knowledge is still incomplete. There are currently no biological markers of disease that can
be used to attribute a particular case to radon progeny exposure, that is, given biological
material from a lung cancer, it cannot be deduced whether the tumor was “caused” by radon
exposure.

Development of risk model °

Previous analysis of the Colorado data indicated that the exposure-response was
non-linear, particularly at high exposures, Analyses also revealed that the exposure-response
relationship varied with attained age and time since last exposure. Although our intent was to
characterize risk as precisely as possible, we made several simplications to the models to
enhance their usefulness in developing a practical scheme based on PCS.

For simplicity, age ‘was categorized into four levels, <50, 50-59, 60-69 and 270 years of
age, and time since last exposure was categorized into three levels, <10, 10-19 and 220 years
since last exposure. The following RR model along with several variants were fit to the data:

RR = 1+ Bx W Xba X Bag : a
where w is cumulative WLM and the exposure-response parameter, « is a “non-linear”
effect of exposure, ,,, denotes the effects of the three lime since last exposure categories,
Deo Grog 220 and 6,,. denotes the effects of the four age categories, Os, 959.59 Om 9 and
@.5. For identifiability, we set $.;=1-0 and 8.5;=1.0. Results of the model fitting art shown
in Table 1. The table shows that the exposure-response relationship was significantly
modified by both age and time since last exposure. These models were fit assuming a

multiplicative association for radon progeny exposure and smoking status.

Based on mode! A in Table 1, approximately 77% of lung cancer cases can be attributed

19



mines over this time period. Figure 2 shows the number of measurements made in the mines
over this same period of time. The number of measurements peaked in the late 1950s and
early 1960s and declined thereafter. These figures make it clear that if compensation criteria
are based on duration of time spent in underground, as suggested by the Advisory Panel,
calendar period of mining must also be considered.
Estimation of other mine exposures

For the present analysis, data were available on years of previous underground hard rock
mining, as well as an estimate of WLM accumulated in these mines. There were no data
available on other specific exposures that may have been encountered in the uranium mines,
such as arsenic-containing dusts or silica, or in the previous hard rock mines.
Information on tobacco use

Data on smoking were obtained at the annual censuses of miners and from mai)
questionnaires, obtained on one to four occasions between 1950 and 1960 and at other times
between 1963-69 (NRC 1988; Whittemore and McMillan 1983).
New Mexico uranium miners
Cohort definition and follow-up

The State of New Mexico was one of the four States included in the Colorado Plateau
uranium miners study of the PHS. In 1977 a separate study cohort was established by
investigators from the University of New Mexico. The cohort consisted of men who had
undergone a mining-related physical examination at the Grants Clinic in Grants, New Mexico

and who had worked at least one year underground in New Mexico prior to December 31,

13

doit 5 ook ah imabn

cdc deta! avite cit een es'nteime tn ii



k

exposure-response for Native-American miners was less than for non-Native American miners,
i.e., the difference in the exposure-response by smoking status was smaller for Native
American miners than for non-Native American miners, suggesting less of a difference
between Native American smokers and non-smokers. Since the data on Native American
miners are too limited to estimate an exposure-response trend specific for ethniticity and since
Native Americans who do smoke are generally light smokers, we classify all Native American

miners as non-smokers for risk estimation purposes.

Topic 4; Risk modeling based on duration of exposure and year first exposed

The President’s Advisory Panel voiced concern about the misclassification of exposures
and the inability to document exposure rates in miners for individuals seeking compensation.
While some estimates of exposures may over-estimate true exposures, there was particular
concern about possible under-estimation of exposures, and the possibility of unfair denial of
compensation. The major source of error in exposure assessment is the uncertainty in
exposure rates for the various mines and tunnels.

The risk models presented in Table 1 were based on the association of WLM and lung
cancer risk, with modification by age and time since last exposure. This approach may be
termed a science-based approach, in that the goal is to explore the underlying relationship
between exposure and disease, and provide insights into the carcinogenic process. For the
purpose of compensation, however, the calculation of a miner’s WLM is often problematic

and perceived to be unfair by applicants. An alternative approach relates cancer risk directly

to duration of exposure and calendar year of employment, along with other factors. It would

23



The analyses presented in this appendix use outcome and exposure information as
provided by study investigators. For comparing the likelihood that an observed lung cancer in
@ miner is due to radon progeny exposure using non- WLM-based criteria, we assume that at a
minimum a worker is able to document work history, including calendar years of underground
employment.

This appendix considers five topics ‘elated to the evaluation of whether an observed
lung cancer in a miner is the result of exposure to radon and its decay products accrued
during time as an underground miner.

(1) Pooling data from two studies of uranium miners, risk models ‘are developed that reflect
the causal association between radon progeny exposure and lung cancer occurrence.
These models form the basis for recommending new guidelines for compensation.
Because the risk model is derived directly from an analysis of a substantial fraction of
all eligible miners, the recommended criteria incorporates a direct evaluation of the
likelihood that a lung cancer was the result of exposure to radon progeny.

(2) The scientific basis for the joint association of radon progeny exposure and cigarette use
and the occurrence of lung cancer is reviewed. Based on analysis of available miner
data, the most appropriate description of the joint RRs can be interpreted to specifying
eligibility criteria for non-smokers which are less stringent than for smokers. However,
results to date indicate that due to chance alone, the joint RRs are also interpretable as

; consistent with using the same compensation criteria for smokers and non-smokers.

(3) Evidence indicates that Native Americans do not smoke or are generally light smokers.

aa Anecdotal reports suggest that radon progeny exposure may be especially deleterious in



to radon progeny exposure.
Cohort differences

The populations from the two studies arise from the same mining area of the U.S.,
although different criteria which were used to define entry into the cohort meant that mean
exposure differed substantially. Model (1) was modified to test for cohort differences by
replacing the exposure-response parameter B with study-specific parameters, Boo and By.
The estimates were similar; the exposure-response estimate was about 30% greater for New

Mexico, but a score test revealed that this difference was not statistically significant, p=0.75.

Topic 2: Cigarette smoking and lung cancer risk due to radon

Published analyses of the Colorado data and the New Mexico indicate that data are
consistent with a multiplicative relationship between smoking and WLM, but that the data are
also statistically consistent with a relationship less than multiplicative. A multiplicative
relationship implies that the proportion of lung cancers due to radon progeny exposure is same
in smokers and non-smokers. (Because the lung cancer rate is substantially greater in
smokers, the model also implies that the absolute number of lung cancer cases due to radon is
greater in smokers than non-smokers.) In determining risk, a multiplicative association
suggests that the same model, and therefore the same compensation criteria, should be applied
to smokers and non-smokers. ee

Analyses indicate that the most appropriate model is less than multiplicative. This

implies that the increase in the RR with radon progeny exposure is somewhat greater in

non-smokers that in smokers. The difficulty in applying a sub-multiplicative relationship is

20



pe

ee

accurate WLM for a miner with lung cancer. In response to their concerns, the Committee
developed a risk mode] based on duration of underground mining and year of first
employment underground, factors which are likely known with a high degree of accuracy.
However, another source of uncertainty that has yet to be considered arises from statistical
sampling variability. The (asymptotic) statistical variability is captured in the covariance
matrix of the parameter estimates. However, for the complex models described in Tables 1
and 2, calculating the variance estimate for a given set of covariates (age, time since last
exposure, and WLM, or duration of exposure and year of first exposure, as well as smoking
status) is not a sumple matter, since one must account for the variance and covariance in the
estimation of all the parameters. As suggested in the BEIR IV Report, a simple informal
approximation is available, recognizing that statistical uncertainty is only one of many sources
of uncertainty that burden accurate risk estimation. Stratifying on categories of attained age,
calendar year of follow-up, study, ethnicity, previous mining experience and smoking status,
we fit a simple constant RR model in WLM, namely,

RR=1+Bxw
Although this model does not represent the appropriate RR relationship, the estimate of B®
does represent a kind of average excess risk for the study population as a whole over the
period of observation. Further, the estimate of variance then represents the approximate -
sampling variability for the data. In fitting the constant RR model in WLM to the data from
the two cohorts, we obtained a multiplicative standard error of 1.25 for the estimate of B.
Thus, to ensure that the computed RR for a miner captures his true level of risk with 80

percent assurance or 90 percent assurance the excess RR in model (1), i.e., RR-1, should be

26



There were 464 lung cancer cases with information on smoking habits, with 47 cases
among non-smokers. With so few non-smoking lung cancer cases, we could not fit a separate
model for non-smoking miners. To adjust the model for smoking status, mode! (1) was fit to
all data, Table 1. (Smoking was still included as a stratification factor in the background.)
Parameter values for the effécts of non-linearity (x), age and time since exposure were then
fixed, with only the B parameter free to be éstitnated. The model was modified by replacing
the exposure-response parameter 8 with smoking-specific parameters, B,, and Beau. then fit to
the data on miners with smoking information. The changes in the 8,, and B,,,, telative to the
overall B were then applied to the model for all data (Table 1 column A).

4

In the miners with smoking information, relative to the overall B, the estimate for
smokers was reduced by a factor of 0.8, while the estimate for non-smokers was increase by a
factor of 3.0. For all data combined, the results in Table 1 coluran A were modified to reflect

smoking status, B,.=3.0x0,22=0.66 replaces B for non-smokers and By =0.8x0.22=0.18 for

smokers for the computation of PCs. L

Topic 3: Radon risk among Native Americans

‘

As indicated previously, there was little quantitative information that allows comparisons
of lung cancer risk with radon progeny exposure among Native American miners with
non-Native American miners. There was suggestive evidence that among smokers and among
non-smokers the estimated exposure-response for Native American miners was greater than

for non-Native American miners; however, the difference was not statistically significant

(p=0.18). In addition, the relative impact of smoking on the estimated radon

22



pedis tes

association between radon progeny exposure and tung cancer. The BEIR IV analysis
indicated that the RR of lung cancer increases with increasing WLM (as impled in the
RECA-based model), but that the exposure-response relationship decreases with attained age
and time since the exposure occurred. In the more recent analysis of 11 miner cohorts,
analysis showed that in every cohort examined, a constant relative risk model did not provide
an adequate fit to the data.

Among miners in the pooled Colorado and New Mexico studies, the RECA criterion that
distinguishes lung cancer events under age 45 years has little impact on eligibility for
compensation. Of the 470 lung cancer deaths in the joint population, 32 tung cancer cases
occurred under age 45 years, with 27 lung cancer cases in smokers. ‘al five non-smokers
qualify for compensation. Of the 27 smokers, twenty qualify under RECA criteria. All, but
two workers, had exposures above 500 WLM (and thus qualified under the general exposure
criterion). Only two smokers under age 45 had exposures in the 200-300 WLM range.
(Using the model defined below, all 27 smokers would be eligible for compensation, since
their disease occurred at such young ages, when lung cancer is usually quite rare.)

Definition of probability of causation

Probability of causation is the chance that an observed tung cancer was due to radon
progeny exposure, i.e., PC is the increase in the probability of lung cancer due to exposure.
Formally, for 2 lung cancer cases occuring at age a, for an individual with WLM exposure w,

and with other factors z, the PC is defined as

W


with a multiplicative association. An association which is less than multiplicative implies that
the RR for radon progeny is greater in non-smokers than in smokers. In the pooled analysis
: of 11 cohort studies, non-smokers had a 3-fold greater exposure-response relationship than
smokers. Because radon progeny exposure multiplies the background disease rate and
because smokers have a much higher lung cancer rate than non-smokers, a “greater than
additive” association results in a radiogenic excess that is substantially greater in smokers than
- in non-smokers.
Effect of radon progeny exposure in Native Americans

Because of small numbers of study subjects, analyses of Native American miners have
been limited. There have been case reports of lung cancer among Native Americaus of the
outhwest, who were involved in mining. In the most complete analysis, Samet et al. (1984)
eported on 32 lung cancer cases diagnosed between 1969 and 1982, in which 23 had a
ented history of uranium mining, while none of the 64 matched controls had been
miiranium miners. This suggests a high RR with uranium mining in Native Americans, who
ditionally do not smoke or are light smokers. However, results are insufficient for

titative risk estimation or for comparison with non-Native American miners.

bort studies of U.S. uranium miners
plorado Plateau uranium miners

wt definition and follow-up
The Colorado Plateau uranium miners study was one of the earliest of the modern

lemiologic studies to document increased hung cancer risk with exposure to radon progeny



at tung cancer occurrence and with more than 20 years since last exposure.

The correlation coefficient for PCyza4 and PCyy, Was 0.88, Figure 5 shows a plot of
PCyiss and PC,,, for the 470 lung cancer cases. (The correlation coefficient for PCy,,, and
PCarca is 0.65 and for PC,,, and PCpeca is 0.57.) PCyrm and PC,,, agree on the compensation
classification of $9% (419/470) of cases. Using PCa, as the sole basis for compensation, 2%
(6/349) of lung cancers would be inappropriately compensated, while 45 or 12% (45/388)
would be inappropriatedly rejected.

The rate of rejecting deserving lung cancer cases under a PC,,, scheme could be reduced

by lowing the 0.5 criterion for the PC, with the corresponding consequence of increasing the

5
x number of non-deserving cases who are compensated. However, as suggested by Figure 4, no
bee -

= obvious and reasonable reduced standard (any vertical line) would capture a large majority of
ie . :

€ incorrectly rejected cases without markedly increasing the number of inappropriately

compensated cases.

Development of model-based compensation criteria

aa The characterization of lung cancer events that are more likely than not due to radon

progeny exposure can be re-expressed as PC20.5, which is equivalent to all cases with a

computed RR22.0. Based on model (1), this characterization can be defined in terms of the
level of WLM exposure for fixed categories of time since last exposure and age by solving
the expression, PC=(RR-1)/RR, for w, i-e.,

Tables of exposures for fixed categories of time since last exposure and attained age were

computed for RR=2.0. Table 3a shows WLM values for smokers, where Bou (=0.8xB}

29



been due to the causal agent.

The validity of a PC is based on the validity of the mode! used to describe the
disease-exposure association, If the model omits important factors or misspecifies the
functional form of the relationship, then PCs computed from the model may not measure the
true likelihood of the event being caused by the exposure. With observational data and
without a precise understanding of the biological mechanisms of the carcinogenesis process at
the level of the cell and of the organ system, the true risk relationship, and thus the true PC,
can never be determined with absolute certainty. However, a risk model that provides a more
accurate characterization of the observed data will necessarily result in a computed PC which
has greater validity,

In the view of this Committee; fairness in the compensation criteria is reflected in the
PC. The more accurate the model used to compute PCs the greater fairness there is in the
determination of eligibility. The risk model which is developed below fits the pooled
Colorado and New Mexico miner data significantly better than the model implied by the
RECA criteria. Thus, criteria based on the new risk model would imply a greater faimess
than the RECA criteria. ke

Current scientific understanding about the relationship between radon progeny exposure
and lung cancer does not support the RECA criteria, and therefore the RECA criteria should
not be viewed as the benchmark of fairness by which to judgé any new criteria. It is not the
case that new criteria can be fair only if greater numbers of lung cancer cases are
compensated. As a result, using criteria based on a new risk model, some lung cancer cases

may be eligible for compensation who would do not satisfy current RECA criteria.


Native Americans. Data are examined and recommendations are suggested.

(4) In response to concerns of the President's Advisor Panel on Human Radiation
Experimentation on uncertainties associated with the estimation of a worker's exposure,
an alternative to a WLM-based risk model is developed. This non-WLM based. model is
used to recommerid an alternative scheme for establishing compensation eligibility when
information on mine location is missing or when measurement data are missing or
inadequate for accurate estimation of WLM.

(5) RECA criteria for compensation require that all exposures occurs prior to 1972, that for
non-smokers total cumulative exposure should equal or exceed 200 WLM, and that for
smokers exposure should equal or exceed 500 WLM or if lung cancer occurs under 45
years of age exposure should equal or exceed 300 WLM. Based on current
epidemiologic findings, these criteria do not reflect a scientifically sound attribution of
risk of lung cancer risk from radon progeny exposure, and therefore do not appropriately
discriminate lung cancer cases that are more likely than not due to. radon ‘progeny

exposure.

:

One issue of importance is the requisite “fairness” of the RECA compensation criteria

for lung cancers in uranium miners. It is worth considering what is meant by fairness in the

context of tung cancer attributable to radon progeny exposure. Compensation for lung cancer

is based on whether it was more likely than not due to radon progeny exposure. This
stipulation can be reflected in the probability of causation (PC), which is a probabilistic
statement of the likelihood that a lung cancer case is due to radon progeny exposure. A PC

equal to or greater than 0.5 indicates that the disease event was more likely than not to have



prepay en

P(lung cancer given w and z)~P(lung cancer given w=0 and z)
P(lung cancer given w and 2)

PC(w,2) =

Dividing the nummerator and denominator by the disease probability in the non-exposed, one

obtains the PC as a function of the RR, namely,

RRw.2)-1

PC(wa, - RR(w,z)

For a given level of exposure w and values for other factors z, the RR with exposure is
determined by the risk model.
Topic 1; Modeling lung cancer risk and WLM

In modeling risk in miners, we have the benefit of having daia on a substantial
proportion of the population of interest, thus obviating the need for an extreme extrapolation
from one population to another, possibly unrelated, population. Thus, risk levels can be based
directly on relevant epidemiologic data. While the Colorado study provides limited
information for exposures below 100-200 WLM, there are substantial data from New Mexico
study in this range, where exposures were lower. In the New Mexico miners, mean year of
first employment occurred about a decade later, in 1965, after substantial control measures

had been put in place. Patterns of risk with exposure in the Colorado and New Mexico
studies were consistent with patterns found in other populations of radon-exposed miners. .
This suggests that the observed patterns of risk in the data are not.astifacts, but likely reflect
underlying biological processes.

It is worth noting that, while there has been considerable advances in recent years in

understanding the nature of the carcinogenic process from exposure to alpha particles,

18



workers. For exposures in miners, the multiplicative effects were assumed unchanged for all
ages and without regard to years since last exposure. Recent analyses have shown that these
simple characterizations of risk from radon progeny exposure are incorrect. In 1988, the NAS
BEIR IV Committee was the first expert committee to conduct original analyses using modern
statistical methodology. The model linking WLM and lung cancer risk was complex. The
BEIR IV anlaysis showed that over a broad range of exposures RRs were generally consistent
with a linear relationship; however, they also showed that the RR relationship was not
constant, but decreased with current age of the individual and with time since the exposure
occurred, These patterns of variation in the exposure-response relationship have since been
x

sustained in a pooled analysis by Lubin et al. (1994; 1995) using data from 11 cohort studies,
including the four in the BEIR IV Report.
Effects of smoking on risk of lung cancer due to radon progeny exposure

Cigarette smoking is the major cause of lung cancer. Several investigators have
considered the effects of smoking on the estimates of risk from exposure to radon progeny.
Analyses have provided convincing evidence that, although the majority of miners were
smokers, the increased risk with WLM exposure is not due solely to smoking. Based on
formal analyses, with the Colorado study providing the most complete information, it has
generally been concluded that there is a synergistic relationship for WLM and smoking, that
is, the Jung cancer risk for exposure to both agents is greater than the sum of the risks for
each exposure individually.

Analyses support a joint association which is most likely intermediate between

multiplicative and additive, although analyses also indicate that the association is consistent



be expected that duration of exposure and year could be known and verified with great
accuracy, and therefore subject to less misclassification, and would be preceived to be fairer
by applicants. The approach has the advantage that the calculation of risk appears more
transparent.

It should be noted that’ WLM is not a true measure of lung dose, but is a surrogate of
the true lung dose. Likewise, exposure determined in terms of duration of underground
employment (and calendar year of first exposure) is also a surrogate of the true lung dose.
Because exposure rate is ignored, it must be expected that duration of exposure (and calendar
year of first exposure) may be less adequate than WLM as a surrogate for the true dose.

For simplicity in risk modeling and in risk determination, we use ‘year of first exposure,
rather than year of exposure. Since periods of work histories which were free of exposure
constitute a relatively smal! proportion of total exposure durations, modeling duration and year
exposed, or duration and year first exposed will define similar levels of risk. We defined
three categories for year of first radon progeny exposure, <195-54, 1955-59, and 21960. The
RR of lung cancer was modeled in duration of exposure (denoted by d), as follows

RR =14 Bx dTM x yee X Pre ¥ Page * ieee 1 (2)
where in contrast to model (1) B now denotes the duration-response parameter, The
parameters 7,;, denotes three parameters for categories of year of first radon exposure,
<195-54, 1955-59, 21960, while $,,, and 6,,, represent parameters for categories of time since
last exposure categories and attained age, and v represents a parameter of the effect of
continuous age minus 60 years. An empirical evaluation indicated that the inclusion of

continuous age significantly improved model fit.

24



HERG LIBRARY

FINAL REPORT
-OF THE
RADIATION EXPOSURE COMPENSATION
ACT COMMITTEE

SUBMITTED TO THE
HUMAN RADIATION INTERAGENCY
WORKING GROUP

JULY, 1996


for to 1951, only a few measurements of radon were made. During the period

;tadon progeny measurements were made by the PHS, several State agencies and the

eau of Mines. Data were considered representative of areas in which most of the

@ worked. After 1960 and until the latest samples were collected in 1968, most samples

en by mine inspectors. Between 1951-68, almost 43,000 measurements of radon

2,500 mines were obtained. However, as noted by Holaday (NRC 1988, quoted

et al. 1971), most measurements were made for control purposes and may: have

nated exposure to workers.

tittle over 10% of the miners had their exposure estimated entirely from measurements

ihe mines, Measures of exposure to radon progeny were generally collected using
method, which was an area sample based on alpha counts collected using a
sup apparatus. When multiple samples were available in a given mine and year, the
“mean was used to characterize annual WL levels. When measurements were not
for a given mine and year, three different methods were used to assign WL levels.

data for a mine were incomplete, the annual average WL was used for the level

‘niaximum of two years from the measured year. (2) When gaps of greater than four
ted for a mine, averages of other mines in the same geographic area were used to

wal WL levels. (3) If no data existed in adjacent years or within the geographic

imates of WL were made based on knowledge of ore bodies, ventilation practices,

REF factors thought to influence radon progeny levels.

amount of time each miner worked underground was obtained from several sources.

Were collected at the time of the physical examination, one of the defining criteria

11



(Lundin et al. 1971), Extensions of the follow-up through 1977, 1982, 1987 and 1990 were
subsequently carried out. Initially established by the U.S. Public Health Service (PHS), the
cohort follow-up is now under the auspices of the U.S. National Institute for Occupational
Safety and Health. Work history information was available through 1969; little additional
exposure is thought to have occurred after that time since many workers had retired and many
uranium mines in the Colorado Plateau had closed. In contrast to the pooled analysis by Al ah
Lubin et al. (1994), we include all miners and all exposures in the analysis. en
The current analysis utilizes the most recent update with follow-up through 1990. A ia
total of 115 workers, including 12 lung cancer cases, in the Colorado cohort qualified for and
nN
were included in the cohort study of New Mexico uranium miners (defined as all underground
miners medically evaluated at the Grants Clinic, Grants, New Mexico). These miners were
included only once in the pooled analysis.
Members of the Colorado Plateau study cohort included workers in the four States of
Arizona, Colorado, New Mexico, and Utah, who had completed at least one month of >
Ounderground uranium mining, who volunteered for at least one medical examination between
1950 and 1960, and who provided personal and occupational data of sufficient detai) for
follow-up and for exposure estimation. Information on follow-up and ascertainment of vital
status was obtained from records of mining companies, state vital statistics offices, the U.S.
Social Security Administration, the Internal Revenue Service, the Veteran’s Administration
and, most recently, the National Death Index, and by direct contact. Cause of death was

determined from State death certificates,

Estimation of radon progeny exposure

10


RR-1 y*

ws [——_——_.
BX Bite X Sage

replaces B from Table 1, and for non-smokers, where f,, (=3-0xf) replaces B. The table
illustrates the impact of age at lung cancer and time since last exposure, The older the case
occurs and the more distant in time since the last exposure the less the likelihood that the
observed disease was due to radon progeny exposure, and therefore the greater the WLM
required to meet the more likely than not criterion. The values of WLM that are required to
meet the "more likely than not" criterion with 80 percent and 90 percent assurance are shown
in Tables 3b and 3c, respectively. .

A similar approach for adjusting the risk model for smoking status was used with
model (2), using 0.9 and 2.4 as adjustment factors for 8 for smokers and non-smokers,
respectively. Table 4a shows duration of exposure in years that would be require to meet a
“more likely than not” criterion of lung cancer causality. The values of duration that are
required to meet the "more likely than not” criterion with 80 percent and 90 percent assurance
are shown in Tables 4b and 4c, respectively.

The criteria defined by Tables 3 and 4 were applied to the 470 jung cancer cases
occurring in the pooled Colorado and New Mexico data (Tables 5 and 6). Using model (1)
and the WLM-based approach, 388 (83%) cases would be eligible for compensation, with ‘the
numbers increasing to 401 (85%) or 403 (86%) after accounting for a 80 percent or 90
percent level of assurance in the RRs. ‘Under model (2) and the duration/year first
exposed-based approach, 349 (74%) cases would be eligible, with the number increasing to

394 (84%) or 408 (87%) after accounting for the level of assurance.

30

E
4



Conversely, some lung cancer cases who would not be eligible under a new scheme,
indicating that their lung cancer is more likely than not due to factors other than radon
progeny exposure, may be eligible under current RECA criteria.

Finally, true dose to lung tissue from radon and its progeny can never be known with
certainty. WLM estimates in the miner data are approximations and are themselves subject to
uncertainty. Thus, any WLM-based model wiich is deveioped, while having greater validity
than the RECA-based model, cannot be considered as the “gold standard” that identifies the
true level of risk,

Before proceeding, we briefly review the scientific basis for the causal relationship

4

between radon progeny exposure in miners and Jung cancer, define the data available for

analysis, and describe the methods of analysis, including the computation of the PC.

Scientific basis for the association of radon progeny exposure and lung cancer
Effects of radon progeny exposure in underground miners

Since the mid-1980s, expert committees have sought to characterize risk of lung cancer
from exposure to radon and radon progeny. Two early reports, by the National Council on
Radiation Protection and Measurements (NCRP 1984) and by the International Commission
on Radiation Protection (ICRP 1987), relied on heuristic summarizations of existing results in
the literature. The NCRP committee assumed that the effects of exposure directly added to
the age-specific lung cancer rate in non-exposed workers, and assumed no synergistic effects

of cigarette smoking. In the Latter report, the ICRP committee assumed a constant RR model,

whereby the effects of exposure multiple the age-specific lung cancer rate in non-exposed

4
3
F
4
}
q
j


the limited amount of data on lung cancer risk in non-smokers. This leads to a lack of
adequate power to estimate precisely the nature of the joint association. An estimate of the
“degree of sub-multiplicativeness” depends heavily on the postulated form for the joint
association. Since a multiplicative model is consistent, the difficulty in the estimation of any
particular sub-multiplicative model has lead most researchers and expert committees to apply
the same risk model to smokers and non-smokérs. This is the approach taken by the
Environmental Protection Agency in applying miner-based risk models in the estimation of
lung cancer risk from residential radon exposure.

Although the precise form for the joint RR is uncertain, current analyses indicate that the
absolute risk of lung cancer due to radon progeny exposure and to cigarette smoking do not
simply add. In the general population the lung cancer rate among smokers is 10-20 fold
greater than the lung cancer rate among non-smokers. An additive RR association for WLM
and smoking would imply that the excess rate of lung cancer due to radon is the same in
smokers and in non-smokers, Data indicate that more radon-attributable lung cancer cases
occur among smokers than non-smokers.

In the pooled analysis, Lubin et al. (1994; 1995) separated data into smokers and
non-smokers and found that the exposure-response in non-smokers was about three times the
exposure-response in smoker, indicative of a sub-multiplicative association. ,

Because there is evidence that Native American miners either did not smoke or were
light smoker, Native American miners with missing data on smoking was classified as a
non-smoker. In the poofed data, 427 miners, including 6 lung cancer cases, had no

information on smoking, and were omitted from the smoking analysis.

21



wre aay

multiplied by the factor 1.3=1.25'** or 1,4=1.25', respectively. Because the approach is an
approximation and the sampling variability is only one of the multiple sources of uncertainty
in risk estimation, the limits are referred to as levels of assurance rather than levels of
confidence which carries the connotation of a statistical confidence interval, which has a
precise sampling interpretation and beyond our intended usage.

Model (2) in duration of exposure and year of first exposure lends itself to a similar
approximation, using a constant RR model in duration of exposure, as opposed to WLM. In
this case, the 80 percent assurance or 90 percent assurance levels are given by multiplying the
excess RR in model (2) by the factors 2.2 or 2.8, respectively.

4

Adjusting the compensation criteria by a level of assurance decreases the number of

faise negative claims, i.e., rejecting claimants deserving of compensation. As a consequence,

however, there is an increase in the number of false positive claims, i-e., compensating

claimants who fails to meet a more Sikely than not criterion.

Topic 5: Comparisons PCs for data-based risk mode! and RECA-based models

As indicated above, the RECA-based model does not adequately account for patterns of
risk, which therefore implies that RECA criteria do not adequately and fairly identify high
risk workers. Using the 470 lung cancer deaths in the Colorado and New Mexico studies, we
compute PCs using three different approaches: (i) based on the RRs‘derived from the RECA
criteria, denoted PCpgca; (ii) based on fitting model (1) which utilizes the relationship of risk
with WLM and other factors, denoted PCy,y4; and (iii) based on model (2) which utilizes the

relationship of risk with duration of exposure, calendar year of first exposure and other

27



Sr Su

an inverse exposure-rate effect, that is, for the same total exposure, exposures occurring over

ok.

a shorter duration of time (at a higher exposure rate) were less harmful than exposures
occurring over a longer duration (at a lower exposure rate). In the analysis of the Colorado
and New Mexico data which is presented below, the inverse exposure-rate effect was not
statistically significant and ‘will be omitted. The pooled analyses also found that risk from
radon progeny exposure did not depend on thé age at which a miner was first exposed.

The procedures used in the BEIR IV and NCI-ied pooled analyses are the same as the
methods used in the analysis presented in this appendix. Specific information is provided in
those reports.

ny

RECA does not establish an entitlement program whereby all lung cancer cases are
compensated, but seeks to establish a program of fairly compensating lung cancer cases for
harm done during the mining of uranium. In this regard, the most appropriate basis for
making such a determination is the application of the most relevant data. There have been
two epidemiologic studies of uranium miners in the four state region of Arizona, Colorado,
New Mexico, and Utah. “One study was initiated in the 1960s by the U.S. Public Health
Service and currently is being conducted by the National Institute for Occupational Safety and
Health (Lundin et al. 1971; Hornung and Meinhardt 1987). A second study was initated at
the University of New Mexico (Samet et al. 1991), The study populations constitute a
substantial fraction of all miners eligible for compensation under RECA, and thus are directly
relevant to establishing factors predictive of lung cancer occurrence. The two studies include

nearly 190,000 person-years of observation and 470 lung cancer cases, and provide the data

used in our evaluation of compensation criteria.



1976 (Samet et al. 1991). The Grants Clinic opened in 1957 and performed most pre-
employment as well as follow-up examinations for area mines. Although the geographic area
of the New Mexico study was spatially related to the source area of workers for the Colorado
Plateau cohort and although some workers qualified for inclusion in both cohorts, the New
Mexico workers had substantially lower mean exposures.

Vital status was determined by searching’New Mexico vital statistics records, the New
Mexico Tumor Registry and State drivers’ license records. The cohort was also matched
against the records of the Social Security Administration and the U.S. National Death Index.
Death certificates were obtained for the deceased and cause of death ascertained. After

4

excluding men with missing work history, birth year, or follow-up information, vitat status of

.

3,469 workers could be ascertained; 390 workers have a verified cause of death.
Estimation of radon progeny exposure
Measurements of radon and its progeny were obtained from various sources. Prior to
1968, data on radon progeny came from the State Mine Inspector, the State Health and
Environment Department, and records from the Colorado Plateau study. These data were in
ithe form of annual estimates for individual mines.

From 1967-1985, estimates of WLM exposure were taken from the WLM reports for
individual miners from mining companies in the area, submitted to the U.S. Bureau of Mines
or its successor agencies, the Mining Enforcement and Safety Administration, and the Mine
Safety and Health Administration. Records were generally complete for the larger mining

companies, while generally not available for the smaller companies (Morgan and Samet 1986).

Estimation of other mine exposures

14



RECA criteria and characterization of lung cancer risk

RECA criteria were meant to identify lung cancer cases which were more likely than not
due to radon progeny exposure. This can be restated as a PC greater than or equal to 0.5.
Since a linear mode! for the RR in WLM is a good first order approximation, RECA criteria
can be re-expressed in terms of the RR of lung cancer for cumulative exposure (w), smoking
status and age. For non-smokers, the specification of a PC of 6.5 at 200 WLM implies a
linear RR. model which is independent of age, namely,

RR,(w) = 1+ 0.005 x w

For smokers, the specification of a PC of 0.5 at 300 WLM for ages under 45 years and 500
WLM at ages 45 years and older implies the following RR models,
RRypk<ss(W) = 1 + 0.0033 x w
for under age 45 years, and
RRomoas(¥) = 1 + 0.002 x w
for ages 45 years and older.

Direct analysis of data reveals that these RR patterns do not provide an adequate fit.
Compared to the models developed below, the RECA-based model for the RR is statistically
rejected, p<0.001. °

For current ages 45 years and over (the majority of miners), the 500 WLM criterion
implies that the excess RR of lung cancer increases 0.2% per WLM and that the increase is
constant with age and other factors. The BEIR IV Report (1988) demonstrated conclusively

that a model in which the RR depends only on WLM does not adequately describe the

16



miners, including a study of Colorado Plateau uranium miners (NRC 1988). The BEIR IV
Committee reiterated the causal link of radon progeny exposure to lung cancer risk, and
developed a risk model that described the exposure disease relationship. The BEIR IV model
has been the principal model used for the evaluation of radon risks from exposures in mines
and in homes. More recently, building on the work in the BEIR IV Report, scientists of the
U.S. National Cancer Institute in collaboration with principal investigators of all (eleven)
cohort studies of radon-exposed underground miners conducted a pooled analysis of original
data (Lubin et al. 1994; 1995), including recent updates of studies of Colorado Plateau
uranium miners (Hornung and Meinhardt 1987) and New Mexico uranium miners (Samet et
al. 1991). The pooled analysis included a total of nearly 1.2 miflion person-years of
observation and over 2,700 Jung cancer deaths, nearly eight times the data available to the
BEIR IV Committee.

The BEIR IV analysis and the NCI-led analysis showed that relative risk (RR) of Jung
cancer was strongly related to cumulative radon progeny exposure in units of Working Level
Months' (WLM). The analyses indicated that the exposure-response relationship in the RR
was complicated by a dependence on other factors, notably that the exposure-réSionse
relationship declined with current age (referred to as attained age) and time sinee the exposure
occurred. These patterns indicate that the effects of exposure vary in a complex way as an
individual ages. In terms of the RR, the effects of exposure lessen as the age of a miner

increases and with time since cessation of radon progeny exposure. The analyses also found

One Working Level (WL) equals any combination of radon progeny in one liter of air which results in the
emission of 130,000 MeV of energy from alpha particles. WLM is the product of time, in units of 170 hours,
and WL.


Results of modeling are shown in Table 2. Non-linearity of the duration-response
relationship is only marginally significant, while year first exposed, time since last exposure,
and attained age are significant modifiers of risk.

Although not directly comparable, the deviance (a measure of model fit) was
substantially smaller using model (1) then model (2); for the “full” models, deviance values
were 2,509.9 on 17,218 degrees of freedom for the WLM-based model, and 2569.9 on 17,216
degrees of freedom for the duration-based model without continuous age“and 2557.7 on
17,215 degrees of freedom for the duration-based mode] with continuous age. This suggests
that model (2) does not fit the data quite as well as model (1). The attributable risk of lung
cancer using the duration-year first exposure model A in Table 2 was B%, similar to the 77%
attributable risk based on mode! {1) using WLM. (Note this is slightiy higher than reported in
Lubin et al. (1994), due to the inclusion of all exposure data. Lubin et al. excluded exposures
above 3,200 WLM.)

Modification of the B estimate in the duration-based model (2) for smoking status was
carried out as for model (1). Model (2) was fit to the miners with smoking information, all
parameters, except B, were fixed. The model then was refit with B,,,. and Bas replacing p.
Relative to the overall B, the estimate for smokers was reduced by a factor of 0.9, while the
estimate for non-smokers was increase by a factor of 2.4. For all data combined, the results
in Table 2 column A were modified to reflect smoking status, B,,=2.4 x22.3=54.4 (with
rounding) replaces B for non-smokers and. P..40.9x22.3=19.0 for smokers for the
computation of PCs.

The President’s Advisory Panel was concerned by the purported difficulty computing an

25



fees

Appendix
Establishment of Data-Based Criteria

for Radon-Associated Lung Cancer

Introduction

This appendix presents the scientific basis for suggested revisions of criteria for the
compensation of lung cancer among underground uranium miners from the eligible regions of
the U.S. Radioactive radon (more specifically radon-222, hereafter referred to as radon) is an
inert gas and is a decay product of radium-226, the fifth progeny of uranium-238 which is
ubiquitous in the crustal rock of the earth. Radon can migrate from soils and rocks and
accumulate in enclosed areas, such as homes and underground mines. The carcinogenic
potential of radon derives principatly from alpha particles (helium nuclei) emitted during
radioactive decay of the short-lived progeny of radon-222, that is, polonium-218 and
polonium-214.

Since the 1970s, a substantial amount of research, epidemiologic studies in humans,
experimental animal studies, and in vitro cellular studies, has been published, that establishes
the causal link between exposure to radon and its decay products and the development of lung

2v

cancer. Periodically, the body of research material has been reviewed by expert committees.

Reviews have been conducted by the National Council on Radiation Protection and
Measurements (NCRP 1984) and by the International Commission-on Radiation Protection
(ICRP 1987). In 1988, the fourth National Academy of Sciences’ Committee on the
Biological Effects of Ionizing Radiations (BEIR IV) evaluated the scientific literature, and for

the first time, conducted a pooled analysis of original data from four epidemiologic studies of

&

AS g &

Fr tee CayeoW ; vnuerg:

eats amarncarne we mevude


factors, denoted PCy.

While PCy, is computed from what is thought to be the most appropriate model, any
comparisons among these PC values is problematic, since the “true” relationship between
exposure and disease cannot be known precisely.

Figure 3 compares PCarca With PCy, Dotted lines denote a PC value of 0.5. The
PCyim points follow curved paths of related Values because of the categorical representations
of age and time since last exposure. There were 296 lung cancer cases with both PCpeca and
PCyin greater than or equal to 0.5, termed the “true positives”; 22 cases with PCreca 20.5
and PCy, y<0.5, termed the “false positives”, who would be compensated under RECA
criteria, but not under the more appropriate risk model; 92 cases with PC geca<0.5 and
PCy20.5, termed “false negatives”, who would not be compensated under RECA criteria,
but would be compensated under the more appropriate risk model; and 60 cases with
PCgeca*9.5 and PCy 4<0.5, termed “true negatives”, who would not be compensated under
either approach. Out of 388 (296+92) lung cancer cases who would be compensated based on
the more scientifically-based model, 92 or 24% would be ineligible under RECA criteria.

As indicated, the age 45 criterion for smokers has little practical impact on determining
compensation. The question might arise whether any single WLM could ever adeguately
identify high-risk cases. Figure 4 plots PCy, based on mode! (1), by cumulative WLM
exposure. As illustrated, a simple criterion based on a single WLM {which would be
represented by a vertical line) is insufficient to seggregate lung cancer cases with high and

low values for PC. For example, several cases with an estimated exposure of over 1,000

WLM had a value for PCy, of less than 0.5. These cases are individuals over the age of 70

28



for enrollment into the cohort. This information was used to provide work histories to that
point in uranium mines, as well as past employment in other hard rock mines. Additional

data were collected from mining company records and from an annua! census taken by the
PHS and by the Colorado Bureau of Mines.

The number of hours per month underground was taken to be 170. No adjustment was
made for vacations, sick leave, or overtime. ‘However, gaps in employment histories of one
month or more were recorded and, when available, taken into account in the exposure
estimates.

When a census identified a miner with no work history at a particular mine, the miner
was assumed to have worked in that mine for a period of six months prior and subsequent to
the census date. ;

A relatively small proportion of the study group had WLM estimates based completely
on measured data. While extrapolated data may be subject to greater error, the extent of
misspecification depended largely on estimation procedure. An analysis of error. found a
coefficient of variation of 112% that included sampling error, counting error, and
environmental fluctuations over each year (Hornung 1985). The coefficient of variation in the

a

three estimation procedures ranged from 121% to 186%. When all estimation procedures

were averaged over the number of measurements and years worked for each miner, the
coefficient of variation for cumulative WLM was approximately .97%.

Exposure rates in WL have decreased dramatically over time. Figure 1 shows mean
exposure rates in WL for 1956-69. The primary factor in the decline was the introduction of

ventilation systems in mines. There is approximately a 20-fold reduction in mean WL for the

12



For each worker, information from Clinic records was available on whether he
previously worked in a hard rock mine.
Information on tobacco use

Clinic records indicated smoking status; current smoker, former smoker, or never

smoked.

Material and methods of analysis
Pooled miner data

We developed risk models by directly pooling data from the Colorado and New Mexico
studies. In the Colorado and New Mexico studies, there was a total of 7,475 miners, who
accrued 187,406 person-years of observation and'470 lung cancer deaths. There were 951
Native American miners with a total of 35 Jung cancers. For Colorado and New Mexico
miners, mean cumulative exposures among exposed were 681 and 110 WLM, respectively.
Relative risk regression models

Poisson regression methods (more correctly called piecewise-exponential regression
models) were applied to the pooled data. Methods are based on a RR model which. is finear
in WLM exposure, but allows for variations with other factors. These methods are similar to
and are detailed in the BEIR IV Report and Lubin et al. (1994). The background tisk of Lung
cancer among “non-exposed” miners was specified using factors for study, attained age,
calendar year, previous hard rock mining experience, smoking status, and ethnicity (whites

and Native Americans). These factors were adjusted in the modeling by categorizing the

variables and including stratum-specific parameters.

15