THE U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES
 

PUBLIC HEALTH SERVICE
 

CENTERS FOR DISEASE CONTROL AND PREVENTION
 

NATIONAL INSTITUTE FOR OCCUPATIONAL SAFETY AND HEALTH
 

convenes the 

ADVISORY BOARD ON
 

RADIATION AND WORKER HEALTH
 

VOLUME II 

The verbatim transcript of the Meeting of the 

Advisory Board on Radiation and Worker Health held 

at the the Hyatt Regency Denver, Denver, Colorado, 

on Tuesday, July 2, 2002. 

NANCY LEE & ASSOCIATES 

Certified Verbatim Reporters
 

P. O. Box 451196
 

Atlanta, Georgia 31145-9196
 

(404) 315-8305
 
 

2 

C O N T E N T S 

VOLUME II
 

July 2, 2002
 

PARTICIPANTS (by group, in alphabetical order) ..... 3 
 

REGISTRATION AND WELCOME 

Dr. Ziemer ...................... 6 
 

THE ROLE OF UNCERTAINTY ANALYSIS IN NIOSH-IREP 

Dr. Hoffman ..................... 8 
 

RADIATION EFFECTIVENESS FACTORS (REF)

Dr. Kocher ...................... 34 
 

CASE STUDY RESULTS USING NIOSH-IREP
 

Mr. Thomas ...................... 88 
 

BOARD WORK SCHEDULE 

Ms. Homer, Dr. Ziemer, Mr. Elliott ......... 134 
 

ADMINISTRATIVE HOUSEKEEPING 

Mr. Elliott .................... 156 
 

PUBLIC COMMENT PERIOD
 

Mr. Miller ..................... 160 
 

Mr. Schofield ................... 165 
 

NIOSH-IREP TECHNICAL DOCUMENTATION, SUBJECT MATTER

EXPERT COMMENTS, AND RADIATION EFFECTIVENESS

FACTORS PAPER 

Board Discussion ................ 165, 266
 

SPECIAL EXPOSURE COHORT PETITIONING NPRM
 

Mr. Katz ...................... 169 
 

Board Discussion .................. 180 
 

DOSE RECONSTRUCTION WORKGROUP RECOMMENDATION 

Mr. Griffon .................... 230 
 

Vote ....................... 245 
 

ADJOURN ....................... 246 
 
3 

CERTIFICATE OF REPORTER . . . . . . . . . . . . . . . . 248 
 
4 

P A R T I C I P A N T S 

(By Group, in Alphabetical Order) 

ADVISORY BOARD MEMBERS 

CHAIR 

PAUL L. ZIEMER, Ph.D.

Professor Emeritus 

School of Health Sciences 

Purdue University

Lafayette, Indiana 

EXECUTIVE SECRETARY 

LARRY J. ELLIOTT 

Director, Office of Compensation Analysis and Support

National Institute for Occupational Safety and Health

Centers for Disease Control & Prevention 

Cincinnati, Ohio 

MEMBERSHIP 

HENRY A. ANDERSON, M.D.

Chief Medical Officer 

Occupational and Environmental Health

Wisconsin Division of Public Health 

Madison, Wisconsin 

ANTONIO ANDRADE, Ph.D.

Group Leader, Radiation Protection Services Group

Los Alamos National Laboratory

Los Alamos, New Mexico 

ROY LYNCH DeHART, M.D., M.P.H.

Director 

The Vanderbilt Center for Occupational and Environmental

Medicine 

Professor of Medicine 

Nashville, Tennessee 

RICHARD LEE ESPINOSA 

Sheet Metal Workers Union Local #49 

Johnson Controls 

Los Alamos National Laboratory 
5 

Espanola, New Mexico 

SALLY L. GADOLA, M.S., R.N., COHN-S

Occupational Health Nurse Specialist

Oak Ridge Associated Universities

Occupational Health

Oak Ridge, Tennessee 

MARK A. GRIFFON 

President 

Creative Pollution Solutions, Inc.

Salem, New Hampshire 

JAMES MALCOM MELIUS, M.D., Ph.D.

Director 

New York State Laborors’ Health and Safety Trust Fund

Albany, New York 

WANDA I. MUNN 

Senior Nuclear Engineer (Retired)

Richland, Washington 

ROBERT W. PRESLEY 

Special Projects Engineer

BWXT Y-12 National Security Complex

Clinton, Tennessee 

GENEVIEVE S. ROESSLER, Ph.D.

Professor Emeritus 

University of Florida

Elysian, Minnesota 

INVITED SPEAKERS 

TED KATZ, M.P.A.

Policy Analyst

National Institute of Occupational Safety and Health

Atlanta, Georgia 

NIOSH STAFF/VENDORS 

MARY ARMSTRONG, Office of General Counsel

CORRINE HOMER, NIOSH 
6 

MARIE MURRAY, Writer/Editor

JIM NETON, NIOSH

KIM NEWSOM, Certified Court Reporter 

AUDIENCE PARTICIPANTS 

RICHARD MILLER 

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P R O C E E D I N G S 

8:29 a.m. 

DR. ZIEMER: Good morning, everyone. I want 

to call us back to order for our second day of 

our fifth meeting. 

I think that everybody I see was probably 

here yesterday. If there is anyone who was not 

here yesterday, I'd like to ask you to please 

register in the log book back on the table. I 

have just one other announcement at this time for 

the members of the Advisory Board, and that is if 

you have more materials than you wish to carry 

aboard the plane and want those shipped to you, 

please let Cori know and she'll make arrangements 

with you to ship whatever materials you want her 

to -- within limits, I suppose, but anyway --

UNIDENTIFIED: If you're not shipping 

antiques – 

(Laughter) 

DR. ZIEMER: Right, antiques that you've 

bought. 

We have a full session this morning. We're 

pleased to have several speakers here that will 

be addressing the IREP risk models, the 

uncertainty analysis, and the radiation 

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effectiveness factors. Those speakers are Dr. 

Owen Hoffman, Brian Thomas, and David Kocher. 

These three gentlemen are with SENES Oak Ridge, 

and I might tell you that that particular group 

originally worked with NCI and had a contract, I 

believe, with NCI to update the 1985 models; and 

then more recently then has had a contract with 

NIOSH to make the NCI-IREP adapted to the NIOSH 

approach. So they've been very heavily involved 

in the risk models, the uncertainty analysis, and 

radiation effectiveness factors. 

So we're going to begin with Dr. Owen 

Hoffman, and then that'll be followed by a 

presentation by Brian Thomas, and then 

presentation by David Kocher. We've set aside 

two hours for these three presentations. There 

will be time during each of those, I think, for 

some discussion, even though we have a separate 

discussion period later. 

Now one thing I want to mention to you that 

-- and Owen has already suggested that we do this 

-- and that is that if there are certain 

questions that he feels might be better answered 

by others who are not here, and more specifically 

by Dr. Land, we will in a sense collect those 

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questions. Dr. Land is standing by at his office 

and will join us, if needed, by conference call 

during the discussion period. So if questions 

are identified that either you wish to direct to 

Dr. Land or that Owen or his colleagues believe 

would be best answered by Dr. Land, we will set 

those questions aside until the 10:45 discussion 

period, at which time Dr. Land will be available 

to join us by conference call or speaker phone, I 

guess. 

So with that, Owen, we'll let you kick it 

off, and then your other colleagues can join you 

as needed along the way. We appreciate your 

being here. 

DR. HOFFMAN: I think with all the meetings 

I've attended and all the times I've had to do 

this, that this would be automatic. It's a 

pleasure to be invited to address you this 

morning. We've been involved for a period of 

perhaps three years in adapting the Interactive 

RadioEpidemiological Program for calculating 

probability of causation. And as Paul Ziemer 

mentioned we first started this under contract 

with the National Cancer Institute, and most 

recently have had a contract to make this program 

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available over the web for NIOSH in facilitating 

their implementation of worker's compensation 

legislation. 

When I was asked by Jim Neton to come here, 

the issue at hand was can we increase the 

transparency of IREP? Evidently at your last 

meeting there was quite a bit of conversation 

from around the table and from the audience that 

the web version appeared to be somewhat like a 

black box, and that IREP wasn't as transparent as 

it could be. Well, our objective today before 

you is to try to make things as transparent as 

possible, and we are prepared to answer any 

question that you have. If you'd like to see 

what changes would be made in the final result as 

the result of changing input assumptions, we'll 

do that. We've got the source code with us, and 

so we're prepared to give you complete insight 

into this code. 

Those of us from SENES Oak Ridge really had 

involvement with the code itself. The decisions 

about the risk coefficients, the actual models to 

be used in transferring the risk from Japanese to 

the U.S. population have been the responsibility 

of the scientists working with the National 

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Cancer Institute. 

The estimation of the probability that past 

exposure to radiation caused a diagnosed cancer 

is primarily the product of three simple factors: 

quantifying the organ-specific exposure, 

translating that exposure into risk, and 

accounting for uncertainty in these two steps 

that then is put into the mathematical 

transformation that accounts for a probability of 

causation, whereby probability of causation is 

simply the risk from radiation divided by the 

risk from radiation plus the risk from all other 

sources. 

Probability of causation is sometimes 

referred to as assigned share. Assigned share is 

the fraction of disease in a heterogenous 

population that would not have occurred in the 

absence of that exposure for all individuals of 

the same exposure category, such as dose, gender, 

age at exposure, age at diagnosis, time between 

exposure and onset of disease, ethnic background, 

et cetera. Assigned share is a conceptually 

measurable quantity. You can measure it. 

Probability of causation for an individual is not 

measurable. An individual's either going to get 

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disease from exposure or he's not going to get 

disease. For an individual, probability of 

causation is simply the weight of evidence that 

the disease could have been caused by that 

exposure. Assigned share, however, is a 

attribute of a population and is a measurable 

quantity. 

The basic calculation of probability of 

causation in the Interactive RadioEpidemiological 

Program is simply the ratio of excess relative 

risk divided by excess relative risk plus one. 

The quantity excess relative risk plus one is 

known in epidemiological circles as the relative 

risk, so excess relative risk divided by relative 

risk equals probability of causation. 

The excess relative risk is a product of risk 

coefficient, excess relative risk per unit dose 

at sievert times the dose. And it is the 

uncertainty in the risk coefficient times the 

uncertainty in dose that gives us the uncertainty 

in the excess relative risk. So you see that the 

uncertainty in probability of causation is just a 

function of the uncertainty in the calculated 

excess relative risk. 

The program IREP is probably the most 

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extensive use of full quantitative uncertainty 

analysis and risk assessment to date, so it's a 

major step forward in how we calculate the risk 

from radiation -- in fact, how we calculate the 

risk from any type of hazardous substance. 

Uncertainty is considered using probability 

distributions, and probability distributions are 

assigned to the organ equivalent dose. This must 

be defined by those responsible for doing the 

dose reconstruction. The original relative 

excess risk per unit dose is also considered as a 

probability distribution, but what goes into this 

is the original statistical uncertainty in the 

dose response as defined by age at time of 

exposure, gender, attained age at the time of 

onset of the disease, and numerous other factors. 

But there's also bias or uncertain bias that 

is accounted for due to the random systematic 

errors associated with the original dosimetry 

that was incorporated in the analysis of the 

atomic bomb survivors. Well, this accounts for 

the fact that -- what is it -- BS-86 dosimetry is 

subject to update, and what kind of uncertainties 

would be introduced as a result of that impending 

update. 

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Uncertainty is also assigned to the selection 

of different mathematical models used to transfer 

the observed risk in the Japanese population to a 

member of the U.S. population, and this primarily 

accounts for differences in background incidence 

rates and differences between an additive, a 

multiplicative, and/or any combination of 

additive and multiplicative models for 

transferring risk from one population to another. 

David Kocher is here to talk about one of the 

areas where there's been a major improvement in 

the way we look at quantification of radiation 

risk, and that is the assignment of probability 

distributions to account for the uncertainty in 

the radiation effectiveness of exposure to 

radiation types other than high energy gamma 

rays. Why high energy gamma rays? It's because 

that's what the Japanese survivor data is 

primarily based on. And now we're looking at 

very low energy gammas like X-rays or low energy 

betas like tritium, alpha particles or various 

energies of neutrons, we will have probability 

distributions assigned to those. And as David 

will mention, these probability distributions 

don't necessarily overlap with the default 

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assumptions recommended by national committees 

that recommend values for radiation protection 

purposes. 

One of the areas that I know has been a 

subject of interest among your committee is what 

do we do about extrapolation from information 

from the Japanese survivors to conditions where 

individuals have been exposed at low doses and at 

low dose rates. Low dose rates mean chronic 

exposures, where there are several exposures in 

sequence over a number of years. 

Well, this is accounted for as what's called 

a DDREF. That just means a dose and dose-rate 

effectiveness factor. It's using the denominator 

of the equation, so the higher the value of the 

DDREF or dose and dose-rate effectiveness factor, 

the lower is the adjustment of risk. The DDREF 

is used for both acute and chronic exposures to 

low LET radiation. But for acute exposure it 

only comes in when the exposures are below 

something that ranges between two and 20 

centisieverts. As you will see, there is a small 

possibility accounted for for an inverse dose 

rate effect for both low and high linear energy 

transfer radiation. This means that there is a 

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possibility accounted for that the DDREF may be 

superlinear or less than one. 

Now the probability distributions used in 

IREP mostly reflect uncertainty that accounts for 

our subjective states of knowledge, as opposed to 

variability associated with an experimental 

design or repetitive observations. This is 

important to keep in mind. The probability 

distributions that describe stochastic 

variability from random observations in an 

experiment, these distributions must obey the 

laws of nature. Normal distributions, lognormal 

distributions are typically the most common that 

come out of such experiments. 

State of knowledge distributions can be any 

shape necessary to represent the space within 

which the true but unknown value is likely to 

occur. And in IREP you'll see that there are a 

whole variety of distribution functions that are 

used to express our state of knowledge. Some are 

discrete, with weights given at specific values. 

Some are continuous -- normal, lognormal, uniform 

distributions, triangular, trapezoidal. And many 

are hybrids of various distributions to reflect 

the impact of alternative datasets. It's the 

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most, I would say, sophisticated use of combining 

various sets that contribute to our state of 

knowledge to represent this within a state of 

knowledge probability distribution. 

To give you an example, here is the current 

distribution used in IREP for the dose and doserate effectiveness factor for solid tumors, 

except for breast and thyroid. And you can see 

that the primary weight is given to values 

between 1.0 and 3. A value of 1.0 means that 

there is complete linearity between health 

effects seen at high acute exposures and that 

that occurs at low doses and low dose rates. The 

higher the value of the DDREF, the more there is 

an adjustment downward in risk, the more the risk 

is suppressed; which means that exposure to 

chronic doses will give a lower risk. Notice 

that there is about a 80 percent probability for 

values between one and two; about a 15 percent 

probability for values at three and/or greater; a 

five percent probability for values less than 

one; and a 25 percent probability for values at 

one or less. 

Now if we look at breast and thyroid, almost 

the same but not quite. There's increased weight 

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of evidence for linearity. Still the bulk of the 

distribution is between 1 and 3; a small 

probability out at 4.0; and about the same 

probability, five percent, for values less than 

1. The reason for this is the increased evidence 

for these two organs that radiogenic cancer is 

linear. 

Now some of you asked about, well, how does 

this whole thing work, and how does Monte Carlo 

simulation affect the final outcome? What 

happens is that we have the probability of 

causation model. This is the Interactive 

RadioEpidemiological Program. This is a 

mathematical model that translates dose and 

disease into probability of causation. All of 

the uncertain inputs are expressed as a variety 

of probability distributions. One value at 

random is selected from each distribution to 

produce a randomized outcome. This is repeated 

over and over until there are a large number of 

possible outcomes that are tabulated, and from 

this we can get a central estimate, and in this 

case a 95 percent confidence interval. 

For the purposes of adjudication of claims, 

the Veterans Administration and NIOSH and the 

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Department of Labor -- actually it's in the --

the acronym, I can't pronounce it -- it's in the 

law that the upper 99th percentile of this 

population of numbers will be used for decisionmaking and the adjudication of claims. And the 

reason why such an extreme value is used is to 

give the benefit of the doubt to those who have 

been exposed. This is not a decision we have 

made. This is a decision that's was made 

external to the effort that we have put into 

quantifying uncertainty. 

In fact, I read the minutes of your last 

meeting, and in those minutes there is numerous 

discussions about all the decisions that have 

been made within IREP to be claimant-friendly. 

We have made not a single one. Not a single 

assumption that we have made that has been 

intentionally made to be claimant-friendly. What 

we've tried to do is to capture our state of 

knowledge quantitatively, albeit many of these 

decisions are the result of our collective 

judgment, but subject to peer review. And we 

have structured IREP in such a way that in the 

future if there is a need for updating, it can be 

readily updated. 

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Now here's an example of results that are 

produced by IREP, and the example is a person 

exposed at age 24 who has come down with thyroid 

cancer at age 60. He was exposed to a thyroid 

dose of -- here I have 15 centigray, but 15 

centigray and 15 centisieverts are identical for 

low LET radiation to high energy gammas. The 

dose is uncertain, but we’ve given a modest 

uncertainty which would be a geometric standard 

deviation of 1.4. That's about a factor of two 

either side of this central estimate. 

As a result of 2,000 Monte Carlo simulations 

using Median Latin Hypercube Sampling -- and I 

won't go into that, but that's the mechanism 

that's used for sampling -- here is the outcome. 

Notice that the central estimate only shows about 

a 12 percent probability of causation. The upper 

95th percentile often used for decision-making 

would still be less than a 40 percent probability 

of causation. However, at the 99th percentile, 

that percentile that has been deliberately chosen 

for decision-making, that would cause this person 

to be eligible for claims. 

A feature of IREP that I know that some of 

you aren't familiar with, and this is an 

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important feature because we know that we're 

working in an atmosphere of imperfect knowledge. 

We know that although we have tried to account 

for all sources of uncertainty, that the state of 

knowledge progresses on. And so in addition to 

building this code so it can be readily updated, 

we've also allowed for additional sources of 

uncertainty to be included with adequate 

justification. This justification should require 

written rationale. 

And what we have within IREP -- and Brian 

Thomas will demonstrate this -- is an additional 

variable that functions like an overall bias 

correction factor that is uncertain, with the 

central value and the width of the uncertainty in 

this parameter, will adjust the final excess 

relative risk. The rationale for such adjustment 

could be an individual whose background rates of 

cancer are known to be significantly different 

from those of the national average, updates in 

radiogenic cancer risk for certain disease end 

points, or as new information comes forward from 

worker populations. This back door can be used 

to justify additional modifications to the 

overall outcome. 

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But the point I want to make is that it was 

our intent that this just not be used willynilly; that, Larry, there should be good, strong 

scientific rationale for its implementation. 

The default of this additional uncertainty 

factor is a lognormal distribution with a mean of 

one and a geometric standard deviation of one. 

What does that mean? Means it's constant. 

There's no effect at all currently. But if the 

mean were kept at one and this geometric standard 

deviation were changed to, let's say, 1.4, that 

would increase the overall uncertainty in the 

expression of probability of causation. If the 

geometric mean were to change to two, it means 

that we would have an overall bias whereby we 

felt that the current estimates in IREP were 

underestimating the probability of causation, and 

this could be used to adjust the entire 

distribution upward by a factor of two. If this 

were to go down to, let's say, .33, it means that 

we felt we were overestimating the probability of 

causation, and the whole distribution could be 

adjusted the other way by a factor of three. 

So in summary in this introductory 

presentation, IREP starts with original risk 

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factors that come from the follow-up of the 

lifespan study of the Japanese cohort that is 

formed from the survivors of the atomic bombings 

of Hiroshima and Nagasaki. What's new is unlike 

past risk estimates that are based on mortality, 

this one is now based on incidence. And the 

basic data used in IREP is incidence-based. I 

think this is the first time anywhere in any 

radiation risk assessments that the incidence 

data have been used directly, as opposed to risk 

estimates being derived from mortality 

statistics. 

The only organs not using the Japanese data 

would be the thyroid, in which case the pooled 

study from Ron, et al. in 1995 is the basic 

dataset, and for lung cancer exposures to radon 

is used as the primary dataset for the case where 

exposures to radon are explicitly quantified in 

terms of working level months’ exposure. 

These original epidemiological estimates are 

adjusted for errors in the epidemiological 

dosimetry. Those errors are further adjusted for 

the uncertainty associated with the transfer of 

risk from the Japanese to the U.S. population, 

and this accounts for both the uncertainty in the 

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models as well as uncertainty in the differences 

in the background incidence rates. 

For low dose and chronic exposures, it's 

further adjusted for that dose and dose-rate 

effectiveness factor. And then the final excess 

relative risk per sievert can be adjusted using 

this user or claimant-justifiable uncertainty 

factor. To date it hasn't been used, and to date 

it is just simply set as a constant. 

That's my introduction. 

DR. ZIEMER: Thank you, Owen. I think we'll 

take a few moments for some questions here. Let 

me begin simply by asking you, in our handout 

there are three slides that deal with dose and 

dose-rate effectiveness factor that you either 

omitted or are holding for later. Were you 

intending not to cover those? 

DR. HOFFMAN: You led right into the reason 

that I decided to hold them, because I wanted to 

wait for a question to come up. 

(Laughter) 

DR. HOFFMAN: Because I know this has been a 

subject of interest, but I didn't want to give 

you everything I knew. 

DR. ZIEMER: Is there anything else you're 

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holding back? 

DR. HOFFMAN: Hoping that a question would 

come forward, I used the advanced features of 

PowerPoint to hide these slides -- but you have 

them in your handouts -- to show what other 

distributions have people used in quantifying the 

uncertainty in radiogenic cancer risk. 

The first attempt to formally quantify 

radiogenic cancer risk was in Publication 126 of 

the National Council on Radiation Protection and 

Measurements. And Dr. Charles Land, Andre 

Bouville, and Warren Sinclair were the principal 

authors of that report. That report used a state 

of knowledge distribution -- no named shape to 

this; it looks like a compounded series of 

triangular distributions with the left-hand side 

truncated at 1, peak value at 2, and then 

diminishing but stopping at 5.0. 

Now the interesting part of this distribution 

is that linearity or 1.0 is not sampled at all, 

so there's no weight given to 1.0 here. There is 

weight given to values slightly above 1.0, but in 

a continuous distribution like that neither 

values at 5 or at 1 are sampled. This was a 

subject that was brought up in the Science 

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Advisory Board review of EPA's uncertainty in 

radiogenic cancer risk, and Gen and I were 

associated with that effort. 

Well, here's what EPA did. And this is 1999, 

EPA's addendum to their radiogenic cancer risk. 

And this is the small report written on their 

attempt to quantify uncertainty in radiogenic 

cancer risk, and this is the distribution that 

they put in for all solid tumors other than 

breast and thyroid. Again, it goes from 1 to 

very small weights given to values greater than 

5. However, most of the distribution is between 

1 and 2. Because it's a continuous distribution, 

values at 1 aren't sampled. And again this was a 

subject that we discussed in our Science Advisory 

Board review, and EPA's answer was, well, if we 

put some weight here at 1, it would only change 

the overall results by about 10 percent. So they 

didn't do it. 

This was an issue that I think over the last 

few years we battled and debated amongst the team 

of us working on IREP, and finally what 

influenced us to try for something different was 

the dose reconstruction for Rocky Flats. And 

this is Warren Sinclair, Helen Grogan, and 

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others, who looked at the NCRP distribution and 

said, well, there's evidence from the Japanese 

bomb survivors, and some animal experiments as 

well as some other human epidemiological studies, 

that says that even some superlinearity cannot be 

discounted. And so they went down as low as .2, 

but basically used the NCRP distribution and 

added this small probability to an inverse dose 

rate effect. 

We looked at the information and said that, 

well, basically there is not a whole lot of 

epidemiological and experimental evidence to 

allow us to dictate a distribution of any shape, 

and that's why we put weights at discrete values 

and used a discrete distribution for both breast 

and thyroid and distributions for all other solid 

tumors. 

Now for leukemia there is no DDREF used. 

It's just a -- basically it's a linear quadratic 

dose response. And that linear quadratic dose 

response has the effect that at low chronic 

exposures the risk is about a factor of two less 

than it would be at high acute exposures. 

I'm not hiding any other slides. You've now 

seen all of them. 

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DR. ROESSLER: You led right into a question 

I have, and that's why do you use the DDREF for 

the solid tumors and then the linear quadratic 

for leukemia, when aren't they essentially the 

same? Or is there some fine difference that I'm 

not recognizing? Or are you trying to make it 

line up with the BEIR reports? 

DR. HOFFMAN: Neither, neither. This is --

and here's a case where the ultimate authority on 

that is Charles Land. 

But since I've got the floor I will try to 

mimic what I know his answer would be, and that 

is that the data are far better developed for 

leukemia than perhaps any other organ, and it is 

clear from the statistical analysis of those data 

that it follows a linear quadratic relationship. 

It's also clear, however, that in looking at all 

other solid tumors that it is not a linear 

quadratic relationship. And in fact, for the 

range over which one sees a statistically 

significant excess relative risk, the model is 

more linear than anything else. 

But we can reserve that as one of the 

questions we ask Charles when he gets on the line 

to get his viewpoint on it. 

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MR. GRIFFON: I guess I was looking for one 

other hidden overhead there. You mentioned that 

the analysis of the Hiroshima data showed some 

superlinearity, and I wondered did they recommend 

a separate distribution for the DDREF value? You 

said Grogan incorporated that into their 

distribution. Did the Hiroshima researchers --

DR. HOFFMAN: No. 

MR. GRIFFON: -- recommend any distribution? 

DR. HOFFMAN: No, they just report their 

observations. They make no recommendations. 

MR. GRIFFON: Can you give the reference for 

that? What reference, and what was their 

citation? Some superlinearity, or was it more 

specific? 

DR. HOFFMAN: Well, I believe it's the most 

recent publication on cancer mortality by Preston 

and Pierce -- either Preston and Pierce or Pierce 

and Preston, 1996, Radiation Research. I think 

if you look in the back of your documentation of 

Charles' report that I think has been circulated 

to all of you, the exact citation's in there. 

Yes, Gen. 

DR. ROESSLER: I thought it was interesting 

you talked about the ability of IREP to deal with 

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additional sources of uncertainty. And I’m 

wondering on the thyroid, now that the Hanford 

Thyroid Disease Study -- do you feel like you're 

getting in a corner? -- now that the results of 

that study are final, will that make any impact 

on the adjustment of the geometric mean in IREP? 

DR. HOFFMAN: I'm going to try to divorce my 

personal opinion on that subject with what I 

would consider a more direct answer, and the 

direct answer is that IREP is amenable to 

upgrades in the state of knowledge as the state 

of knowledge evolves. And I think the final 

Hanford Thyroid Disease Study has only been out 

for a matter of days. And I don't know about 

you, but I have not even had a chance to read it 

to know what effect that would have. 

My personal opinion is I still don't think it 

has the power to sort out signal from the noise. 

And I think if one looks at the confidence 

intervals that would take into account 

uncertainty in dosimetry, especially shared 

sources of uncertainty and uncertainty that would 

be associated with what I call differential bias 

-– in other words, the potential to underestimate 

the high end of the distribution and overestimate 

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the low end of the distribution. You see those 

confidence intervals that clearly overlap risk 

coefficients in IREP. But I say that having seen 

the previous Hanford Thyroid Disease Study. I 

haven't look at this final version. 

The bottom line is as the state of knowledge 

changes, IREP is amenable to updating. And one 

of the advantages in having it on the web is you 

can update it in one place and that update is 

available to the world, as opposed to putting it 

on CDs and having to generate thousands of new 

CDs every time there's an update. 

DR. DEHART: Your comment just covered what I 

was going to say, that is the dynamic process of 

IREP over time. In that context, then, as 

epidemiological studies come forward, how are you 

validating and making adjustments? 

DR. HOFFMAN: Well, our future role with IREP 

is uncertain, and so I can't answer that 

question. I can just say the design is that it's 

amenable to frequent updates. And each new 

epidemiological piece of information is a form of 

validation. And if it becomes clear that the 

upper bound of these uncertainty distributions 

are simply rewarding for the presence of lack of 

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knowledge, well, new information should justify a 

change. 

Now of course the political difficulty is 

this, is what happens in the presence of lack of 

knowledge that a person today qualifies for 

compensation, and then as new knowledge comes 

forward the person is suddenly ineligible? 

That's outside the realm of our influence. 

That's your job, to deal with these really 

difficult situations whereby simply by rewarding 

for uncertainty that a person could be eligible 

for compensation today and not be eligible for 

compensation as the state of knowledge improves. 

DR. ZIEMER: Tony. 

DR. ANDRADE: I gather that if I were to ask 

you what was the real baseline baseline start for 

IREP, you would probably say the ICRP-60 risk 

coefficients insofar as calculating excess ERR, 

the excess risk -- no? 

DR. HOFFMAN: I'm glad you said that. No. 

No, ICRP-60 is 1990. The real baseline baseline 

is the 1994 Thompson, et al. report and its 

associated datasets in radiation research. 

But the National Cancer Institute made new 

analyses on that data, so you can't just get into 

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Thompson 1994 and map directly from that study 

onto what's in IREP. There have been -- and it's 

described in the write-up -- numerous re-analyses 

of age at time of exposure, time since exposure, 

attained age effects, gender effects in order to 

build in as much defensible specificity as is 

possible. And it probably could go on and on, 

but at some point you have to draw things to a 

close. And what you're seeing is the outcome of 

three years' worth of work. 

DR. ANDRADE: Okay. Well, my point was going 

to be simply this, is that you've used 

information that has evolved tremendously since 

ICRP was put out, and even ICRP-60 attempted to 

use factors including gender, time at -- during 

the lifetime at which the person was exposed, 

age, that sort of thing. 

And so what I wanted to do is just clarify or 

address a comment that was made yesterday, that 

apparently we in the health physics community 

have been trying to use only Japanese survival 

data to calculate these probabilities -- or risk 

coefficients, let's put it that way, let’s be 

more precise -- risk coefficients. And the 

answer to that is that that is not true. We have 

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used all sorts of studies, one of which, only one 

of which has been the Japanese survivor data. 

And I just wanted to emphasize that point for the 

audience here in general. 

DR. HOFFMAN: I wish I could adopt your 

enthusiasm. The truth is that the bulk of this 

really is the Japanese survivors data. But the 

radon, the radon cohorts and the thyroid are 

exceptions to that. I think if there is a major 

-- a major upgrade to all of this would be to 

include within the uncertainty analysis other 

options from other studies, such as worker 

studies and looking at outcomes from those. But 

that will be the job of a committee with more 

resources than what was available to the 

committee that put this together. 

DR. ANDRADE: Exactly. But for example, in 

the case of lung cancer, the radon data and the 

radon studies would heavily weigh into those risk 

coefficients. 

DR. HOFFMAN: In this case lung cancer itself 

does come from Japanese survivors, as long as the 

exposure is coming from low LET radiation. But 

for radon exposure directly, the working level 

month being the source of exposure, then it 

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changes over to use radon cohorts. And the bulk 

of that is the uranium miners. 

Well, if I might introduce the next speaker 

DR. ZIEMER: Yes, please. 

DR. HOFFMAN: When we were invited to come, a 

person that I felt was absolutely essential to be 

here is the person responsible for, I think, one 

of the major contributions to IREP. And this 

contribution has been done under the sponsorship 

of NIOSH, and that is to address the risk of 

other radiation types other than high energy 

gammas. That was an assignment given to us, 

assignment that I charged Dr. David Kocher with. 

Dr. David Kocher is a health physicist that's 

had 30 years experience at Oak Ridge National 

Laboratory. Some of you from the health physics 

community are well aware of his publications. 

We've had the privilege of having Dave work with 

us for over a year now at SENES Oak Ridge. And 

Dave does things the old-fashioned way -- that 

is, with overheads. 

DR. KOCHER: Anybody remember lantern slides? 

That's sort of where I come from. 

Owen gave a good introduction to my remarks 

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when he commented that we've been looking at 

issues of how different types of radiation differ 

in their effectiveness in causing cancers in 

humans. And we have looked at neutrons, alpha 

particles, photons of different energies, and 

electrons of different energies. We haven't yet 

gotten into some real exotic stuff like nuons and 

very high energy neutrons, things that probably 

aren't encountered everyday in the Department of 

Energy system, but who knows? 

What is new and exciting about all of this, 

as far as I'm concerned? Well, these different 

effectivenesses have been taken into account in 

radiation protection for 40 years now. ICRP-2 

had some assumptions about the effectiveness of 

alpha particles relative to gamma rays, and 

neutrons have been well known and studied, going 

back to the beginning of radiation biology. But 

what has never really been done in a broad scope 

before is to express these factors in terms of 

uncertainty. 

In radiation protection you choose point 

values -- 20 for alpha particles, you're all 

familiar with this. But for purposes here of 

calculating the probability of causation of a 

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cancer in a real person who got a real dose, and 

if you want to express your state of knowledge, 

you must do this using uncertainty. 

And there have been some limited efforts in 

other areas in the recent past -- for example, 

the Rocky Flats dose reconstruction did 

incorporate uncertainties in biological 

effectiveness of alpha particles from plutonium 

in that analysis. It has not yet been applied to 

real people. Tritium has been looked at from an 

uncertainty point of view in a limited context 

that Owen and Brian worked on for Berkeley Labs. 

But this is really the first time that I'm aware 

of that a broad approach to trying to capture 

uncertainty in a human health risk assessment has 

been done. So therefore we will be subject to 

lots of potshots, and deservedly so. 

I know you all have read, from cover to 

cover, the 77-page report which was posted on the 

Internet not too long ago. That's an awful lot 

of stuff. And let me really tell you in 30 

seconds what I tried to do there. I tried to 

disclose, as fully and completely as I could, the 

thought process we went through to try to develop 

uncertainty distributions for these different 

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factors. If you go into ICRP and try to discover 

how do they come up with 20 for alpha particles 

or whatever, complete silence -- absolute, 

complete silence. 

So really the bulk of this 77-page current 

version of this report is I tried to explain what 

we did. What we did has a lot of weaknesses. It 

has some strengths. What I'm going to try to do 

today -- I don't want to go too much into a lot 

of technical detail here, because I know most of 

you aren't necessarily that interested in really 

the fine details. But your mother said you've 

got to eat your spinach every once in a while, so 

there will be a little bit of that. But what I 

really want to try to do is to give you a feeling 

of what we did. What were the sources of 

information that we had to develop uncertainty 

distributions for different radiation types? 

What were the judgments that we made to come up 

with our final answer? And what are the 

weaknesses, what are areas where I am absolutely 

sure that better work could be done? 

And I'll try to point in those directions, 

because there are a couple of areas here where we 

really are looking -- we eagerly would like to 

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have positive feedback or helpful comments and 

suggestions from anyone. We are open to changes 

in any of this. But I will try to point out to 

you a few areas that I feel like particular 

attention could be paid to doing things better. 

Well, there's an awful lot of information in 

the radiobiological literature on the biological 

effectiveness of different radiation types. RBE, 

that's the acronym in radiation biology that 

stands for relative biological effectiveness. 

But we have a new term, REF, radiation 

effectiveness factor, and it's explained in the 

report. But the short answer is that what we are 

coming up with is not RBEs, because RBE is what 

you get when you do a specific radiobiological 

experiment. And I can say, mercifully, that we 

don't have a lot of human data on what we're 

looking for. So we need a new word, and I'm glad 

that you all are using radiation effectiveness 

factor in your everyday lingo, because I 

certainly hope this term catches on. 

But there's enough literature data out there 

that could fill this room, and we just -- there 

was no way to go back and review all this from 

scratch. So we depended very heavily on past 

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reviews and analysis of this wealth of data by 

various expert groups in this alphabet soup of 

organizations. Some of these you may not know. 

ICRU is the International Commission on Radiation 

Units. They're kind of like the ICRP. The NRPB 

is the national authority in Great Britain. 

Our work has been through two rounds of 

external peer reviews, and we've incorporated a 

lot of comments that we got from experts in the 

field. And we have used the recent primary 

literature to some extent to fill out because a 

lot of these things are getting a little bit old. 

The NCRP report, for example, is from 1990, and 

there has been some work since then. But by and 

large, we relied on expert groups who know far 

more about radiation biology than I do to look 

through all this data and assess the experiments 

that are good from those that are not so good, 

and what did they think this meant in terms of 

RBEs, et cetera. 

I'm not going to go through the equations in 

any detail, but I did want to show you how these 

things -- these quantities are used in actually 

calculating cancer risks. And I've got two pages 

of equations, and I'll really just show you one 

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equation to give you a sense of how this works. 

The quantity we're trying to calculate over 

here is risk, and we express it in terms of 

excess -- well, it's just the excess relative 

risk, is what you want at the end. That's what 

goes into a calculation of PC, as Owen showed. 

You start with some estimate of absorbed dose, 

and here's the risk coefficient that you get from 

the atomic bomb survivor data. This is some kind 

of -- I call it an ERR per gray, some people call 

it an ERR per sievert. They're basically the 

same. This is high energy gamma rays that have a 

defined biological effectiveness of one. 

And if you're going to -- in some of the 

equations, not always, this is adjusted by the 

DDREF that Owen talked about. This is a thing 

that has an uncertainty distribution with a 

central value somewhere between one and two. And 

I never remember what the central value is --

1.6, something like that. 

And then this REF is just a multiplier. It 

just adjusts for the effectiveness of the 

different radiation type. And basically all this 

means -- it's really a simple concept -– if you 

give a certain absorbed dose of gamma rays to a 

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mouse, and you give the same absorbed dose of 

neutrons to the same mice, you're going to see 

more cancers in the mice than you do -- from 

neutrons than you do from the gamma ray 

exposures. They have a different effectiveness 

in causing the response that you're looking for, 

and that effectiveness is captured in this REF. 

It's a very simple concept. So this just kind of 

shows you how they're used. 

And I'm not going to go into the difference 

between high and low doses and dose rates. 

That's for the health physics aficionados on the 

committee to look at and see what you think about 

it. I realize that certain things are just too 

painful. 

I'm going to skip -- well, Owen did mention 

this, and I'll show you again. For all solid 

tumors there's a linear dose response in the 

atomic bomb survivor data. But -- Gen, this is 

the answer to your question -- it's linear 

quadratic for leukemias, and this is what the 

data show. They show linear quadratic for 

leukemias, but they look linear for everything 

else. So that's the assumption that Charles Land 

made. And enough of that. 

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Now here's something -- half of this should 

be familiar to many of you. The column for ICRU 

may not be quite so familiar. But this is how 

biological effectiveness is taken into account in 

radiation protection today. And again, radiation 

protection is not about estimating real risks to 

real people from an actual exposure. That's not 

what radiation protection is about. Radiation 

protection is about controlling doses, period. 

So they have standard assumptions. A point 

estimate of 20 for alpha particles, 20 for 

neutrons of unknown energy -- and the ICRP has a 

function I'll show you later that accounts for 

the energy dependence of the neutron weighting 

factor -- one for all electrons, and one for all 

gamma rays. 

Now as we go ahead, you'll probably be 

keeping score on how I'm doing relative to this 

curve, to this set of numbers. Well, our 

distributions for alpha particles will encompass 

this, and our distribution for fission neutrons 

will encompass this, but we will depart from 

these numbers here at the lower energies. 

A question came up over here when Owen was 

talking about what have we done about the ICRP 

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assumptions as we got into this. We did not 

start with an assumption that these values were 

the correct -- were the best central estimates of 

anything. We looked at what the data told us. 

And if the ICRP numbers fell within our 

distributions, fine. If they didn't, well, 

that's the way the mop flops. That's all I can 

say. We did not assume that they had the right 

answer, mainly because they didn't really 

disclose where these numbers came from. 

So a key point to remember here is we're 

applying subjective judgment to a lot of data, 

and we absolutely acknowledge that knowledgeable 

individuals could look at the same information we 

looked at and come to somewhat different 

conclusions. I don't think the conclusions could 

be radically different, but you could certainly 

-- there's a lot of judgment in here. And again, 

the whole purpose of my paper was to try to 

disclose our judgments as best we could, and to 

express where the weaknesses are. But we did not 

assume that ICRP had the right answer. 

So I just want to go through the different 

radiation types that we looked at and give you a 

flavor for the kinds of data that we used and the 

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kind of judgments that we made. And I'm going to 

start with neutrons. 

Historically, neutrons have been the 

radiations that have been the most studied of 

all. Back in the sixties and seventies and 

eighties there were a lot of data on RBEs and 

neutrons in all kinds of biological systems 

ranging from simple cells up to whole organisms, 

plants and animals, the whole nine yards. But 

there are data in mice that actually where tumors 

themselves were the end point. They actually 

measured tumor induction in mice exposed to 

neutrons compared with some reference radiation, 

either X-rays or high energy gamma rays. And as 

Owen mentioned, we use high energy gamma rays as 

our radiation that has a defined REF of one, 

because that's the conditions under which the Abomb survivors were exposed. 

And again, going to reviews of the 

literature, there was a lot of data on RBE for 

life-shortening and induction of specific 

cancers, and life-shortening in these mice is due 

almost entirely to cancer induction. There's 

very little else that's killing them. And you 

find a range of RBEs -– and I just give you these 

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numbers, you don't have to pay any particular 

attention to this -- and from this you can just 

derive some kind of distribution. And we're 

trying to make life simple, and we're trying to 

choose familiar distributions when they can be 

justified. And lognormal is one of the most 

familiar distributions in natural systems, 

especially when the data are highly variable. 

Where the range from the low end to the high end 

is fairly large, lognormal often describes what's 

going on. 

And from this range of data, we just said 

there's a 95 percent chance that the REF in 

humans lies between 2 and 30. That's a fairly 

wide range. That's a range of 15. The central 

estimate here is at 7.7. 

Now some of you are already maybe keeping 

score, and here we're saying a central estimate 

at 7.7, where the big boys say it should be up 

around 20. Well, something I didn't talk about 

is that this is an REF at high acute doses. It 

doesn't have a DDREF in it. So more or less you 

need to multiply this value by a factor of about 

two if you want to compare it with the number 20. 

And this is explained in excruciating detail in 

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the paper, but I don't want to talk about it 

here. So this number has to be multiplied 

roughly by a factor of two, and this for acute 

exposure only, so that's around 15 to 16, which 

is pretty close to 20. But there's a substantial 

range of 15 here between the lower and upper end 

of that confidence interval. 

I felt like the situation for fission 

neutrons in solid tumors and leukemias is in 

pretty good shape, because there are animal data, 

data on whole animals with the cancers that we're 

interested in as the biological response that was 

being measured. But still there are problems 

that we talk about in the paper, about are the 

mice data relevant for humans? A human doesn't 

look like a mouse. And those of you who know 

anything about neutrons, this is a very 

complicated type of radiation in terms of how it 

interacts with tissue. You get all kinds of 

secondary radiations. And if you had a 

monoenergetic neutron incident on the skin of a 

mouse, the spectrum of radiations inside that 

mouse is going to be very different from the 

spectrum of radiations in a deep-lying organ of a 

human being. 

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And we really haven't done much with that, 

and that's an area where perhaps something could 

be done. We basically just said that the mice 

data apply to humans. But that's an area where I 

think, as this method gets fine-tuned as we go 

along, where something more could be done. It's 

quite possible, I think, that the mouse data tend 

to overestimate the biological effectiveness in 

humans rather than underestimate. So in a sense, 

if you want to claim do we have a bias, it's a 

little bit on the claimant-friendly side, I 

think. But this is a matter of science that 

could be worked out, and we could do more here. 

This next slide is not in your package, but 

in case some of you have never seen what a 

lognormal distribution looks like before, this is 

the distribution that I described on the previous 

slide. When plotted on a linear scale -- this is 

REF on this scale, and here's probability on the 

vertical scale -- a lognormal distribution tends 

to be skewed to the left, and the 50th percentile 

is somewhere about here and the 95th is from 2 to 

30. That's basically what a lognormal 

distribution looks like. And as this range gets 

bigger it gets more and more skewed to the left, 

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with a very long tail going out to the right. 

And of course, only 95 percent of the values are 

shown here. There are two and a half percent 

that lie out here, and there's another two and a 

half percent -- down to zero is show -- but 

there's two and a half percent of the values lie 

beyond the right-hand side of that curve. The 

beauty of lognormal distributions, they never go 

negative. 

We did the same thing for leukemias, for both 

alpha particles and leukemias -- sorry, for both 

alpha particles and neutrons. There was 

convincing evidence from the literature that the 

biological effectiveness was different for 

leukemias and solid tumors. These are two 

entirely different types of cancers, so there's 

no reason that they have to be the same. And in 

general, RBEs for leukemias are less than RBEs 

for solid tumors, and we've incorporated that in 

what we did. We have separate distributions for 

leukemias and solid tumors for the high LET 

radiations. And again there are data on 

mice, and we went through, and 

it ranges from this to that, 

and we had another lognormal 

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distribution. 

Now here, this is a number which you could 

directly compare with the ICRP, because this is 

at low doses and low dose rates. That's what 

this L stands for. In fact, almost all our 

distributions are at low doses and dose rates. 

The only one that isn't is the solid tumors and 

neutrons. And here the confidence interval we 

just said dose from 2 to 60. That's a range of 

30, and the median is about 11. Well, 11 

compared with 20, that's a factor of two. But 

remember, the ICRP is coming up with a single 

number that's supposed to cover everything, and 

if they had to pick a single number they would 

probably bias it toward the solid tumor numbers 

rather than leukemias to be safe. But who knows 

what the process is they went through, because 

they haven't told anybody. 

Now one of the complications about neutrons 

-- and Owen mentioned this -- is that there's 

some data in the radiobiological literature, and 

there's a lot of calculations which show that the 

-- suggest that the biological effectiveness of 

neutrons is energy dependent. Now most of the 

experiments are done for fission neutrons, and 

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that's a spectrum of neutrons over a wide energy 

range. But by and large, most of those neutrons 

are in the energy from -- this is .1 MeV here up 

to about 2, is this break point. And the fission 

neutron experiments lay up here in the region of 

maximum biological effectiveness. 

But there's calculations going back 30 years 

now, and a lot -- and some radiobiological 

studies which show that as you get away from this 

range from .1 to 2 MeV the biological 

effectiveness drops off in this direction, and as 

you go toward higher energies. And this is just 

a reflection of as the energy changes, you get a 

different mix of secondary radiations that are 

actually delivering the dose. That's what this 

is all about. Neutrons don't do anything by 

themselves. They cause dose only because of the 

secondary radiations they produce. 

And this solid curve is the standard ICRP 

assumption that many of you are familiar with, 

that the value -– here’s 20 for .1 to 2 MeV. It 

drops by a factor of two out here down to 10 keV, 

another factor of two down to 5 at the lowest 

energies, and similar as you go up. But what 

really impressed me is kind of the database for 

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that step function curve. I don't know whether 

impressed is quite the right word. The data are 

sparse. Everybody used fission neutrons, and not 

too many people have studied neutrons of other 

energies in experiments. And I have two slides 

here that show, at least according to an NRPB 

review, really almost the entire data in this 

area. 

Now here's one dataset. Here's the fission 

neutrons kind of up in here. Here's one dataset 

that maybe sort of shows what's going on that 

matches that other curve. But here's another one 

that it's okay up here, but there's a point way 

out here. And you can find other studies in the 

literature that don't really show much of a step 

function, like the ICRP said. Here's just one 

more example of the same thing. The open 

symbols, they kind of fall off as you go up here. 

But this, here's a dataset, who knows what that 

one's doing in terms of energy dependence. 

So the point I want to make is that that nice 

little step function curve that the ICRP assumes 

today has a fairly shaky database in terms of the 

actual radiobiological information that goes into 

that. A lot of what goes into that is 

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theoretical calculation of how neutrons interact 

with tissue at different energies, and what are 

the secondary radiations they produce. But it's 

not really been verified experimentally. I wish 

-- I'm a humble physicist. I don't know much 

about this biology stuff. But really, no data on 

thermal neutrons. I guess that's a hard 

experiment. But we didn't find any data on 

thermal neutrons, which is often something of 

interest. 

So what did we do about this in terms of the 

REF for different energy ranges? Here's where we 

get really into the idea of subjective states of 

knowledge distributions that Owen emphasized. 

What I’m going to show you next doesn't resemble 

anything that you would actually measure if you 

did the experiment. It's just to try to 

represent what do we know about the REF for 

neutrons of energies other than fission neutrons. 

And we assumed that these distributions should 

have three properties. 

The first was that the REF should not be less 

than one, and this is a simple assumption that 

neutrons of any energy should not be biologically 

less effective than high energy gamma rays. High 

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energy gamma rays is our defined REF of one, so 

neutrons should not be less biologically 

effective than high energy gammas. That's 

assumption number one. 

Assumption number two is we assumed the ICRP 

step function reduces the weighting factor for 

fission neutrons by either a factor of two or 

four as you step up or down in energy, and we 

assumed that the median of our distributions for 

fission neutrons should be reduced by about that 

amount. In other words, we assumed that the ICRP 

step function that I showed you more or less 

represents the energy distribution -- the energy 

dependence of REF. 

But there's certainly uncertainty in that 

adjustment, as I showed you on those two plots of 

the data. The data are pretty shaky. So we 

reduced the upper confidence limit by an amount 

less than that to represent uncertainty in that 

adjustment. In other words, the uncertainty 

distribution is going to be broader at these 

other energies than it was for fission neutrons. 

Now we started with a lognormal distribution 

for fission neutrons, which was already highly 

skewed to the left. And if you fix the lower 

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bound and lower the median by a certain amount, 

and lower the upper confidence limit by less than 

that, you're going to get a distribution that's 

more highly skewed to the left, and it's going to 

have a long tail. Here we tried to make life 

simple. We just fabricated a distribution that 

would have these properties but would look 

simple. Now this is obviously not a distribution 

that you would ever measure in an experiment, but 

it has the three properties that I showed on the 

previous slide. 

This is just one example. This is a case 

where the median value is reduced by a factor of 

two compared with the distribution for fission 

neutrons, but the upper confidence limit was 

reduced by something less than a factor of two, 

around a factor of 1.7, 1.8, something like that. 

It's explained in detail in the report. And we 

just arbitrarily assumed that we would describe 

these distributions by what I call a piece-wise 

uniform distribution that had three pieces. It's 

uniform between one and some number, uniform 

between that number and another number, and then 

a third tail that goes way out here. And we just 

fixed the number of steps at three. And 

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furthermore, in every case we said there's going 

to be a 30 percent weight to this part, a 50 

percent weight to this part, and a 20 percent 

weight to this part. 

Now these judgments are obviously arbitrary. 

There's an infinite number of probability 

distributions that would meet the three 

conditions that I showed on the previous slide. 

And we just wanted to have something that was 

visually and conceptually fairly simple. 

So all we have to do once we have these 

definitions is we just adjust these three numbers 

until we get the conditions that we wanted on the 

previous slide. And it's just -- it looks 

simple, but you would never measure anything like 

this. But this captures the state of knowledge 

about REF at these other energies, and the state 

of knowledge is not real good. 

Okay, I'm going to move on to alpha 

particles. I think in general alpha particles is 

a radiation type for which what we have come up 

with would be most subject to adjustment by 

further input. There's a lot of uncertainty in 

what to do. A lot of uncertainty in what to do, 

and our judgments could be wrong, or they could 

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be not as good as they should be. And I want to 

try to indicate where the weak parts are. 

Let's look first at solid tumors. Here again 

we're fairly fortunate in that there's a lot of 

data in various small mammals -- dogs, rats, mice 

-- looking at induction of bone and lung tumors 

by alpha-emitting radionuclides like plutonium 

and americium, a lot of data on RB and E systems, 

a lot of data on different kinds of responses in 

cell systems. And you find a wide range of RBEs, 

down from about 5 at the low end -- these are 

central estimates -- range from about 5 at the 

low end to somewhere in the range of 60 to 100 at 

the high end. And here again, just to keep life 

simple, we describe this range of values by a 

lognormal distribution where 95 percent of the 

values were in the interval from about 3 to 80, 

and the median here is 15. 

Now here's an area -- and this is not in our 

report, but I'm going to put it out to you. It's 

possible that the median of this distribution is 

a bit too low, that we might actually be better 

off in this case coming up with some kind of a 

hybrid distribution that has this confidence 

interval, but has the median shifted up somewhat. 

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And if you just kind of look at -- if you just 

plot all the data, you get the impression that 

it possibly could be a little higher, but not by 

a great deal. But this is an area where I think 

as this work evolves we might want to look at 

this again. Of course, this is not the final 

answer. We have this inverse dose rate effect 

that hasn't been applied yet that I haven't 

talked about. So that's one area where we might 

do a little bit more. I think I'm pretty 

comfortable with the range here. There's just 

not very much beyond 80, and there's hardly 

anything, virtually nothing below 3. 

Where we're really skating on thin ice -- and 

I think no one really knows what to do about this 

-- is the question of alpha particles and 

leukemia. What's the problem here? The good 

news, in a way, is that there are data in humans, 

possible data in humans for the effectiveness of 

alpha particles in causing leukemias. The 

problem with the available data is that they're 

contradictory, and that there's a lot of problems 

in the data themselves. And it's very, very hard 

to sort this out. 

The essential problem with alpha particles 

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and leukemia is this: The question of how to 

estimate the dose to radiosensitive cells in bone 

marrow. The whole problem of dosimetry is highly 

uncertain, so it's very, very hard -- when you 

try to look at the various human studies it's 

very, very hard to sort out issues of dosimetry 

versus issues of biological effectiveness of 

alpha particles. And what we have attempted to 

do -- what I have attempted to do, I can't blame 

this on Owen or Iulian -- what I attempted to do 

was say, look, if the dosimetrists have a 

problem, go fix it. I'm not going to bury 

uncertainty in -- I'm not going to bury a problem 

with dosimetry in the REF. I want to try to 

assess what is the REF, assuming that the 

dosimetry is right. And if you have a dosimetry 

problem, go take care of it, but I'm not going to 

blame -- I'm not going to incorporate your 

dosimetry problem in an estimate of REF. But I'm 

sure we have done some of that, just because the 

data are all we have. 

Now let me just briefly try to describe what 

the problem is and what we tried to do about it. 

There's a group of medical patients out there 

called the Thorotrast patients. These are people 

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60 

that were given a special substance that 

contained thorium for medical treatment. And 

these people received fairly high doses of alpha 

particles to bone marrow. And these people, 

these patients were followed over time, and lo 

and behold, there were excess leukemias seen in 

these populations. And you could derive an 

estimate of leukemia risk in those patients. And 

by comparing the leukemia risk in those patients 

with leukemia risks in the A-bomb survivors, you 

could estimate an RBE for alpha particles in 

leukemias, and you get something that ranges from 

about 1 to 15. 

Well, this is a good dataset in the sense 

that it's data on humans. It shows an effect. 

You could use it. But the problem here is that 

Thorotrast is a special chemical form. It's 

called a colloid. Colloids are kind of large 

globs of stuff that kind of remain suspended in a 

liquid medium. Milk is a colloid. Milk is a 

colloid. So what happens in the Thorotrast 

patients is that the thorium in this stuff 

remains suspended in bone marrow, and perhaps 

more or less irradiates the marrow uniformly. 

But radionuclides that DOE workers get exposed 

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61 

to, they're not colloids. And they probably get 

deposited very quickly on the surfaces of bone, 

and in some cases then translocate into the bone 

volume. And of course alpha particles have a 

very short range in tissue or in matter. That's 

a fundamental problem here. 

So the way that marrow is irradiated by the 

Thorotrast patients is very different from what 

you get from a DOE worker who is exposed to 

plutonium. So this dataset may have nothing to 

do with exposures of DOE workers. It doesn't 

describe the exposure pattern at all. So it's 

questionable whether you could really use this. 

There are other groups of populations that 

were exposed to alpha particles, the radium dial 

painters being the example that people are most 

familiar with. These are a group of young women 

who received high doses of radium, and the data 

seemed to suggest -- well, there's been no 

observed excess of leukemias in the dial 

painters. 

Now here again, there's a lot of problems 

with this study. What do you mean by no excess 

leukemias? I haven't yet seen a really good 

statistical distribution that showed a confidence 

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interval in a risk coefficient. People just tend 

to focus on a central estimate, and say I don't 

see anything. But there needs to be more work 

done in uncertainties in this population. 

There's a group of medical patients exposed 

to radium 224. No excess leukemias, 

statistically significant excess leukemias seen 

in those populations. 

Another problem with the dial painters is 

that leukemia is a disease that, if you're going 

to get it, it tends to come fairly early after a 

radiation exposure. And there are some serious 

questions about whether the early follow-up of 

the dial painters was sufficient to have actually 

caught the leukemias that they might have gotten. 

So there's a lot of problems in this dataset. 

But if you take the standard ICRP dosimetry 

model for radium in bone, you would predict a 

substantial increase in leukemias in these 

populations where none is seen. Well, there are 

two ways you could interpret this. Either the 

RBE is very low, or there's a problem in 

dosimetry -- and I personally think that there's 

a problem in dosimetry, that we don't want to 

muck up our REF with that. But here's a dataset 

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that shows no effect. 

A third source of information is data on 

neutrons. It's been widely understood for many 

years that neutrons and alpha particles are quite 

-- should be quite a bit alike in terms of their 

biological effectiveness. They're both high LET 

radiations. The calculations all show that the 

effectiveness should be more or less the same. 

So there are the data on the mouse studies that I 

showed you previously that could provide a marker 

for what the leukemia risk for alpha particles 

is. 

So we have these different datasets, and 

here's an example, a clear example of applying 

just purely subjective judgment. We constructed 

a hybrid distribution where we gave different 

weights to these different pieces of evidence. 

The weights that we assigned are obviously 

somewhat arbitrary. And we've gotten feedback 

already -- you know, I wouldn't do it that way. 

And that kind of feedback is welcome, and we want 

more of it. 

We gave, as indicated here, 50 percent weight 

to the data in the Thorotrast patients. Here 

again, we clearly are irradiating the right cells 

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64 

in this group. So if the dosimetry model for the 

other alpha emitters was correct, this probably 

gives you some idea of what it ought to be. 

We gave 25 percent weight to the fact that 

there's no excess leukemias in these other human 

populations. Here again, we did not allow the 

value to go below one, and we feel pretty 

confident that if the cells are being irradiated 

that alpha particles are at least as effective as 

high energy gammas in causing leukemia. If you 

take the data straight away, what EPA did here is 

they assigned a uniform distribution from zero to 

one, what they called the effective RBE. We said 

it really shouldn't be less than one, if the 

dosimetry's right. 

And we gave a 25 percent weight to the 

distribution for fission neutrons. But I would 

say this is the weakest. This is the weakest 

distribution we came up with, just because the 

data are so contradictory and there are serious 

problems with dosimetry here. 

Something else that I think I would do, if I 

revisit this again, is see what we might learn 

from animal studies about alpha particles and 

leukemia. And most of the animal studies have 

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65 

focused on bone cancer and not leukemia, but what 

can we learn from the animal studies in regard to 

alpha particles and leukemia? I think there's a 

lot of work to be done here. 

What does a distribution like this look like? 

I just gave you a couple of plots here. Here's 

our 25 percent weight at the value one gives you 

a spike, and the other two, which were lognormal 

distributions, give you something here with a 

very long tail going out. Distributions like 

this are sometimes a little easier to understand 

if you plot them in terms of a cumulative 

distribution. In other words, sort of integrate 

under that curve as you go from left to right. 

What this number is, this says here that 50 

percent of the values are less than this number, 

75 percent are less than this number, going on 

up, you have this long tail. This is a 

cumulative probability distribution rather than a 

frequency distribution. 

Owen mentioned this inverse dose-rate effect. 

For both neutrons and alpha particles, there is 

weak evidence in animal studies and some weak 

evidence in the uranium miner data for radon of 

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rate effect. And what this means is -- suppose 

you did two experiments where you deliver the 

same total dose to two groups at different rates. 

One group gets the same -- a given dose at a 

fairly high dose rate, and the second group gets 

the same total dose but at a much lower dose 

rate. There's weak evidence that at the lower 

dose rate that the risk increases slightly. This 

is what Owen referred to as a superlinear 

response. 

And the evidence is weak, and because the 

evidence is weak the correction that we applied 

for this is small. It's a small correction to 

the REFs for chronic exposure to neutrons and 

alpha particles. Well, all exposures to alpha 

particles are chronic, because these alpha 

emitters have fairly long half-lives. And I 

don't think we have anybody that was standing in 

an unshielded beam of a pulsed alpha source, and 

I don't think you're going to find that one very 

often. So alpha particles are always chronic. 

Neutrons in some cases certainly are. 

And we used a discrete distribution where we 

gave most of the weight to the value one simply 

because the evidence that this effect actually 

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67 

exists is quite weak. But there's some evidence 

that the inverse dose-rate effect could be as 

high as three, and we gave successively smaller 

weights going from one up to three. And on 

average, the correction was 40 percent for 

neutrons and 20 percent for alpha particles, 

fairly small. But it's in there. It's in there, 

and you can certainly change this. But you just 

don't see this in all studies. 

My personal opinion is that it's already 

incorporated in the data for alpha particles 

because they are delivered chronically to begin 

with. 

And if you apply the inverse dose-rate effect 

to the data for alpha particles in solid tumors 

you get something that's kind of lognormal, but 

it's even more skewed to the left than before. 

We started with a lognormal distribution from 3 

to 60, I think it was -- 3 to 80, and adjusted by 

the inverse dose-rate effect. It now goes from 

3.4 up to 100, and there are a few values that 

straggle out here beyond 100. And the median 

here is 18, and this is the number that you would 

compare with the standard ICRP assumption of 20, 

because again all exposures to alpha particles 

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are chronic. 

So we're pretty close. But I think some 

justification could -- some thought could be 

given to whether we could start with something 

other than a lognormal distribution and maybe 

have this median go up a bit. But that's -- it's 

all judgment. It's all judgment. We just don't 

have any data. 

I'm going to skip the next one, I think. Oh, 

here's our funky hybrid distribution for 

leukemias with the inverse dose-rate effect. 

This is the one where we had 25 percent weight 

for one, and then kind of a lognormal-looking 

distribution that tailed out here. Now when you 

apply this inverse dose-rate effect where almost 

all the weight gets at one, you have a spike here 

and very skewed to the left, but still numbers 

dribbling on out here to the high side. Here the 

median is four. This shows a clear difference 

between leukemias and solid tumors for alpha 

particles. Here the median was four. On the 

previous one it was 18. 

And again, I think a lot of work needs to be 

done here. I can't tell you -- I don't have any 

confidence in my state of knowledge about what 

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alpha particles and leukemias are all about 

because the dosimetry problems are so severe. My 

gut feeling is that if you use the standard ICRP 

dosimetry models and you put this REF in those 

models, you're probably going to overestimate the 

leukemia risk. But again, I think if the 

dosimetrists have a problem they should go fix 

it, and we shouldn't bury their problems in the 

biological effectiveness factor. And if you have 

ideas about that, we welcome them. But that's my 

bias. I don't want to take their problems under 

my tent. 

And this just shows the same thing in a 

cumulative distribution. It rises very steeply, 

and then this long tail. 

So for neutrons and alpha particles, our 

distributions clearly encompass what the ICRP has 

done. We have a broad range of uncertainty, 

which is different. 

Now when we get into photons, things change. 

Here's a curve that the ICRU published 15 years 

ago in a nice little report; it's only about 20 

pages thick. This is a calculation of the 

quality factor for photons as a function of 

energy. Our reference radiation is cobalt-60 

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high energy gamma rays, which is out at this end 

of the curve. And you can see that in the 

calculation, the biological effectiveness goes 

up. And here in the range of X-rays, it's about 

twice as effective as high energy gamma rays. 

And this report had an extensive discussion 

of the data that supported this conclusion. And 

the ICRU report said there is clear evidence that 

X-rays, 280 to 250 kVp X-rays are twice as 

effective as high energy gamma rays in causing 

stochastic effects, said that right there in the 

report. And this is a dataset and a conclusion 

that the ICRP has never adopted in anything they 

did. They have assumed that the biological 

effectiveness of photons of any energy from 50 

electron volts up to 100 MeV is the same. And if 

we look in ICRP-60 for an explanation of this, 

they say we don't think it would be helpful to do 

anything different. 

But here's a hint. The evidence is fairly 

compelling. This is a calculation, but there's a 

lot of data that say that X-rays are twice as 

effective as gamma rays. And I'm going to kind 

of go through the data and show you what we did 

about it. So here's a place where we part 

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71 

company from ICRP. 

Somewhat surprising to me, historically there 

were not that many experiments that were designed 

to study the biological effectiveness of lower 

energy X-rays. X-rays were one of the reference 

radiations that people often used to study 

neutrons. But there weren't a whole lot of 

studies that just looked at X-rays themselves as 

the radiation under study, but there was a lot of 

data on a particular kind of end point in a cell 

system. And you could say, well, what relevance 

does this have for induction of cancers in 

humans, and that's a fair comment. 

DR. ZIEMER: Could I interrupt and ask you to 

clarify? Are you or they using the kVp value 

like -- is this --

DR. KOCHER: Okay --

DR. ZIEMER: In other words --

DR. KOCHER: This is a double dose of 

spinach. 

DR. ZIEMER: Yeah, because the --

DR. KOCHER: The energies --

DR. ZIEMER: A 250 kVp X-ray spectrum has 

virtually no 250 kVp X-ray -- or kV X-rays in it. 

DR. KOCHER: I will take the time to explain 

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why we assigned REF to this energy range. But 

Dr. Ziemer's point is this: If you have an X-ray 

tube that you apply this potential difference to, 

the energies of X-rays tend to be a lot lower 

than this --

DR. ZIEMER: About a third. 

DR. KOCHER: -- by about a third. The peak 

of this -- you get a spectrum of X-rays, and the 

peak is in the 50 to 70 keV region. It depends 

on how it's filtered, and everybody does it 

different. 

But yeah, what you're actually measuring here 

is the biological effectiveness of X-rays in the 

50 to 70, 50 to 80 keV region. And I'll have to 

come back in a second as to why we assumed that 

those data apply in the energy range of 30 to 

250. That's a good point. 

These are the studies that the ICRU pointed 

to to say that there's a clear difference between 

X-rays and high energy gamma rays. And all the 

data ranges from a low of about 1.5 up to a high 

of -- central estimate of about 4, with fairly 

large uncertainty. And it was on the basis of 

this that the ICRU said that there's a clear 

difference of about a factor of two between these 

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73 

low energy X-rays and high energy gamma rays. 

Now here's another case -- initially we were 

just going to use this dataset. But as a result 

of one of the rounds of technical reviews and 

some further thinking on our own part, there are 

data in humans that can be used -- well, I'm 

skipping ahead. Let me go to this line here. 

These are studies where the biological 

effectiveness of X-rays was studied directly. 

But there are other studies where you can infer 

the RBE for X-rays indirectly in the following 

way: You do a study of neutrons, you're trying 

to investigate the biological effectiveness of 

neutrons. And you do one set of measurements 

with high energy gamma rays as your reference 

radiation, and you do another set of measurements 

with X-rays as your reference radiation. The 

difference in RBE for those two studies gives you 

an indirect measure of RBE for the X-rays. 

Because you're going to see a difference in the 

two results for neutrons, and you can compare 

those two to infer what the RBE for X-rays was. 

And there's a lot of studies, and they're listed 

in nauseating detail in the report. And these 

again show a clear difference of about one and a 

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half to about three between X-rays and high 

energy gamma rays. 

Now the third piece of information, there are 

data in humans that can be used to investigate 

the question of are X-rays biologically more 

effective in causing cancers in humans than high 

energy gamma rays, because you have the A-bomb 

survivors where children had their thyroids 

irradiated by high energy gamma rays, but there 

are all these studies of children who were given 

X-rays for various medical treatments. These are 

fairly large populations, and they've been 

studied. And so you can compare the thyroid 

cancer risks in the A-bomb survivors with the 

thyroid cancer risks in these other medical 

groups to infer an RBE. And unfortunately, the 

statistics are so poor in these data that the RBE 

that you infer ranges all over the map. You can 

get -- the 95 percent confidence interval ranges 

from an RBE of .2 up to 4, so you can get any 

number you want. 

But what I think is kind of striking -- and 

they are even poorer datasets for other cancers, 

like breast cancer and colon cancer and a few 

others -- none of these datasets show a clear 

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difference between X-rays and gamma rays. By the 

same token, none of them show that there's not a 

difference. You can't infer anything from 

something like this about the effectiveness of Xrays relative to gamma rays. And what I think is 

kind of striking is that the central estimates 

tend to cluster near one to two. You don't ever 

find an outlier out there, which is kind of what 

you would expect on pure random grounds. So we 

took this as a dataset that we could apply some 

weight to. 

So we have different sets of information, and 

as I did for alpha particles and leukemias, we 

just gave different weights to this information 

to come up with some kind of a hybrid 

distribution. And here we felt that the evidence 

from the non-human studies was just fairly 

compelling, so we gave a 75 percent weight to a 

distribution between one and five. It was a 

combination of the data on the dicentric 

chromosomes and all the indirect inferences --

there were about 10 or 15 of them that I listed 

in the report, all of which showed a clear effect 

-- so we gave a 75 percent weight to that. 

But we gave a small but still substantial 

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76 

weight to the possibility that there's no 

difference in humans. Again, the human data 

neither support nor refute any assumption you 

want to make. So we just said, well, maybe 

there's no difference. So we just assigned a 25 

percent weight to the fact that there would be no 

difference. And the result is a 95 percent 

probability that it's somewhere between one and 

nearly five, and a median of about 1.9. 

Now how did we take this data for a very 

limited range of X-ray energies and assume that 

it applies between 30 and 250 keV? Well, that 

goes back to this curve right here. We said 

we're going to trust the ICRU calculation where 

the radiation quality is flat over this entire 

energy range. And this mean here is at 30, 

roughly. And your guess is as good as mine as to 

where you want to draw the cut-off up here, but 

we put it at 250, which is about here. So we 

said everything in here is twice as effective, 

roughly, as out here, which is our reference 

radiation. So the 30 to 250 comes from assuming 

that this curve is right. But in fact, as Dr. 

Ziemer pointed out, all the data are in a fairly 

narrow range of energies down here, so it's an 

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77 

inference from the calculation. 

Well, the other thing that you see from this 

curve is as you go below 30 keV that the 

biological effectiveness starts to rise, and so 

below 30 keV we assumed that this curve would be 

more or less correct. We were not aware of any 

actual radiobiological data that investigated 

this low energy range, but we assumed that this 

curve was more or less correct in going below 30 

keV. And because of that, we increased the 

previous distribution by a triangular 

distribution as we went below 30 keV. The mean 

of that rising curve is about 1.3. We didn't 

figure that it was worth actually having this be 

energy-dependent. We just applied the same 

distribution at any energy below 30 and gave it a 

triangular distribution. So that increases the 

biological effectiveness even more as you go 

below 30 keV. 

And what you get when you do that -- here's 

our 25 percent weight at one, smeared out by a 

triangular distribution, and then the rest of the 

lognormal similarly smeared out. This is a 

probability distribution for the lowest energy 

photons, median of about 2.4. And there are lots 

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78 

of calculations out there. This is an 

interesting problem for breast cancer in women, 

because they're starting to use really low energy 

X-rays to do this. And people have done a lot of 

calculations using different assumptions about 

radiation quality. And they come up with numbers 

that agree with the ICRU curve, but I don't know 

of any real data to describe this problem. If 

those of you in the medical community on this 

Board know about it, let me know. 

So for photons we are certainly departing 

from the standard ICRP assumption that it's one 

for everything. So we have an increased 

effectiveness as we go below 250 keV, a further 

increase as we go below 30, but some weight given 

to values less than one. There is this little 

tail down here. 

The last category is electrons. The only 

radiation that I know of that's been studied is 

tritium beta particles, because tritium is a 

radionuclide that's encountered often in the work 

place. It's been studied six ways from Sunday, 

as reviewed by Tore Straume and Carsten and 

documented in our report. The history of this in 

terms of radiation protection, I think, is quite 

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interesting. 

What did ICRP do 40 years ago, Paul? Do you 

remember this? 

DR. ZIEMER: I can't remember back 40 years. 

(Laughter) 

DR. KOCHER: Well, I was in high school, so I 

can't be expected -- anyhow, in ICRP Publication 

2, the exposure limits for tritium incorporated 

an RBE of 1.7. This is 1960, so this phenomenon 

was known. But that increase -- this was the 

famous N factor in the equation H equals DQN. 

I'm really digging deep into ancient history 

here. This N factor was -- the ICRP had was to 

account for anything else that you wanted to put 

in the equation. It went from absorbed dose to 

dose equivalent. And they assumed N equals 1.7 

for tritium beta particles back in 1960. Well, 

that was dropped beginning in publication 26, and 

it's still not there. So this has a history of 

being used, but it's not used today. ICRP today 

says the biological effectiveness of tritium beta 

particles is one. 

Well, you could argue this till the cows come 

home. There's all kinds of data on various kinds 

of biological systems that says it's not one, and 

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this has been written about by many different 

people. No data on cancer induction in humans, 

so who knows what the story really is. But we 

said there's all this data in various biological 

systems; we ought to use it. There's probably 20 

or 30 good experiments out there that show a 

clear increase in biological effectiveness for 

these very low energy electrons. 

The RBE's range from about one to two at the 

low end up to about six at the high end, and 

we've excluded these really unusual chemical 

forms of things that get bound to DNA and don't 

really mimic what tritiated water would do in the 

human body. But still you get up to about six. 

And here again, the standard ol' lognormal 

distribution from a low of about 1.2 up to about 

5, median of about 2.4; 2.3 is a number that 

you'll find in ancient literature in some cases. 

So this is a clear effect that the ICRP doesn't 

have in their model. 

One of the problems here, of course, is that 

these energies of beta particles are very low; 

4.7 keV, I think, is the average energy of that 

spectrum, and the endpoint of that spectrum is 

less than 15 keV. So these are very, very low 

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81 

energy electrons, but they show a clear effect. 

And you're going to have tritium exposures in 

your claimants, that's for sure. 

Well, at that point we kind of went off the 

deep end, and here's where I don't really -- I 

won't give you an extra dose of spinach on this 

one. But we just wondered, these energies of 

tritium beta particles are so low, is there some 

intermediate energy electron, range of 

intermediate energy electrons where the 

biological effectiveness would be lower than for 

tritium beta particles, but would still be 

greater than one? And we went through a long 

song and dance -- and it's in the report -- that 

for energies from about 15 to 60 keV there ought 

to be an increase, just based on physical 

grounds, looking at what are the radiations that 

electrons produce when they interact with matter, 

and going back to the ICRU curve for photons. 

But I won't take time to do that here. 

But if the Board members who are interested 

in this problem want to review what I have in the 

report and comment on it I'd appreciate it, and I 

think NIOSH would, too. I don't think you're 

going to encounter a lot of cases where 

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intermediate energy electrons, say between 50 and 

60 keV, are important. Carbon 14 is the only one 

that I know of that falls in that group, and I 

don't really know what kind of exposures to 

carbon 14 you're going to have out there. But we 

haven't done anything about that. 

The other thing that we did not touch is this 

whole question of these really low energy Augeremitting radionuclides, and these are electron 

energies that are often a keV or thereabouts or 

less. And sometimes those radionuclides get 

incorporated directly into DNA, so the RBE can be 

huge. But that's a special problem that we have 

ducked, and I think rightly so. If you think 

somebody was exposed to Auger-emitting 

radionuclides in the work place and they were 

incorporated into DNA, you really need to look at 

that as a special case. 

Okay, let me just try to sum up here what we 

have done, just a kind of two-page summary of the 

different radiation types and what we developed. 

Photons is a case where we clearly have 

departed from the standard ICRP assumption. We 

have separate distributions of an REF that are 

greater than one, and entered one distribution 

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for energies less than 30 keV and another for 

energies between 30 and 250. This distribution 

is based on data for X-rays, most of whose 

energies are in the 50 to 80 keV region, combined 

with the ICRU curve which says that radiation 

quality should be flat between about 30 and 250. 

Applies to all cancers equally. 

Electrons, we have just a single distribution 

for tritium beta particles, for reasons that are 

explained in the document, we assume applies out 

to energies of 15 keV, but nothing in the 

intermediate energy range. That's something that 

could come in the future, I think. Again, 

applies to all cancers. 

What's really nice, I think, that helps kind 

of tie this all together, the distribution for 

the tritium beta particles is essentially 

identical to the distribution for the lowest 

energy photons. Which if you know about the 

physics of how photons interact with matter, this 

is as it should be. Less than 30 keV photons, 

the dose is delivered by electrons whose energy 

is 15 keV or less. So this is really nice. The 

radiobiological data and the calculations have a 

nice story that ties together, so I'm pretty 

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84 

confident about this. 

For alpha particles we have separate 

distributions for leukemias and solid tumors, 

again based on the evidence which says that for 

high LET radiations the difference in 

effectiveness does depend on whether you have 

this kind of cancer or this kind of cancer. 

Again, I think that the shakiest part of our 

entire analysis is alpha particles and leukemias. 

And I really welcome comments about what we might 

do about this. 

These distributions are independent of 

energy. The good news about radioactive decay is 

that the range of alpha particle energies is very 

limited. It's about four to eight MeV is all you 

get. 

And we apply an inverse dose-rate effect in 

all cases. All exposures to alpha particles are 

assumed to be chronic. And again, the central 

estimate here at the end of the day was about 18, 

which is more or less 20, but it's a broad range 

of values. Again, you have to think about 

uncertainty, not just where the central estimate 

lies, and there's a lot of uncertainty in these 

REFs. 

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85 

And lastly, for neutrons, again we 

distinguish between leukemias and solid tumors; 

and furthermore, we have an energy-dependent REF. 

We have these five energy bins as defined by 

ICRP. So we have three sets of distributions, 

each for the two different types of cancer. And 

we have a correction for the inverse dose-rate 

effect that would be applied only in cases of 

chronic exposure to neutrons. 

Well, after that spinach you can have some 

chocolate ice cream for lunch, I guess. You've 

got to balance the diet here. I'm sorry about 

that, but I really didn't know how to talk about 

this without making it painful. 

DR. ZIEMER: Thank you very much. An 

extremely interesting approach that's been used 

to what clearly would be a difficult problem if 

point values were used on all of these things. 

DR. KOCHER: Yeah, I might comment. The 

atomic veterans' dose reconstructions haven't 

done any of this. Of course, the presumption was 

that they don't have a lot of problems with alpha 

particles and neutrons, but of course they do 

have some. The veterans got some neutrons, and 

some veterans got some plutonium. But they have 

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86 

basically in that work assumed point estimates as 

developed by the protection authorities, so this 

is breaking new ground. 

DR. ZIEMER: And it's taking into 

consideration a wide variety of studies, some of 

which appear to us now to conflict in terms of 

what they tell us. 

DR. KOCHER: Yes. 

DR. ZIEMER: So you've given some weight to 

DR. KOCHER: And there were always questions 

about how to apply data in different biological 

systems to humans. This is really in the realm 

of what do you do. That's a problem for 

neutrons, could be a problem for alpha particles. 

The dicentric chromosome aberrations, is that 

relevant for induction of cancer in humans or 

not? I don't know. We've gotten feedback both 

ways as to whether those datasets are useful. 

But we tried -- again, we tried to be honest 

about what we did, warts and all, warts and all. 

DR. ZIEMER: And Owen, we appreciate the 

comment, a sort of correction that we have 

assumed that you built in biases. Actually those 

biases come, in terms of application to 

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87 

compensation, come in terms of where you draw the 

cut-off, and that's more of a political/legal 

issue. So I think we're seeing at least an 

attempt here to be sort of neutral on how you do 

this. 

DR. KOCHER: Yes, sir, I --

DR. ZIEMER: And let the science try to speak 

for itself. 

DR. KOCHER: Exactly. That's exactly what I 

did. And the science is imperfect, there's no 

question about it. But we did not try to start 

out -- I did not try to start out with a certain 

bias as to what we should do, just let the data 

speak to us and see what we get. 

DR. ZIEMER: Well, let's take a couple of 

minutes here for additional questions, then we 

need to take a break. Yeah, Gen. 

DR. ROESSLER: Well, David, that was 

wonderful. I read your report on the airplane, 

and I wasn't even tempted to look at my novel. 

It was so interesting and so refreshing to see --

DR. KOCHER: Are you having trouble sleeping 

at night? 

(Laughter) 

DR. ROESSLER: No, well, except thinking 

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88 

about a few things here. But I think the 

science, the degree to which you've applied 

science, really should be applauded. And the 

honesty with which you talk about things, because 

I was going to really nag at you about the 

leukemias and alpha particles. 

DR. KOCHER: Please. 

DR. ROESSLER: Well, you already -- there's 

nothing left, because you already admitted the 

weak points. And I guess the one thing that 

maybe isn't quite reflected correctly in your 

paper is when you put that 50 percent weight on 

the Thorotrast patients, it seems as though it 

all came from that one paper, the Hunacek and 

Kathren. But in fact, it really -- there's more 

DR. KOCHER: They reviewed -- they did a 

review of the other studies as part of their 

work, is where that comes from. 

DR. ROESSLER: Yeah, so it's really not based 

just on those two autopsies, but --

DR. KOCHER: No. 

DR. ROESSLER: -- the rest -- yeah. And I 

think maybe in the way the paper's written, it 

implies that it was just that one. 

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89 

DR. KOCHER: Yeah, I need to make it clear 

that when we used that paper that I was using 

information that they got from all the previous 

studies. 

DR. ROESSLER: Yeah, I think that would help. 

DR. KOCHER: They were really the ones that 

pointed out the uncertainties in dosimetry in the 

other studies. And that's where the range in 

values comes from, is the difficulty in 

estimating dose. But yes, I will do that. 

DR. ROESSLER: That's my only comment. 

DR. ZIEMER: Other comments? It's the point 

at which the desire for a break overcomes the 

desire to --

DR. HOFFMAN: Just a suggestion, that is 

definitely have a break now, after which there's 

a discussion period? 

DR. ZIEMER: Yes, we're coming back. 

DR. HOFFMAN: Much of what Brian Thomas is 

scheduled to present leads right into discussion, 

because this next rather brief presentation is an 

attempt to sum it up. And the bottom line is two 

individuals with the same disease and the same 

dose don't necessarily get the same probability 

of causation. 

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90 

DR. ZIEMER: That’s right, we do have to hear 

from Brian yet. But I think we're close enough 

to the hour, and there's enough squirming going 

on, to necessitate a break. 

(Whereupon, a break was taken at 10:21 a.m.) 

DR. ZIEMER: Before our discussion period 

we're going to hear from Brian Thomas. 

Brian, if you're set, let's go. 

MR. THOMAS: Now I'm sure everyone had a good 

time so far with the previous presentations, and 

what I'm going to do is just run through a real 

quick PowerPoint presentation that I've prepared 

that has two or three case studies in it. 

Then we're going to get right into the model, 

and I have some bad news about the model, and 

then some accompanying good news. The bad news 

is that the Internet server that houses NIOSHIREP is not accessible this morning, for one 

reason or another. We -- wonderful, we hear. We 

have some people working on that, because there 

are some things that we would like to show you 

that we’ve just added in the past week, and 

that's under the view model details button on the 

web. 

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91 

So in the event that we don't get to access 

that web site today, next time you get on it look 

down at the bottom of that main screen. There's 

a button that says “View Model Details.” You can 

access lots more details now than you could just 

two or three weeks ago. And there's even some 

additional calculation buttons under the view 

model details now that will show you the exact 

original ERR per sievert value that was used for 

the case you're running. And then you can see 

the ERR per sievert after it's been adjusted for 

the errors in dosimetry, after the values have 

been transferred to the U.S. population, and then 

after they've been adjusted by the DDREF, and 

then the final. So all of those are there as 

buttons you can click and calculate. 

Probably what we'll do today, once I run 

through this real brief PowerPoint talk, is we'll 

get right into the source code, and I'll show you 

kind of how it's laid out. It's not as userfriendly for it to give you a copy. It'd be a 

little harder for you to browse through than it 

would be to run it on the web. But we'll go 

through some of that. If there's questions that 

come up, we'll immediately be able to address 

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92 

those within the model. 

So I'm going to start out by just discussing 

some of the required model inputs. You guys are 

extremely familiar with this, but I at least have 

a slide that will touch on them. I'm going to 

show you some results from two or three case 

studies that we've come up with. And the purpose 

of this entire talk is just to show you that two 

people that were exposed the same way might not 

have the same probability of causation. 

And just a note about the results that I'm 

going to be showing today, the slides up here 

were done with 1,000 iterations. And if you guys 

have read the rules, the Department of Labor are 

going to be using 2,000 iterations for all their 

runs. 

The inputs for the personal information 

include the individual's gender, their year of 

birth, the year that they were diagnosed with a 

particular cancer. Then you'll need to select 

from a pull-down menu a cancer model. There are 

30 cancer models included in NIOSH-IREP, and 

there's even a category called other and illdefined sites. So if someone has a cancer that's 

not included with one of those models, that would 

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93 

be the model that would be used. A couple of 

other things that need to be entered, if the 

individual has lung cancer, the smoking history 

needs to be selected. If the individual has skin 

cancer, they need to select the ethnic origin. 

The exposure to be entered, this will be done 

by the people who do the dose reconstruction from 

NIOSH. All these things will be entered: The 

number of exposures that an individual had --

this could be multiple exposures in one year, 

some acute exposures that a person had; could 

represent one exposure per year, which would be a 

chronic exposure over an entire year, and so you 

could have several of those; the year of each 

exposure; the exposure rate -- whether they got 

the dose acutely just in a short period of time, 

or whether they got it over a long period of 

time; the radiation type, which is what David 

Kocher just went into; and of course the organ 

dose. 

Now some of the advanced features. Owen 

touched a little bit on the user-defined 

uncertainty distribution already. Also, the 

simulation sample size can be edited. By 

default, the Department of Labor will be doing 

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94 

2,000, but anyone else looking at the model on 

the web, you can pre-select that, any value you'd 

like. Same thing with the random number seed, 

and that simply is just a value which the Monte 

Carlo simulations use as a starting point. 

So the main question is will two individuals 

who receive the same dose have the same 

probability of developing cancer? Here's a case. 

This is a female -- there's actually going to be 

two cases. Age at exposure for the first female, 

she's 20 years old. She gets cancer when she's 

50. Liver cancer, one exposure to chronic 

photons, energy range 30 to 250 keV, and I've 

just entered a constant dose of ten 

centisieverts. 

And what you see in the first column here is 

the first individual. This is the one that was 

exposed at age 20, and this is the individual 

exposed at age 40. And so what this shows you is 

the dependence on age at exposure. You can see 

that the person who was exposed at a younger age 

would qualify under the current regulations, and 

the person who was exposed at an older age would 

not. 

I have a case here, just to show you a little 

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95 

bit how smoking history affects the probability 

of causation. We have a male exposed at age 20, 

diagnosed with lung cancer at age 50. Case 2A, 

he never smoked, case 2B, he smoked one to two 

packs per day. And I just put a dose in here of 

50 centisieverts. I selected the dose in a way 

that the 99th percentile would be at or around 

the 50 percent. So you see the person who never 

smoked has the higher probability of causation 

than the person who did smoke. 

And here's an example just to show the time 

since exposure effect, the time between when 

they're exposed and when they're diagnosed. And 

so what we have here is an individual exposed at 

age 20. One of them gets cancer at age 25, the 

other at age 35. This is lung cancer, and 

neither individual has smoked, 50 centisieverts. 

And so what you see here is that the person who 

got the cancer earlier has a lower probability of 

causation. And so immediately you think, well, 

that's kind of weird, but not really. With all 

cancers, as you know, there's a latency effect. 

And so if I get exposed today and get cancer 

tomorrow, that's not really practical. It takes 

time for those cancer cells to develop. 

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96 

And there is an S-shaped curve then in NIOSHIREP to account for this. It doesn't just go 

five years and then have a steep incline there. 

It's an S-shaped curve. And so there is still 

some probability that a person who gets the 

cancer five years later is -- there's still some 

probability that that exposure caused their 

radiation, but not as likely as someone who got 

the cancer 15 years later from the same exposure. 

And so normally at this point what I was 

planning on doing is click this button, and it 

would take us right on line and we'd run a few 

more examples. I don't know if you guys have 

been on line recently, but one of the neat 

advancements that we’ve added to this thing since 

we traveled around to the Department of Labor 

sites in April is that right on the front screen 

there are two buttons now instead of just one. 

The Department of Labor claims examiners had 

expressed an interest in reducing the number of 

mouse clicks that it took to process a claim. 

And so what we've done is right on that front 

page we've provided the option. They can --

well, an individual using the code can click on 

the first button, and that will take you right 

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97 

into the input screen. You can manually input 

everything. Or you can click on the second 

button, and what that will allow the claims 

examiners to do is to use a pre-formatted input 

file prepared by NIOSH. They'll just locate it 

on their hard drive, upload it. All the fields 

will be pre-selected for them, so it reduces the 

possibility of errors in entering it more than 

once. 

So what we're going to do at this point -- so 

you saw with the slides my conclusions that two 

people can have a different probability of 

causation for the same dose. So now we're going 

to get right into the model, and I'm going to 

show you just a little bit in here -- I might run 

one example, and then we'll start with some 

questions and run some specific examples. 

Now when we first began working with the 

National Cancer Institute -- do we already have a 

question, before I get started? 

MR. GRIFFON: Yeah, just one question. 

MR. THOMAS: Sure, go ahead. 

MR. GRIFFON: This model you're running right 

now, it is Version 5.2, and it's running in 

Analytica. Is this -- we've been told that this 

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98 

new version of IREP isn't available on CD, and it 

looks like this might be. This is something the 

Board has asked for for review purposes. 

MR. THOMAS: I stayed awake late last night 

cleaning this thing up to be able to show you 

just in case, and it still would require some 

time to clean up a little more. And we can have 

some discussions about how feasible that would 

be. I think the primary concern with spreading a 

lot of CD versions around would be that -- well, 

let me start by saying the reason that we went to 

the web was two-fold. 

First of all, almost everyone is familiar 

with a web browser, and they can navigate around 

with the little finger and click back and forth. 

Most people aren't that familiar with the 

Analytica programming platform, and so it's a 

little harder to navigate around in there. So 

that's one reason we went to the web-based 

approach. 

The other reason is that as updates occur, 

it's much easier to change it once on one server 

computer, and then everyone accessing it from 

that day on is getting the current version. So 

the fear is that we would float a lot of CD 

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99 

versions around, the model gets updated, and then 

someone would run one of the CD versions and get 

a different answer than what comes on line. So 

perhaps there's a way to release a CD that's just 

for review purposes, never to be intended to 

process claims with or to compare to what's on 

line. 

DR. ANDERSON: Self-destruct. 

MR. THOMAS: Self-destruct in five days, 

okay. 

DR. ANDERSON: Like all that test software 

you can get off the --

MR. THOMAS: Exactly, yeah. Okay. 

So what I'm going to do is -- yes, we are in 

software called Analytica. When we began working 

with the National Cancer Institute we chose 

Analytica because when presenting to the public 

it's really nice to be able to show diagrams and 

things like that as opposed to trying to show 

them some C code or Fortran code, or even Excel 

is really hard to go through that with the 

public. And this does the same calculations, and 

deals a lot easier with arrays of data. And so 

we chose Analytica for that reason. It includes 

uncertainty analysis software right in it, so 

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100 

it's really nice. And we did release a CD 

version, Version 2.1, for the NAS review 

committee to have. And overwhelming comments 

were it's too hard for them to navigate through, 

and so that just again pushed us to go towards 

the web version. 

And it's not going to look exactly like what 

you're used to on the web, but still has the same 

inputs. Just to let you know how this works is 

this program is housed on a server computer. 

Every time you log on to the web, enter all your 

inputs, and click calculate it is submitting 

those inputs into the server, opening a copy of 

this software, running it, and then submitting 

the answers back to your web page. So every 

calculation is done live. A lot of times what 

you see on the web is a calculator, but it's just 

look-up tables. This thing is done live every 

time -- 2,000 iterations, 10,000 iterations, 

whatever you choose. 

So this is our main input screen that we've 

created in Analytica. Notice there are quite a 

few more pieces of personal information to be 

entered on the web version, or that you can enter 

on the web version. Those are programmed in the 

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101 

HTML. They don't even need to call out here, 

because it's things like the claimant's Social 

Security number and those sorts of things that 

don't need to be passed across the web. They can 

just stay right on your machine. 

And so -- but you can see a pull-down menu 

for gender; the birth year, you just type it in; 

the year of diagnosis; you select from the type 

of cancers. On this version the ethnic origin I 

have right on the screen. On the web version 

it's down one level; there's one more button to 

press to get to that. The lung cancer entries 

are here. This is where you would enter things 

like the smoking history, the radon exposures. 

Advanced features would include the userdefined uncertainty distribution, and on the web 

there's an advanced features button which --

that's also where you would change the number of 

iterations or the simulation sample size and the 

random number seed. 

Here in this step three, enter exposure 

information, this is where you would first of all 

type the number of exposures, and then based on 

the number of exposures you type there that's how 

many doses will be used from this table. And so 

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102 

what we have done is -- this is one of the things 

that's sort of confusing about this version. 

When we first created it, it would create -- it 

was sort of an interactive table. Depending on 

the number of exposures you typed right on the 

front, it would create only one column for only 

one exposure, you type it in and go on. 

What we've done is we have allowed up to 200 

exposures from the web. So the web version works 

just like that. You type in two exposures, 

you're going to get a place to type in two doses 

and all the corresponding information. But in 

this version, what you have to realize is that 

only the first column is going to be used in the 

calculation because I only had one on the 

previous screen. So if I had had ten there, 

it'll use the first ten columns of data. 

Now another thing that's not as friendly in 

this version is that you need to physically type 

in the distribution to be used. It's not in a 

pull-down menu like on the web, so you have to 

know the spelling and you have to capitalize the 

first letter. And we have three parameters to 

define. This is just like on the web, so the 

first number would be for a lognormal, the 

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103 

geometric mean. And there's a lot of help right 

on the web site if you click, and it'll tell you 

what to type in for each distribution. 

Now the exposure rate is either a lower case 

C for chronic or a lower case A for acute. On 

the web it's a pull-down menu between acute and 

chronic. Radiation type, there are eleven 

different radiation types that you can choose 

from. Again, on the web it's a pull-down menu, 

and here you have to know that E-1 stands for 

electrons, energies less than 50 keV. So if 

something like this ever did get distributed, 

we'd need to put a little help file right beside 

that to tell exactly what those energies are so 

you can play with the different ones. I've made 

myself a little list, so if we go in and play 

with them today, we're all set. 

So the one example I'm going to run first, 

it's for a male born in 1900 -- and the reason I 

picked 1900 is because it's easy to add 30 and 50 

or whatever to -- so they're born in 1900. 

They're exposed in 1930, so they're 30 years old. 

We're going to define their dose as a lognormal 

distribution with a geometric mean of 20 

centisieverts and a geometric standard deviation 

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104 

of 1.4, which is about a factor of two. This 

person was exposed to a chronic dose of high 

energy photons. This is energies greater than 

250 keV. And they got cancer, they got liver 

cancer, in 1950. 

So let's run NIOSH-IREP. And you notice I 

have two buttons here. One is this table of 

results. All it is is just the percentiles, the 

1st through the 99th percentiles. The other one 

has a little bit more information in it, summary, 

it'll list their cancer type and those sorts of 

things, the birth year and year of diagnosis. 

Okay, so we see that this individual clearly 

qualified for what I entered. So that's how the 

results look here. 

If you remember, on the web you get a really 

nice page that you can either save electronically 

or print out that has every piece of information 

that went into the run, including simulation 

sample size, the random number seed, all the 

exposure information, so that years from now you 

could take that sheet of paper and rerun the 

model and get exactly the same result. 

Now it just turns out that this projector is 

the same projector that we own at our office, so 

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105 

I know that it has this feature where it will let 

us enlarge, if I aim it right at it, and you can 

see those results. I apologize for those of you 

in the back. I didn't think to do that earlier. 

So again, the 99th percentile is what we're 

concerned about for compensation purposes. 

Okay, so that's an example that kind of shows 

you how this Analytica version works. Let me 

show you one more piece of information that is 

really cool, and this is also available on the 

web just as tables. There's a button down at the 

very bottom called intermediate results or -- I 

can't remember the exact wording, but it's more 

results that you can go in and see the excess 

relative risk that was calculated, and you can 

see the breakdown of the contribution to 

variance. 

So what I have here -- and I apologize, 

because I know that at least one person has 

complained that on the web we used to have these 

pie charts like this, and I just created these in 

a little picture editor program just to show that 

it's broken into three pieces. This doesn't --

this is not intended to show which one is --

they're all equal size. But when you click this 

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106 

little calculation button, you're going to get a 

table that is live that has to do with this exact 

case we just looked at. 

Now what you see at the very top -- there's 

really no need for us to look at this one because 

we have sources other than radon. If we had had 

radon sources and we had had some user-defined 

additional uncertainty, then this would be broken 

into three components. When we click it now, 

it's only broken into one component; 100 percent 

of it goes to the excess relative risk, sources 

other than radon. So you can see the little 

arrow that goes across here. If we had had radon 

sources, we could go here and see the breakdown 

of the ERR for radon. Since we don't, we're 

going to go to the left, and we're going to look 

at a breakdown of everything that it takes to 

calculate the excess relative risk. 

One component is dose, and you'll remember we 

had some small uncertainty on the dose; the RBE, 

which has now been updated to be REF; and then 

the adjusted ERR per sievert. Now the word 

“adjusted” just simply means that it's been 

through all the adjustments now. This is not 

looking at the original ERR per sievert. This is 

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107 

including all the uncertainty for the DDREF, for 

the transfer to the U.S. populations, for bias 

and uncertainty with everything else. 

Okay. So then let's go and look at that. 

And what you see, that the organ dose plays a 

little bit into it. So the organ dose plays a 

little bit into the uncertainty because it had a 

GSD of about 1.4. 

But you can see that the ERR per sievert 

dominates the uncertainty here. So let's zoom 

out, and let's go find out -- let's look at a 

breakdown now of the adjusted ERR per sievert. 

So what you see in this list is the original ERR 

per sievert. This is what came straight from the 

-- this just includes the statistics on the 

Japanese survivor data. 

Errors in dosimetry accounts for a very small 

amount of the uncertainty. Transfer to the U.S. 

population in this case is the largest 

uncertainty, and that has to do with the 

backgrounds, it has to do with whether they use 

an additive or a multiplicative approach when we 

use the Japanese data for U.S. population. This 

is your DDREF that Owen went into and showed you 

the distribution for. You can see that it 

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108 

affects about 23 percent. And again, this is not 

23 percent of the total uncertainty. This is 23 

percent of that 80-some percent that we looked at 

before. So it's 23 percent of this piece, which 

was 80 percent of the total. 

Now of course this lung -- adjustment for 

smoking doesn't play into this because we're 

looking at liver cancer. On the web when you 

click intermediate results, it'll bring up 

separate tables for lung so you won't see that 

blank line, because that might confuse someone if 

it says lung cancer and they know they selected 

liver. 

So that's a really nice tool for analyzing 

like what you guys want to do, to look through 

the model. 

Okay, what's next? Any questions? What do 

we want to look at? 

DR. HOFFMAN: Brian, last evening when we 

just arrived, I think it was Rich Miller cornered 

us and said he really doesn't like what we've 

done through the DDREF. He says that it isn't 

really conclusive that DDREF is absolutely 

linear, and therefore we should use 1.0 and not 

this 20 distribution that we've put in. Well, 

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109 

now that Brian has pulled up the source code, go 

in and change the DDREF to 1.0 and see what the 

difference would be. Show them the original 

calculations that we have here, and then replace 

the distribution with just simply 1.0 and show 

them what the difference would be. 

MR. THOMAS: I'm jotting down some numbers. 

DR. HOFFMAN: Yeah, here it's -- you'll have 

to memorize it -- the 99th percentile, it falls 

at 50.8, and 50th percentile is at 12.6. 

MR. THOMAS: So what we've done on the web 

version under view model details is just taken 

screen shots of each of these screens that I'm 

going to go through. This is the screen that I 

was mentioning earlier. Now there's calculation 

buttons -- there's actually a link which will 

show up right here on the page. You click that, 

and it'll bring you to another web page that will 

have all the buttons on top of each other. You 

can just click each one and see the adjustments, 

see what effect the adjustments have. 

So we are going to go right into this DDREF, 

and instead of using a distribution we're going 

to replace all this -- I'm going to cut it so I 

can paste it back in a moment. Don't anyone 

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110 

worry, this isn't the official one that's on --

this is just -- this is only my copy, don't 

worry. 

(Laughter) 

MR. THOMAS: Okay. So we've changed the 

DDREF, and I'm going to click run here to show 

you that one, that's what it's going to use now 

for the DDREF. So we'll go right back to the 

front page, put calculate, see what difference it 

makes. 

Well, it's not exactly the same number, and 

you can see -- remember we had about 13 or 14 for 

the midpoint, now we have 19. And the 99th 

percentile used to be 51, now we have 55. And 

this is based on 2,000 iterations. So it makes 

some small difference, which we saw previously 

when we looked at those pie charts. We saw that 

it did have some effect on the overall 

uncertainty, but it's not a significant source of 

uncertainty. 

DR. HOFFMAN: The other thing to show there 

is by changing the DDREF, the midpoint comes up 

almost a factor of two, but at the 99th 

percentile --

MR. THOMAS: Well, it was 13 and -- well, 

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okay, if you round down to ten or up to 20 --

DR. HOFFMAN: But the 99th percentile is just 

a few percentage points. 

DR. ZIEMER: Let's see if there are 

additional questions or comments. Anything you'd 

like demonstrated here, or varied or massaged? 

Larry. 

MR. ELLIOTT: Brian --

MR. THOMAS: Yes. 

MR. ELLIOTT: -- on the web version from the 

early Version 2.1 Analytica that was sent out as 

a disk, in that 2.1 version there was the ability 

to look at the risk coefficients in the models. 

And we've had some concerns and comments that in 

the web version that's been up lately we weren't 

showing that. And there's good reason for that, 

that that was based on NCI's release of their 

documentation and what we had adapted from them. 

But now, as of today, the risk coefficients are 

viewable and available. Correct? 

MR. THOMAS: Yes. Now that's a good point, 

Larry. 

MR. ELLIOTT: If we can get the server up. 

MR. THOMAS: Yes, exactly. Larry, that's a 

good point, and perhaps what I could do is take a 

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moment just to show you, or maybe those of you 

who have not browsed through a CD version, where 

those things are, and kind of how they're used in 

NIOSH-IREP. 

What you'll have access to on the web, those 

five buttons that I discussed, one of -- actually 

one of the buttons will be before any truncations 

are made. So for cancers like uterus, where 

there's a negative dose response in some cases, 

the negative values are preserved. They're 

there. You can see them. The very next step 

truncates everything at zero, because we won't 

use the negative values in the calculation. So 

you'll see both of those buttons, and it'll be 

for the case that you have selected on the front 

screen. So if you wanted to look at a different 

cancer type just select a different cancer on 

your pull-down menu, and go right back and click 

calculate and that'll let you see any of those 

coefficients. 

So we're going to go right into the original 

ERR per sievert data, and these are actually the 

nodes that get referenced from the web, so it 

calls out and uses those. This ERR per sievert 

database is actually a separate Analytica file. 

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And we had toyed with the idea of putting these 

things into an Access database and hitting it. 

It might even make it a little more efficient. 

If you've played with the web version, let's 

say two months ago versus last week, you see a 

significant speed increase. It used to take 

somewhere around -- just for a really simple case 

it would take somewhere around 10 to 15 seconds 

to get your answer back. For a very complex case 

it would take minutes to an hour to get back. 

This is someone who might have been exposed to 

100 different exposures, three exposures per year 

for 30 years. So it's probably not that 

uncommon. 

So for that reason we went into the model, 

and we ran some diagnostics on it and found out 

where the roadblocks were, and we tried to 

alleviate as many of those as possible. And so 

now, after you do the very first run on the web, 

what that does is establishes the connection. So 

that one's still going to take anywhere from five 

to ten seconds. After that it's almost 

instantaneous. As soon as you click the button 

-- and I don't know how this all works -- but it 

sends it across the line and right back to you 

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just really, really fast. 

So anyway, I digressed from talking about 

Access. These are in a separate file, and what 

we've done is created some different groups. 

There's a PDF file you can download right from 

the web. If you click on this node on the web, 

it'll give you the option to download a PDF file 

that discusses these different answer groupings, 

and it shows you all the elaborate equations that 

went into those. 

Now Charles Land did all the statistical 

analysis on this data, and he sent us a list of 

about 15 percentiles, ranging from the 1st to the 

99th, that described the distribution that he 

felt best represented the Japanese data for all 

these cancer types. What we have done is taken 

that list of 15 and done just one more step of 

analysis, and instead of having only 15 values to 

describe it we've done some cubic spline 

interpolation, and what that has done for us is 

created 100 values that we can sample from as 

opposed to just the 15. And so what you will 

see, if you look at any one of those cancers, is 

a list of 101, actually, 101 values, because we 

had to have a midpoint, and these are in 

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increasing order. 

Yes. 

MR. GRIFFON: I should say we won't see this 

on the web, am I correct or incorrect? 

MR. THOMAS: That's right, you won't see 101 

values. Every time we've presented this we've 

had the opportunity to explain what those 101 

values are. For just someone that got a hold of 

a CD, it might be a little harder -- or even if 

we had that on the web -- it's a little harder 

for someone to understand what those 100 values 

are. So what we present is the step right after 

this, where we create the distribution out of it. 

So we show the distribution on the web, and it'll 

allow you to see seven to ten percentiles from 

the 1st to the 99th. So you'll see a range 

similar to this, but it won't be 100 values. And 

so at least on a CD version this is the place 

where someone could look at those 100 values for 

every cancer type. 

And then what's done immediately is we pull 

in that ERR per sievert from the database. We 

use 101 probabilities. These probabilities go 

from zero to one, and that just defines what each 

of those values are. And then we create the 

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distribution in this step. And so this is, 

again, for liver. So this is very similar to 

what the web version will show you, and actually 

it'll look more like this, so you'll get a table 

that looks a lot like that now. And again, this 

is the original ERR per sievert. 

We have a step here where we correlate for 

multiple exposures. This is the value before 

it's truncated, so that's the one that gets 

pulled out. This is after it's truncated to 

zero. 

Then we make the adjustments for errors in 

dosimetry, and this is discussed very well on the 

web. The exact numbers and distributions that we 

used in the model are provided on the web. This 

is where we adjust for the model mixture factor. 

There's a good discussion of that in Charles 

Land's report on IREP. 

The last step is to adjust for the DDREF. 

And as Owen showed you, that's in the 

denominator, so you divide by that and it takes 

you right to the final ERR per sievert. You 

multiply that times the organ dose. Within this 

organ dose is where Dave Kocher's work comes into 

play, the RBE, which now is the REF. And so what 

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you see here is the programming behind the 

photons, electrons, alpha, and neutrons. This 

pulls all of them into one file, one database, 

and then this one pulls out just the one that we 

need for the model. This is what sends it out to 

the Internet version. 

So there's lots of nodes in here that won't 

mean much to the average person looking at this 

code. But we have tried to at least keep it 

relatively easy to understand. Most people who 

program in Analytica use it with influence 

diagrams, and so in this case they would have 

excess relative risk sitting here, relative risk 

sitting here, and probability of causation down 

here with arrows going in, just showing that that 

node depends on those. What we did is we just 

created a little equal sign, a line, a times, and 

a 100 so that we could make it look like the 

equation really looks. Now if you go into the 

probability of causation you can see the syntax 

that's used in Analytica, so the total ERR 

divided by the total ERR plus one times 100. 

Now one of the strengths that we found early 

on of Analytica is it first of all it provides 

you a place to type in a variable name. This is 

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-- anytime you use this variable anywhere else in 

Analytica, you just reference or type in A-S, and 

it'll use this node. You can type a title, 

anything you want there. In this description, 

you could put paragraphs of information there, 

references of where it came from. And then of 

course you type the equation in here, shows you 

all the inputs to that. Of course, this one only 

has one input, the total ERR, and it shows 

everywhere that this node is used throughout the 

model. 

A lot of our uncertainty, Monte Carlo-type 

calculations that we did five, six years ago, we 

were doing in a software called Crystal Ball, and 

add-in to Excel. It was a really great program. 

The problem is Excel's two dimensional, and so 

it's hard to program some of these things in 

Excel. And if you guys have done things in 

spreadsheets, you know that if you want to get a 

calculation for different scenarios, you have to 

have it in different cells. All your results 

would be in different cells. The equation is 

just duplicated. And it's easy enough to copy 

down and that sort of thing. But someone 

reviewing that spreadsheet, what we ran into in 

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119 

the past, is they have to review every cell of 

it, and they have to make sure that you've copied 

properly, and that you've held constant the rows 

and the columns and that sort of thing. 

What's nice about Analytica is that the 

equation is only entered one time. So what you 

saw there, that simple equation for probability 

of causation here, is entered one time. So it’s 

really easy for the people who have reviewed this 

so far to just browse through and make sure that 

everything is kosher. 

All right. What else? 

DR. ZIEMER: I think since we actually have 

Dr. Land sort of standing by, I'd like us to see 

if we have questions. We had the one that got 

answered, but if we get Dr. Land on the line we 

may re-ask that question, just to validate the 

answer. 

But are there any other questions that any of 

you want to direct to Dr. Land? Remember now, 

he generated the original NCI stuff upon which 

this is all based. I think originally there was 

some question in the Board as to how we got from 

the NCI stuff to the NIOSH stuff and that kind of 

thing. Maybe that's all clear now. Or are there 

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still questions? I don't want Dr. Land just to 

be twiddling his thumbs for the next two hours 

waiting to hear from the Pentagon or something. 

MR. THOMAS: Yeah, we don't want him to think 

we’ve stood him up. And we can leave this up on 

the screen, too, and so if more questions come up 

DR. ZIEMER: Yeah, we can come back. But I 

want to see, identify --

MR. THOMAS: Certainly. 

DR. ZIEMER: Do any of you have questions 

that you would like Dr. Land to address, which in 

a sense goes back to the original NCI stuff? 

Would that be a fair way to state it? 

MR. THOMAS: (Nods affirmatively) 

DR. ZIEMER: Or are you comfortable now with 

that as the starting point? 

MR. GRIFFON: I think my answer's neither to 

that. I'm not comfortable with it, but I don't 

know if I have questions right now. I've emailed back and forth, and I need to do more work 

on Charles's report that we just got. Some 

things are clearer now. 

I think the reason I'm pushing for this CD 

version again is that -- just in terms of being 

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121 

able to review this. I know the ERR per 

sieverts, as Larry points out, are now going to 

be on the web version. But as I understand it, 

it's still going to be on a case-specific basis. 

In other words, you have to put in age at 

exposure, attained age, and then you get a 

generated profile, as you just showed, that 

generates distribution of the ERR per sievert. 

If we're looking -- if we're concerned about 

factors like age at exposure and how that was 

handled, then that puts the onus on me to sit at 

home and generate -- plug in different ages and 

make my own table, when in fact it already 

exists. So that's the frustration on the 

transparency in terms of being able to review it. 

I should add, I'm not sure that needs to be 

in the web-based version. I'm not even saying 

that. I just think that it would be helpful for 

us to understand. 

DR. ZIEMER: Also I might, before you respond 

there, in terms of Dr. Land, he did indicate that 

he might even prefer, if we had detailed 

questions, that we could just prepare them in 

writing and he would answer them in detail, 

rather than the top of the head on his phone. 

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So maybe what we want to do is call him and 

indicate that the folks this morning did such a 

great job that there are no --

DR. ANDERSON: That he could take the 

afternoon off. 

DR. ZIEMER: Owen. 

DR. HOFFMAN: I took the trouble to read the 

minutes of your last meeting, and what stood out 

to me was this outstanding question: Why is 

there such a big difference between what you get 

out of IREP and what you got out of the CIRRPC 

table in 1985? I think that's the underlying 

question that needs to go to Dr. Land, and I 

think he's prepared to answer it. And so just 

the general question of can you elaborate why the 

differences. 

DR. ZIEMER: That deals with that table that 

was pointed out yesterday, I think. 

Mark, did you have anything? 

MR. GRIFFON: Yeah, I've asked him that in email format, and it's still not -- I think he's 

answered it qualitatively. I'm looking for more 

of a quantitative, and I need to work through the 

math and have -– he's shown the factors that were 

modified that contribute to that difference, but 

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123 

until you sit down and play with some hard 

numbers then -- and part of it's just my 

understanding of how they went from A to B. I'm 

not even -- it’s just the ability to review. 

Part of the other thing about transparency 

was, as Owen pointed out in his presentation, 

this was based on the Thompson data in the 1994 

report, find that’s available. I've looked at 

it. However, as Charles pointed out to me and 

Owen said again, they re-analyzed that data. So 

we can't -- so in terms of comparison, you can't 

really turn to that. So again, we're left as --

we didn't have the data. Now we might have some 

form of it on the web, but we haven't really had 

the opportunity to look at that to make -- to go 

from A to B. 

DR. ZIEMER: And so the bottom line, though, 

is that a brief telephone discussion now may not 

be suitable to answer the question, because you 

want to see some additional -- or have additional 

time to study the material? 

MR. GRIFFON: I don't want to speak for 

everyone. 

DR. ZIEMER: Yeah, for yourself. 

Owen. 

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DR. HOFFMAN: The reason why I'd like to 

encourage you to talk to him is this is what 

we've just gotten via e-mail from Charles, which 

is an attempt on a spreadsheet to explain the 

differences between CIRRPC and IREP. 

DR. ZIEMER: Okay, so --

DR. HOFFMAN: So I think you bring Charles 

on, we get detailed insight to that question. 

DR. ZIEMER: Okay. 

Is Cori still here? 

MS. HOMER: I’m right here. 

DR. ZIEMER: Okay, so I guess we will at 

least ask him to -- and he has a copy of this 

before him, I presume --

MR. THOMAS: Yes, he just e-mailed this to us 

just a few minutes ago. 

(Whereupon, Dr. Charles Land was contacted 

via telephone.) 

DR. ZIEMER: Dr. Land, can you hear me? 

MS. HOMER: Dr. Land? 

DR. LAND: Yes, speaking. 

DR. ZIEMER: Okay, can you hear me from 

there? I'm on a mike here, Dr. Land. 

DR. LAND: I can hear you. 

DR. ZIEMER: Great. Okay. Well, we have the 

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full Advisory Board here. Sorry we're a little 

later than we had planned on. Our original 

papers went a little longer, and then we had 

trouble getting through the phone line here, but 

at least we're here now. 

One of the items that we have before us now 

is some material that I think you just e-mailed 

to the group, because one of the issues that has 

arisen is the differences in the CIRRPC and the 

IREP values that are shown in the June paper. 

We're looking at the material that you sent --

what is this table called? 

DR. LAND: Is it the last table, or the last 

DR. ZIEMER: Well, it's the last table in the 

paper, and then -- yes, table E-4 --

DR. LAND: Uh-huh. 

DR. ZIEMER: Is it E-4? Yes. And the 

differences between the CIRRPC values and the 

IREP values, that has been a bit of an ongoing 

question. And then I guess you have sent, 

relative to that, you have e-mailed some 

information which includes transfer rate and 

DDREF's and so on. So I'm not even sure what to 

ask at this point, but maybe you can simply begin 

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by helping us understand the differences between 

those two. And Mark Griffon has an additional 

comment. 

MR. GRIFFON: I may be able to give people a 

-- Charles, this is Mark Griffon. And I think 

your spreadsheet is what I was also trying to do 

with the e-mail values you sent me, so this is 

helpful. I think what you're trying to 

demonstrate in this spreadsheet is to go from 

table 4-D-2 or D-4-2 -- I forget which -- anyway, 

from the ERR per sievert values to the -- how the 

transfer from the Japanese population and the 

other factors that would affect that to get back 

to the final IREP ERR per sievert value, if I set 

that up right. 

DR. ZIEMER: Did you catch that? 

DR. LAND: Yeah. It sounds as if you have 

the spreadsheet that goes from the median values 

for the uncertainty distributions, the 

statistical uncertainty distributions, and then 

there's a correction for -- immediate correction 

for the uncertainty introduced by the dose 

reconstruction, which is a .82. And then there's 

a -- I'll divide by the DDREF, and then again is 

the median value, and then multiply by a transfer 

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factor which depends on -- really on whether the 

baseline risks are higher or lower in Japan. And 

then the product is essentially the median of the 

IREP, which is -- I think it's in table -- this 

particular case it's table E-2, it's Appendix 

Table E-2. 

DR. ZIEMER: Okay. For the group here, 

that's page 108 of the document, that Appendix E2, right. 

MR. GRIFFON: So Charles, just looking at 

your spreadsheet here because we don't have it, 

we're looking at it on a projector, is it column 

M? Is that the IREP value? And I think column 

C, if I could look back, was the original ERR per 

sievert -- yes, column C, or D and E. D and E 

would have been the original values. 

DR. ZIEMER: Mark is looking at the 

spreadsheet that you e-mailed us. 

DR. LAND: I e-mailed -- is that the -- could 

I ask Owen, is that the same as the spreadsheet I 

DR. ZIEMER: Yeah, the one -- oh, you emailed to Owen? Was it, Owen? 

DR. HOFFMAN: (Nods affirmatively) 

DR. LAND: Okay, right. Okay, then we're on 

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the same page. 

DR. ZIEMER: Okay. 

DR. LAND: The IREP value is in column I. 

DR. ZIEMER: Column I, where it says Japan? 

DR. LAND: It's sheet two of the spreadsheet. 

DR. ZIEMER: Oh, okay. Okay, here we are. 

Okay, we have that. 

DR. LAND: Okay. Then the column N is the 

CIRRPC value, and column G is the multiplication, 

because I don't figure this exercise involving 

columns C, D, E and F is going to be exact, but 

it's good enough. It gets there. And so you can 

see that -- you're starting with C. C is the 

median of the statistical uncertainty 

distribution. Column D, then, is this correction 

factor for the dose reconstruction for the A-bomb 

survivors. That's a .82 except for --

DR. ZIEMER: Right, except for thyroid. 

DR. LAND: -- thyroid. And then there's one 

over the DDREF, right, because you divide by the 

DDREF. It's simpler just to multiply across, and 

that's .6 for most everything except for breast 

and thyroid, which is .66, and for leukemia, 

which is 1. And then there's the transfer, which 

is the -- that's the least easy to explain, but 

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129 

anyway, there you have a really big factor for 

liver and smaller factors for many other things. 

Transfer -- I'm not sure I believe the value for 

stomach. 

UNIDENTIFIED: Yeah, I was questioning --

DR. LAND: I don't think that's right. 

MR. GRIFFON: I think it might have been 9.4 

in the e-mail you sent me. 

DR. LAND: Yeah, I think it's supposed to be 

9.4, and so the value is much larger. 

DR. ZIEMER: We had a different table that --

or Mark did, that showed that value as being 9.4 

for males and 9.3 for females, or something like 

that. 

DR. LAND: Oh, yeah, 9.4. It should be --

somehow it got here as 2.4. Well, I'll just 

change it. And you could change it, too, I 

guess. It's --

DR. ZIEMER: Right. Right, and that -- and 

then the new product, then, is .547 --

DR. LAND: Yeah. 

DR. ZIEMER: Yeah. 

DR. LAND: And then I have the IREP here as 

.13, so I don't --

MR. GRIFFON: Charles, in looking at that one 

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you just changed there, I'm looking at column G 

versus column I now, and that's quite a 

disparity. Unfortunately, that was the one that 

I picked out to try to replicate at home, and I 

was wondering if I was doing something wrong. 

But .54 versus .13 in IREP, seems to me that --

and maybe it's the simplistic form that we're 

doing this analysis in, is that --

DR. LAND: I don't understand this particular 

one, and I -- the first thing that's brought up 

is one that I don't understand. 

(Laughter) 

MR. GRIFFON: It's the first one I reviewed, 

too. 

DR. LAND: Yeah, I really don't understand 

that. I'm going to look at Iulian Apostoaei's 

paper on that, in which he gives the factors. 

DR. ZIEMER: Well, that's something you'll 

need to follow up on, then, and --

DR. LAND: Yeah, I'll follow up on it, yeah. 

DR. ZIEMER: But then can you speak more 

generally to the original question about the 

differences between the CIRRPC and the IREP 

values? 

DR. LAND: Okay. The differences are --

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first place, the NIH -- the table, figure K --

sorry, column K, these are the medians or the 

point estimates that were developed by the NIH, 

the 1985 NIH committee. 

DR. ZIEMER: Right. 

DR. LAND: And they assumed, except for 

breast cancer and thyroid, assumed a quadratic 

dose response. And CIRRPC, which actually sort 

of acts the same way as the DDREF correction in 

the present, except it doesn't have the amount of 

uncertainty in it. And CIRRPC, in the column L 

that's labeled FDL, that's their way -- they're 

moving -- they're making -- they're assuming 

linear dose response, so they're correcting for 

what it would be if the dose response were 

linear. So in effect they're taking away the 

DDREF. This is one of the conservative things 

they did in order to get a screening rule that 

would tend to let in things that -- well, the 

idea was that if something got screened out that 

it would definitely not be qualified for 

compensation, all right? 

And then the other one here is this factor 

FB, which is in column M, and that's taking the 

baseline -- it's a baseline factor, and it has to 

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132 

do with substituting -- rather than the baseline 

for the whole U.S. population, it's the baseline, 

the ten percent baseline -- that is, in the 

lowest ten percent of counties, what was the 

baseline? So there you have this multiplying 

factor here. 

So these two things multiplied together, 

that's a factor of about five. It varies, but 

it's about five, on average. And that's why the 

product in column N, which is the median for this 

distribution or this uncertainty distribution, is 

so much higher. But it's intended to be higher. 

It's deliberately intended to let in as many 

cases as possible that would then be evaluated 

more stringently. 

So there's two things going on here. One is 

these factors here that are intended to boost 

values; and the other thing is that the NIH, in 

the NIH model the transfer between populations 

was assumed to be additive. And that means that 

the coefficients for something like stomach would 

be higher than they would be if you used a 

multiplicative transformation. But anyway, it's 

expanding things, and then for something like 

breast where the U.S. rates are higher, then it 

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would make the excess -- I'm sorry, that would 

make -- yes, that would make the excess relative 

risk lower. 

DR. ZIEMER: Okay. Let me now ask the Board 

if they have any follow-up questions on that at 

this point. 

Mark Griffon. 

MR. GRIFFON: Just one follow-up, are these 

values documented in your report? I don't know 

if these transfer values are documented in your 

report, the recent 2002, June 10th, I guess, 

report. 

DR. ZIEMER: June 11th, yeah. 

DR. LAND: It's -- no. They're described, 

and it tell you how we got them. But that's 

something we just noticed, that we really should 

have a table of them, and we will be putting that 

in either as an errata sheet or as an addendum to 

the report. 

MR. GRIFFON: And just the -- I'm going to 

run through the spreadsheet, too. I think it's 

very useful. I should note there's a couple of 

other differences on the e-mail that you sent me, 

so -- it has liver cancer with a value of 8.3 for 

transfer ratio, so --

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DR. LAND: Oh, you know what? The stuff I 

sent you was -- here's what it is. This was for 

white males or white females, whatever, 

whichever. Anyway, it was for whites, and for 

the -- the ones we're using are for the whole 

population in the country, and there are a number 

of population subgroups that have higher 

baselines. And liver cancer and stomach cancer 

are sort of major examples of that. 

DR. ZIEMER: Let me ask again now, any other 

follow-up questions by the Board here for Dr. 

Land? 

(No responses) 

DR. ZIEMER: Okay. Dr. Land, thank you very 

much. What we'll do, if additional questions 

arise I think what we'll do is ask that the Board 

put them in writing --

DR. LAND: Sure. 

DR. ZIEMER: -- and then we'll shoot them 

back to you. 

DR. LAND: Okay. 

DR. ZIEMER: This has been very helpful. We 

appreciate your taking the time out of your 

schedule to sort of stand by and wait for us to 

call, so we appreciate that. 

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135 

DR. LAND: You're welcome. 

DR. ZIEMER: Thank you very much. Good-bye. 

(End of telephone conference.) 

DR. ZIEMER: Okay. Now does that help some? 

You --

MR. GRIFFON: Yes, yes. 

DR. ZIEMER: Okay. Let's open it back up for 

any questions on any of the material. We are 

going to need to break for lunch, but I think we 

have a few minutes we can continue. 

And Owen, you and the others are going to be 

here for a while after lunch as well, so --

DR. HOFFMAN: We're at your disposal all day. 

DR. ZIEMER: Okay. Well, it is 12:00, and we 

do need to grab a bite to eat. We are shooting 

for a 4:00 adjournment because a number of folks 

have to get to the airport by about 6:00, 6:30 --

that is, they have flights by 6:30, which means 

they need to be at the airport shortly after 4:30 

or roughly. So we're going to shoot for 

adjourning by 4:00, which means the public 

comment period will be moved up. 

Is anyone signed up for public comment today? 

Are any of you that are here know that you're 

going want to --

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136 

MS. HOMER: No. 

DR. ZIEMER: We'll certainly accommodate if 

there are additional public comments, but we do 

want to shoot for adjourning by then. 

We have not only additional discussion on 

this, but we have an updated report on the dose 

reconstruction subgroup, and also a report from 

the group that was looking at comments on the 

rule-making. So we have all of that to do, and 

then talk about when we meet again. 

So it's now 12:00. Let's try to be back by 

1:15 if we can. 

(Whereupon, a lunch break was taken from 

12:00 noon until 1:21 p.m.) 

DR. ZIEMER: Folks, we need to jump ahead a 

little bit on the schedule and do some 

administrative housekeeping, partially because I 

think the earliest flight out now is Tony's, and 

Tony, what time do you have to leave us? You 

have to leave here about 2:00? 

DR. ANDRADE: Around. 

DR. ZIEMER: Around 2:00. 

DR. ANDRADE: Maybe 2:00, 2:30. 

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137 

DR. ZIEMER: 2:00 to 2:30. In any event, we 

want to talk about work schedule and meetings and 

so on. 

A couple of things to keep in mind. Number 

one, it may be by the end of the day today that 

we will still need to polish some comments for 

the proposed rule-making. That would require 

either a face-to-face or a telephone conference. 

Also, the subcommittee workgroup, the 

subgroup, working group -- I forget what the 

proper term is -- the working group dealing with 

our process for overseeing, as it were, the dose 

reconstructions -- that is, the Mark Griffon 

working group -- also wants to plan a meeting in 

Cincinnati, which would include an opportunity to 

see the facilities and look at some dose 

reconstructions and so on. 

One thought was that it might be possible 

somewhere mid to late August to combine those two 

things, so that we could all see the Cincinnati 

facilities and have an opportunity to see what 

the group is doing there, and also to take care 

of both the subcommittee’s activities and have 

even some input on their final recommendations, 

as well as do the final polishing on our 

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138 

comments. 

Now the negative side of all this is that 

between now and then the NIOSH staff is going to 

be extremely busy taking care of the road trips, 

public comments, and related things. I know that 

Larry's availability schedule is very limited. 

His wife is even insisting on some vacation time 

in there. I can't understand why, but in any 

event, those are some options we need to think 

about. 

If it were in August, it would have to be the 

third week, I think. 

MR. ELLIOTT: The week of the 12th. 

DR. ZIEMER: Is that the third week, or it's 

the second full week as far as -- that's the only 

week Larry's available in August, and it's 

available theoretically. You'd be barely back 

from the road shows. 

MR. ELLIOTT: Right. 

MR. PRESLEY: The 12th? 

DR. ZIEMER: The week of the 12th is --

MR. ELLIOTT: The only week I have available 

in August. 

DR. ZIEMER: Then it could be toward the end 

of the week. 

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139 

MR. ELLIOTT: Yeah. 

DR. ZIEMER: But I guess we'd like a little 

input both from staff and from the Board as to 

what your druthers would be. 

I don't know, Mark, on your working group how 

soon you were thinking about meeting in 

Cincinnati, or had you thought about that? 

MR. GRIFFON: As soon as possible. 

DR. ZIEMER: But the staff is not likely 

they're going to want to have you showing up 

before mid-August, because they're going to be 

gone. 

UNIDENTIFIED: Can you just leave a key? 

DR. ZIEMER: Under the mat, okay. 

MR. PRESLEY: Can we come up, the working 

group, the first part of the week, say Monday and 

Tuesday or Tuesday and Wednesday, and then have 

the Board meeting on Thursday and Friday? Or --

DR. ZIEMER: Or 13th, 14th, or something? 

UNIDENTIFIED: The working group would only 

need two days? 

MR. PRESLEY: Yeah. That’s what Mark’s 

talking about. 

DR. ZIEMER: Jim, how much of that would be 

sort of seeing the sights, the facilities, that 

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140 

the full Board might want to be involved with? 

DR. NETON: Well, our facilities aren't very 

extensive. 

DR. ZIEMER: So allow a few minutes for that. 

DR. NETON: I think a five-minute tour -- no, 

a couple of hours to do that. 

I was thinking in terms of the working group. 

To actually sit down, maybe go over a few case 

studies that we could set up with our health 

physicists, and maybe back up a step and actually 

go over our implementation guidelines; and then 

to sit down in a room with some CD-ROMs that has 

data on them would take a couple of days, I 

think. Maybe not full two days, but it would be 

hard-pressed to cram it into one day, I think. 

DR. ZIEMER: That part of it, the working 

group part, would mainly involve you, Jim, and --

DR. NETON: Yeah, that's --

DR. ZIEMER: -- some of your immediate staff, 

so it might not require the rest of the staff? 

DR. NETON: Right, right. I think it's --

DR. ZIEMER: I'm trying to think in terms of 

impact on the ongoing work. 

DR. NETON: Right. Primarily the health 

physicist. We have three health physicists on 

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141 

the staff, and we can move them in and out as 

needed. Each has its own specialty. They have 

an internal dosimetry person, an external, and 

then sort of an overview person, so we could 

rotate them through. We could set you up in a 

conference room with computer terminals and 

whatever we need to facilitate the reviews. 

DR. ZIEMER: Let me ask this question at this 

point. Is there anyone that could not -- we’ll 

start with the working group. Anyone on the 

working group that could not do it that week if 

that turned out to be a desirable week? 

MR. ESPINOSA: On the 16th I've just got to 

be back in Albuquerque by 1:30. 

DR. ZIEMER: All right, on Friday. Yeah, 

okay. But perhaps we could be talking about 

13th, 14th, 15th or something. I'm not even sure 

this group would have to meet the full two days. 

We might overlap on the afternoon of the second 

day or something, and then go into the next day. 

I'm just -- just top of the head. I don't know. 

MR. PRESLEY: Jim, you think -- you said two 

days. Could we schedule Monday and Tuesday for 

us? 

DR. NETON: Yeah, maybe even a day and a 

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142 

half. I think one day would be optimistic to be 

done with everything we wanted to do to go over. 

We spend hours on a telephone conference, and 

we're barely scratching the surface on where 

we're heading. So I'm just -- I think a day, day 

and a half. A day and a half, if not two. 

MR. ELLIOTT: Don't cut yourself short. 

DR. NETON: Okay. 

MR. ELLIOTT: We want to allow you ample 

opportunity to go through all the information you 

want to see. 

DR. NETON: Yeah, I'd rather do it now than 

have to come back for a second trip. 

DR. ZIEMER: Would the 12th and 13th work? 

Are you -- in other words --

UNIDENTIFIED: Is that a Monday and Tuesday? 

DR. ZIEMER: When do you finish the road 

show? 

MR. ELLIOTT: Well, let me go over our plans 

for the road show so everybody can factor that 

into their schedules here. Right now we're 

trying to -- folks back in Cincinnati on my staff 

are trying to work out the logistics. That means 

getting a room where we can have these meetings 

in these locations. 

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143 

But we have targeted, for the week of July --

it'll be starting the 23rd, 24th, and 25th, one 

of those three nights. We would be up in 

Amherst, New York, and then come back to 

Cincinnati and hold a second meeting, a second 

stakeholder meeting somewhere in the Cincinnati 

area. So that's the first two. 

Then the second two would be done the week of 

-- it'd actually be August 7th we would hope to 

be in Richland, and then August 8th we would be 

in Espanola. So you can see what we have lying 

ahead of us. That's if we can get the logistics 

worked out. 

We're going to make one Federal Register 

announcement for all four meetings. We have a 

press release that will be developed and will be 

distributed to the local area media for each of 

these four sites. We have talked with Department 

of Labor about who their points of contact have 

been at these sites to set up their traveling 

resource center meetings or their town hall 

meetings that they've had. And of course we'll 

be working with DOE to try to get the word out 

for those three sites, or three areas where we 

have current active DOE sites that they could get 

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144 

the news to the workers and former workers. 

So today that's the plan. It’s being worked 

on and developed as we speak. 

DR. ANDRADE: Larry, to give you a breather, 

just in case you end up going late that week 

before, would it be better to plan the working 

group on the 13th and the 14th, and the regular 

Advisory Board meeting on Thursday and Friday? 

MR. ELLIOTT: Well, Monday --

DR. ZIEMER: Rich has a problem --

MR. ELLIOTT: Monday's always a good day for 

us when we come back off a weekend and off a 

series of travels, to get our heads back clear 

and collective on a topic. And I appreciate that 

offer. I think Monday -- if you could give us 

Monday the 12th to do that, that would be 

helpful. 

DR. ANDRADE: I think for both meetings, for 

both meetings in case you have to -- in case the 

agenda is such that you don't have to go the full 

second day. That still would be fine, wouldn't 

it? 

MR. ESPINOSA: If it make it easier, I can 

cancel the meeting on the 16th, my meeting. I've 

got plenty of time to cancel that. 

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DR. ZIEMER: Is Rich the only one with a 

conflict that week? 

DR. MELIUS: I've got a problem on the 16th 

also. 

DR. ZIEMER: The 16th also? 

DR. ANDERSON: Yeah, I do, too. 

MR. ELLIOTT: Well, I just wonder maybe if 

you think about the --

DR. ANDERSON: Well, I could -– I was going 

to cancel it. 

MR. ELLIOTT: I think it would be helpful to 

me if you'd talk a little bit about what your 

agenda might be, and whether or not you need two 

days. Maybe you only need a day and a half. But 

I know that won't allow you to get back to where 

you need to be on that Friday, perhaps. 

DR. ZIEMER: He gains a couple of hours, 

though. 

MR. ELLIOTT: You might gain a couple of 

hours, I don't know. 

DR. ZIEMER: Right now it appears that the 

main thing on the agenda would be --

DR. ANDERSON: Finalize our comments. 

DR. ZIEMER: -- to finalize the comments on 

the special cohort rule, and possibly have some 

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146 

input on the oversight of the dose 

reconstructions, because the workgroup will have 

a better feel for how that should proceed. So 

those would be the two main items. I don't know 

that we would even need any speakers -- that is, 

outside speakers -- to come in. 

DR. ANDERSON: Yeah, unless we wanted to hear 

from the VA. 

DR. ZIEMER: Well --

DR. ANDERSON: That would be the only one I 

would think --

MR. ELLIOTT: DTRA. 

DR. ANDERSON: Yeah, I'm sorry. Yeah. 

DR. ZIEMER: So it might well be possible to 

call a day and a half meeting, and the last halfday could be primarily workgroup output so that 

those that had to leave before midday could slip 

out. 

MR. ELLIOTT: Let me suggest this. What if 

the workgroup met all day Tuesday and the first 

half of Wednesday, and you started your meeting 

on the second half of Wednesday and continued it 

through Thursday? And if the workgroup still 

needed to -- absent Rich, maybe -- if you needed 

to stick around, we could still work with you on 

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147 

the Friday morning or Friday all day, if you 

wish. 

DR. ZIEMER: And perhaps that -- that's a 

good suggestion. Perhaps that second half of the 

second day might be the time in which you bring 

the full Board into what your thinking is on the 

dose reconstruction. 

MR. GRIFFON: That sounds good. 

DR. ZIEMER: It appears that we may have some 

degree of unanimity on the 13th, 14th, and 15th. 

Is that right? Or 13th, 14th, 15th, and half the 

16th. 

MR. PRESLEY: Let me throw something out. 

Would we want DTRA to come in that first -- the 

afternoon of the first day, and do their 

presentation before we make any of our 

presentations as a working group? Do we need to 

listen to their presentation? 

MR. ELLIOTT: I can see if they're available 

for that. 

DR. ZIEMER: You're looking at them to 

present to the working group only, or to the full 

Board? 

MR. PRESLEY: No, to the full Board. 

DR. ANDERSON: But on the afternoon of the 

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148 

14th. 

DR. ZIEMER: The afternoon of the 14th. 

MR. PRESLEY: The 14th? 

DR. ZIEMER: Yeah. 

MR. PRESLEY: That way then we've got the 

night of the 14th or the afternoon of the 14th 

when they get through to get our presentation 

ready to give to the full Board on the 15th. 

DR. ZIEMER: As a tentative approach, does 

that sound okay staff-wise, Larry? 

MR. ELLIOTT: If I can get a nod from Jim and 

Cori, because this is going to require Jim's 

staff to support it and Cori to put it in place. 

I think -– we can do it? 

DR. NETON: (Nods affirmatively) 

MS. HOMER: (Nods affirmatively) 

MR. ELLIOTT: We'll make it happen. We'll 

contact the DTRA and see if we can get their 

commitment to present on the afternoon of the 

14th, but that might be contingent on their 

availability. 

DR. ZIEMER: Again, for clarity, working 

group 13th and 14th, full Board afternoon of the 

14th and the 15th, and possibly the first half of 

the 16th -- or did we say --

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149 

UNIDENTIFIED: The working group. 

DR. ZIEMER: -– would stay over if needed, 

okay. So the workgroup would hold -- okay. 

Is that agreeable to everyone? So unless 

some major issue arises that impinges 

particularly on the staff between now and then 

and with the arrangements, I will proceed on that 

basis. And that gives us a little breathing 

space on finalizing comments, so we won't feel 

pressured to try to wrap that up necessarily 

today, although we want to move along on it. 

Cori has distributed a calendar, and I'm 

going to suggest that even though we have already 

set these dates up that you go ahead and block 

off your known conflicts between now and December 

so that they have those. 

Is that good, Cori or is that --

MS. HOMER: We can go -- I'm guessing that 

November will be enough. 

UNIDENTIFIED: Go through November? 

MS. HOMER: Yeah, because going as far as 

December is probably --

MR. ELLIOTT: December is always a confused 

month with the holidays. 

MS. HOMER: Yeah. 

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150 

DR. ZIEMER: Well, the other question to ask 

was does the Board wish to tentatively schedule 

ahead beyond August? 

MR. PRESLEY: It'd be nice. 

(Affirmative responses) 

DR. ZIEMER: To block off dates, not 

necessarily settling where it will be even, but 

to say okay, when would we meet. 

DR. ANDERSON: The week of the 18th. 

DR. ZIEMER: Of what? 

DR. ANDERSON: November. 

MS. MUNN: We can’t do that. 

DR. ANDERSON: Well, we’re meeting already in 

August, so 

DR. ZIEMER: If we meet in August, probably 

would not need to meet in September. I'm not 

sure about October. Again, it's perhaps a little 

dependent on where we feel we are at that point, 

but --

MR. ESPINOSA: Well, as I've said before, I'd 

like to invite everybody to New Mexico. The 

balloon fiesta's in October, at the first, so --

DR. ZIEMER: Is that a bad time to travel 

there, with all the --

MR. ESPINOSA: Not necessarily a bad time to 

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151 

travel. It's a bad time to make hotel 

reservations and such. But if we do it now, it 

might be a possibility to get in. 

MR. PRESLEY: Possibility. 

MR. ESPINOSA: Possibility. 

DR. ANDERSON: Those $400 a night rooms. 

MR. ESPINOSA: Yeah, it's a big event. 

MS. HOMER: That's in October? 

MR. ESPINOSA: It's October, the first week 

of October. 

DR. ANDERSON: First week of October's okay 

for me, so --

DR. ZIEMER: Well, as a practical matter, as 

much as everyone may want to see the balloon 

festival, that in fact is not a good time to go 

to Albuquerque, because that's where we're going 

to have to fly into. 

MR. ELLIOTT: If I may, a practical matter 

also would be to consider what you're going to do 

at that meeting, and I would think it would --

DR. ANDERSON: Watch balloons. 

MR. ELLIOTT: The heavy lifting at that 

meeting probably will be looking at your first 

reviews of completed dose reconstructions. And 

if we are successful in awarding our contract, as 

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we hope we are, I think it's going to be November 

before we're going to have a goodly number of 

those for you to select from. Maybe November 

might be a better time to look at a date. Just a 

suggestion. 

MS. HOMER: And if we need to get together 

for a shorter amount of time, just to address a 

specific issue or two, we can always have a 

conference call. 

DR. ZIEMER: Uh-huh. 

MS. MUNN: Would it be worthwhile to look at 

possibly setting aside a couple of days in late 

September? 

DR. ZIEMER: In what -- when? 

MS. MUNN: In late September, just in case? 

We can always -- it's very easy to cancel. 

Nobody's ever going to cry if we take those dates 

off our calendar. 

MS. HOMER: I have to make all the 

arrangements, and we have to pay late fees if we 

cancel. 

MS. MUNN: Yeah, I understand. 

MS. HOMER: There's cancellation fees, and --

DR. ROESSLER: Then if we juggle other 

meetings and we commit to them, then we move 

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other meetings, and it -- I think we should go 

with what we think is pretty definite. 

DR. ZIEMER: It's a little difficult for me 

to see that we would need to meet as early as 

September if we're meeting in mid-August, and 

Larry suggested November might be a good time in 

terms of having some reconstructions in place. 

DR. ROESSLER: How's your weather in 

November? 

MR. ESPINOSA: Well, you can still get a 

chartered balloon ride. 

(Laughter) 

MR. ESPINOSA: I just feel that it's --

because of the outreach that I've done with Los 

Alamos POWs and other groups in New Mexico, I 

just feel it's really important that this group 

go to New Mexico. For the Board, I would like 

them to see the balloons and everything else like 

that, but it doesn't have to be in October. 

DR. ZIEMER: Let's find out what availability 

is in November. How about the week of November 

4th, any conflicts? 

MS. HOMER: I can’t. I have a meeting that 

week. 

DR. ZIEMER: That week's out. Okay. The 

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week of November 11th? 

MR. ESPINOSA: If I can speak on Andrade's 

behalf, he said that every week -- any time in 

October (sic) except for Thanksgiving weekend. 

DR. ZIEMER: November. 

MR. ESPINOSA: Did I say October? 

DR. ZIEMER: Yeah. 

MR. ESPINOSA: Oh, I meant November. 

DR. ZIEMER: Actually the week of the 11th, 

I'm out of the loop. 

DR. ANDERSON: The 11th is Veteran's Day. 

DR. ZIEMER: The week of the -- when is 

Thanksgiving Day? How about the week of the 

18th? 

MS. MUNN: I'm gone all week. 

DR. ZIEMER: All week? 

MS. MUNN: Uh-huh (affirmative). 

DR. ZIEMER: The week of the 25th getting too 

close to the holidays? 

DR. ANDERSON: Yeah. 

MR. PRESLEY: That is the holiday week. 

DR. ZIEMER: Bad time to travel. 

MR. PRESLEY: Bad time to travel. 

DR. ANDERSON: First week of December. 

MR. ESPINOSA: What about the first -- the 

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11th? 

MR. PRESLEY: Who had problems with the 11th, 

anybody? 

DR. ZIEMER: I'm out all week the 11th. Let 

me ask about the last week of October. 

MS. MUNN: I'm out. 

DR. ANDERSON: I’m out. 

MS. MUNN: But the first few days, the first 

half of the first week in November I could make 

it. 

DR. ZIEMER: Well, somebody --

MS. MUNN: Through the 4th, 5th. 

DR. ZIEMER: Somebody had a conflict in 

November. 

DR. ANDERSON: I do. 

MS. HOMER: Yeah, early November I can’t --

DR. ZIEMER: November isn't looking good, is 

it? 

MS. MUNN: No, it isn’t. 

DR. ZIEMER: How's the third week of October? 

Week of the 21st of October? 

MS. MUNN: Gone. 

DR. ZIEMER: Bad? 

UNIDENTIFIED: Bad. 

UNIDENTIFIED: We’re gone. Different places. 

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UNIDENTIFIED: I’m on vacation. 

UNIDENTIFIED: So am I. 

DR. ZIEMER: How's the week of the 14th of 

October? 

MS. MUNN: 14th? Can do. 

DR. ZIEMER: Bad? 

(Inaudible conversations) 

MR. ESPINOSA: Yeah, keep on going, keep on 

going. 

(Laughter) 

DR. ZIEMER: You can see the slow balloons 

that week, right? 

MR. ELLIOTT: Nobody said they couldn't do 

the 14th, I don't believe. 

MR. ESPINOSA: I don't know about Tony. He 

just talked about November. 

DR. ZIEMER: I think all we would want to do 

is pencil in dates and not ask for hotel 

reservations until next meeting, right? We just 

want to get the Board to block off some dates. 

Do you want to -- is early in the week better 

or --

DR. ANDERSON: Early. 

MS. MUNN: Early. 

DR. ZIEMER: Do you want to travel on a 

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Sunday and meet Monday/Tuesday? 

MS. MUNN: Sure. 

DR. ANDERSON: Monday's a holiday. 

DR. MELIUS: Monday's a holiday. 

DR. ANDERSON: Which is fine. 

DR. ZIEMER: What is it? 

MR. ELLIOTT: Columbus Day. 

DR. ZIEMER: Columbus Day. 

DR. ANDERSON: It’s not in Wisconsin. It’s a 

federal holiday. Too bad. 

MR. ESPINOSA: Would anybody have objections 

traveling that Monday? 

MS. HOMER: Dr. Andrade might. 

MR. PRESLEY: If we have it at Los Alamos, he 

won't have to travel. 

MS. HOMER: Yeah, so he won't have to worry 

about it, will he? 

DR. ZIEMER: We'll have it in Santa Fe or 

Albuquerque. It's very hard to get to Los 

Alamos. Rooms are much more expensive in Santa 

Fe, too. 

MS. HOMER: Yeah, they are. But there are 

places that are covered by per diem. 

DR. ZIEMER: It's not clear to me -- let's 

not spend too much more time. Are we talking 

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about meeting on the 15th and 16th or 14th and 

15th? 

UNIDENTIFIED: 15th and 16th. 

UNIDENTIFIED: I was hoping 14th and 15th. 

MR. ELLIOTT: Can we just block those three 

days out right now, and then make a decision in 

August? In August we would need to make a 

decision so that we can effect a contract with 

the hotel. 

DR. ZIEMER: We'll block off 14, 15, and 16. 

MS. HOMER: Yeah, I’ll have to have 

information soon. 

DR. DEHART: Could I suggest we get an 

alternative week as well in November? I realize 

there was a conflict or two, but if we don't meet 

in October then we'll probably need to. 

DR. ZIEMER: We haven't found any weeks in 

November where everyone's clear. 

DR. DEHART: I understand. That’s a 

secondary goal, recognizing that some --

DR. ZIEMER: Plan B. 

MS. MUNN: Unless we want to have 

Thanksgiving together. 

DR. ZIEMER: The week of the 4th, Cori is not 

available. The week of the 11th, I'm not 

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available. I think the Chairman has to be there, 

and I think Cori's --

MS. HOMER: Yes, you have to be there. 

DR. ZIEMER: The week of the 18th? 

MS. HOMER: No Chairman, no meeting. 

DR. ZIEMER: How many people had conflicts on 

the 18th? One, two --

DR. MELIUS: Depends on what day it is. 

DR. ANDERSON: Yeah, early is all right. 

DR. MELIUS: Early is okay. 

DR. ANDERSON: 18th and 19th is okay. 

DR. ZIEMER: This is a back-up time. Okay, 

November 18th, 19th. 

MS. HOMER: And that's still in Santa Fe? 

DR. ZIEMER: Possibly. Don't make any 

reservations yet. 

MS. HOMER: No, I won't. 

MR. ELLIOTT: In August we'll need to make a 

decision, which of these two dates you've held. 

MR. PRESLEY: So what's the date? 

MS. HOMER: First date was October 14th 

through 16th. We're setting aside November 18th 

and 19th. 

DR. ZIEMER: Pencil those in, folks. Set 

them aside. Thank you. 

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A couple more housekeeping items. 

Larry. 

MR. ELLIOTT: Okay. Under this agenda item 

of housekeeping, if you would please make sure 

before you leave today to give me your 

preparation time so that -- we put a lot of 

information in front of you for your reading 

pleasure, 300-plus pages. The working group 

worked hard and long, I know two different 

sessions. So we need to get that accounted for. 

Secondly, if you haven't noticed in the 

roster, the Board membership roster, your names 

are presented along with your address and 

affiliations and also your appointment dates. 

And you'll notice that your appointment dates, I 

think across the board, expire August, almost all 

of them. Which doesn't mean you're off the hook. 

Under FACA you continue your boardmanship until 

you either extract yourself fully or you're 

relieved from your appointment, even if your 

appointment expires. 

So they do expire in August, but we are 

working diligently toward extending those. And 

so the White House will be -- I hope -- making an 

appointment to extend your memberships to this 

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161 

Board before we have our next meeting. If they 

don't, then you're still on the hook as a Board 

member to continue your involvement until your 

appointment is extended. 

Any questions on that? 

(No responses) 

MR. ELLIOTT: Okay. And I think everybody's 

travel and pay has made your -- I hope. We have 

not heard any complaints to the contrary that 

you've not been -- your automatic deposits 

haven't made it. So we'll leave it at that. 

MR. PRESLEY: Is there any way that we can 

find out when those are made? 

MS. HOMER: That's a good question. Contact 

your bank. 

DR. ZIEMER: Check with your bank. 

MR. PRESLEY: Yeah, that's what we have to 

do, is just call the bank. 

MS. HOMER: We do have -- there are some 

folks that I can contact to get that information 

to you, or just keep an eye on your statement. I 

don't know how you manage your accounts, but we 

check all the time what's coming and going. So 

if you keep a copy of your voucher sheet, then 

you should know exactly what that amount should 

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162 

be. Your travel, nothing is deducted from that 

like it is from your salary, so you'll know 

exactly what the amount is going to be. 

DR. MELIUS: I'm on some other CDC boards, 

and they have some sort of system. They usually 

e-mail me saying expect a travel or whatever 

deposit within the next week, or something like 

that. So there must be some sort of system down 

there. 

MS. HOMER: Well, I know that we have that --

as full-time employees they usually let us know 

by e-mail when a travel payment's going to be 

making it to your account. If you're not 

receiving one, I'm not sure how to request that, 

but I'll check into it. Now you know that you're 

getting salary because I'll send you your 

earnings and leave statement. 

Now Dr. Melius, you're a little different. 

We file a manual on you because you do belong to 

more than one board, so it keeps the accounting 

straight if we file a manual time card for you. 

DR. ZIEMER: Thank you. 

I'm going to ask at this time, since we 

didn't actually call for public comment before 

lunch even though it was on the agenda, were 

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there any public comments? 

(No responses) 

DR. ZIEMER: I think we heard yesterday from 

several of those who were attending. I just want 

to give the opportunity if there are any further 

public comments. 

MR. MILLER: Just to take two minutes very 

briefly, I thought -- it's Richard Miller. 

One of the issues that Owen Hoffman was very 

helpful in bringing up was I guess sort of the 

adaptability of the model. And with the 

exclusion of the worker studies on radon, the 

model does not -- particularly lung cancer models 

-- doesn't particularly account for many of the 

worker epidemiology studies that have been done. 

And I just would encourage you all, 

recognizing you have a full plate at least for 

your next meeting, to think about on a goingforward basis some kind of examination of worker 

epidemiology and how it could, should, might, 

ought not fit in. It's certainly in the statute 

that you're to account for worker epidemiology. 

I certainly think there's room for debate about 

whether the model adequately accounts for the 

uncertainties that exist around the age at 

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exposure question. 

But leaving that for debate for another day, 

I would just strongly encourage you all to think 

about it. This is a worker compensation program, 

and yet very little worker epidemiology has been 

brought to the table in terms of the discussion. 

And the model looks like it's equipped to kind of 

compensate for or adjust for that. 

And one of the issues that's come up is 

should the healthy worker effect be a factor 

that's considered when you look at the baseline 

risks, or whether you want to use population 

averaging. And again, these are the kinds of 

questions which would be, I think, very valuable 

to have examined perhaps at some later date. 

The second question was just a technical one. 

When I was in Los Alamos, we had gotten a number 

of individuals who have already filed claims who 

are survivors for people who worked at the 

accelerator and the Meson facility there. And 

the question was, is NIOSH going to be in a 

position to adjudicate those claims if IREP 

doesn't have that currently in its list of energy 

levels or types of radiation to account for? And 

if so, how are you planning on accounting for 

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those types of claims, or are those just 

automatic candidates for a special cohort? 

I think those are sort of the two key points, 

worker epidemiology and what to do about the 

accelerator population. 

DR. ZIEMER: Thank you very much. On the 

accelerators, I don't know that that would 

necessarily be excluded. We're basically -- are 

these unique particles that aren't covered, or do 

you know? Because they usually are looking at 

secondaries from these --

DR. NETON: Right. I don't know that it 

necessarily follows that these people were 

exposed to particles other than what we've 

covered --

DR. ZIEMER: They are monitored. 

DR. NETON: -- first of all. They are 

monitored. 

Secondly, if there are those instances -- and 

we’ve thought about this when we were moving 

forward with the rule -- that the population of 

personnel or workers that would be exposed to 

such particles would be so small that we would 

address those on an individual basis within the 

dose reconstruction themselves. It would 

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essentially require an effort to go and quantify. 

And given the magnitude of the exposures, 

there may be some -- using our efficiency 

approach, there may be some extremely 

conservative values one could apply, and evaluate 

the case using an efficiency approach thing. And 

as it gets closer and closer to where we had to 

do a full-blown dose reconstruction, we of course 

would commission some sort of a study into that. 

But it doesn't follow that these unusual type 

particles are going to be the predominant 

exposure in those workers at those facilities. 

DR. ZIEMER: Did you have an additional 

comment? 

MR. MILLER: To the extent that -- correct me 

if I'm wrong -- it was my understanding that the 

monitoring devices are relatively recent 

developments, say, in the last 20 years, 

particularly for those types of particles. And I 

wasn't quite sure, is that something that is 

going to pose an obstacle for adjudicating claims 

for, say, prior to 1980 or so? 

DR. ZIEMER: That may be something that has 

to be looked into by the group, but I think the 

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accelerator people have been monitored -– and 

maybe, Tony, you can answer this -- for as long 

as others. And aren't we still looking basically 

at a lot of secondary gammas and maybe some other 

particulates? 

DR. ANDRADE: You're going to have -- of 

course, the potential exists in accelerator 

situations to be -- the highest potential is to 

be irradiated by the direct beam itself or a 

scatter of the direct beam. But then afterwards, 

it's the decay products from the target or target 

areas or misaligned portions, or portions where 

misaligned beams may have hit. And you run the 

gamut of beta gamma emitters, anything that can 

be produced by energetic particles, either 

proton, electron, or heavier ion. 

DR. ZIEMER: There are anecdotal stories 

about early cyclotron workers who aligned beams 

visually -- yes. So there I think -- and the 

biological endpoint was cataracts, which wouldn't 

be covered here. But very definitely an issue 

with some early cyclotron workers. 

Thank you for the comments, though. 

Jim. 

DR. MELIUS: Just to follow up on Richard's 

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comment, there's some epidemiological points that 

have come up relative to the worker populations, 

the healthy worker effect, there are differences 

there. There's also regarding the Japanese 

population in terms of a survivor effect or 

something like that. And I think, to follow up 

on Richard's comment, that it would be worth us 

starting to develop some background and 

discussion on those. And if we could start that 

with the next meeting, it would be helpful. 

Again --

DR. ZIEMER: That would be an item to add to 

the laundry list that we've been accumulating. 

DR. MELIUS: Yeah. 

DR. ZIEMER: Thank you. 

MR. SCHOFIELD: Can I just make one comment? 

In relation to the healthy worker effect, one 

thing that needs to be taken into consideration 

when this is done is the fact that I can't speak 

for other facilities, but at least at Los Alamos 

you go through a physical exam and your 

(inaudible) exam. So people who go into those 

jobs have to be above average in health. And 

those people who start falling down in health 

that normally would be able to keep their 

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169 

positions are weeded out. So that introduces a 

definite bias. 

DR. ZIEMER: Okay. Thank you. 

Now we want to allow a little time for 

additional discussion relating to the papers we 

heard this morning. Owen is still here. I think 

Dave is still here. They're all still here. 

Is there an additional question or comment or 

MR. ELLIOTT: Also at this point on the 

agenda, which is really what we had targeted at 

the 10:45 mark, if there were any questions or 

issues or comments relevant to the NIOSH-IREP 

documentation that was provided to you for 

reading. You heard about the REF from David 

Kocher. 

You've also been provided the subject matter 

expert comments and how those were addressed by 

Mary Schubauer-Berigan through the NIOSH review 

process. So we wanted to -- Mary could not be 

here today. She's in Lyon, France, at IARC. 

Somebody had to do the tough job there. But we 

would like, if you have any issues or questions 

you want to raise about our technical 

documentation, that we can bring Mary back or 

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170 

another NIOSH technical expert back, we'd like to 

hear those and table those till we can get you an 

answer. 

DR. ZIEMER: Mark. 

MR. GRIFFON: I did want to ask -- I think 

I've mentioned this a couple of times -- but I 

would want to request officially that all the 

Board members get copies of this most current 

IREP model on CD. I think we've seen it's 

available. I really think it'd be useful for 

review purposes. 

Larry has a comeback. He doesn't want to 

give it to me. 

MR. ELLIOTT: Well, no, I don't. And here's 

the reason why. We think it needs to be on the 

web in the current version, and that's the 

version that will be used to adjudicate claims. 

If we have a version on a CD floating around, 

we're legally concerned that that version might 

be used to advise a potential claimant what their 

PC might be, and that may be inadvertent and 

cause frustration and disillusionment among the 

claimants population. 

So this is a policy decision that we're 

examining right now. We have to take into 

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consultation general counsel's advice on that 

before we can take a step forward. We've talked 

about this at each meeting. It's present in each 

of the transcripts. And each time I've said, no, 

there's not one available. We are still 

deliberating on whether we can provide it. But 

that's basically the background on why we feel 

strongly we can't provide it. 

MR. GRIFFON: Then if -- I'm not sure that's 

a hurdle that can't be overcome, but if that is 

the case then I would argue that can the on-line 

model include some of these tables. 

I think we're close, and the Excel 

spreadsheet e-mailed today was helpful in 

explaining how you get from X to Y. But it just 

doesn't make -- from a review capacity, from my 

personal need to review this, I really am getting 

kind of tired of entering one at a time cases 

when I know that data's there, and I don't want 

to have to recreate age at exposure distributions 

when I know they already exist in 2.1. But 

that's old, that's old ERR per sievert 

distributions that I'm looking at. I can't turn 

to the Thompson data because they're reanalyzed 

it specifically for this report. 

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So just for the need of transparency, I think 

somehow we have to be able to get to this. And I 

think -- I don't care if it's on the web that way 

or on a CD. I'd prefer a CD, as you know, but --

DR. ZIEMER: The concerns are so noted in the 

DR. MELIUS: Can’t we just get this resolved, 

though? It's --

DR. ZIEMER: Well --

DR. MELIUS: If the counsel has objections 

let's hear them next meeting, and --

DR. ZIEMER: Right. 

DR. MELIUS: -- at least get it settled, 

because --

DR. ZIEMER: Legal counsel does carry weight 

in the agencies, I know. But it may be that some 

of this can be on the on-line version that will 

allow -- and that would probably be the better 

solution. 

MR. GRIFFON: Is there a technical hurdle for 

having the tables? I don't know if that slows 

down --

UNIDENTIFIED: (inaudible response) 

MR. GRIFFON: It doesn't slow down any -- no. 

So having all the tables there would not be a 

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problem on the web version? Okay. 

DR. ZIEMER: Any further comments or 

questions on that material from this morning? 

(No responses) 

DR. ZIEMER: Okay. Now I want to go back for 

a moment to the Special Exposure Cohort, and Ted 

has asked for some additional time to amplify 

some things he talked about yesterday. 

MR. KATZ: Yes. If you recall, I had that 

little snag with the projector not being able to 

go in reverse, and that managed to fluster me 

enough to not say some things I meant to say. 

And I didn't really realize I hadn't said them 

until Tony made the comment that it was his 

perception that -- and here I'm talking about the 

use of a threshold for health endangerment, and 

the use of averaging threshold that you would get 

from using a solid tumor and leukemia as a basis. 

That's creating a threshold in a case where you 

have external exposures, external exposures, 

external dose. 

So when Tony said that seemed to him 

arbitrary, it sort of shocked me into thinking 

what is it I missed saying. And this morning I 

realized that I had sort of skipped through that 

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slide because I couldn't reverse, and hadn't said 

what I wanted to. And then as a result we also 

didn't talk about the slide that we did have up 

there, and I think you all have handouts. And 

this should at least be explained, so you know 

what you have there as well, so I'd like to do 

both those things. 

What I'd like to do is give you as full an 

understanding as possible -- meaning everything 

-- about how we came to the decision of what's in 

there, arriving at that threshold, how that 

evolved, and what the reasoning is. And I hope 

this helps you understand why that's not an 

arbitrary threshold. You may disagree with it, 

and that's good, that's the whole point here is 

to get your feedback. 

DR. ZIEMER: Now which handout are you 

referring to? 

MR. KATZ: I'm sorry. I'm referring to --

it's the handout that was provided late. It was 

a slide that was not in my prepared presentation, 

because it was developed over the weekend at 

night, (inaudible) hard work. So at the top of 

the handout it says “PC Values, 99 Percent 

Credibility Limit.” Everybody on the same page? 

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Okay, so let me just talk about how we got 

there. We started off with really a theoretical 

or a conceptual basis for how we would establish 

this threshold. And the conceptual basis was 

this: We knew that we would have to be making 

subjective judgments about what the actual dose 

levels could have been, as high or higher than 

what. We knew we'd have to do that because we 

can't do a proper dose reconstruction in these 

cases when we're talking about Special Exposure 

Cohort groups. 

As a result, we wanted to have a threshold 

that was as bulletproof as possible in the sense 

that no claimant would take issue with the 

threshold itself. Since they're going to already 

be addressing then the subjective judgment that's 

applied using that threshold, we wanted that to 

be sort of as plain and simple and unarguable as 

possible. 

So we started off as a -- again, it's 

basically purely conceptual -- that we would 

simply have the most radiogenic cancer that 

applies to the exposures that occurred, that 

would be the determinant of the threshold dose 

level. Does everyone follow that? So what that 

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would mean is wherever there were external doses, 

what we would be talking about is using leukemia. 

Simple, simple and plain. Where it was a matter 

of internal doses you'd be going to the relevant 

cancers, right. That's where we started. 

Then we had review of this position, and 

people who didn't have their nose quite so close 

to the paper saw the implications of just that 

conceptual approach which we hadn't considered, 

which is, well, okay, so you're using leukemia 

with external radiation, and that means that you 

could be as low as using a threshold of around 

one rem. And that just seemed to them to be a 

stubborn fact to want to question, then, what is 

the basis for this? How do you end up having a 

threshold which I think would be hard for many to 

accept as a threshold for evaluating health 

endangerment for a class, a threshold that low? 

And explicating further, there was this 

different view which is one we hadn't considered, 

which was that you are -- the job here is to 

characterize health endangerment for the class --

not for a conceptual member, single member of the 

class, the most vulnerable potential conceptual 

member. Does everyone follow that? 

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So that was what was posed to us. Well, 

really this should be representative of the 

class, and how do you do that? And the response 

that we thought of on the cuff there was, well, 

how would we do that if we wanted to do that, 

most simply have a perfectly representative 

threshold? Well, there we then would have to 

have what is in effect a weighted average of the 

doses for all the cancers that are potentially 

related to the exposure, and you would weight 

them by incidence rates. Right? So that the 

more prevalent the cancer in terms of expected 

occurrence among that population the more weight 

that value would have, and you would average 

that. And that would be representative, sort of 

straight, no question about it, representative of 

the class in that sense. 

Now there's problems with doing that 

approach. We didn't think it was feasible to do 

that to start with, as a first issue, because we 

would be working with then expected values for a 

dose that we don't know that we're going to 

assume it could be so high or higher. That's 

what the subjective judgment's going to be made. 

You'd be using that subjective judgment to then 

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come up with a threshold that you're applying 

your subjective judgment against. It just 

doesn't carry water. So we said, that can't be 

done. 

So the next step, then, was what is then a 

practical approach to this if we need a 

representative value? And we also, frankly, were 

concerned because we thought we should be more 

claimant-friendly than that as well. And so that 

made us uncomfortable anyway, that approach, even 

if it were feasible. 

So what's a practical solution to this? And 

the practical solution that occurred to us was 

the one that you have before you, which is to 

simply average, in this case, the two different 

types of cancers, the classes of cancers -- the 

solid tissue cancers and leukemia -- to average 

those dose thresholds and to use that. 

Now I guess it would be more proper if you 

were still working with their incidence rates 

still and weighting it. But again, I just 

explained what the problem is with doing that. 

And in this case we felt that this was a much 

better solution in the sense of being claimantfriendly. Because certainly given the difference 

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179 

in the incidence you would expect for the solid 

tissues and the leukemia, the leukemia is going 

to have far disproportionate weight when you're 

just averaging them. Is that clear? Is that 

clear, what I've explained there? 

So that's how we came about this approach 

that we put before you. And I think that 

explains that fully. I would like to give some 

air time and for you to consider the table and 

the approach we have proposed if we're going to 

go down that route. I don't know, does everyone 

have this table before you? I just want to sort 

of run down these values. 

Now this is just an example. This is just 

one case example. And what we've done here is 

simply taken these PC values you see in the box 

above, the fixed inputs. What these are are 

basically just median values for all the claims 

we've seen so far. So this isn't really -- this 

is just to show you how this would work, but 

these values that you get in the table below 

obviously would differ depending on the values 

that you would actually input. The values we 

used are just median values for all the claims 

that we've received so far. 

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So we have proposed that you would use, in 

the absence of other evidence about the class, 

you would use in effect the lowest latency for 

leukemia, because that would be giving the 

benefit of doubt to the claimants, that would be 

most claimant-friendly. And you can see -- and 

you're also using the most radiosensitive of the 

leukemias, CML in this case, and that ends up 

with a 1.5 rem dose. 

And we would use the highest latency for the 

solid tissue, solid tumors. And in this case it 

turns out to be thyroid, and the dose level is 

nine. 

You're averaging one and a half and nine, and 

you're ending up at what, four and a half? So 

that would be the threshold that we would 

establish if this were a case here, if these were 

the values we were using. 

MR. ELLIOTT: If it were a Special Exposure 

Cohort petition. 

MR. KATZ: Right, exactly. 

MR. ELLIOTT: Not a case. 

MR. KATZ: No. Case, meaning a case of a 

Special Exposure Cohort petition, I'm sorry. 

We're not talking about individual dose -- this 

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181 

isn't about dose reconstructions. 

Then there's, I think, just one other thing 

to say about this when we're talking about 

extremely low levels of exposure, which is when 

we're doing dose reconstructions, if there's a 

component of the dose reconstruction where we 

don't have good information, one approach is to 

simply cap it and do that dose reconstruction 

with that, in effect, maximum dose for that 

element of the dose reconstruction. And that's 

talked about in our rule and so on, how we do 

that. 

So some of these cases, even though you can't 

properly estimate a very low dose, those cases 

would go away. In effect you would still do the 

dose reconstruction. You would give it a maximum 

value. So extremely low dose levels, also you 

have to consider that some of those are going to 

get taken care of by individual dose 

reconstructions, despite the problems there are 

with doing the dose reconstruction about that 

element of the exposure history. 

So anyway, that fully explains what I omitted 

and wanted to address, really to address Tony's 

concern, which is a very important one. 

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182 

DR. ZIEMER: Okay, thank you. Let's see if 

there's any questions on what was just said here 

now. 

Roy. 

DR. DEHART: If I'm understanding this 

correctly, the petitioning group need not have 

leukemic or thyroid cancers in them? 

MR. KATZ: That's right. 

DR. DEHART: And the threshold that you're 

establishing at 5.5 or whatever becomes the 

threshold used in what specific way? 

MR. KATZ: It's the threshold for 

establishing health endangerment. So it is --

right. There may not be any cases of either in 

that class. It's simply the threshold that will 

be used as the bar for making a judgment, then, 

were radiation doses possibly as high as this or 

higher. 

Which raises another point that I have 

omitted that I should point to, when we're 

concerned about the possibly or known leukemia 

case in a class, which is these values that I 

just went through on this table are given the 

most propitious circumstances, that's the value 

you would come up with. But your actual leukemia 

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183 

case may not have incurred the leukemia within a 

five-year latency period, and all the other 

factors may differ. And as you see in this one 

example, the leukemia actually level rises above 

the level of hard tissue in certain 

circumstances. 

So that's just an important, again, 

complication, but to keep in mind. 

DR. ZIEMER: Thank you. 

Now while Tony is still here I'd like us to 

move to the rule-making, which is the 42 CRF 83. 

You recall that yesterday we raised a number of 

issues to be considered. We had a small working 

group last evening or late yesterday afternoon 

that identified some potential -- I don't 

necessarily want to call them fixes -- but 

potential recommendations that were felt perhaps 

would improve the document. And I've asked Tony 

if he would lead us through some of those. I 

think it's safe to say that perhaps the group 

didn't identify everything or capture everything 

that was brought out in the discussion, but this 

is at least a start to what was felt might help 

clarify some of the issues. 

So Tony, if you would take the floor at this 

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184 

time. I know you have to take off soon. Are you 

still okay for a few minutes? 

DR. ANDRADE: Yes. Before I get into detail 

insofar as proposed, very draft proposed changes 

to wording, let me tell you a little bit about 

the philosophy with which we approached the issue 

of trying to clarify some of the language in the 

proposed rule. 

Number one is we wanted to first and foremost 

explain clearly and up front, at least in the 

rule itself -- and perhaps if you all want to go 

back into the preamble and change that, that's 

fine -- that establishing or petitioning for a 

special cohort status is not necessarily a next 

step or a proposed next step seeking remedy in 

case the Secretary has determined that a 

particular -- a particular case now; we're not 

talking about a group of people, but a particular 

case -- just does not meet the threshold for 

action. So that was one. 

DR. ZIEMER: It's not an appeal process for 

DR. ANDRADE: It's not an appeal process. 

DR. ZIEMER: -- for a reconstructed dose that 

did not meet the 50 percent POC. 

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DR. ANDRADE: Exactly. 

Okay. And then when we got down into 83.1, 

what is the purpose of the procedures in this 

part, we wanted to be very clear about how a 

Special Exposure Cohort might be constructed. 

And it appeared to us that the language as 

written leaves the onus on the petitioner, on the 

individual, to go back and petition for such 

status. Again, that conflicts with what I just 

talked about with what I think the philosophy is, 

and it would almost force the person into 

believing that this is the final recourse. 

But beyond that, what is new in our thinking, 

in our collective thinking -- and this was Dr. 

Anderson, Paul, Wanda, and myself -- is that we 

felt that NIOSH and/or NIOSH's contractor should 

bear some responsibility. Now we're not talking 

about putting this in a statement of work, but at 

least being aware of what is going on as dose 

reconstruction efforts occur, such that if they 

start to find commonality in a situation -- in 

other words, somebody has petitioned, yet it 

seems like the dose -- several people, 

individuals, have petitioned. They come from the 

same facility. They've done the same kind of 

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186 

work at the same -- during the same relevant 

period of time, and they start to see commonality 

in activity, that there was a potential for 

missed dose, for example, that they should be at 

least aware of and report that back to NIOSH or 

to HHS. 

And so we wanted to take the onus off the 

individual, who may not be aware of what he, her, 

or their buddies were doing at the same time, and 

put a little bit of responsibility, perhaps 

personal responsibility, back on the contractor. 

Thirdly is just as we were briefed on 

yesterday by the good doctor from Rocky Flats, 

new information can come to light during any part 

of this process. They've just discovered that 

there are body burdens out there for which we may 

not ever find records. I think that in itself 

should trigger or potentially trigger a petition 

for special cohort status. So again, in addition 

to the language that is already in 83.1, we 

propose two more triggers for special cohort 

status. 

And finally -- and perhaps Dr. Ziemer can 

talk a little bit more in detail to this -- we 

felt that as a Board that a lot of the procedures 

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187 

that are described in here, starting under 83.2 

-- how would cancer claimants be affected by the 

procedures in this part, and going on through the 

rest of the proposed rule -- talk about a process 

by which the Board would become involved in those 

decisions, where we would review the decisions of 

HHS in which it has already been determined that 

they're going to go forth with a special cohort 

decision, a positive decision. 

We felt very strongly that it would be nice 

to keep this Board involved, but that we 

shouldn't second-guess the HHS. This is part of 

being petitioner-friendly insofar as positive 

outcomes with respect to going forth with a 

special cohort. We would like to be informed, 

but that's it. 

On the other hand, I think it is more 

important that we be informed of decisions not to 

go forth without some of the details that are in 

here. In other words, we would like to be 

informed of the decisions as to why one would not 

go forth with a petition. I don't think that we 

would like to have people who are personally 

involved come up and petition us. I think that 

would turn us into an adjudicative body. And so 

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we really believe that language in that regard 

should be struck from the record. 

Now I don't have my notes with me. I just 

sealed them in my Fed Ex box. But I know that 

Paul is taking very good notes, and actually 

completing sentences that might be used as 

proposed language. But that's to give you an 

introduction as to what we did yesterday, how we 

feel about the situation, and I think points to 

clarify what this rule is for, what trips this 

rule, and what our role as a Board should be with 

respect to this rule. 

DR. ZIEMER: Thank you, Tony. And with that 

sort of introduction to it, perhaps I can add 

some specificity to specific items here that will 

maybe help clarify some of those issues. 

For example, in 83.1 -- and we may need help 

in the interpretation here -- in 83.1 it appears, 

as Tony has suggested, that the process of 

becoming part of the cohort -- there's a cohort, 

and there's new classes that can be added to it. 

As you read this, that there are not new cohorts. 

There is a special cohort; it exists now. There 

are new classes that are to be added as the 

definition gives here -- yes, class of employees 

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189 

to be added. 

The language in 83.1 says: 

(Reading) HHS will consider adding new 

classes only in response to petitions by or on 

behalf of the employees. 

So it's an employee or a group. I think it 

could be a union group representing employees. 

But nowhere does it speak to NIOSH taking the 

initiative on its own to develop a new class 

based on what its findings are. And as has been 

suggested, perhaps somebody's dose has not been 

reconstructed, and they say, well, I'm not going 

to pursue this any further. But over a period of 

time, perhaps NIOSH finds that there are 10, 15, 

20, or other people from that facility doing a 

similar job for whom doses have not been 

reconstructed. And perhaps these folks don't 

know about each other, don't know that they may 

be a class. 

Was the intent not to have NIOSH be proactive 

in initiating a --

MR. ELLIOTT: Yes, Ted. 

MR. KATZ: Yeah, thank you. Let me -- it's 

Ted Katz -- just address that. When we can't 

complete a dose reconstruction, part of the 

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190 

report that goes to that individual, whether it 

be employee or survivor, saying that we can't 

complete a dose reconstruction, part of the 

service we provide at that point is to tell them 

about the Special Exposure Cohort, and to provide 

them materials to be able to petition and 

encourage them to petition. So --

DR. ZIEMER: Understood. But if they don't? 

MR. KATZ: No, I understand. I understand, I 

understand. But the interpretation of the law, 

EEOICPA, that was given at least, was that the 

starting process for considering a class was a 

petition by a class of employees. So EEOICPA 

didn't authorize HHS to establish petitions on 

its own initiative, but that in response to 

petitions, and that's why it's written the way it 

is. 

DR. ZIEMER: Does it prohibit it? 

MR. KATZ: No, and there's no language in 

EEOICPA that says HHS must not, cannot, should 

not, whatever. And of course, EEOICPA addressed 

the President, not HHS. But anyway -- do this on 

its own initiative. It laid out that these 

classes would be considered in response to 

petitions. 

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191 

DR. ZIEMER: Well, that was a concern, 

though, that it gives the impression, even though 

in reality this might not occur. You do advise 

them to do this and so on. It gives the 

impression that unless that individual does 

something, even if we know that there appears to 

be a class out here, unless those folks do 

something nothing's going to happen. 

DR. MELIUS: Can I just ask some 

clarification? I guess if I understand you 

right, Ted, you're saying that there has to be 

some sort of active, affirmative process back by 

the claimant to request --

DR. ZIEMER: To trigger --

DR. MELIUS: -- being part of the Special 

Exposure Cohort. Does that necessarily, though, 

have to require them to name the class and things 

like that? I think --

MR. KATZ: Right. No --

DR. MELIUS: If it were like a check box --

MR. KATZ: And in effect, it is. 

DR. MELIUS: -- yes, I want to be considered 

– 

MR. KATZ: Yes, and --

DR. MELIUS: Well, that's not clear. 

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MR. KATZ: Well, that's -- no, that may not 

be clear. But in effect, all they are providing 

is their personal information, their contact 

information and so on, and the finding that 

NIOSH, in their case, couldn't complete a dose 

reconstruction. 

DR. MELIUS: Right. You already have all 

this. You've already sort of know their -- you 

know all this about them. If all you need is 

some sort of an affirmation back that they want 

to be considered --

MR. KATZ: Well, and that's in effect what 

we're getting, right. 

DR. MELIUS: Well, it's not clear --

MR. KATZ: I don't know, a check box or 

whether they're filling out their name and 

address. But it's not a burden, what we're 

asking, just for them to affirm that they want to 

be part of the class, part of the cohort. 

DR. ANDRADE: Well, once again, Ted, it's 

just appearances, I think. You all may be 

planning and actually doing this already, and 

advising them about the possibility. However, I 

think it would be wise to consider just an extra 

line or two in the proposed rule, such that it is 

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193 

clear that if evidence to that effect comes up, 

if there is some possibility that they could be 

part of the cohort, they might want to petition. 

MR. ELLIOTT: I appreciate the fact that --

this has been very beneficial to hear your 

thoughts on this. And it is not clear, I 

believe, as I've read it, reread it myself. And 

we can certainly take your comments into account 

and reflect upon them. 

I wanted to comment on the second point you 

made about putting the burden on us. We believe 

the burden is on us, and we need to make that 

clear. It's not on our contractor, it's on us. 

And it's on us to monitor the results of dose 

reconstructions coming out of our contractor, and 

observing where dose reconstructions seem to be 

on shaky ground or they can't do a dose 

reconstruction, and what that means for that 

potential class and how we can get an affirmation 

from an individual or individuals from that 

class. And yes, we may get one that says no, but 

hopefully we'll find somebody else who will stand 

up and say yes, we need to have a review for us 

as a class. 

DR. ZIEMER: And our thought is that this 

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again is partially a perception thing, but you 

certainly want to show that NIOSH is going to be 

proactive in making some of these things happen, 

even if you still require the petition. 

In 83.5 there's a definition of the class of 

employees that says they have similar experience, 

they worked at a similar facility, and so on. We 

felt that it was probably also important to 

include -- and I think you intended to do this --

include the similarity of time periods. It's not 

just that here's somebody in 1955 that worked at 

Los Alamos as a, let's say, a glove box operator, 

and someone in 1980 that did that. Generally 

these are also time-related as well as -- and so 

we're simply suggesting that that be included in 

some way in the definition there. 

In section 83.1 --

DR. MELIUS: Paul, before you --

DR. ZIEMER: Oh, yeah. 

DR. MELIUS: On that same issue, it's the 

issue I brought up when we were at lunch. And 

part of it's a factual question. Are there 

itinerant groups of workers that move from 

facility to facility? Because you've got 

classes, a person at a facility -- and again, 

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this may be a small portion of who's out there --

but it may be easier to identify the class as a 

particular group that does a task, moving from 

facility to facility. Certainly in the 

commercial and nuclear power there's a more 

highly --

MR. KATZ: This is another issue of 

interpretation of the legislation, which defines 

classes as being at a facility, though -- so the 

legislation seemed pretty clear to HHS in 

interpreting the legislation that the definition 

is -- adheres to a facility, and hence that's why 

we discussed before about needing different 

petitions separately for different facilities. 

DR. ZIEMER: But it wouldn't really exclude, 

Jim, I think what you're talking about, because 

one of these special classes may be part of their 

time at some particular facility where such an 

exposure did occur, or multiple facilities. 

DR. NETON: On a practical basis --

DR. ZIEMER: You could even name multiple 

facilities, but there --

DR. NETON: No, it would have to be one 

facility. But on a practical basis -- I could 

think of an example, health physics technicians, 

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rad techs that jump from -- to support certain 

things. Their exposure profiles are going to be 

very different, more than likely, at different 

facilities. So it wouldn't be easy to group them 

if they worked at Los Alamos and then moved to 

Fernald. Fernald you'd have uranium exposures; 

Los Alamos you have something else; Rocky Flats. 

So I don't think it even makes a practical sense 

to lump them into one category of workers who 

jumped from facility to facility. They could be 

considered at multiple facilities, I suspect, a 

Special Exposure Cohort if there was evidence. 

DR. MELIUS: Yeah, but -- again, I'm not sure 

how practical this is or meaningful, and I don't 

want to belabor it. But in essence it may be 

their cumulative exposure over those facilities, 

because that exposure differs so much, it makes 

it hard to reconstruct their doses, so to speak. 

And I'm just thinking --

DR. NETON: I'm having trouble envisioning a 

class like that, but you are right. If there was 

such a class, I think --

DR. ZIEMER: But all they really need is one 

facility where you couldn't reconstruct. 

DR. NETON: Well, and --

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DR. ZIEMER: They were all -- that was common 

to all the exposed --

DR. NETON: But the exposure would have to be 

sufficiently large to --

DR. ZIEMER: Correct. 

DR. NETON: -- pass the bar test. 

DR. MELIUS: Yeah, but because it would be --

it's depends on obviously the fact pattern. 

DR. NETON: Right. 

DR. ZIEMER: Yes, a comment? 

MS. GADOLA: From attending some of the 

employees meetings in Oak Ridge, there have been 

employees that claimed that they were 

construction workers or maintenance workers that 

moved from facility to facility, and they 

envision that their dose reconstruction would be 

very difficult to obtain, and that sometimes they 

were working -- this is according to them --

sometimes they were working in areas which at 

first they were told they did not have to be 

badged, and then after they were there for a 

while they were given dosimeter badges. 

MR. PRESLEY: That's correct. 

MS. GADOLA: So it would seem that 

maintenance workers and construction workers 

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might possibly be their own cohort or fall into a 

special cohort. But according to how you're 

defining it, they wouldn't be able to. Is that 

correct? 

DR. ZIEMER: They still have to link it to 

some facility, not just be a construction worker, 

right? They would have to -- you would want to 

be able to show that when they worked, say, at 

Oak Ridge they didn't have -- they couldn't 

reconstruct. 

MR. PRESLEY: What Sally’s talking about is 

at Oak Ridge they had three plants -– I'm sorry, 

Bob Presley -- at Oak Ridge you had three plants. 

And so what we did is we had one prime 

construction contractor for all three plants, and 

those people would move around. One week they 

may be working at Y-12, the next week they may be 

working at ORNL, the next week at K-25. So that 

did happen in Oak Ridge. 

MR. KATZ: So that get at the question of how 

you define a facility, too. 

DR. ZIEMER: Right, right. 

MR. PRESLEY: Yes, that's correct. 

DR. ZIEMER: But all it would take would be 

for one of those, let's say Y-12, where the dose 

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couldn't be reconstructed, even if the others 

could, and it was sufficiently large, then they 

meet the criteria. 

DR. MELIUS: Yeah, I'm just worried about 

them getting defined as a class. I don't have 

the law here, and I'm not sure what your counsel 

said. But if we could sort of look in and follow 

up on this it would be helpful to make sure we're 

not -- by some of these definitions we're not 

excluding somebody, a group that moves from 

facility to facility, or that we may change the 

definitions here somehow to make it -- facilitate 

that kind of a designation. 

MR. PRESLEY: And the other thing is, since a 

lot of these people, they're in their seventies, 

late sixties, early seventies, even eighties, 

we've changed prime construction contractors 

about four or five times. Records, things like 

that, are almost nil. 

DR. ANDERSON: This is just partly a followup on should NIOSH be proactive. Do you foresee 

that NIOSH will publish on a regular basis the 

characteristics of those people that don't -- you 

can't do dose reconstructions? 

I think our group concern was it's kind of --

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it's all very individual-oriented, but the 

individual is very isolated. And so to expect 

that individual to either go out and find these, 

unless your report back to them that says, well, 

you ought to contact da-da-da, or we're aware of 

X, Y, Z, you then -- you could either be 

proactive and do it yourself, or if you put out a 

report then unions or others who could file 

petitions could analyze that data. But if the 

individual data isn't available, the only people 

who could do any kind of characterization to look 

for commonality would be NIOSH. 

So that was our concern, is that you will 

know something but the individual won't, and so 

they won't move forward, and therefore there's 

some view that a class is being covered up 

because you can't let people know about it. 

MR. KATZ: But so -- I just want 

clarification on part of what you're saying. 

You're saying that when we let an individual know 

that we can't do their dose reconstruction, we 

tell them that they should file for a class. 

You're saying that they would be more persuaded 

to actually do that if they knew other 

individuals were in their same bag, than they 

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would be -- is that what you're saying? 

DR. ANDERSON: Well, if you get a letter back 

saying you your dose can't be reconstructed, does 

that mean de facto you're -- if you just say, oh, 

maybe I'm a special class, I'm going to ask you, 

NIOSH, to investigate whether I am in a special 

class. And all I have to do is say, okay, am I 

in a special class? Then you evaluate whether 

you're going to evaluate it, and you turn around 

and say, yes, we'll evaluate it. If that's the 

intent, then it's very easy. But if --

MR. KATZ: Right, but that part is, I hope, 

clear in the rule. In fact, in that case we are 

telling them that they should petition to be part 

of the Special Exposure Cohort, and there's no 

further consideration about the petition being 

evaluated. It will be evaluated. 

DR. ANDERSON: See, I don't think that's 

clear in there, that in fact everybody who you 

can't reconstruct their dose is --

MR. KATZ: I see, so --

DR. ANDERSON: -- all you've got to do is 

mail it back to you. 

MR. KATZ: Let me explain. And maybe this is 

addressed in the preamble, maybe it's not. But 

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the dose reconstruction rule states very clearly 

that whenever we can't do a dose reconstruction, 

we will provide them with the materials and 

information about filing to be part of the 

Special Exposure Cohort. That's part of the --

DR. ANDERSON: Yeah, but I mean to say --

MR. KATZ: -- dose reconstruction rule 

already. It's separate from this rule, but 

that's a guaranteed element of completing that 

dose reconstruction, and in effect not being able 

to. 

DR. ANDERSON: Yeah. I mean I guess the how 

to file is a different issue from --

MR. KATZ: That's what their --

DR. ANDERSON: -- you are eligible to be 

evaluated. 

MR. KATZ: And this Board actually gave us 

advice on this, and we took the Board's advice 

about giving them -- not just telling them that 

they're eligible, but in fact telling them how to 

do it and giving them the materials do to it. So 

that is part of the dose reconstruction rule 

already, to not just tell them they're eligible, 

but to give them materials to file, encourage 

them to file. And that part will happen. 

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203 

So I guess an individual might decide, well, 

I don't want to be bothered or whatever, but 

we're certainly going to encourage them to file, 

and we're giving them all the materials to file. 

And there's nothing more to be done. That 

petition will be evaluated by NIOSH, by the 

Board, by HHS. 

DR. ANDERSON: Okay. See, I'm confused by 

when you say materials. To me, that's the form 

you need to fill out, versus here is the 

rationale we've provided for you why you could be 

a class, and that you will then evaluate that, as 

opposed to they send it back and you say, no, we 

won't accept this --

MR. KATZ: No. 

DR. ANDERSON: -- evaluate this. 

DR. MELIUS: They do say they will accept it. 

DR. ANDERSON: Okay. 

MR. KATZ: It's a --

DR. MELIUS: I think what we were saying 

before is that should be as claimant-friendly as 

possible. 

MR. KATZ: Yes, and I --

DR. MELIUS: You're going to have survivors 

that have waited some length of time and so 

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204 

forth. 

The other part of that, though, I think would 

be useful is if you could publish in a nonidentifiable form sort of a listing of those 

people that you couldn't complete dose 

reconstructions on. That's my point about 

there's no really criteria out there for people 

to understand who that -- so for people --

MR. ELLIOTT: It gets in a class. 

DR. MELIUS: Yeah --

MR. ELLIOTT: How do we define the class? 

DR. MELIUS: Right. 

MR. ELLIOTT: We think there's a class here. 

DR. MELIUS: Yeah. 

MR. ELLIOTT: And we're going to have the 

Board review it after we've done our research to 

define the demographics of that class. And once 

the Board says, yeah, we agree, and then we go 

forward with announcement, publication --

DR. MELIUS: No, before that, though. I'm 

saying --

MR. ELLIOTT: Jim --

DR. MELIUS: -- it's when you have 

individuals of why you can't complete their dose 

reconstructions, can you publish or make 

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205 

available in some way that as a listing, not 

identifiable? 

MR. KATZ: Right, this is entirely separate. 

Jim's just wanting some accounting of when we 

can't do dose reconstructions, let the world know 

that we can't. 

DR. MELIUS: That way if I'm a potential -- a 

union, say, or somebody that would be -- or 

someone in a similar situation, maybe rather than 

applying individually, I say look, that's -- you 

ought to get together a petition and do that. 

You've already got some information on this. 

You've already made a preliminary finding. It 

should be easier to go through with. It would 

also, I think, help inform people about this on 

this case-by-case --

DR. ZIEMER: Mark. 

MR. GRIFFON: Just a question to follow up on 

Larry's part of it, which is once you have a 

class established and you release the criteria in 

the Federal Register, I'm wondering, in 

establishing that it seems to me that NIOSH may 

actually identify coworkers from the original --

as you're going to do this research you're going 

to identify potential people that would fall into 

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206 

that SEC. 

So I'm wondering about notification. 

Obviously once that SEC is released, defined and 

released to the Federal Register, people can 

apply and say that they meet it or don't meet it. 

But if you already know a group and found some --

maybe they didn't fail a dose reconstruction. 

Maybe you've never heard from them before, but 

you identify them in doing your coworker 

analysis. Would there be a proactive sort of 

notification process to reach out to those people 

and say, hey, in our -- just asking. 

MR. ELLIOTT: It's a point worth considering, 

but we've not examined it in that way as to 

whether or not we need a notification piece here. 

We have talked with Labor, and have an 

understanding of how they see their job in 

dealing with claims that come forward and 

identifying them -- oh, well, NIOSH has 

established or HHS has established a new class 

for the Special Exposure Cohort and this claimant 

fits into that, so we don't send it to NIOSH for 

dose reconstruction. It's got one of the 22 

cancers, they're awarded their compensation. And 

so we have that in place. 

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But we've not talked about or thought about 

or considered -– this is something we should, I 

think, take up and deliberate on. The risk you 

run is you don't know where to find some of these 

people. You may not know how to get at them. 

You miss people. But it's probably better -- a 

benefit rather than a detriment to do it. 

DR. ZIEMER: You're saying if you know 

already because you maybe interviewed them to try 

to reconstruct somebody else's dose or something 

like that. 

MR. GRIFFON: Yeah, or in just doing your 

analysis for, say, if one person fails, you can't 

reconstruct a dose, and in doing that analysis 

you find all these other coworkers. They may not 

have even applied through the process. 

DR. ZIEMER: They may not have cancer. 

MR. GRIFFON: May not have cancer, but you 

know that they fall into the Special Exposure 

Cohort. So rather than put the burden on -- I 

think it's just the proactive --

DR. ZIEMER: Okay, let me continue a moment. 

In section 83.1, Tony made the remark about 

making it clear to people that this is not an 

appeal process for individuals for whom dose 

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208 

reconstruction didn't lead to compensation. And 

we're actually going to suggest possibly adding a 

statement in 3.1 that says what are the purpose 

of the procedures, and we're suggesting to add a 

sentence or two that also says what the purpose 

is not, and it's not an appeal process. If you 

had a dose reconstruction that failed to lead to 

compensation, this is not plan B. So that's just 

a clarification for people to understand what 

this is about. 

Then in section 83.10, this is a section that 

gets very specific about some roles for this 

Board. And our small group felt like we were 

much too involved in the sort of day-to-day 

operation of the process, or in the loop too 

early. 

For example, in 83.10 subparagraph (b)(2) it 

talks about petitioners who fail to meet the 

requirements. If they have a petition that 

doesn't meet the requirements, and so they're 

going to be turned down, it basically says that 

they're going to be turned down -- this 

recommendation for turning them down is going to 

be reviewed by the Board, as if the Board is 

going to second-guess this in some way. It's 

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209 

already stated they don't meet the requirements 

of the petition. That's the basis for turning 

them down. We felt like that's a staff function 

at this point, and we were -- unless we 

misunderstood this. 

And then in the subparagraph (3) it says HHS 

will report the recommended finding and its basis 

to the Board. HHS will consider recommendations 

of the Board before producing a final decision on 

whether or not to select the petition. But we 

felt like at that point, we're not creating a new 

class. We're just saying somebody -- the 

petition didn't meet the requirements. If it 

doesn't meet the requirements, why do we need to 

even review it? 

MR. KATZ: Right. And the reason that's 

there -- and this is a valid issue for comment, 

particularly by the Board -- but that's there 

because it was our view that claimants would 

expect that they would get some sort of hearing 

by the Board because the Board's named in 

EEOICPA, and so on; that in their cases, then, 

for those individuals, if the Board didn't look 

at that decision they would feel like, well, I 

was supposed to have a chance with the Board, to 

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210 

petition the Board, and in fact I never even --

HHS never let me get to the Board. So that's --

that's why that's there. 

DR. ZIEMER: Well, and perhaps this needs 

further discussion, but is it really a petition 

to the Board, or is it a petition to HHS? 

MR. KATZ: Well, in the language of EEOICPA, 

in effect it's a petition to the Board. It's a 

petition to the Board to consider their class, in 

effect. But HHS -- there's prerogative here. 

HHS is given the role of considering these 

petitions to the full Board before advancing them 

to the Board, and you could read it to say that 

HHS has the right to decide without involving the 

Board where it doesn't believe a petition meets 

sort of basic requirements for being a valid 

petition. 

DR. ZIEMER: I think that was our point, that 

-- there's two prongs to this. One is the 

petition doesn't meet the requirements, so it's 

not going to go any further. The other is the 

petition does meet the requirements, and it's 

going to move up and has the potential of 

becoming a new class, which definitely requires 

some Board action. 

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211 

But we just wanted to raise this issue with 

the full Board. Our small group felt like the 

Board's involvement was too early here. We're 

getting more involved in the day-to-day 

management of that activity. And we haven't 

discussed this with the full Board, but we're 

just raising this issue and wanted to get some 

feedback. 

And then in item (4), or item (c), 83.10(c), 

NIOSH will present the petitions selected for 

evaluation to the Board, with plans specific to 

evaluating each petition. What we think is 

intended here, and it's not clear, is that it's 

petitions that NIOSH intends to evaluate, or 

maybe we both are. But this has to do with 

informing the Board that here's a petition we 

plan to evaluate, and here is the evaluation plan 

that we plan to use. 

Is that correct, Ted? 

MR. KATZ: That's completely correct. So the 

next step, after you've decided which petitions 

need to be evaluated, is to present those so 

you're aware of these are new petitions that are 

going to be coming up. You won't be having to 

address them at that point --

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212 

DR. ZIEMER: But this evaluation is NIOSH's 

evaluation? 

MR. KATZ: NIOSH is the first step, right. 

Exactly. 

DR. ZIEMER: It sounds like NIOSH is 

presenting this to the Board for evaluation. 

It's just a wording --

MR. KATZ: Okay. It's NIOSH that takes the 

first step at --

DR. ZIEMER: It's just informing us that you 

plan to evaluate it, and here’s the evaluation --

MR. KATZ: Right. The Board will be 

evaluating it later, too, so it's --

DR. ZIEMER: Right. 

MR. KATZ: The whole process of evaluation 

will have to occur. That’s what --

DR. ZIEMER: We're just asking for clarity 

there, so at this step it's the NIOSH evaluation. 

DR. MELIUS: If I read this, I think 

literally it says it takes two Board meetings to 

get something into an evaluation -- the first 

Board meeting for the Board to say go ahead, the 

second Board meeting for NIOSH to present its 

evaluation plan for the approved petition. 

MR. KATZ: No, because the Board doesn't have 

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213 

to say go ahead. So we will go ahead as soon as 

-- as soon as a petition meets, we will be going 

ahead. And when the next Board meeting arises, 

we will then go -- there'll be a generic plan for 

how we evaluate these, but we'll present specific 

plans when that Board occurs. But we'll have 

gone ahead. 

DR. MELIUS: Okay. I don't think it's 

completely clear in here. 

The other point, I think, going back to the 

earlier issue also, is that I think -- maybe this 

was my other meeting with you, the stakeholder's 

meeting -- but the idea that there's this 30-day 

period. If there's something missing in the 

application, you'll get back to the -- NIOSH will 

get back to the petitioner asking for whatever's 

missing, further information and so forth, and 

give them time to present that. So then it 

should be -- hopefully a lot of this stuff gets 

addressed -- either makes it or it doesn't at 

that point. 

MR. KATZ: That's right. That's right, 

that's not a 30-day period. It's as long as it 

takes between us and the petitioners. But we'll 

do what we can to help the petitioner do all the 

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214 

petitioner can. 

MR. ELLIOTT: For 83.10(4)(c), we just 

thought the Board would want to be -- would want 

to have an opportunity to weigh in on the plan, 

for a specific plan, the specific petition plan -

-

DR. ZIEMER: Yeah, I don't think we have 

trouble with that. We had more trouble with 

trying to figure out whether this was telling 

people that the Board is going to do the 

evaluation, NIOSH is presenting this to the Board 

for evaluation. It's just getting the wording 

clear that -- it needs to be will present its 

evaluation, NIOSH's evaluation package to the 

Board. It's a semantics thing there. 

And then later there's a Board review 

process. NIOSH comes back and says here's our 

findings, then we weigh in. And then conceivably 

NIOSH could say we turned it down, and the Board 

could say, well, we think it should go forward. 

Both could turn it down. Both could endorse it. 

And then it's reported to the Secretary. 

Now one question in 83.13, then, is the Board 

will review the petition and NIOSH evaluation at 

a meeting to which the petitioners are invited. 

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And we're just asking the question at this point, 

is it necessary to invite the petitioners to this 

meeting? Or does that -- would you only do that 

in cases where you thought there was going to be 

some really big issue that has -- we're 

concerned, particularly if there's 90 cases, that 

petitioners are going to want to come and not 

just tell you in two minutes what their petition 

is. 

MR. KATZ: The petitioner is likely to want 

to come if they see our report and the report is 

not an affirmative report. They're likely to 

want to be able to make a case to the Board. And 

since it's the Board they're petitioning, we 

thought they should have an opportunity to 

actually come before the Board, as opposed to 

being kept at, in effect, at arm's length with us 

in between. 

DR. ANDERSON: I think our discussion was 

more if your recommendation is to accept, then I 

think our sense on the Board is why would we 

necessarily stand in the way of that? Why would 

you ask somebody to come in to make an 

impassioned plea when the decision is to move 

forward? 

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MR. KATZ: Well, in an affirmative case, 

they're not likely to -- they don't have a lot of 

motivation to come in and make a plea. But I 

suppose they could still want to address you. 

DR. ANDERSON: But we could turn down your 

proposed --

MR. KATZ: You could reject our --

DR. MELIUS: See, I don't think there's a way 

of avoiding inviting them. 

MR. KATZ: I just think that's a necessary 

element. 

DR. MELIUS: There's also issues of --

remember, it’s not just the petition, but it's 

also --

DR. ZIEMER: This is more than inviting. 

This is inviting them to present views and 

evidence. And suddenly you're going to have 

attorneys present, and then the Board's going to 

say, well, then do we need attorneys present? It 

seems to me that this starts looking more and 

more like a formal adjudication process of a 

document. 

What is the wording that is driving this in 

the original -- do you have the original 

legislation that says the -- that talks about 

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petitioning the Board versus --

MR. KATZ: It's actually in the rule. 

DR. ZIEMER: But what are the words? 

MR. KATZ: I have it here. Liz just handed 

it to me, so let me just read it to you verbatim. 

DR. ZIEMER: While you're looking at that, 

because it's really the Secretary that makes the 

decision; the Board does not make a decision. 

It's one other piece of information that the 

Secretary weighs together with the staff 

recommendation. So I would sort of argue, is 

that really a petition to the Board if the Board 

doesn't make the decision? The Board makes a 

recommendation. It looks more like a petition to 

the Secretary. Otherwise, the only thing the 

Secretary could do is accept that, unless they're 

Sally's got a question, while they're --

MS. GADOLA: I'm good at complicating things. 

I brought this up yesterday, because it also says 

in the rule about the silica and about silicosis. 

And the way that I read it is that it is also 

possible for people that have silicosis to also 

petition for a special cohort. I know that the 

rest of this all talks about radiation, but when 

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you go right back to the very beginning it says 

people that worked with silica and developed 

silicosis with the Department of Energy. And so 

if there is a special cohort out there, can they 

come in? No? 

MR. ELLIOTT: Somebody'd better help me out 

here, but I don't believe the Act specifies the 

Special Exposure Cohort to include silicosis, 

silicosis or beryllium. It's only cancer. It's 

radiation injury only. And whatever 

Congressional rationale for all of that was, we'd 

have to go back to Dave Michaels or Richard 

Miller or somebody else. But the Special 

Exposure Cohort that's been established is for 

radiation injury -- i.e., cancer. Not a 

deterministic effect, but stochastic effects. 

DR. ANDERSON: Because it's tied into dose 

reconstruction. 

MR. ELLIOTT: That's right. 

DR. ANDERSON: You don't have to do dose 

reconstruction for silicosis. 

DR. ZIEMER: Right. And endangered health 

for this thing is defined as reasonable 

likelihood that radiation dose may have caused a 

specified cancer. 

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MS. GADOLA: I guess I was reading it when it 

talks about the background and the statutory 

authority right at the beginning. And when it 

talks about that it was established benefits as 

compensation to covered employees suffering from 

designated illnesses occurred as a result of 

their exposure to radiation, beryllium, or silica 

while in the performance of duty for the 

Department of Energy. 

MR. ELLIOTT: But that is referring to the 

Act itself, not to the Special Exposure Cohort. 

That's the background on why the Act -- that's 

the enabling legislation. 

MS. GADOLA: And they did establish one 

special cohort. 

MR. ELLIOTT: There's only one Special 

Exposure Cohort. That's it. One. And we're 

talking about adding classes to that Special 

Exposure Cohort, and those classes have to have 

had their health endangered by radiation exposure 

where we cannot do a dose reconstruction. Simply 

put, that's where we're bound by the Act. 

MS. GADOLA: Okay. I just wanted to have it 

clarified again. 

MR. ELLIOTT: If I can, I think Liz has 

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pointed out -- this may be what they're 

discussing back there -- but of the Act, this is 

the EEOICPA Act, Section 36.26, Designation of 

Additional Members of the Special Exposure 

Cohort, (a), subsection (a), Advice on Additional 

Members: 

(Reading) The Advisory Board on Radiation and 

Worker Health under Section 36.24 shall advise 

the President whether there is a class of 

employees at any Department of Energy facility 

who likely were exposed to radiation at that 

facility, but for whom it is not feasible to 

estimate with sufficient accuracy the radiation 

dose they received. 

So Ted, is that where you're --

MR. KATZ: Here it is. And it's the way it's 

written, it's tucked under, so you have to refer 

to another paragraph to know what they're talking 

about. But in paragraph 3(1) it says: 

(Reading) The President shall request advice 

under paragraph 1 -- that's what I think you were 

reading -- after consideration of petitions by 

classes of employees described in that paragraph 

for such advice. 

So petitioners are petitioning for advice by 

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the Board. That's what their petition is for, 

advice for their -- they want the Board to advise 

the President about a class of employees. Does 

that -- it is actually straightforward, except 

it's not written neatly. 

MR. ELLIOTT: And the President has delegated 

that duty to the Secretary of HHS. 

DR. MELIUS: Does that explain this 

appearance and present evidence portion of it? 

That's my -- I think that's our question. It's 

not -- that actually sounds to me --

DR. ZIEMER: My question had to do with who 

is the petition to. 

DR. MELIUS: Right. 

DR. ZIEMER: That's your point, too, then. 

The President shall request advice under 

paragraph 1 after consideration of petitions --

this is the President after consideration of 

petitions, but now HHS Secretary becomes the 

surrogate for the President, so he's considering 

the petitions in that paragraph. 

DR. ANDERSON: Asking for advice. 

MR. PRESLEY: But would they not come before 

the Board and present their case, and then we 

would be the ones to go back to the Secretary of 

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Health and Human Services with advice on who? 

That's the way I understand it. 

DR. ZIEMER: Well, I don't know if we can --

I think the staff has interpreted this to mean 

that the petitions come to the Board. 

MR. KATZ: The petitions are addressed to the 

Board, in effect. By this language --

DR. ZIEMER: By this language. 

MR. KATZ: Yes. 

DR. ZIEMER: In the law. 

MR. KATZ: Right. 

DR. ZIEMER: Yeah. I think I'm asking 

whether -- I think it could easily be interpreted 

differently than that. 

The Advisory Board advises the President --

i.e., the Secretary of Health and Human Services 

-- whether there's a class of employees for whom 

it's not feasible to estimate dose. The advice 

of the Advisory Board shall be based on exposure 

assessment by health professionals, and so on. 

And the President shall request advice after 

consideration of petitions. It doesn't say 

petitions to whom, but it does say petitions by 

classes of employees in that paragraph. 

MR. KATZ: It's petitions for such advice, 

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and the advice is coming from the Board, so it's 

for Board advice. This is what these are 

petitions for, for Board advice. 

DR. ZIEMER: I don't see where you're linking 

that. 

MR. KATZ: It's the rest of that sentence. 

After consideration of petitions by classes of 

employees described in that paragraph for such 

advice, the last three words of that sentence. 

DR. ZIEMER: Shall request advice under 

paragraph 1? 

DR. ANDERSON: A mistake has been made. 

(Laughter) 

MR. GRIFFON: Yes, we're here. 

UNIDENTIFIED: About those submissions for 

extension of term. 

(Laughter) 

UNIDENTIFIED: You want to back down now? 

DR. ANDERSON: August 4th is looking real 

good. 

MR. ELLIOTT: I sense the Board interest to 

get out of a little work here. Welcome to my 

world. 

(Laughter) 

DR. MELIUS: But don't worry, Larry, you'll 

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suffer under this one, too. 

DR. ZIEMER: To me, this wording is not at 

all clear cut, but I think --

MR. PRESLEY: Let Mary speak. 

MS. ARMSTRONG: As I understand it, the 

concern is having a Board meeting turn into a 

hearing. 

DR. ZIEMER: Is the petitioner really 

petitioning the Board, or is the petitioner 

petitioning the Health and Human Services 

Secretary? Because that is the person who makes 

the decision, based on advice from (inaudible). 

MS. ARMSTRONG: The Secretary -- and I'm just 

saying he at this point because the Secretary is 

a he at this point -- makes the final 

determination. That's clear from the statute. 

It says that the Secretary determines upon advice 

of the Board. At this point we have it set up 

that, because of the wording in the statute, that 

the petition is for a petition for that process 

to begin, including the petition to the Board for 

that advice. 

Your concern is you don't want this Board 

meeting turning into a hearing. These Board 

meetings are public. There's always going to be 

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-- the petitioner, if they want to sit in the 

audience and make their public comment, that's 

what FACA is. These are public meetings. If 

there's a concern that we're going to have a 

trial type hearing at these particular meetings, 

we can take a look at this and try to make sure 

that this is a determination based on a written 

record with an opportunity for a public comment 

period, but not necessarily a representation and 

hearings and witnesses, et cetera. 

Is that what the concern is, basically? 

(Affirmative nods) 

DR. ZIEMER: Actually, what our subcommittee 

-- and again, we're just raising this to the full 

Board as to what our -– our concern was really 

with the paragraph that says that petitioners are 

going to be invited to present views and evidence 

at a Board meeting. 

MS. ARMSTRONG: And what you, I think, were 

wanting is that all evidence be presented to the 

Agency at the time the petition is made, and that 

you all will be able to make your recommendations 

based upon whatever has been presented to the 

Agency. Is that basically --

DR. ZIEMER: Well, I'm not even sure we got 

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that far. We really were concerned about the 

implications of this, because it starts to look 

like an adjudicatory hearing. 

MS. ARMSTRONG: A hearing, or a trial-type 

hearing. 

DR. ZIEMER: And maybe the intent there was 

simply that this is going to be on the docket for 

that meeting, and that you're invited to attend 

and listen to the deliberations and whatever. 

The wording in here looks very much like it's a 

formal hearing because it talks about presenting 

evidence and so forth. 

MS. ARMSTRONG: Okay. 

DR. ZIEMER: We're only raising it today as a 

concern. We don't have a proposed solution, but 

I think we would like to think about it and maybe 

have the staff --

MS. ARMSTRONG: And have us think about it, 

too. 

DR. ZIEMER: I don't think the issue of who 

to petition; that's sort of secondary. 

MS. ARMSTRONG: As much as how the hearing or 

how the Board's consideration --

DR. ZIEMER: (Inaudible) -- the issue remains 

the same. Does our thing become a formal 

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hearing? 

MS. ARMSTRONG: Right. Okay. And I think 

that would --

DR. ZIEMER: If we can find words to take 

care of that, at least for our subgroup that was 

what our concern was. 

MS. ARMSTRONG: And I guess I should identify 

myself for the record. I'm Mary Armstrong. I'm 

the senior attorney for NIOSH. 

DR. ZIEMER: And our concern is not so much 

getting out of work, as much as it is when -- for 

example, it was suggested there might be 90 such 

petitions. And we're going to have a hearing 

that takes less than an hour, there's 90 hours. 

Well, let's see, that's only about ten days a 

year out of -- that's about how many days we'll 

meet this year. 

MS. ARMSTRONG: Right. Right. I can 

understand the concern, and I think we need to 

look at how this is structured. 

DR. ZIEMER: And then -- let's see. Well, I 

think that took care of sort of the major things 

we were wrestling with. There are probably some 

other details, but I'm going to suggest to the 

Board that if it's agreeable we'll ask the four 

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individuals -- and I'll take the lead in this --

to put some of this stuff in more formal words 

for our next meeting, and we'll work amongst 

ourselves and then prepare a straw man, if that's 

agreeable, with any other input that --

DR. ANDERSON: Yeah. 

DR. MELIUS: Yeah. Who should we get that 

input to, that's my question. 

DR. ZIEMER: Me. 

DR. MELIUS: Okay. 

MR. GRIFFON: I was just going to ask --

DR. ZIEMER: I don't want to volunteer Tony. 

MR. GRIFFON: I was going to ask if -- it was 

a working group, so maybe minutes of your -- did 

you take minutes? 

DR. ZIEMER: It was really an ad hoc --

MR. GRIFFON: It was ad hoc, okay. 

DR. ZIEMER: -- group. But we can formalize 

it, I think, if that's necessary. I'll simply 

exercise the prerogative to appoint this as a 

working group. And it's Henry and Wanda and Tony 

and me. We can probably add another person if 

somebody wants to be involved -- okay, and Sally 

-- and we'll work up some straw man words for the 

next meeting. 

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MR. GRIFFON: Did you consider other issues, 

particularly one of my favorite issues that I've 

been talking to Jim Neton to some extent on, with 

sufficient accuracy and how that was handled. 

And also definitions of feasibility. I don't 

know if you got around to discussing those. 

DR. ZIEMER: We didn't. 

MR. GRIFFON: I know we brought them up as 

issues. 

DR. ZIEMER: And if there are particular 

places -- what we're trying to do is say where 

would you put some of these things, and what 

would you say. And if you have suggestions --

insert the following -- we can add that. Thank 

you. 

MR. ELLIOTT: I'd just remind, as a working 

group, whatever your deliberations come to be and 

you exchange those, we can do that on the web 

site because we have to make that public. 

DR. ZIEMER: Right. 

MR. ELLIOTT: So keep that in mind. 

DR. ZIEMER: So I'll copy you on anything 

that we send out. 

Now let's -- do we need a break yet? 

UNIDENTIFIED: Yes, we’re over. 

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DR. ZIEMER: Oh, we do. Can we make this 

break fairly fast? 

How long will your report take, Mark? 

MR. GRIFFON: I hope not long. It's similar 

to the presentation, so we just refined some 

language around those four major --

DR. ZIEMER: We don't need final action 

today, or do we? 

MR. GRIFFON: No. We did word it in a formal 

recommendation, but we wanted to do our follow-up 

with NIOSH. 

DR. ZIEMER: Let's take ten, and then we'll 

reconvene. 

(Whereupon, a break was taken at 3:22 p.m.) 

DR. ZIEMER: Brian Thomas has some additional 

information, I think, on why -- perhaps it's why 

disks cannot be made available. 

Is that a good way to put it, Brian? 

MR. THOMAS: I grabbed my laptop computer 

after this whole thing came up just a little 

while ago, and I was trying to look at the 

feasibility of putting some tables on line and 

trying to answer some of the questions that Mark 

had. What Mark was saying, that he liked the CD 

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version because it provides all the data at once 

without having to select different cancer types 

and ages at exposure. And in fact, the CD 

version doesn't do that. We're kind of limited 

by the same sorts of things that we have on the 

web now. Let me bring it up. 

We had thought at some point that we'd like 

to have printed tables, printed tables had been 

requested of us. And at that point we got to 

thinking about how in the world could that 

happen, because what we're talking about here is 

three and four-dimensional tables. There’s just 

lots of data. That was one of the main reasons 

we went away from the look-up tables that they 

did back in 1985, because now this thing is so 

much more complex. And let me show you what I 

mean. 

I had showed you this earlier, the way the 

different cancers are grouped, but let's just 

look at this again. Group one cancers, the data 

here is a function of age at exposure, and 

there's 70 of those; so just imagine now in Excel 

you have 70 rows. Attained age, we now take 

those up, I think, to 80, and so there's 80. So 

you've got 70 by 80, that sounds simple. 

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But then you've got all the uncertainty. And 

so if you put at the very minimum five of the 

percentiles -- the 1st, 5th, 50th, 95th, and 99th 

-– then that's five more tables just like that. 

And on top of that, we have gender. And so just 

immediately, with all the group one cancers and 

most all of the group two cancers, you have four 

dimensions to try to print out or to provide on 

the web. Group three cancers, some of those are 

a little simpler and could be on one page. 

But that's the reason that we had gone with 

the approach that we have on the web now, which 

is doing a calculation for one age at exposure 

and one time since exposure, and it provides all 

your uncertainty with it. Now, what the web 

version or what this version does, what we looked 

at before is that it brings it in still just for 

one age at exposure, time since exposure, 

whatever's selected on that main screen is all we 

see here in this column. 

And so I'm sensing what Mark's question is 

here -- and so I'm going to go right back to that 

main data real quickly -- and he's thinking what 

about this 101 values? It's really simple there. 

And in effect, it is. But you notice there there 

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is no attained age effect, there's no age at 

exposure there yet. That's a multiplicative 

factor. It's another uncertain factor that we 

apply after this point. 

And so these values could easily be provided, 

but then there would need to be this 

multiplication of the additional factor in some 

cases. And where to apply that and what that 

factor is is discussed in that PDF file that 

comes along with this. 

MR. GRIFFON: Can I just -- is that the --

that would be the newly-analyzed Thompson data? 

MR. THOMAS: Yes. 

MR. GRIFFON: I think, for me, that's useful, 

too. Also, I guess I'm thinking back to 2.1, 

you're saying that in those cases the tables were 

constructed differently, so therefore you had --

I think you had tables going across for attained 

age, or for age at exposure versus your --

MR. THOMAS: That's right. For a number of 

the cancer types in version 2.1, the way we 

handled attained age and age at exposure was 

different. And so these tables did include all 

the information. And one of the nice things I 

had mentioned about Analytica is the way that it 

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handles multi-dimensional arrays. But that's 

hard to print that out. It's hard to visualize 

four dimensions for someone. 

So anyway, that was my only comment. We can 

now --

MR. GRIFFON: That data right there would 

satisfy my need. I think that data, along with 

the PDF document describing the equations and the 

age-dependency on those various equations for 

cancer groups, you can get from the beginning 

point to your endpoint. So that would suffice 

what I was requesting. 

MR. THOMAS: Okay. And so maybe what we 

could do instead of the 101 values there --

because what we'd have to do with that as well is 

provide you with the 101 probabilities that went 

along with it -- but perhaps we could provide a 

smaller number of those. And then with that 

information, plus what you'd have with that PDF 

file, you could essentially work through the 

calculation yourself. 

DR. ZIEMER: Well, let me suggest that 

perhaps you folks can discuss that further, and 

if others want copies they can work on that or 

talk to you about it. 

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Thank you very much. 

MR. THOMAS: Sure. 

DR. ZIEMER: I'd like to ask Mark Griffon now 

to present the status of your recommendations 

from the working group. 

MR. GRIFFON: I think we worked on this 

yesterday afternoon in our working group. And we 

tried to put -- this is again a straw man of some 

recommendations of what I presented in the 

morning yesterday, and basically broke up into 

three groups: the independent panel, this notion 

of forming the independent panel; the case 

selection; and then the scope of work for the 

panel. 

First, the working group recommends having a 

review panel with independent experts, along with 

Board representation and Board oversight. That's 

exactly as we stated yesterday in the 

presentation. The working group proposes that 

the panel be comprised of two groups, each 

consisting of one expert -- parentheses, 

contractor -- and two Board members. And in 

addition to that, we're recommending four to six 

experts in total be identified so that they're 

available on an as-needed basis. 

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The reason for that is we're envisioning --

and if I get this wrong from the rest of the 

group, please chime in -- but we envisioned we 

might need to rotate subgroups. We might need 

certain expertise at certain sites or certain --

for example, like accelerator exposures or 

something like that. So you may have to rotate 

these experts on these two groups. 

And the reason for the two groups, at least 

initially, we felt we've got to start at least 

with two groups just to be able to scale up for 

the number of cases we're going to be reviewing. 

And we may need more, but we also recognize the 

total pool that we may have to work from for 

experts may be limited. So we have -- that's 

where we came out on those numbers. And again, 

this being a draft. 

Why don't I go through it all, then people 

can comment on it and give us --

The groups within the -- this is as mentioned 

yesterday -- the groups within the panel would 

work separately, but as a control we’d give the 

same case to both groups and see how they came 

out on it -- hopefully they came out the same --

for quality control purpose. 

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Case selection was the next topic we tried to 

cover. The workgroup recommends that the Board 

should select the cases for review. Again, that 

was in the presentation yesterday. The workgroup 

recommends a stratified sampling of cases based 

on the following parameters: 

The site -- and when we said by site, we do 

say weighted based on number of claims per site. 

And we also felt that we might -- we want to 

revisit this a little bit, because we didn't know 

the distribution by sites. We didn't have that 

data with us yesterday to look at. But at least 

some parameter based on site, we thought was 

important. Some percentage of the awarded claims 

-- that's awarded claims; some percentage of 

denied claims; some percentage of the cases for 

which the dose could not be reconstructed, as 

well. 

And I just wanted to mention one thing we did 

consider initially was -- and I think Henry 

brought it up yesterday -- was the idea of having 

some sort of appeals process. And if people 

appeal their dose reconstruction, then we might 

sample a group, might sample from that group of 

people that appealed. 

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Larry met with us yesterday about -- that 

basically reviewing appeals was not a good idea 

because it's getting into the adjudication 

process, right. However -- and it's not in our 

parameters here, but I'm just throwing out there; 

it's something we discussed, and I still feel 

like we might want to consider it -- is if we had 

a group of the appeals pooled and we sampled them 

on a deidentified basis, it might be a parameter 

we might want to sample from. And I don't know 

if that steps over that line, and I would ask for 

advice on that. But it's something we discussed. 

It didn't make our recommendation here, but it's 

something that I was interested in and just 

wanted to throw it out there for discussion 

possibly. 

The workgroup also recommended that the first 

ten cases which are completed be assessed by the 

panel. Part of this was we understand, or at 

least we get the sense, that the first ten cases 

that are completed are likely to be awarded, and 

probably low-hanging fruit, if I can use that 

term. But we thought it might be beneficial at 

least to get the independent panel, their feet 

wet on what these cases are going to look like, 

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how much time may be involved. Although these 

may be simpler cases, it was a starting point to 

get the panel engaged on these cases. So that 

was a recommendation. 

Finally, the scope and protocol. The 

workgroup recommends that the Board establish the 

scope of work and the protocols for the panel. 

The workgroup recommends that the scope include 

the following: 

One -- and this was not in our presentation 

yesterday, but it came from comments -- the panel 

should assess the methods for dose 

reconstructions. And that comes from the statute 

where there were actually two items, two tasks. 

Second, the panel should determine whether or 

not the dose reconstruction -- or the 

reconstruction of the dose provides a reasonable 

estimate of the dose, at least as needed to 

determine eligibility. 

Three, the panel should determine whether or 

not the assumptions, individual case assumptions 

or assumptions applicable to multiple cases, made 

for the dose reconstruction are credible. 

And finally, the panel should determine 

whether or not the data from DOE or other source 

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is of sufficient quality necessary to obtain a 

reasonable estimate of dose. All right. 

And I think that's it. That's what we boiled 

things down to as a start of the recommendation 

for this. 

DR. ZIEMER: This recommendation, in essence, 

comes to the full Board as a recommended 

procedure for the Board to use in going forward. 

Keep in mind that if it is adopted it can be 

modified at any time. This is not set in stone 

forever. It could be viewed as a starting 

procedure, that we would expect as we gained 

experience to modify, add to, change, and so on. 

Further, this is not a recommendation to the 

Secretary or anything like that. This is an 

internal document. 

MR. GRIFFON: We feel --

DR. ZIEMER: The existence of a procedure to 

do this could, of course, be reported to the 

Secretary as part of our ongoing work, and the 

fact that this is being done. 

But I guess what I would ask the Board today 

is are you ready to adopt this now, or do you 

feel like you need more time to look at it, again 

keeping in mind you could adopt this today and 

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change it at the next meeting, or modify it? 

This is not a once for all thing. 

DR. MELIUS: I would suggest that we do adopt 

it, recognizing that there will be some changes 

along the way. At the next meeting the workgroup 

is going to be going over some of the records, 

and may deal with some of the procedural issues 

in more detail and so forth. But at the same 

time I think, since some outside consultants need 

to be hired and we know that's going to take some 

time, that we get started on this. 

So I really think we should try to adopt 

these recommendations at this meeting so that we 

can at least get that part of the process going, 

have a basic understanding of the parameters of 

the review, and so through the August meeting 

we'll be able to get underway a little bit more 

with this process. 

DR. ZIEMER: Thank you. 

Wanda. 

MS. MUNN: I guess I'm not really wild about 

what we're seeing here. I think an objective 

reader could probably, with appropriate selection 

of a few numbers, work into two FTEs for the next 

year, given this. And maybe that's a part of the 

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objective. I don't know. 

I'm really concerned, first of all, that any, 

for example, search for outside consultants has 

to come from somewhere. Whether this Board is 

expected to do this or whether this is going to 

fall on staff again, while they're out there 

trying to expedite all this other stuff that 

we're asking them to do, go out and also do a 

worldwide search for the appropriate experts to 

fit on here. 

And I had thought that our earlier 

discussions had focused around the possibility of 

a very small number of cases being overviewed, 

with perhaps a couple of experts and possibly one 

member of this Board. I was a little surprised 

to see two Board members and a hired gun being 

proposed. 

I understand -- I think I understand -- what 

the workgroup is trying to do here. But I really 

have to express some reservations about the 

extent of what I think I see here. 

DR. ZIEMER: Larry, do you want to comment on 

that? 

And Mark, you may wish to respond. 

MR. ELLIOTT: I appreciate your comments, 

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Wanda, but I am very pleased to see this. I 

think that we need to have this, because it falls 

upon us at NIOSH to put in place the support to 

the Board and these contractors. And the sooner 

we can get started on that, the sooner the Board 

can start its review of dose reconstructions. 

And I don't see that's an inordinate amount of 

resources that's being requested here. I think 

it's an appropriate amount at this time, and 

certainly can be modified as we go forward, as 

needed. 

I would also like to make sure that you 

understand that the first ten cases that are 

going to be completed that we're working on now, 

they are the low-hanging fruit, but they're both 

extremes. So the first ten are going to 

represent awards and denials -- we think. We 

think --

UNIDENTIFIED: Parenthetically, it might be 

the easier ones, then, right? 

MR. ELLIOTT: We think. We don't know how 

they're all going to shake out, and which of the 

first ten is going to be really representative. 

But we're working on those that we think are 

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compensable cases. 

And the last thing I'd like to comment on is 

your -- what didn't make this list. I would just 

ask you -- I know the workgroup took to heart 

what Mary had to say. And I would point to the 

fact that you are looking at denied cases, and in 

those denied cases you are going to see some that 

represent those that go forward for appeals. 

That, I think, should be sufficient to attend to 

your interest about what an appealed denial looks 

like versus a denial that somebody just said, 

okay, I accept it. So I would ask you to make 

sure you consider Mary's advice and counsel on --

MR. GRIFFON: Well, I actually think we, as a 

group, I think the majority was in that opinion. 

And that's why I presented it kind of as a 

minority. And I'm not sure where I come down on 

it yet. I just wanted to leave it on the table a 

little bit, and partially because -- Henry 

introduced that concept, so it did some up as a 

comment yesterday from the Board, and so I didn't 

want to just rule it out from there. 

Also partially because I felt like maybe that 

was at least some indirect way that we were 

paying attention to those that did appeal the 

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process, without stepping over the bounds of the 

adjudicatory process. That was another thought 

in my mind, was that it was a way -- while we are 

sampling from -- we may not be -- denials, but if 

we could say we were sampling from appeals that 

may still not satisfy that individual that 

appealed, because we may not get his or her case. 

But it was sort of one way to pay attention 

specifically to that subset of denials. I hear 

what you're saying, but --

The other thing I wanted to respond to was --

well, two things. One, I think that I just want 

clarification. I think Wanda's question about 

who is going to find these experts, and we have 

been going around on this, and who are going to 

be the available pool of experts that can do this 

work. But I think that the Board -- it is a 

Board task to identify the experts. It's NIOSH's 

role to contract with them, certainly. But I 

think if this panel's to have independent 

expertise to review NIOSH, I think we have to 

make sure that these are our picks, the Board's 

picks. I think that's a very important 

distinction in defining independence for this 

panel. I'll leave it at that. 

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Then the other question about the amount of 

work and the two full-time equivalents, Wanda, we 

specifically -- because we had this discussion, 

too. And part of the reason we left out in 

yesterday's presentation, I put down a tiered 

approach of different levels at which we might 

review cases. And we just said, geez, at that 

third level, the most in-depth level, it's 

getting into a lot of work. And before we can 

even get down into those kind of protocols, we 

thought it wise to go to NIOSH and review some 

real cases and see actually what the magnitude of 

what we're asking for is. 

So I thought that we tried to stick to the 

broad scope in protocol rather than -- but we 

still want to define, and that's where this would 

just be a first draft of a procedure or 

something, but we want to further define 

protocols. And then I think the Board will 

respond to those protocols as well. 

DR. ZIEMER: Mark, in presenting this you 

didn't explicitly recommend its adoption. But I 

think that was implied in the presentation, and 

since this is a subgroup of the Board that's 

recommending its adoption that becomes an 

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official motion. I'm going to consider it as 

such. 

It doesn't require a second, since it's from 

an official body of the Board. And we've already 

had some discussion, but adoption of this 

protocol as a procedure for moving forward is 

officially on the table for discussion. 

Further -- Jim. 

DR. MELIUS: I have another plan. It's not 

directly relevant to -- concerning the motion. 

So we can either do it now or do it later, but 

one -- so stop me if you want to, into this. It 

shouldn't take long. 

One way around this dilemma, this getting 

involved in an appeals process and so forth, is 

that there's certainly also -- there's a back and 

forth that goes on between NIOSH and the claimant 

during the dose reconstruction process. And 

there'd be awareness on the part of the NIOSH 

staff that there's some dispute over some of the 

factual information, or there may be a 

particularly difficult technical issue involved 

in the dose reconstruction or whatever. 

It would seem to me that there should be a 

way for NIOSH to refer some of these issues into 

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this review Board group to look at in a way that 

would address these, short of the appeals process 

and staying out of that appeals process. And I 

think that may be a way of also helping with the 

credibility of the process. Because if there is 

this kind of issue that's in dispute, or sort of 

new area or whatever, conflicting approaches or 

whatever, that having -- the Board having 

reviewed it as part of the process, I think, may 

be helpful. 

And I'd like -- I guess I would request that 

Larry and Jim and other people sort of explore 

ways of doing that, again keeping us out of the 

appeals process. 

DR. ZIEMER: Right, you want to be sure that 

we're simply reviewing the process, and not part 

of the process. 

MR. ELLIOTT: I guess that would be my 

concern. I appreciate your comment, Jim, and I 

think it merits our consideration and discussion. 

But we do want to do that. You're to review 

completed dose reconstructions. And I don't know 

if that really -- we need to talk about that. We 

need to get general counsel's advice on that as 

well. 

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DR. ZIEMER: Well, again, as experience is 

gained, we'll have some further insights. 

DR. NETON: I would point out, in a random 

sampling process you're going to run across 

these, I guess what you’d consider contentious 

dose reconstructions, because the administrative 

record that is associated with all of these cases 

has every single piece of correspondence and 

transmittal and whatever we've done in that 

administrative record. So you will, on a random 

basis at least, tend to run into these cases in 

your sampling. 

DR. MELIUS: I guess it's when they're 

contentious in a technical way or something, not 

as -- as opposed to -- I think that's what we're 

trying to get at, process for you to access us, 

because those are the ones where the credibility 

of the process is more at stake than -- if 

somebody's going to appeal --

DR. ZIEMER: Well, and there may be issues 

that can be brought to the Board in a generic 

fashion that are triggered by a particular --

DR. MELIUS: Right. 

MR. ELLIOTT: It may not be claim-specific, 

but methodologic issue-specific. 

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DR. MELIUS: Yeah. 

MR. ELLIOTT: Maybe that's the way to get at. 

But it's something we need -- we certainly should 

look at, and I agree. But I'm worried about --

we can't violate this what we consider to be the 

development of the claim and the administrative 

record that goes forward, and that's what you 

need to review as a completed dose 

reconstruction. 

DR. ZIEMER: Roy has a comment. 

DR. DEHART: Can I call for the vote? I'm 

having to leave. 

DR. ZIEMER: Yeah. The question’s been 

called for. I'm going to take that as an 

informal call for the question. 

DR. DEHART: Yes, it is. 

DR. ZIEMER: We're not going to vote on 

limiting debate. 

All who favor adopting this procedure, say 

aye. 

(Ayes respond) 

DR. ZIEMER: All opposed, say no. 

(No response) 

DR. ZIEMER: The procedure is adopted. 

Thank you very much, Mark, and the working 

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group for that. 

MR. ELLIOTT: If I could make one more 

comment, and that is the surrounding -- I 

appreciate the Board's need to be independent and 

identify, but it's a procurement issue. So we're 

going to have to work together on how we put that 

in place. There are certain ways we can do sole 

source, and there's certain ways we can't do sole 

source. We also have to wait and see what this 

pool of available remaining experts looks like. 

DR. ZIEMER: As the Chair packs up his things 

to catch a plane, I'm going to ask for a motion 

to adjourn. 

MR. GRIFFON: Motion to adjourn. 

DR. MELIUS: We all want to spend time 

discussing that. 

(Laughter) 

DR. ZIEMER: All in favor will head out. 

(Whereupon, the meeting was adjourned at 

3:58 p.m.) 

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___________________________

253 

C E R T I F I C A T E 

STATE OF GEORGIA )

)

COUNTY OF DEKALB ) 

I, KIM S. NEWSOM, being a Certified Court 

Reporter in and for the State of Georgia, do hereby 

certify that the foregoing transcript, consisting of 

270 pages, was reduced to typewriting by me personally 

or under my direct supervision, and is a true, 

complete, and correct transcript of the aforesaid 

proceedings reported by me. 

I further certify that I am not related to, 

employed by, counsel to, or attorney for any parties, 

attorneys, or counsel involved herein; nor am I 

financially interested in this matter. 

WITNESS MY HAND AND OFFICIAL SEAL this 23rd day of 

July, 2002. 

KIM S. NEWSOM, CCR-CVR

CCR No. B-1642 

(SEAL)