association. We concur that there is need for a much
more formalized evaluation of the radiogenicity of CLL than was possible in the initial
development of NIOSH-IREP. It would have been preferable to use uncertain estimates of CLL
risk from the same source as for other marginally (or non-) radiogenic cancers. The approach
used for other rare cancers (to use risk coefficients for clusters of “similar” cancers) is not
suitable for CLL, however. The only similar cancers for CLL model estimation are other
leukemias, and it has been well-established in studies among radiation-exposed Western
populations that risk coefficients for CLL, if non-zero at all, are much lower than those for other
leukemias. It would not therefore be appropriate to use risk coefficients from other leukemia
models for CLL.

Dr. Richardson has pointed to evidence from the combined international nuclear workers
studies that the upper bound on the confidence interval of risk coefficients for CLL is higher than
for ALL. This argument does not seem compelling. No association was found between CLL and
radiation exposure in that cohort, despite the occurrence of 27 deaths from CLL (Cardis et al
1995a, p. 57). There were only eleven cases of ALL mortality in that cohort (Cardis et al. 1995a,
p. 60), and just four of these had radiation exposures above 1 cSv (rem). Combined with the
weight of evidence from other epidemiological studies, and from the treatment of CLL by various
expert committees, this argument does not, at this time, justify the use of risk coefficients derived
from ALL, or any other leukemia model, in the Japanese A-bomb survivor cohort for CLL.

In summary, no modification was made to the approach used in NIOSH-IREP for CLL,
which is considered non-radiogenic for purposes of DOE workers’ compensation. As stated in
the technical documentation, however, and in the rule on probability of causation under
EEOICPA (42 CFR 81), this determination is subject to modification as advances in knowledge
warrant.

17


Figure 1. Group mean (+ 1 standard error) upper 95% confidence interval estimate of the
ERR at 1 Sv (from Table 1), by cancer inducibility category (as classified by Boice et al.
1996). Cross-hatched bar shows result excluding salivary cancers,

NO Ww &
J n I

Upper 95% ERR/Sv

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Inducibility by ionizing radiation



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Table 1. Upper 95% confidence interval estimate (two-sided) of ERR/Sv, from analysis of
cancer incidence in Japanese A-bomb survivors (Thompson et al. 1994 and Preston et al.
1994). Inducibility category was classified by Boice et al. (1996).

Cancer n Upper 95%CL of _Inducibility
ERR/Sv category
Leukemia, excluding CLL 141 5.6 4
Female breast 289 2.2 1
Thyroid 129 2.1 1
Bladder 115 24 2
Ovary 66 2.3 2
Lung (incl. trachea and bronchus) 449 1.4 2
Colon 223 1.3 2
Stomach 1305 0.5 2
Oesophagus 84 1 2
Non-melanoma skin 91 1.9 3
Kidney 34 2.2 3
Rectum 179 0.75 3
Uterus 349 0.1 3
Salivary gland 13 6 3
Liver 283 0.92 3
Nervous system 69 1.3 3
Prostate 61 1.2 4
Oral & pharynx 64 0.93 4
Pancreas 122 0.82 4
Gall-bladder 143 0.72 4


“age at diagnosis”, the risk increases for increasing age at last exposure. For all other cancers,
the NCI models incorporate a trend of decreasing risk per unit dose for exposure ages between 15
and 30, and assumes constancy thereafter.

The NCI-IREP modeling approach requires further evaluation in future versions of
NIOSH-IREP, as there are alternative ways of modeling the data. For example, a recent re-
analysis of the A-bomb survivors suggests that, excluding the hormonally-related cancers (such
as breast and thyroid), no variation by age-at-exposure is indicated for remaining cancers after
accounting for attained age (Pierce and Mendelsohn 1996).

The second point argued by Dr. Richardson is that there may be increased dose-related
risks for some cancers at higher ages at exposure (among adults), which should be incorporated
into NIOSH-IREP. This observation has been made in analyses of certain DOE populations
(Wing 2000, Richardson et al. 2001), but not in other populations of nuclear workers in which it
has been evaluated, nor in analyses of the atomic bomb survivors and those exposed to high
doses of x-rays during medical treatments (Cardis et al. 1995a, b; Gilbert 2000). While this is an
area of intense current research, it is very difficult to conclusively elucidate effects of age-at-
exposure among complex exposure patterns typified by nuclear workers. Incorporating other
temporal factors, such as time since exposure, birth cohort, and temporal aspects of exposure
assessment accuracy, could produce risk models that fit as well as those focusing on age at
exposure in these cohorts (Gilbert 2000, Wing 2000). While it is plausible that an increased
effect of age-at-exposure exists, it must be incorporated into the context and examination of
alternative modeling possibilities. Currently, it is not feasible to defensibly do so. We believe
this is a high-priority area of research with important implications for future versions of NIOSH-
IREP.

In summary, we believe that greater attention should be given to variation in sensitivity to
radiation for various adult ages at exposure, by cancer groupings, in future revisions of NIOSH-
IREP. No changes regarding age-at-exposure modeling have been made in the current revision of
NIOSH-IREP.

13


Figure 4. Draft and final DDREF distribution used in NIOSH-IREP for (a) all solid
cancers except breast and thyroid and (b) breast and thyroid cancer.

(a)

0.35 9
034

0.25 4
024

0.15 4

Probability density

0.05 4

()

Probability density