NEAL R. GROSS

COURT REPORTERS AND TRANSCRIBERS 

1323 RHODE ISLAND AVE., N.W. 

(202) 234-4433 WASHINGTON, D.C. 20005-3701 www.nealrgross.com 

U.S. Department of Health and Human Services

Centers for Disease Control

National Institute for Occupational

Safety and Health

National Institute for Occupational Safety and 

Health

Advisory Board on Radiation and Worker Health

Joint Meeting of the Savannah River Site (SRS)

And

Special Exposure Cohort (SEC) Issues

Work Groups

Friday

November 20, 2020 

The Work Groups convened at 10:30 a.m., Eastern 

Standard Time, via video teleconference, Henry 

Anderson and Bradley Clawson, Co-Chairs, presiding.
2 

Present: 

Henry Anderson, Co-Chair

Bradley P. Clawson, Co-Chair

Josie Beach, Member

James E. Lockey, Member

Genevieve S. Roessler, Member

Phillip Schofield, Member

Paul L. Ziemer, Member

Also Present: 

Rashaun Roberts, Designated Federal Official

Nancy Adams, NIOSH Contractor

Matt Arno, ORAU

Bob Barton, SC&A

Elizabeth Brackett, ORAU

Ron Buchanan, SC&A

Zaida Burgos, CDC

Grady Calhoun, NIOSH/ORAU

John Cardarelli, NIOSH/ORAU

Nancy Chalmers, MJW Companies

Joe Fitzgerald, SC&A

Roger Halsey, ORAU

Patrick Kelly, SC&A

Joyce Lipsztein, SC&A

Mike Mahathy, ORAU

Jenny Naylor, HHS OGC

Chuck Nelson, NIOSH/ORAU

LaVon Rutherford, NIOSH/ORAU

Tim Taulbee, NIOSH/ORAU
3 

Contents

National Institute for Occupational Safety and 

Health Advisory Board on Radiation and Worker 

Health Joint Meeting of the Savannah River Site 

(SRS) And Special Exposure Cohort (SEC) Issues 

Work Groups Friday November 20, 2020 1

Welcome and Roll Call/Introductions 4

Issue 5: Stratification of Co-Exposure Models 

(NIOSH "Stratification Refinement" White 

Paper) 6

Issue 6: Status of Open Issues on SRS CoExposure Model (OTIB-0081 Rev. 4) 74

Issue 7: Petitioner Comments 152

Issue 8: Work Group Discussion 153

Adjournment 158
4

Proceedings

(10:31 a.m.)

Welcome and Roll Call/Introductions

Dr. Roberts: I'd like to wish everyone a good 

morning. This is, of course, the Advisory Board on 

Radiation and Worker Health. And I'm Rashaun 

Roberts. I'm the DFO for the Advisory Board.

This is the second and final day of the two day joint 

meeting of the Savannah River Site, or SRS as we 

call it, Work Group and SEC Issues Work Group.

I want to let meeting attendees on Zoom and 

attendees who might be participating by telephone 

only that the agenda and all of the background 

documents and presentations for both days of this 

meeting are on the NIOSH website under the 

Schedule of Meetings if you look under the month of 

November.

If you've taken a look at the agenda, you will notice 

that today's session is focused both on SRS and SEC 

issue Working Group business whereas the first 

session on November 17 that we had earlier this 

week was focused mainly on SRS.

So with that said, let's start roll call and conflict of 

interest as usual. I will let everyone know that in 

order to sit on the SRS Working Group, members of 

that group cannot have a conflict of interest for that 

site. So let's go ahead into roll call.

(Roll call.)

Dr. Roberts: Being that he doesn't seem to be on, let 

me ask the Work Groups shall we continue?

Member Lockey: Did anybody give him a call?

Ms. Burgos: I'm going to give him a call. This is 

Zaida.

Chair Clawson: This is Brad. We can go ahead and 
5

continue on.

Dr. Roberts: Okay. Okay. While Zaida is trying to get 

ahold of him, let me just go through this. So thank 

you. Welcome again to all of you. I just will go over 

a couple of additional items before I give the floor 

over to Brad and Andy if he joins us. They are the 

Chairs of the SRS and SEC Issues Work Groups, 

respectively.

In order to keep things running smoothly, and I'd 

really like to emphasize this, and so that everyone 

speaking can be clearly understood and we don't 

need to interrupt presentations, please make sure 

that you mute your telephone unless you need to 

speak.

If you don't have a mute button on the phone, press 

star 6 to mute and star 6 to take yourself off. And I 

can hear something in the background right now. So 

if you're not on mute, if you could check your phone.

If you're on Zoom, the mute button is at the bottom 

lower left-hand corner of the screen. So, again, if 

you're not the speaker, it's really important for you 

to check Zoom or to check your phone, especially if 

you need to attend to another phone call or some 

other business because there's been some situations 

where we can hear other conversations taking place, 

and it's rather disruptive. So please try to be as 

mindful as you can.

So once again, if you didn't hear earlier, the agenda 

and the presentations and background materials that 

are relevant to today's meeting can be found on the 

NIOSH DCAS website. All of the materials were sent 

to Board members prior to the staff meeting.

If you were in on the November 17 session, you 

might have heard some discussion about making a 

change to today's session. But ultimately, the Chairs 

decided not to make any changes just for your 

information.

So with that, I'll check in one more time to see if Andy
6

has joined us.

Chair Anderson: I'm here.

Dr. Roberts: Oh, hi. Good morning.

Chair Anderson: I'm here. I can't get my video 

working, but I'm here.

Dr. Roberts: Okay. But you can hear okay. Alright.

Chair Anderson: I can hear you.

Dr. Roberts: Is that correct?

Chair Anderson: Yes.

Dr. Roberts: Okay. Well, you have joined in the nick 

of time for me to turn this meeting over to you and 

to Brad.

Chair Anderson: Okay.

Dr. Roberts: Are you ready?

Chair Anderson: Yes.

Dr. Roberts: Okay.

Issue 5: Stratification of Co-Exposure Models 

(NIOSH "Stratification Refinement" White Paper)

Chair Clawson: Andy, since this is a Savannah River 

stratification, if it's alright I'll take over on this. I'd 

like to welcome everybody here today. Thank you for 

taking your time out of your day so we can work on 

this. Tim, I believe, you're the first presenter on this 

so I'll turn it over to you.

Dr. Taulbee: Okay. Thank you, Brad. I appreciate 

that. What we're going to talk about first here is the 

Savannah River Site plutonium construction trade 

workers stratification.

And we're calling it a refinement because this is 

effectively an expansion or a more detailed 

evaluation of something that we had done a few 
7

years ago. But it does address some of the questions 

that came up during the discussions a few days ago 

on November 17.

So before I get going here, I really want to recognize 

the ORAU Team. They did the lion's share of this 

work, and I really appreciate all that they did in 

pulling this information together so.

I'm going to go over a little bit of background, the 

evaluation that we did, some more summary and 

conclusions and then open it up for questions. So 

hopefully this will go fairly quick.

So from the background standpoint, when we made 

the construction, or when we made the initial coexposure model for the Savannah River Site, we did 

a single co-exposure model where we actually had all 

construction trades workers and operations workers 

combined together.

One of the comments that came back was that we 

should really break out construction trades from the 

non-construction trades workers. And so we did that. 

And so in the current revision of OTIB-81, which is 

the Savannah River Site co-exposure model, nonconstruction trades workers are broken out from 

construction trades workers.

This resulted in a further concern that the 

construction trades workers in the co-exposure 

model are a combination of prime construction trades 

workers. These would be construction trades workers 

working for DuPont and subcontractor construction

trades workers.

So the current discussion and the concern is should 

the subcontractor construction trades workers be 

combined with the prime construction trades workers 

into a single co-exposure model?

And our position is that if prime construction trades

workers had similar exposure conditions to the 

subcontractor construction trades workers, then the 

bioassay data from the primes and thus the intakes 
8

on that data may be used to assign the intakes to 

unmonitored subcontractor construction trades 

workers.

It is our position the exposure conditions and the 

potential for intakes were similar amongst all 

construction trades workers. And this comes into play 

where a lot of the what I'm going to call prime 

construction trades workers were primarily 

millwrights and what they call at Savannah River Site 

E&I technicians, electronics and instrumentation 

technicians. And so they would be doing a lot of the 

same type of work that Savannah River would bring 

in subcontractors for. Okay?

So a timeline of these discussions is this kind of 

started around the 2017 time period. There was a 

joint SRS, SEC Issues Work Group meeting. And then 

in August, we had sent out an email memo to Mr. 

Clawson and Dr. Melius, at the time he was the Chair 

of the SEC Issues Work Group. And this is where they 

had asked for additional analysis and clarification.

So in May of 2019, we wrote a White Paper. And 

that's the topic of today's presentation right now. We 

entitled it the SRS Plutonium Construction Trades 

Worker Stratification Refinement. And that's what 

this presentation is about.

Now what you're going to see next is on November 

12 SC&A did a memo review of that White Paper, and 

Bob Barton is going to talk about that next. And then 

in March of this year, we wrote a response to SC&A's 

comments with regards to the information I'm about 

to present to you.

So this is the sequence. This is how we're getting to 

this. It really -- the first -- this presentation and the 

November 12th just didn't make it onto the agenda 

due to timing type of issues for the last time we met, 

which was December of last year.

So we're kind of backing up a little to fill in the gaps. 

Remember, we were all supposed to have a March 

Work Group meeting that got delayed or actually 
9

cancelled due to COVID and so we're kind of picking 

up what would have been presented in March. Okay?

So let me start with what we did in this White Paper, 

what our evaluation was. So our evaluation plan here 

was to compare subcontractor construction trades 

workers and prime construction trades worker 

bioassay for commonly monitored radionuclides 

onsite over a period of time.

So we looked at possible candidates that we could do 

this with, plutonium, uranium, mixed fission products 

or tritium. We had previously done one, a comparison 

of the tritium and had presented that previously to 

the Work Group. But the actinides are really the crux 

of the concern here. And so we selected plutonium to 

compare from that standpoint.

So one of the other questions we had was do we look 

at all years or select years? Well, this is a large effort 

to do. This is not trivial to go through and do this type 

of comparison and separating people out.

So we decided to evaluate five years through the 

period of 1972 through 1988 and basically at three 

year intervals. Okay? And so we honestly started with 

the end. And 1986 is where there was previous 

concerns that subcontractor construction trades 

workers were demonstrating a potential higher 

exposure than prime construction trades workers. So 

we started with 1986 and then kind of worked 

backwards and decided on let's do five years every 

third year going backwards.

Can we evaluate more years? Yes. We certainly can. 

But we don't feel that this is necessary. We feel that 

this answers the question.

So this is the current SRS plutonium construction 

trades worker model that is in OTIB-81. And I've 

highlighted here the five years that we evaluated. 

Again, we started with 1986 and kind of worked back 

on three year intervals. And so these are the years 

that we are evaluating.
10

So the source data for the plutonium co-exposure 

models is from claimant files. This is the NOCTS data. 

And that was done -- it was started years ago. 

Instead of trying to code all of the plutonium 

logbooks, which would be a massive effort, we felt 

we have sufficient information in the claimant files to 

just use the claimant data to develop the plutonium 

co-exposure models.

However, one of the things we noted when we started 

doing this is that the data is highly censored for most 

years and in particular for subcontractor construction 

trades worker populations. So if you're looking at a 

lot of censored data at .1 dpm per day, it looks like, 

well, as you will see on the next slide or so, that 

virtually all of the data is censored.

To increase the number of uncensored results, we 

considered an additional source. And this was the 

plutonium bioassay logbooks. And in these logbooks, 

the data is actually not censored.

On the individual cards we get from the Department 

of Energy in processing claims, the data is censored. 

It will say less than .1 dpm per day typically, but in 

the logbooks there is the raw result.

So, wait a minute. Okay. So this is the time weighted 

one person, one statistic plutonium results. And 

again, the censoring level is .1 dpm. And so this is 

when we went through and separated the prime 

construction trades workers from the subcontractor 

construction trades workers. And I've got the two 

columns there divided by the blue line of primes on 

the left and subcontractors on the right.

And what you'll see from this for these five years is 

that the primes for the 50th percentile were slightly 

higher than the subcontractor construction trades 

workers with the exception of 1980. The same holds 

true for the 84th percentile, except for 1980 and 

1986. Okay?

So this is what it looks like graphically using box 

plots. And the dotted line across the center of the 
11

graph there, that's the censoring level. And so what 

you see here is that there's not much difference 

between these two populations, at least from our 

standpoint in what we were looking at.

What you do see, especially in '83, '86 is that there 

are a lot more subcontractor construction trades 

workers. And this is predominantly because we 

supplemented their data with the bioassay logbooks, 

and we did not for the prime CTWs.

We had over 30 results for the primes that were not 

censored. And we could go to the logbooks and 

instead of trying to randomly select, we just coded 

them all. Okay?

And so if you look at the numbers here, 65 prime 

construction trades workers in 1983, 641 of the 

subcontractors. There are many more than 65 prime 

construction trades workers at the Savannah River 

Site. These are actually just the claimants. Okay? So 

I just want to make that clear here.

This is what happens when you expand the box plots

on a log scale that makes them easier to see and 

easier to see that separation. What you see is that 

central tendency of the box between the 25th 

percentile and the 75th percentile. They match quite 

closely all the way through.

You do see in 1986 that the 95th percentile of the 

subcontractor construction trades is higher than the 

primes. Also in 1980 and I believe 1983, too, it's also 

slightly higher. Okay?

So we're not seeing a big difference in the TWOPOS 

results. But remember the TWOPOS results are used

to develop the intake model. Okay? Those are not 

directly used when we do dose reconstruction. We 

use the intake model.

So if you recall what we do from the intakes to 

determine these intakes is we take the 50th 

percentile, the TWOPOS data, and we fit it into what 

this particular case is an example of. And in this 
12

particular case, we're fitting the 50th percentile of the 

subcontractor CTWs from 1973 to 1978. And this 

would be for Type M plutonium.

And here is for the latter years, for the 1979 to 1987 

time period. And that's what that would look like. It's 

a higher intake that we would assign. So really the 

intakes is what we should be comparing here.

And so for Type M plutonium, what we find for the 

'73 to '78 time period is that the prime construction 

trades workers have a higher 50th percentile, a 

higher 84th percentile and a higher 95th percentile. 

However, from 1979 to 1987, the primes have a 

higher 50th percentile but a lower 95th percentile. 

The geometric standard deviation is higher for the 

subcontractor, showing there is more variability in 

that time period. Okay? And so this is for Type M.

For Type S plutonium, again, we see the exact same 

thing. And so in the '73 to '78 time period, the intake 

is about 100 dpm per day higher for the prime 

construction trades workers versus the 

subcontractors. The opposite is true for the '79 to '87. 

The subcontractors are about 50 dpm per day higher 

than the prime subcontractors.

So which is more claimant favorable? Well, it depends 

upon the individual worker as to which time period 

they worked in as well as what their exposure profile 

looks like.

So our summary in conclusion is that, you know, over 

95 percent of the TWOPOS data results are less than 

the reporting level of .1 dpm per day. So 95 percent 

of the data for all construction trades workers is 

censored. Okay?

We had to go back to the original records in order to 

make it uncensored to try and 

get what these lower levels are. There's no apparent 

difference between the prime construction trades 

workers and the subcontractor construction trades 

workers with regards to TWOPOS. There's no 
13

practical difference between the two groups when 

intakes are modeled.

So there's really no evidence of a significant 

difference between the prime construction trades 

workers and the subcontractor construction trades 

workers. So our conclusion is that the exposure 

conditions and the potential for intakes were similar 

among all construction trades workers therefore 

combined strata is appropriate. With that, I'll be 

happy to answer any questions.

Okay. Not hearing any, then let me go and share 

Bob's presentation. SC&A will --

Member Beach: You know, Tim, while you're doing 

that I do have one quick question. How can you be 

sure of the completeness of the data? How can you 

be sure that all the construction subcontractors are 

even in the logbooks?

Dr. Taulbee: Well, let me go back up then. Sorry.

Member Beach: Oh, I apologize.

Dr. Taulbee: No, no. That's alright. That's alright. 

This is important. Let me go back up to that 

presentation. Why am I not seeing it now? Share. Oh, 

okay. Let me stop sharing that one and share a 

different presentation here. There we go. Okay.

It kind of goes back up here to this particular slide, 

Josie. When you look at 1983 and 1986, I mean, 

we're looking at 1,000 bioassay of subcontractor 

construction trades and in 1983, 600.

It kind of runs down to, I mean, is this complete? 

Was everything -- I mean, this follows on to the 

discussion from Tuesday for sure. You know, there is 

a lot of work that is going on in this time period, but 

just the sheer numbers of bioassay tells us that we're 

not missing a huge portion, at least that's what it tells 

me.

Now when we get into these earlier years, '74 and 
14

'77, there could be. But in this time period we also 

know that DuPont didn't use a lot of subs in that time 

period. They did a lot of the in-house work 

themselves.

In 1980 is when they began to ramp up. And that's 

where we begin to see these large numbers of using 

subcontractor construction trades. So can we be 100 

percent sure? No. But if everybody was monitored, 

okay, then we wouldn't need a co-exposure model. I 

mean, that's just a simple fact.

So we know not everybody was monitored. How do 

we know that we feel like we've got enough data? Oh, 

it has to do with the abundance of the data here. I 

mean, 641 subcontractor construction trades 

workers were monitored for plutonium.

As John showed on Tuesday, over this time period of 

1972 to 1990, or 1989 rather, we have 11,000 

subcontractor construction trades workers bioassay -

- I'm sorry -- amongst 7,000 individual subcontractor 

construction trades. So it's the abundance of data 

that gives us some assurance here that we've got a 

large population that is monitored and that a coexposure model is reasonable.

Chair Clawson: Tim, this is Brad. One of the things 

that bothers me on this, now Savannah River is 

different than any of the other sites. Their 

maintenance program, everything else like that, they 

use trades. They use construction trades. That's why 

you have a DuPont construction trades workers. 

Those people can go from there, back into the hall 

and back and forth without too much trouble.

A lot of them stayed there for quite a while. The thing 

that does bother me is that if you look at the DuPont 

construction trades those numbers are almost half in 

some cases what the construction trades is.

To me, I would see that it -- because they are the 

ones that are doing all the maintenance on the work 

and maintaining the whole Savannah River Site. And 

there's not that many numbers there.
15

Dr. Taulbee: Again, this is the claimants. Okay? For 

the years 1970 -- all of the black box plots here that 

you're seeing, these are claimants only. Alright?

The red ones that you're seeing, these are ones that 

have been supplemented with logbooks. So you 

really can't compare these two the way that I have. 

What you should be comparing is just the 

concentration, not as much the numbers. Because if 

we were to add in all of the prime construction trades 

workers here -- and I'm sorry that keeps popping up

that way -- for, like, 1983, like I said, there's many 

more than 65. There are probably going to go on --

somewhere around 300 to 400 prime construction 

trades workers that could be added in here.

But what you see in those distributions of their 

bioassays, they're not that different, even with these 

smaller numbers for the primes.

Chair Clawson: Okay. It's just to me it's misleading a 

little bit that way and --

Dr. Taulbee: So I'm trying to be clear.

(Simultaneous speaking.)

Chair Clawson: -- Savannah River Site period. And it 

just kind of was a little bit interesting to me.

Member Lockey: Tim?

Dr. Taulbee: Yes, sir.

Member Lockey: Jim Lockey. What percentage of 

subcontractors did not have bioassay data again?

Dr. Taulbee: Did not have bioassay data?

Member Lockey: Yes.

Dr. Taulbee: Within this particular population, this 

one here we just looked at the plutonium bioassay 

itself. You'd have to look at RPRT-00944 in order to 

tease that out.
16

And from there we broke out the plutonium bioassay 

-- or not the plutonium. We combined plutonium and 

mixed fission product bioassay. We broke out tritium.

And so the numbers are around -- sorry, I'm being 

weird here because I don't have the exact numbers 

right in front of me. But it's somewhere around 40 to 

50 percent of the actual claimants that we have did 

not have -- subcontractors did not have plutonium 

bioassay for these five years.

Member Lockey: For those five years.

Dr. Taulbee: Yes.

Member Lockey: Okay.

Dr. Taulbee: Of the claimants, between 40 and 50 

percent of them that were externally monitored have 

plutonium bioassay.

Member Lockey: Okay. And you could link those 

bioassays to specific job tasks?

Dr. Taulbee: To specific trades, not to -- the job tasks 

comparison was RPRT-0092 that we talked about on 

Tuesday. And we can only link them for one area. And 

that was one of the discussions. But this particular 

dataset is looking at all areas.

Member Lockey: So you linked it to a specific trade.

Dr. Taulbee: Yes.

Member Lockey: So we're comparing trade against 

trade. Okay. I thought that's what happened, and it 

confirms what I heard on Monday. But I just wanted 

to make sure. There's so much data that we have to 

look at or have looked at that sometimes it makes 

your head swirl. But your answer is consistent with 

what I thought you were going to say.

Dr. Taulbee: Okay. Thank you. Are there other 

questions? Okay. Alright. Now let me try then, Bob, 

to share your presentation. Alright. Go ahead.
17

Mr. Barton: Alright. Thank you, Tim. Again, this is 

sort of our review that came out last fall of what Tim 

just described, that analysis, which was really a 

refinement of the 2017 analysis that sort of broached 

this entire question about stratification.

Before we really get started in here, I would just like 

to reiterate the caution in that the decision to stratify 

is really in my mind more of a Site Profile issue that 

comes up when you've already decided that a 

plutonium co-exposure model is feasible for all the 

parties involved, which includes the subcontractors.

And that's why I cautioned that on Tuesday. But 

there are -- this discussion does provide a good 

perspective. But I'm not sure we want to belabor it 

too much because I do see it as more in the Site 

Profile realm.

But anyway, I don't know if those of you who were 

on the call a little bit early the way we were going to 

structure this is that we'll sort of go through my 

presentation and then pause at each finding and 

observation and switch over to NIOSH's response so 

that we don't sort of have all of these things washed 

out by going full presentation, full presentation and 

then trying to discuss them all at the end.

So as we go to each one, SC&A will present its 

finding, and NIOSH will present its response. And I 

believe the best thing to do at each point would be to 

have a discussion of that among the Work Group or 

Work Groups rather. So we'll get started.

Next slide, please. Alright. A lot of these first slides 

are just, again, reiterating the background of this 

issue and sort of the timeline that Tim already laid

out. So we're going to sort of breeze through these.

But, again, that key question is do subcontractor 

trade workers have the same exposure potential as 

primes? And the test that was put to this was to look 

at available plutonium data, which for the prime

contractors was just claimant data and then 

expanded for certain years for subcontractors. I 
18

believe among those five years it was expanded for 

three of them, Tim, or was it four? I think maybe it 

was three.

Dr. Taulbee: It was actually four. I think on my slide 

there is an error that I actually meant to point out. 

There are four of the five years we did supplement 

with the bioassay logbooks. You are correct.

Mr. Barton: Okay. Alright. If we can go to the next 

slide again, this is sort of just giving the history of 

how we got there, which Tim has already presented.

Again, this was the conclusion in 2017. And really 

what gave us the most pause is sort of this notion in 

here of the exceptions where subcontractors appear 

to be higher in the original analysis in 2017.

And it was, what? The conclusion was this 

observation is somewhat supported by the 

contemporary interviews with subcontractor CTWs. 

Subcontractor CTWs indicated they were called in for 

more contaminated work to save the exposure of the 

onsite CTWs.

And so we saw this and said, well, this would indicate 

to us that stratification should be considered not just 

pulling out construction trade workers alone but 

possibly separating out primes and subcontractors.

Next slide, please. Again, this is just reiterating what 

the path forward was. I don't think there's anything 

more to say about this slide other than NIOSH 

wanted to go back and look at a little bit more data 

and refine it is exactly as it's called.

Next slide, please. And so Tim already showed this

table. And, again, these are intakes that we're 

looking at, which ultimately is what is applied to a 

claimant who is unmonitored and needs the coexposure model.

And we're going to certainly get into this in a little bit 

but just to call your attention to that last column, the 

95th percentile ratio. As Tim pointed out, at the 95th 
19

percentile in the 70s, the prime contractors appear to 

have higher intakes based on this analysis. But once 

you get into the '79 to '87 time frame, it's the 

subcontractors that are higher by about 15 to 20 

percent.

Next slide, please. And so this is the refined NIOSH 

conclusion. Tim just went over this. And ultimately 

what they're saying is that even though for the 

different periods, you know, '70 to '78, the 95th 

percentile for DuPont is higher, as I just said, '79 to 

'87, subcontractors are higher, again, at the 95th 

percentile.

And that's due to that higher geometric standard 

deviation of the data, which you saw that box plot 

that Tim just put up, especially in those later 80 --

the two later 80 years. You see a lot of subcontractor 

datapoints that are well above what the highest 

DuPont construction trades were, again, in those 

1980s periods. And, again, this does give us pause 

as we move forward as you will see in Finding 1.

Next slide, please. And this comes out of the NIOSH 

original analysis that NIOSH presented in 2017. And 

the only thing I really want to point out here, and, 

again, this is the number of monitored claims. It 

breaks it out by DuPont and subcontractors.

And just if you look towards that 1980, towards the 

right of the graph, you can see that the number of 

subcontractors is significantly increased as well as 

the percentage compared to DuPont. So, again, 

that's the 1980 period where we observed as the 

95th percentile a 15 to 20 percent increase in the 

calculated intakes for subs versus DuPont. 

Next slide, please. And, again, this is essentially the 

same thing. If you look, again, this is the claimant 

population, the subcontractors versus all workers 

combined, again, in the claimant population.

As you can see, it goes from '72 into the late 90s. 

And it slowly increases through the 70s. And you sort 

of hit your maximum there in the mid-1980s up to 
20

1990. So, again, this is just sort of giving perspective 

on the increase in the use of subcontractors during 

that 1980s period, which I believe was referred to as 

the late DuPont era in NIOSH's presentations on 

Tuesday whereas the 70s was referred to as the midDuPont era.

So there is sort of a delineation there, which I believe 

is why in the stratification analysis when calculating 

the intakes, and Tim, you can clarify this certainly, 

that's why I believe it was sort of separated into the 

1970s and the 1980s for comparison purposes. I 

don't know if you want to -- or is that correct?

Dr. Taulbee: Actually when we do the intake 

modeling, we don't consider from that standpoint. We 

look at what is the bioassay data doing because we're 

modeling a chronic intake. And so the results were 

generally lower in the 1970s compared to the 1980s. 

That was the reason for the break. It really wasn't 

due to this change in the number of workers.

Mr. Barton: Okay. Thank you for that clarification. 

But I think it's important to remember -- the reason 

I put these charts in here, and these were compiled 

by NIOSH, it just appears that the use of 

subcontractors increased significantly in that 1980 

period. And that's really the only point I wanted to 

make with those two slides here.

So we can move on to the next one. Alright. This 

brings us to Finding 1. And I'll read this into the 

record.

In SC&A's opinion, the conclusion that subcontractor 

construction trade workers had higher excretion 

rates and derived intakes at the 95th percentile for 

the period 1979 to 1986 is significant from the 

standpoint of considering stratification because the 

95th percentile is what is proposed for assignment to 

unmonitored subcontract construction workers.

And so that's our finding. What we're basically saying 

here is that, you know, you can look at the medium 

--
21

Dr. Taulbee: Bob, we lost you.

Dr. Roberts: Bob? Let me see. He's still talking. Let 

me see if I can --

Dr. Taulbee: Is anybody able to hear Bob?

Dr. Roberts: I can't.

Chair Clawson: No.

Dr. Roberts: No.

Member Beach: No.

Chair Clawson: I cannot hear him.

Dr. Roberts: I sent him a message.

COURT REPORTER: This is the Court Reporter. I 

suggest we go off the record until we have him back.

Dr. Roberts: Okay. Thank you.

(Whereupon, the above-entitled matter went off the 

record at 11:12 a.m. and resumed at 11:13 a.m.)

Mr. Barton: Alright. So as we can see, this is SC&A's 

finding. And we believe that it's significant to look at 

the 95th percentile because what we're really talking 

about is bounding doses here. And so in that box plot 

that was in the previous presentation if you looked at 

those 1980s years, at the very top of the distribution, 

you had a lot of subcontractor results compared to 

the DuPont.

And if we're really talking about bounding doses here 

to unmonitored subcontractor workers, I think it's 

important to be able to look at the 95th percentile. 

And NIOSH has a different viewpoint on that, which I 

think we're going to get into.

And, Tim, this might be the time to bounce over to 

your presentation so that NIOSH can present their 

view and their response and then we can discuss 

what's really the appropriate metric to look at.
22

Dr. Taulbee: Okay. Alright. Let me -- can everybody 

see the slides here? Okay. So our response to SC&A's 

finding here, for one thing I want to make clear to 

the Work Groups is that the 95th percentile is not 

exclusively used in dose reconstruction. It's a 

bounding scenario, but we do use it occasionally.

The decision as to what should be assigned is made 

during the dose reconstruction process, and it's 

based on the total information in the claim. There are 

some subcontractor construction trades workers who 

were never exposed.

They did their work onsite. They talked about that 

they were doing new construction. They weren't 

wearing external dosimetry. And so we assigned an 

environmental dose, a minimum exposure, for those 

particular workers.

There are others who occasionally went into an area. 

And we've assigned the geometric mean in the 50th 

percentile. But as Bob pointed out there are some 

who we would assign the 95th percentile to. So it's 

not a one size fits all when it comes to dose 

reconstruction and how this gets used.

So if you go back to the comparison that I was talking 

about, and the comparison of the TWOPOS and the 

intakes, the TWOPOS results, the 50th percentile is 

higher for four of the five years that we compared. 

The 84th percentile is higher for the prime 

construction trades workers for three of the five 

years. But when you come to the intake models, the 

50th percentile is higher for prime construction 

trades workers in both intake periods.

The primes was higher in the second intake period --

or the subcontractor construction trades is higher in 

the second intake period.

So it really depends upon the individual claim, which 

is going to be more claimant favorable. But there's 

no apparent difference that we felt would support 

stratification on this and that's our current position.
23

We're not refuting that some of the construction 

trades workers subcontractors were potentially 

exposed higher than the primes, especially in the 

1980s. But the bulk of the data doesn't, in our 

opinion, support stratification for unmonitored 

subcontractor construction trades workers.

And a lot of it goes back to this particular slide in that 

we look at the totality of the claim. We don't just 

blindly assign the 95th percentile. There are some 

that we assign an environmental dose to. And some 

we assign geometric mean or the 50th percentile so.

With that, Bob, let me bring back your presentation. 

Actually, does the Work Group -- I mean, probably 

before we move on we should address questions, 

right? Is that agreeable, Bob, to both of ours? Okay.

Mr. Barton: Yes. I'd like to hear if the Work Group 

has any questions and then I do have a comment on 

this issue, which is applicable not just to this but as 

sort of a programmatic policy of how we apply these 

different percentiles and how co-exposure models 

are actually used in practice because I think it's 

possible that we over at SC&A are mistaken on what 

NIOSH's policy was. But we can get into that -- I 

prefer to open it up to the Work Group if there are 

any direct questions or comments first. Unless you 

want me to --

Member Beach: Well, one question I have is, Tim, 

how do you determine which exposure to assign, the 

environmental, the 50th, the 95th percentile? Where 

does that information come from? Is it from the 

workers, from their bioassay?

Dr. Taulbee: It's a combination of multiple sources 

when the dose reconstruction is done. And 

professional judgment plays a role in that. And that's 

where the Dose Reconstruction Review 

Subcommittee comes into play.

And they review these claims and go through and 

look at which decision we made and how we assigned 

the doses. And, you know, there isn't any, you know, 
24

like I said, one size fits all. It depends on the 

individual claim.

There could be claims where it's a non-metabolic 

cancer, and we go ahead and assign the 95th 

percentile because it's not going to make a difference 

in the claim decision. And so we'll be overestimating 

that particular dose.

And then in other ones when you're looking at a best 

estimate, which is what the Dose Reconstruction 

Subcommittee focuses on is the best estimate cases, 

and where that professional judgment is used.

Ms. Naylor: Tim, this is Jenny. But I just wanted to 

make sure that you could discuss the various factors 

that the dose reconstructors evaluate when assigning 

those percentiles.

Dr. Taulbee: Sure. I mean, some of the things that I 

would be looking at if I was a dose reconstructor 

would be looking at whether they were externally 

monitored, which areas that they were working in 

from that standpoint as to whether I would be 

assigning the environmental dose or the 50th 

percentile or the 95th percentile.

I mean, if I've got somebody who only worked around 

the reactor areas and, you know, they mentioned 

that they were specifically working in the spent fuel 

areas, then we might assign the 50th percentile so. 

Does that help?

Ms. Naylor: That's great. Thank you.

Member Lockey: Hey, Bob. Jim Lockey. I'm looking 

at this slide, your SC&A Finding Number 1. I'm trying 

to get my head around what you're trying to say 

here. What would you propose? That everybody be 

assigned a 95th percentile? Is that what --

Mr. Barton: No. What I'm saying is that when you 

make the decision to stratify or not, you want to 

consider whether there's a group out there at the 

upper end of their distribution. They're actually doing 
25

more contaminated work and had a higher exposure 

potential.

So I think when you're talking about purely bounding 

doses and the decision to stratify, I think looking at 

the 95th percentile is the metric you want to look at 

because that's what you're going to essentially apply 

when you have a worker who you feel is significantly 

exposed but does not have monitoring records for 

whatever reason, either they were lost, destroyed or 

they just weren't monitored and should have been.

So I think, you know, I'm not saying the 95th 

percentile should be applied to everyone, but I think 

that when you're talking about the decision to 

stratify, that is the metric you want to look at.

And to sort of get into that a little bit, I mean, I was 

looking through some transcripts when we discussed 

this back in 2017 because, again, SC&A may just be 

confused. But our impression was that was the metric 

we were looking at.

So in a 2017 meeting of the SEC Issues Work Group, 

this is on Page 26 and 27, and I believe this is you, 

Tim, you had similar conclusions. So we didn't see 

any systemic difference between DuPont construction 

trade workers and subcontractor construction trade 

workers.

There's a few years where plutonium bioassay is 

higher for subcontractors than DuPont construction 

trades workers, but it's not systematic. The last five 

years there's three years where subcontractors were 

higher and two years where they were lower.

Therefore, we feel that the application of the 95th 

percentile of the combined construction trades 

workers coworker model, the unmonitored 

construction trades worker would be bounding. So 

even back then, we're looking at that 95th percentile.

Also in 2017, and Brad, you'll appreciate this because 

this actually comes from a -- I took this out of one of 

the Fernald transcripts. And the quote is, our general 
26

guidance on coworker models is for a coworker model 

if someone that is not monitored and is occasionally 

exposed, they get the 50th percentile. If someone is 

not monitored but it looks like they are probably 

regularly exposed, they get the 95th.

But I think that application of the 95th percentile is 

very important to think about here in the context of 

applying co-exposure models. Again, our impression 

was that essentially if, you know, you never really 

entered radiologically areas, if you were 

administrative or something like that, or you just 

worked outside, environmental dose is appropriate.

If you were someone who only occasionally entered 

radiological areas but weren't really considered a rad 

worker, per se, then you get the 50th. And if you 

were a rad worker that was unmonitored, then you 

get the 95th.

And that's what we get from this quote out of Fernald 

and that, by the way, was Stu Hinnefeld who said 

that. And that's on Page 95 of that transcript. So, 

again --

Dr. Taulbee: I believe that matches what I had said 

earlier as well.

Mr. Barton: Right.

Member Lockey: This is Jim Lockey. That was my 

point. I was trying to figure out how you two differed. 

And I couldn't understand how you were differing.

Member Ziemer: This is Ziemer. Can I comment on 

that as well? So this actually is an overarching issue. 

And we've been looking for consistency across all the 

different sites for this.

So I'm sort of trying to understand Bob, what your 

point is. Are you saying that maybe the wrong 95th 

percentile that they're using for those that need to 

have the 95th percentile assigned? That --

Mr. Barton: Right. My point is that if we're trying to 
27

bound doses to workers, whether it be DuPont or 

subcontractors, you would want to apply the 95th 

percentile to workers to bound those exposures. And 

this is the decision whether we need to pull out the 

subcontractors that have their own separate 

distributions for that purpose. And these are looking 

--

Member Ziemer: You're saying that it may be a 

different 95th percentile value rather than the 

combined. Is that my understanding?

Mr. Barton: No. What I'm saying is the analysis shows 

that in the model 1979 and 1987 period at that higher 

end, the subcontractors had higher intakes, 

calculated intakes than DuPont. So if they did not 

have their own separate model, you'd be applying the 

DuPont intakes or the combined intakes.

And so when we're talking about, again, this is the 

decision to stratify and again this is sort of getting 

back to this is really a Site Profile issue in that it's 

assumed that co-exposure modeling is appropriate 

and can be applied to subcontractors that at least for 

that latter period when you look at those higher end 

calculated intakes, the subcontractors are higher 

than DuPont.

And that's really our only point here. Not that, well, 

they're not 50th percentile according to this sort of 

scoping calculation is different than DuPont workers.

Now the inverse is true for the 1970s. But in that 

latter period, the 1980s, it would appear that if you 

wanted to bound unmonitored exposures to 

subcontractors, it might be appropriate to pull them 

out, again, for the 1980s because at that higher end 

exposure, they're higher than the DuPonts.

Now the inverse is true for the 70s. And that might 

be very well appropriate that subcontractors in the 

70s may, if they had their own co-exposure model, 

the DuPont would probably bound them.

However, I would caution that when we talked about 
28

job specific bioassays back on Tuesday and 

specifically plutonium, which is the analyte we're 

looking at here, SC&A found that the subcontractors 

on job plans were only monitored or effectively 

monitored, in other words being on the same job plan 

as the monitored worker about 65 percent of the time 

and that's in the 70s.

And actually that's only for '72 to '74 because there 

was no analysis available for '75 to '79. So we found 

what we feel are deficiencies in that job plan 

monitoring in the 70s for plutonium.

Now in the 80s we actually agreed -- we're pretty 

much in lockstep with NIOSH that the follow-up for 

job specific M plutonium in the 1980s was around, 

like, 97 percent. And I think we pretty much agreed 

exactly on that number.

So while the data appears to say that in that 70s 

period where you did have fewer subcontractors 

onsite doing work and that DuPont work might have 

been higher based on this analysis, again, we found 

that there were concerns with the job specific 

monitoring of those subcontractors in the 70s.

And, again, we came up with 65 percent effectively 

monitored. That's directly monitored via urinalysis or 

working with someone who would be included in a 

co-exposure model. So I throw out that caution 

there. I'm not sure if that answers your question 

necessarily.

Member Lockey: Bob, Jim Lockey. So are you 

proposing that -- what you're saying is take the 

exposure data for the subcontractors and separate it 

from the primes. Correct?

Mr. Barton: That's the question on the table. And I 

think it's certainly for the 1980s when we feel that at 

least for plutonium, the job specific monitoring data 

is very good. And, I mean, again 97 percent 

effectively monitored, there's not going to be a lot 

based on that analysis.
29

Now, again, that's only one area. But based on the 

analysis of RPRT-0092, again, this all assumes that 

co-exposure monitoring for plutonium is feasible for 

subcontractors.

Member Lockey: I understand that. So if by 

combining them, you're actually lowering the 95 

percent bounding for the subcontractors were if you 

are separated, the 95 percent bounding for the 

subcontractors may likely be higher. Is that what 

you're saying?

Mr. Barton: For the model '79 to '87 period, that's 

what the analysis shows. And, again, the inverse is 

true. DuPont at the 95th percentile bounds the 

subcontractors. But, again, we do have concerns 

about the plutonium data.

We didn't get too much into the plutonium aspect of 

it on Tuesday. A lot of the brunt of the discussion was 

about limitations of the sampling itself, which in the 

70s was only '72 to '74 with no comparison available. 

'75 to '79 in only A area for the entire period. So we 

do have concerns about whether it's feasible to use a 

co-exposure model during the 70s.

And then you get into the 80s where we really don't 

have the same concern for plutonium at least. But 

then when you look at should we pull out -- if we can 

do co-exposure modeling, if it's decided that that's 

feasible during the 1980s, is it appropriate to pull out 

the subs considering they have increased calculated 

intakes per this analysis when compared to DuPont?

Member Lockey: And the subs are going to have a 

higher 95 percent bounding limit if you do it that way 

than the primes in that time frame.

Mr. Barton: In that time frame.

Member Lockey: So, Tim, can you answer that?

Dr. Taulbee: Yes. I mean, our approach is to combine 

these two together. They're all construction trades 

workers combined together. And so the 95th 
30

percentile for this latter time period of say the '79 to 

1987 time period would be a combination of this 279 

plus 326. So it would be around 300 dpm per day.

So if we were to break it out, you do see this 

difference here, which would be about 20 percent 

higher. And we just don't see that this is a reason, 

you know, a 20 percent difference to stratify off of 

somebody -- off of people who are -- to stratify these 

two groups -- I'm trying to use the right -- I'm trying 

think of the right words here.

For this particular time period, that's what this data 

shows. The earlier time period, it's the inverse. Okay? 

And up here, it's, like, 50th percentile, or 50. No, it's 

about 30 percent.

Okay. We just aren't seeing the value of going 

through this effort to do it. Can we do it? Sure. We 

can. But it just -- we feel that combining the 

construction trades workers together is the 

appropriate way to go for this.

Keep in mind that when we do our full co-exposure 

analysis, okay, we are looking at generally over about 

this time interval, yes, about 10 years is generally 

and then you see another drop in the values due to 

bioassay monitoring type of methods.

This is just a further substratification. And I guess 

we're looking for feedback from the Board, you know, 

from that standpoint. Is this worth going through and 

breaking it apart?

Member Schofield: Tim, this is Phil. I've got a 

question for you. Now this is based on Pu.

Dr. Taulbee: Yes, sir.

Member Schofield: But we do know a lot of those 

people also were exposed to fission products and 

americium and strontium, you know, some of these 

others. So how are you -- are you taking those into 

a factor or just using -- it looks like you're only using 

plutonium to make this co-worker model.
31

Dr. Taulbee: No, no, no.

Member Schofield: Is that correct?

Dr. Taulbee: That's not correct. We only did this 

evaluation for plutonium. Okay? We did not do this 

evaluation for the mixed fission products, for the 

americium or the uranium bioassay. Okay?

This was a comparison to see do we see a difference 

and should we stratify? That's all that this evaluation 

was. We have co-exposure models for construction 

trades workers combined for all nine radionuclides. 

Okay?

Member Schofield: Oh, okay. Well, that answers my 

question. Thank you.

Dr. Taulbee: Yes. We just did this separation just for 

plutonium so that we could do a comparison. What 

we thought would be an easy comparison, it's not as 

easy as we thought.

Member Ziemer: This is Ziemer. I have another 

question in terms of the practical dose 

reconstruction.

Does the dose reconstructor, it's easy to see where a 

given worker could have a life -- his lifetime exposure 

may overlap these different periods. Do they use a 

95th percentile value year-by-year when they dose 

reconstruct?

Dr. Taulbee: Actually what they would do in this 

particular case is they would assign it -- this is dpm 

per day. And so this would be on a per day within this 

time interval of 1979 through 1987.

Member Ziemer: Right. So if you had a given worker, 

there is a pretty good chance that part of the time his 

value would be overestimated because he would be 

in the -- and part of the time it's underestimated.

Dr. Taulbee: Exactly. If you consider a worker that 

say started in 1975 and worked through 1985, 

they're going to bridge these two gaps. And in one 
32

case it's more claimant favorable for them to be 

combined under the combined CTW method and the 

other part it's better for them to be separated.

How can you make that determination? You know, 

that's part of why we feel it's better to combine it all

together. And, you know, keep in mind that this is 

the 95th percentile here. So this is the upper bound 

of what a worker would be exposed to. And we're 

applying it to everybody who wouldn't be monitored 

that we feel should have been monitored and was 

doing significant work in a rad area.

Member Ziemer: And it's already highly claimant 

favorable to start with.

Dr. Taulbee: Yes, sir.

Member Ziemer: So it's the issue of is it really worth 

the detail because you're going to end up part of the 

time going the opposite direction. So what's the 

point, you know?

Dr. Taulbee: That's our exact position. Yes, sir.

Member Ziemer: I think it makes sense just to use 

the combined one. But I'm not on the Working Group 

that makes this decision.

Chair Clawson: Oh, Paul, but this is kind of an 

overarching issue for all of the sites. This is what part 

of the issue is we are talking about Savannah River 

in this aspect.

But what Tim is asking us and also the Board is they 

want to do this to all of the sites. And this is an 

overarching issue for every one of the sites.

Member Ziemer: Well, I think the decision on the 

95th percentile and 50th and so on, that decision has 

been made already. So that's not new here. I think 

the only issue here right now is very site specific on 

treating this particular situation for Savannah River.

The use of the 95th percentile today as an issue, the 

overarching issue, I believe, that's already been 
33

applied, is it not, Tim?

Dr. Taulbee: No, it is. But Brad is right here. And 

what our question back to you is because this is an 

overarching issue for all of the co-exposure models, 

what level of detail do we need to go down to?

And that's where this is the SEC Issues Work Group's 

role, I think, is do we need to substratify amongst 

subcontractors versus primes for, you know, any site 

from that particular standpoint?

I mean, there could be times when we run into this 

exact same scenario at another site. You know, I 

don't know, Idaho or Hanford or one of the other sites 

where we run into a time period where if we broke it 

out we would see a subcontractor pool that would be 

higher for a few years than the in-house folks and 

vice versa, like we're seeing here with the '73 to '78 

time period.

And that's the question back to you all. We don't see 

a significant difference here that warrants 

substratification. And if you go back to the intake 

modeling, let me pull that up quickly here if I can, 

and I can't pull it up quickly. Give me just a second, 

sir.

Mr. Barton: Well, Tim, while you're doing that, in 

preparation for this meeting, I hope everybody can 

hear me, I actually ran this series of reports, 2017, 

the refinement, SC&A's review and then the NIOSH's 

responses past one of our statisticians, Richard 

Griffiths. Unfortunately he had a prior commitment 

so he couldn't be on this call.

But he did provide some comments that are directly 

related to this and it's that notion of significant 

difference. And what he said was the determination 

of no significant difference between the two 

populations appears to be subjective here.

There's no formal statistical test that was applied to 

quantify and conclude the two groups are either not 

different, different or there's a degree of difference in 
34

there. And Richard Griffiths, again our statistician, 

also noted this is of particular import because we're 

only looking at a sample of the population under 

consideration here and not the full population.

So, again, the decision of whether the groups are 

different or not is somewhat subjective here from 

SC&A's viewpoint.

Chair Anderson: I mean, and I would just add the 

other issue is it almost has to be on a specific basis 

because you may not have sufficient samples to 

substratify. Here you have a lot of test results so you 

can consider is it important to self-break out the 

groups? But in many of the sites, the numbers are 

not sufficient to be stable enough to allow you to 

break them out.

So kind of the first decision is how strong does that 

statistical number need to be to be able to say we 

should consider substratifying or not. Here this is 

probably -- maybe Hanford has a similar large 

number of samples. But this is one that has so many 

samples that you can consider substratifying.

Dr. Taulbee: And if you look at the combined model 

over a time period here, this is all of the data. This is 

the current construction trades worker plutonium coexposure model.

You'll see that it changes a little bit over time. But 

the actual model of the intake -- this is the bioassay 

data. The actual intake doesn't change much because 

we're fitting all of this data together.

And so when I go back to this particular graph here, 

there would be additional years in here. And we 

would come up with an intake that's not perfect. It 

doesn't go through all of the lines as you can clearly 

see from this particular modeling.

So this small -- this difference that we're seeing here 

in the intake, we just don't believe that it is 

significant enough, if I can use that term, to separate 

from a practical standpoint.
35

We disagree with SC&A on that. But, again, you 

know, if the Work Group feels differently, we can do 

it. It is possible.

Chair Clawson: Well, Tim, what are you asking the 

Work Group for period?

(Simultaneous speaking.)

Dr. Taulbee: What I'm asking for is the approval or 

the okay to combine the DuPont construction trades 

workers with the subcontractor construction trades 

workers in our co-exposure models. That's what I'm 

asking for.

Member Lockey: Tim, this is Jim.

Chair Clawson: Is this only pertaining to Savannah 

River? Because to me I'm kind of under the 

impression that this is an overarching issue because 

I think that we've run into this at almost every large 

site that we've got.

And I just want to be clear what you're asking for us 

to do. Because to tell you the truth, as Bob has said 

already, this is a Site Profile issue. So I just wanted 

to fully understand what you are asking this Work 

Group for.

Dr. Taulbee: Effectively the agreement that our 

stratification method of combining the construction 

trades workers together is appropriate for this 

particular site. That's the first thing that I'm asking 

for.

The second part that you're absolutely right on, Brad, 

that I'm asking for or I guess looking for is do we 

need to go through this type of analysis at all the 

sites, which I hope we don't, or do we have to go 

through this type of analysis at other sites to 

demonstrate that it's okay to combine them?

Member Lockey: Tim, it's Jim Lockey. I wanted to 

follow-up on what Bob was saying. There is no 

statistical methodology available to determine 
36

whether there are differences in these two 

approaches?

Mr. Barton: Tim, I think that question was for you.

Member Lockey: It is for Tim.

Dr. Taulbee: Oh, I'm sorry.

Member Beach: You said Bob though, yes.

Member Lockey: No, I said the question is to Tim. But 

Bob inferred that there was no statistical model 

available that they could discern that could help us in 

this issue.

Dr. Taulbee: We looked --

Member Lockey: It sounds like it comes down to a 

value judgment. Is that correct?

Dr. Taulbee: Yes, it does at this point. And part of 

this goes back to the initial reason to stratify or not 

stratify for even construction trades workers. We've 

looked at using a single model way back in 2015, I 

think.

We proposed multiple different testing, tests, 

statistical tests that could be done. We settled on one 

called the Peto-Prentice test.

This was presented to the SEC Issues Work Group if 

you recall. And the whole discussion kind of came 

down to was does it even have sufficient power in 

order to do this? And power is not the right word 

there. My statisticians are cringing when I said that. 

But is it possible to even discern a difference amongst 

the different groups?

And because we couldn't come to a conclusion then 

on a statistical testing method, we went ahead for 

CTWs at Savannah River and said, okay, we'll just 

stratify a priori and just break them apart now and 

then we don't have to try and have that discussion.

Well, we've ended up in that discussion now between 
37

DuPont construction trades workers and 

subcontractor construction trades workers. So that's 

really the overarching issue of whether we need to 

use a statistical test and then if we do, what test 

should we use?

Member Lockey: Okay. So my next point, and this is 

for Tim, as a practical perspective by combining 

them, by combining the contractors and 

subcontractors, if you look at how that impacts an 

individual dose reconstruction, on some individuals, 

you're going to have a dose reconstruction that is 

somewhat lower and in other individuals you're going 

to have a dose reconstruction that's somewhat 

higher. You're sort of homogenizing the data. Am I 

reading that correctly?

Dr. Taulbee: Yes, you are. But the example that I was 

pointing out, though, is if you consider a 

subcontractor who started say 1975 --

Member Lockey: Right.

Dr. Taulbee: -- and ended in 1985, he's going to 

straddle both ways, and I have no idea which way 

that claim would come out.

Member Lockey: And so I think I understand. So this 

is really a value judgment for the Board. What we're 

doing is some people are going to get a dose 

reconstruction using this combined approach that's 

higher than probably is truth and then other people 

are probably going to have a dose reconstruction 

that's lower and does not necessarily represent truth 

for that person, but it's been homogenized across the 

whole group. I don't know where else we can take it. 

But that's, I think, the end result here.

Dr. Taulbee: Right. But one thing I would point out is 

the discussion that we're doing right now is at the 

95th percentile.

Member Lockey: Right. I understand --

(Simultaneous speaking.)
38

Dr. Taulbee: This is a --

Member Lockey: But the subcontractors in the late 

90s, if we stratify them, they're going to have a 

higher dose reconstruction because their 95 percent 

is going to be higher.

Dr. Taulbee: That is true.

Member Lockey: Right.

Member Beach: But there's a judgment call there, 

too.

Member Lockey: No, that's right. It comes down to, 

you know, are we averaging out over the whole 

population and are we okay with that? Because some 

people are going to have a falsely higher dose than 

they really got and some people are going to have a 

falsely lower dose than they really got. That's the end 

result of this.

Dr. Taulbee: It's the really got part of your sentence 

there, Dr. Lockey, that I'm having a little difficulty 

with because we're using the 95th percentile.

You know, in reality the whole distribution is what we 

believe. Now we're assigning the 95th because it 

makes it easier to do that. So, you know, there's very 

few in my mind that are going to be getting, in 

reality, greater than that 95th percentile of the 

combined model. I just don't --

Member Lockey: But that 95th percentile, Tim, would 

change if you stratified it?

Dr. Taulbee: Yes, it would.

Member Lockey: Yes. And you said there's a 20 

percent difference maybe, right?

Dr. Taulbee: Right. That's correct.

Member Lockey: So that's what I'm trying to deal 

with. And that 20 percent now, if you combine them 

is being leveled --
39

Dr. Taulbee: Yes.

Member Lockey: -- across that whole population to a 

certain extent.

Dr. Taulbee: To a certain extent, yes, sir.

(Simultaneous speaking.)

Member Ziemer: Tim, either way, either way at the 

95th percentile for an unmonitored worker, you're 

giving them what is likely completely friendly notes, 

user friendly notes. In other words --

Member Lockey: Jim Lockey, I agree with that. You 

know, I agree with that. I'm just trying to --

Member Ziemer: So it's a little more user friendly, 

maybe slightly so? But it's --

Member Lockey: Is it worth the --

Member Ziemer: In my mind, it's not going to make 

much difference.

Member Lockey: Yes. Is it worth the effort? And I 

would say probably not, not worth the effort.

Member Ziemer: But this decision on which to use 

still looks to me to be site specific, for example, if this 

stratification thing across the board looked different 

all the way across, we wouldn't have this question. I 

mean, it's up and down.

And for a given worker, it's going to pretty much level 

out between various years. If it was consistently 

higher and significantly so all the way, we wouldn't 

have this question. We could say, yes, stratify. But 

that doesn't mean that would occur on another site.

Chair Anderson: The other thing is the exposures in 

different time periods of the subcontractors may be 

different. And that's really what this is saying.

And, therefore, you could stratify in some time 

periods where I would say that the differences 
40

between the groups in the -- and after '80 you have 

a great deal more data. And you look at the 

exposures there between '73 to '78. And we know the 

quality of that and the areas that were sampled is 

quite different than that in the 80s.

So the quality of the data and the extensiveness of it 

in the 80s is much better than it is in the early years. 

So combining all those and then, you know, we may 

want to stratify in certain time periods might be -- at 

other sites that might be what you need to do 

because it has to do with what kind of work the 

subcontractors are doing if that's different in different 

periods.

I mean, the comments made that the subcontractors 

were brought in for some of the dirtier jobs whether 

that's true or not, I'm not sure we've established. But 

that certainly could account for why it's higher, the 

differences between the two groups are different at 

some periods of time because of the use of the 

subcontractors.

Mr. Barton: This is Bob. I would also reiterate that, 

again, this is sort of a Site Profile issue because all of 

this -- the question of whether to stratify or not 

assumes that a co-exposure model for 

subcontractors is feasible.

And not to completely rehash Tuesday's discussion, 

but SC&A does still have some concerns about the 

plutonium monitoring for job specific subcontractors 

during the 70s, namely we only have that comparison 

for '72 to '74 for subcontractors and only for A area. 

There's no analysis for '75 to '79. And just for '72 to 

'74, we only found 65 percent effectively monitored.

Again, that's the combination of those who actually 

have records or are on a job plan with someone who 

has records, which would feed into this co-exposure 

model. So we have concerns about the data for 

subcontractors in the 70s whereas in the 80s we do 

not.

But in the 80s based on this scoping analysis, it 
41

appears that there is somewhat of a difference for 

subcontractors when we don't have those job specific 

concerns based on the RPRT-0092 analysis.

I just want to try to keep this in perspective that what 

we're really talking about is a Site Profile issue that 

already assumes that co-exposure modeling is 

feasible for both periods under evaluation here.

Chair Anderson: And also we have to remember this 

data that's being used is only in the claimants. And 

so there may be -- and, again, so the claimants are 

more apt to be people that have cancers. And we 

don't know is that representative of the whole 

workforce? And those who file, we don't know are the 

subcontractors less represented than the claimants 

because they're not as aware?

Chair Clawson: This is Brad. I would also like to say, 

you know, all of this is bounding on the completeness 

of the data. And if we don't have the completeness of 

the data and feel comfortable with the data and 

everything else like that, I don't see where we can 

because I think that we're stratifying what we do 

have but it may not be the best for either side.

Member Lockey: But, Brad, Jim Lockey. Even if the 

SEC gets sort of through with the whole Board, 

NIOSH still has to use this data.

Chair Clawson: And I understand that, Jim.

Member Lockey: So we should make a decision 

whether on this data we want them to stratify or nonstratify. Don't you need an opinion from us, Tim, on 

that?

Dr. Taulbee: Yes. However, it depends upon how you 

define -- or if the Board does recommend the Class, 

it depends upon how that gets defined.

If you say we can't do plutonium dose 

reconstructions or plutonium dose reconstructions 

are not feasible for subcontractor construction trades 

workers, then no, we wouldn't, you know, because 
42

we wouldn't be able to set a maximum dose.

Member Lockey: Okay.

Dr. Taulbee: So it really depends upon how the -- if 

you recommend a Class and the Board approves it 

and so forth, it depends upon how that gets defined.

Chair Clawson: Well, I've got a -- for the Work Group, 

I've sent out a proposed Class. And once everybody 

has made their reviews on that, that will come into 

use.

So really what you're telling us, Tim, is until we define 

this Class, this is kind of a moot point for right now, 

are we going to stratify or not?

Dr. Taulbee: Yes. I guess based on that that would 

be correct.

Chair Clawson: So --

Dr. Taulbee: Okay.

Member Ziemer: Could you put that chart back up 

that you had on before with the -- yes, okay. If you 

look at those 95th percentile things, I'm just looking 

at the '79 to '87 one right now.

The 279 versus 326, I think you've got to understand 

that this is one of those things where we carry things 

out to decimal places that are way beyond the 

significance of the number. The 279 is probably 300. 

And the 326 is probably 300.

Dr. Taulbee: Yes, sir.

Member Ziemer: You know, plus or minus. Okay? So 

we have much more significance before us than the 

actual number is. To me, those two numbers are the 

same. The same with the 16 and 90. We don't know 

those numbers that closely anyway. If they were way 

apart, like, 200 and 2,000, that's very different. But 

279 and 326, it's the same number as far as I'm 

concerned.
43

Dr. Taulbee: Yes, sir. I agree wholeheartedly with 

you. And how do we --

Member Ziemer: See, I can't justify stratification on 

the numbers that are before us here.

Now I do have some other issues I'm going to have 

to bring up at some point when we get into the 

definition because there's a downside to the 

definition of what we use for if we say we can't 

reconstruct dose with some accuracy.

The downside of that is going to be those individuals 

who do not meet the 250 day requirement or who do 

not have a covered cancer. For those individuals if we 

tell them that we cannot reconstruct any internal 

dose for them, they're not going to be very happy.

They're going to say, you know what? We have all 

kinds of internal dose data and even though you say 

it's not sufficiently accurate, how about if you give 

me what is there? Take that distribution and give me 

at least some of that rather than cancelling all my 

internal dose out because you can't reconstruct your 

accuracy.

We can expect someone whose claim is turned down 

on a partial dose reconstruction because we don't 

give them any internal dose that we're going to have 

lawsuits. I think we already have some of that type. 

So, you know, we have a lot of internal dose data. 

We're saying we can't use that for somebody?

Chair Clawson: Well, I thought we had a statement 

at the end of that that unless they had personal 

dosimetry that we would be able to utilize what we 

did have for that individual.

Member Ziemer: Yes. But you might have someone 

who has no personal dosimetry.

Chair Clawson: Okay.

Member Ziemer: I mean, bioassay. Well, we don't 

need to discuss that now. But I think it's going to be 
44

very important if we say we can't reconstruct with 

sufficient accuracy, we're going to have to address 

very carefully what that really means, particularly in 

the presence of all of this internal dose data.

We have a lot of internal dose data and to say that 

we can only use it if we can tie it in with some work 

order, that's a stretch for me.

Ms. Naylor: And I also want to just remind the Board 

that, yes, we do have lawsuits that have been filed 

against the Department on the partial dose 

reconstruction issues for a site that has SEC Class, 

just like what Dr. Ziemer has said. And also Bradley 

brought out the issues about using their personal 

dosimetry data. 

At the end of that sentence, that boilerplate 

sentence, they also said that could be interpreted or 

be reconstructed by using existing models. So if we 

don't have a method to actually use their data in 

some way to reconstruct a reasonable dose for them, 

then we cannot reconstruct those doses because of 

the SEC Class determination.

Mr. Barton: Tim, you want to clarify that, or should 

I?

(No response.)

Mr. Barton: If you determine that -- Dr. Ziemer, 

you're correct, it would be for an unmonitored 

worker, however, if you're monitored you can use 

that worker's data to reconstruct doses and any SEC 

determination would not hinder that.

Member Ziemer: No, no. I'm talking about someone 

--

Mr. Barton: Unmonitored?

Member Ziemer: -- unmonitored, yes.

Mr. Barton: Yes. Correct. I just wanted to make sure 

that that was understood. So all the data that we 

have that is tied to any future claimants would or 
45

could potentially be used for a partial dose 

reconstruction regardless of any SEC determination. 

It would simply be those workers who are 

unmonitored.

Dr. Taulbee: That is correct. But one thing I would 

point out to you, Bob, is that by -- depending upon 

what you do with the -- or how you talk about the coexposure model here that not everybody under an 

SEC -- in fact only about the additional 30 percent of 

the claimants fall under that SEC. So there is a large 

fraction of people who could benefit under a -- using 

co-exposure model such as this one. So that is an 

important aspect here.

Chair Clawson: Plus, Tim, it could also go the other 

way, too, depending on where you're sitting in the --

(Simultaneous speaking.)

Dr. Taulbee: Yes, sir.

Chair Clawson: -- you got it on both sides. So no 

matter what we take, no matter what stand we take 

we are all taking a chance that we're not going to do 

-- get the best for everybody. And we've known that 

from day one. We are trying to get the best product 

out to the over -- the biggest group that we can. It is 

something -- and I understand what you're talking 

about, Paul, and I deal with this on a daily basis of, 

what is the best for the majority of the people? 

Unfortunately, we're not going to be able to give the 

best to 100 percent of us. We're trying, but it is the 

nature of the good. This is what they handed us and 

this is what we deal with.

Ms. Naylor: Brad, I don't think the issue here is about 

sort of who wins or loses, but I think in designating 

SEC Class we have to articulate a scientific basis to 

support this recommendation to the Secretary that 

dose cannot be reconstructed because that basis is -

- what goes up to the Secretary is taken under 

advisement in making that final decision.

And so if that -- the science that's been articulated 
46

by the Board is really not strong enough and some 

claimants end up with a partial dose as a result of 

that, then we have to defend the Agency's action in 

some way by using the Board's rationale. And that 

could be very challenging for the Agency to do.

So what I'm asking is just that the Board actually fully 

articulate what is it that's causing these data not to 

be good enough to do dose reconstruction so that we 

are accountable to all the claimants here.

Chair Clawson: Jenny, I understand what you're 

saying, and I guess that's the million dollar question, 

but it kind of comes back to what I say, too. We're 

doing the best scientific possible that we can for the 

people, and what's going to give them the correct 

dose, too. Because we're also held under another 

standard, too. Scientifically is it feasible or not? And 

that's been in question many times itself. So I 

understand what you're saying.

Dr. Taulbee: I mean, our -- NIOSH's position right 

now is that dose reconstruction is feasible for the unmonitored workers using the co-exposure models. 

Member Lockey: Tim, Jim Lockey. And that's about 

using best case situation, about 50 percent, right?

Dr. Taulbee: That is correct. Yes, sir.

Member Lockey: And so, I mean, really then the 

remaining 50 percent is assumption, I take it. Right?

Dr. Taulbee: Yes, sir, I -- well --

Member Lockey: I mean, we went back and looked at 

the records at Savannah River to see if there were 

going to be -- provide us any additional help in 

relationship to bioassay data that specifically was 

related to job tasks for the subcontractors.

Dr. Taulbee: Well, that was -- we went back to look 

at specific RWPs or job plans in order to verify that 

those workers were monitored or that we felt should 

have been monitored were monitored, that they were 
47

sufficiently represented in the co-exposure model. 

Okay? 

But the other component to that is, what do we see 

in the individual claimant files? And we're seeing that 

-- at least for plutonium, amongst this time interval, 

that around 50 percent roughly of the 

workers/subcontractors are monitored. And when 

you combine that with the much higher percentage 

monitored of the prime contractors, that that 

combined model would cover that 50 percent that is 

not monitored.

Member Lockey: Alright. So what it really comes 

down to is that 50 percent adequate? I mean, that's 

a question you even asked on Monday: is that 

adequate?

Dr. Taulbee: Yes.

Member Lockey: Right. 

Dr. Taulbee: We believe that it is. We believe that the 

combined representation between the two is 

adequate, keeping in mind that many of that 50 

percent that we're talking about that was not 

monitored didn't need to be monitored necessarily. 

Okay? Not everybody who went into an area was 

exposed. We know that at least 20 percent of the 

subcontractors didn't get an external dosimetry 

badge. Okay? They didn't even go into an area. So -

-

Chair Clawson: And, Tim, vice versa with that 50 

percent. All that 50 percent there probably didn't 

have to be monitored either. You're telling us 

basically to flip a coin and which one comes out the 

best. It's a 50/50 deal. And I'm not -- I thought that's 

why we had the SECs. And I still feel this is an SEC 

for this.

Dr. Taulbee: I guess, how do we want to proceed 

here, to go on through these, because I guess we're 

not -- I mean, I've heard from -- at least Dr. Ziemer's 

opinion on the sub-stratification here, but perhaps 
48

you're -- I mean, well, not perhaps, but, Brad, you're 

right as far as how this plays out as to whether this 

is even going to be important that we stratify or 

whether we even have a model. So, okay.

Chair Clawson: Well, this comes down to how we 

wrote that. 

Member Lockey: Well, Brad, Jim. This is Jim, Brad. 

That aside, if we're just looking at the question of the 

stratification versus not, I don't think it's worth -- I 

would say it's not worth stratifying. I'm just looking 

at that as a stand-alone issue here. Okay?

Chair Clawson: Okay. I understand what you're 

saying there and --

Member Beach: Is this a decision that has to be made 

today? Can we go through the rest of the slides as an 

informational and then see where we're at after the 

Board meeting, or after this comes up for a vote? 

Dr. Taulbee: I'm okay with that. We could go through 

the other observation. We could respond to it. Then 

we could just go through the rest of it and I guess 

just answer burning questions or clarification-type of 

questions, if you'd like. That would -- that's fine with 

me.

Member Ziemer: This is Ziemer again. Could I also 

ask -- and I don't know who to ask this of, perhaps 

Andy and maybe Rashaun. I'm not sure exactly what 

the SEC Work Group's role is in this, whether or not 

we vote on that or we're just here in some advisory 

capacity, because I went back and reviewed our 

responsibilities that we're officially charged with and 

they are twofold: one is to handle those cases where 

there is a situation where NIOSH has found that it 

could not reconstruct dose for one or two people, the 

so-called 83.14 situations, that we would review 

those. 

And the other was to consider conditions where 

there's a significant exposure in addition to criticality 

that could occur in less than 250 days. Those are the 
49

two main things we're responsible to look at. 

And I know we've been brought in on some other 

things relating to SEC, but I'm not sure exactly what 

our responsibility is other than perhaps giving our 

opinions on some of these issues.

Dr. Taulbee: If I could remind you of the co-exposure 

model: you guys had a very large role in that of 

getting the implementation guide through and 

agreeing to that. So that's -- this is actually a spinoff 

of that aspect of it. 

Member Ziemer: Yeah, I don't know if the Board 

actually charged us with doing that or -- it doesn't 

show up in our official list of responsibilities.

Member Beach: I think that was a -- Dr. Melius back 

in, what, 2017 or earlier --

(Simultaneous speaking.)

Member Ziemer: Asked us to do that.

Member Beach: Yeah.

Member Ziemer: Yeah. Yeah.

Member Beach: So maybe that definition needs to be 

updated. Good call, Paul.

Member Ziemer: Well, I mean, we're all glad to give 

our opinions, but I think the ball is actually in the --

Savannah River's Work Group on how to handle these 

things, on this particular case, in my opinion. 

Member Beach: Well, and I think --

Dr. Taulbee: And actually --

Member Beach: Oh, go ahead. Sorry, Tim.

Dr. Taulbee: Actually I think that's some good 

guidance here for us from that standpoint, because 

we are facing a potential for stratification amongst 

other co-exposure models and I'm getting the 

impression from the SEC Issues Work Group perhaps 
50

that you feel that it -- the decision to stratify might 

be based upon the individual Work Groups. Is that --

am I interpreting that correctly?

Member Lockey: I would say yes.

Member Beach: Yes.

Member Lockey: I don't think we can have a onesize-fits-all approach to this.

Chair Anderson: Yeah, I would agree with that.

Chair Clawson: And this is how you got drug into it, 

Paul, is because they wanted to use this as a test for 

us of the -- to be able to make an overall, but the 

thing is, is we can't. Each site is going to be different. 

I don't think we can pick a one-size-fits-all. I really 

don't.

Member Beach: Well, I think --

Dr. Taulbee: On the stratification. Yeah, I agree with 

that.

Member Beach: Yeah, and I think this was --

Dr. Taulbee: That's what I --

Member Beach: Oh. Sorry, Paul. I think this was for 

us, not necessarily for the Savannah River Site. It 

was for us to see if it could -- if it was possible based 

on our procedure.

Chair Clawson: Right. You're absolutely correct, 

Josie. 

Member Beach: Well, the other part of this is we don't 

usually settle Site Profile issues until after all SEC 

issues are settled. And I understand this is happening 

simultaneously, but because you've called for a vote, 

it kind of derails this and we should just go back to 

this as informational at this point, in my opinion.

Chair Clawson: Well, right. And that's why I didn't 

think that we were going to go over this, but I also 
51

have to give due diligence to NIOSH and SC&A for 

the work they did do. And Tim did want to go over 

this. And it's good information for us and especially 

what Jenny brought up to us and stuff, in looking at 

our proposal of -- for that SEC to be able to make it 

the best that we can. And I appreciate that, Jenny.

Chair Anderson: Moving right along. Chair Clawson: 

Right.

Mr. Barton: Okay. Really that Finding 1 is the whole 

question about whether to stratify or not. The 

remainder of the findings and observations are 

concerns SC&A had with the overall method used to 

get to that table that we've been looking at for the 

past hour-and-a-half or so.

So Observation 1 is just pointing out that this 

analysis of whether to stratify or not was only limited 

to the five years. There is more data out there. They 

have all of the logbooks, but those logbooks were 

only used for subcontractors for three of those five 

years evaluated, or maybe it's four. It's three or four. 

And one of the things we looked at was that is there 

a difference by adding in all of this logbook data just 

for subcontractors whereas we used solely NOCTS for 

prime contractors, and for subcontractors we use a 

combination of the logbook data, which is essentially 

all of the data for those years where it was used and 

comparing those two.

And so given that was only five years and this is an 

observation, not a finding, what we're pointing out is, 

is more analysis may be warranted here given that 

we saw what we felt was a difference in that 1980 

period for the subcontractors. The magnitude of that

difference of course is subjective, and as Dr. Lockey 

pointed, is a value judgment. So that's SC&A 

Observation 1.

Dr. Taulbee: Okay. Let me do this then. We'll go to 

the -- sorry for the delay there, folks.

So our response to the observation is that we agree 

that while additional data would provide a more 
52

comprehensive analysis, the current assessment we 

felt was sufficient to conclude that further 

stratification wasn't necessary. The five 

noncontiguous years cover the DuPont era in the SEC 

-- during the SEC range in question that's currently 

being evaluated by the Advisory Board.

Considering the entire set of entire set of analysis 

including the TWOPOS and the intake results, again 

we feel there's no apparent difference between the 

primes and the subs. As Dr. Ziemer had pointed out, 

just the numbers of the 95th percentile, the 279 

versus the 326, they're basically within the precision 

of assigning or estimating what those intakes are.

Now, let's go back to your presentation, Bob.

Okay. Before we go on, is this working for people for 

us backing -- going back and forth? Is this okay?

Member Beach: Yeah, is there a way you can leave 

them both not side by side so it's easier for you to 

switch back and forth like I think John was doing on 

Tuesday, or is this the best --

Dr. Taulbee: No, I can try. 

Member Beach: I just thought it might be easier for 

you, Tim, but otherwise --

(Simultaneous speaking.)

Dr. Taulbee: Can you all see my screen now?

Member Beach: Yeah.

Dr. Taulbee: Okay. So there's that one. Let me put

Bob's here. And then this is my responses. Okay. 

We'll do this and see how it goes.

Mr. Barton: Okay. This is moving onto Finding 2, 

which really has to do with -- we took a look at how 

subcontractors were identified in this analysis and 

whether there might be an issue with prime 

contractors being inadvertently included in the 

subcontractor population or vice versa.
53

Now what NIOSH does is they look at payroll IDs and 

associate certain number references with 

subcontractors. That's based on the dosimetry 

records. So what we did is we went in and we pulled 

35 random claims from this analysis that were 

identified by NIOSH's subcontractors and then we 

went into the computer-aided telephone interviews 

and the Department of Labor file for those 

individuals. And what we found is that 13 of the 35 

subcontractors, or roughly 37 percent, we found 

evidence that designating them as subcontractors 

may have been incorrect. And really the description 

is contained in Table 3 of SC&A's report. And what I 

want to do is just quickly go through that.

And before I give Jenny a heart attack, this is only 

based on what was publicly available. So there will 

be no redacted information in what I'm about to say.

But again, 13 of those 35 randomly selected 

subcontractors, we found evidence that suggests 

they were actually prime. So we have 13 cases and 

we just assign them an arbitrary letter for each case. 

And again that's Table 3 in SC&A's memo.

For Case A, again DOL has specified that the covered 

employees were DuPont, Bechtel, and Westinghouse. 

They did not specify a subcontractor. 

Case B. Again employment was established as a 

contractor. DuPont, Bechtel, and Westinghouse. We 

also found a medical report for that individual that 

listed employer as DuPont. 

Case C. Again only DuPont and Bechtel are listed on 

the claimant's employment form. And the 

termination form says only Bechtel.

Case D. This person appeared to move between 

subcontractor and prime contractor for brief periods. 

So it's a question of which strata they would be in, or 

potential strata based on the employment time, what 

time during their employment.

Case E. In the Statement of Accepted Facts by 
54

Department of Labor, claimant worked for DuPont 

and Bechtel at Savannah River.

Case F. Employment verification indicates contractor 

rather than the option of subcontractor, which 

appears on that form.

Case G. Again, the contractor is designated by DOL, 

not subcontractor. 

Case H. This person was associated with the -- a 

certain corporation that appears to be a 

subcontractor, but that was changed to DuPont and 

then Bechtel in April of 1972, which is exactly the 

period or the start of the period, or close enough to 

the start of the period that we're looking at. And in 

that case other information in that file indicated a 

different payroll number than what was used by 

NIOSH to definitively place that worker either in the 

subcontract or prime contractor strata for this 

analysis.

Case I. There was as Request for Review by Medical 

Panels. That's the official name of the form. And it 

affirms employment by the contractors. And a 

detailed work history that was provided by the 

Energy employee indicates that the energy employee 

started with DuPont. 

Case J. Again, DOL indicates DuPont Construction. 

Also other correspondence in that DOL file and 

correspondence with the EE, that's Energy employee, 

indicates DuPont. And the interview with the Energy 

employee also indicates DuPont.

Case K. Again, we had a medical form that was in the 

'80s that lists DuPont under usual occupation.

Case L. There's a referral document to NIOSH that 

indicates Bechtel and DuPont, and DOL checks 

contractor, not subcontractor. 

And the last case, the 13th case, the Statement of 

Accepted Facts lists employer as Westinghouse from 
55

1978 to 2000. Of course Westinghouse didn't take 

over until the late 1980s, April 1989 I believe. But 

again another medical record during this period 

indicates Westinghouse, Bechtel.

So those are the 13 cases among 35 randomly 

selected cases that were identified as subcontractors, 

where when we go into the Department of Labor files, 

that designation may have been incorrect based 

solely on the payroll ID, which is what NIOSH used 

to establish whether someone was a subcontract 

worker or a prime contract worker.

Now it must be noted -- some 13 out of 35, roughly 

37 percent, we found evidence that suggests they 

were actually primes. The remaining 22 of 35 all had 

evidence in the Department of Labor files that their 

employment was with subcontractors such as MillerDunn or MK-Ferguson, that sort of thing.

What we also did -- so that was sort of one way. 

We're going to test the subcontractors to see if 

they're really subcontractors. We also pulled in an 

additional 25 claims that were designated as prime 

contractors based on payroll ID and all of those

appear to be correctly categorized.

So that's the subject of Finding 2 in SC&A's review 

and it appears to be a one-way street. We found 

some evidence that subs might not have actually 

been subs, but it appears to be 100 percent that 

those designated as prime were in fact prime.

Dr. Taulbee: Okay. There's a bit of a 

misunderstanding here between, apparently, us and 

SC&A as to what we were defining as a subcontractor 

CTW. 

There's two operating divisions at Savannah River. 

Both had construction trades workers: operations 

and construction. We used the five-digit payroll ID as 

the basis for the subcontractors. These were all under 

what you would call the construction division, if you 

will. They had specific contracts for electricians --

subcontractor; sorry, for electricians using Miller-
56

Dunn. For pipefitters, it was BF Shaw. For North 

Brothers, those were -- those covered the insulators.

But not all of the construction trades workers had a 

subcontractor. DuPont Construction would hire 

directly out of the union hall, so they would appear 

to be a prime CTW, but these are generally 

temporary workers like a subcontractor. I don't 

believe that there was ever a subcontractor for the 

sheet metal workers, which you will see as several of 

the ones that Bob noted in their listing of 13 there.

So it wasn't specific from that standpoint when they 

hired out of the union hall. Okay? They could very 

easily have been as DuPont Construction and work 

for four months and then they left. They went to 

another place. 

So we called those -- the thing that DuPont did was 

they included them in the construction trades worker 

division with that prefix that they had for that job 

code, or for that particular trade. So there's a fivedigit payroll that had a two-digit prefix to it. So we 

can identify who was a laborer, who was a sheet 

metal worker, who was a pipefitter. So we can go 

down to that level of detail from the quarterly 

dosimetry.

So regardless -- like I said, DuPont assigned these 

workers that five-digit payroll, which is why we used

it that way. The people we are calling DuPont 

Construction are the people who were until Roll 2. 

These workers were all typically Roll 4, sometimes 

Roll 5, and then later it was more Roll 6 toward the 

end of the DuPont construction, the late DuPont era.

So the DuPont Construction that we're referring to 

are the maintenance mechanics and the E&I 

technicians. And those were Roll 2. They did not have 

five-digit payroll IDs. They were the ones who were 

solely DuPont, which is what Bob noted there 

whenever they did that check of the DuPont people. 

There was no other subcontractors for them. 

These are people who were under Roll 2. They were 
57

-- some sites might refer to them as technicians, but 

these were actually maintenance mechanics, building 

mechanics and E&I technicians. So they would be 

doing some of the electronics work. It depended upon 

what was going on and somewhat dealing with DavisBacon-type of rules of how much would be spent on 

a particular job.

Those are the DuPont construction folks, the Roll 2 

folks. They do not have a five-digit payroll ID. The 

five-digit payroll ID folks: those could have been 

hired out of a union hall or one of those other 

subcontractors, so you will see the mix that Bob is 

displaying there. We believe the five-digit payroll ID

is the best indicator of these subcontractor 

construction trades workers that would put that 

whole group together, your temporary transient 

workers, as I believe I've heard Joe refer to them. 

Questions?

Mr. Barton: Brad, you're on mute, if you were trying 

to say something.

Chair Clawson: Yes. Thanks, Bob. But you're not 100 

percent.

Here's one of the things we found out in the 

interviews and talking with people: I've always told 

you that Savannah River is a unique site because for 

their workforce for their maintenance and everything 

else like that, they use the trades. There are some 

trades that have been there for 25 years, but the 

thing is it's not uncommon for them to be working for 

DuPont for six months, all of a sudden drag up and 

go onto a construction site and still be on Savannah 

River. And they said that their numbers never 

changed. One of them had worked on construction 

for two-and-a-half years and then went back to 

DuPont as a maintenance person for them. 

Dr. Taulbee: That is correct. And those people were 

all given that five-digit payroll ID. The DuPont folks 

that I was talking about, the E&I technicians, I do not 

believe that they were the ones they were bouncing 

back and forth. The five-digit payroll ID out of Roll 4, 
58

they were the ones bouncing back and forth.

Chair Clawson: Okay. Because one of the big drivers 

on this -- and this is what all of them have told us --

Savannah River paid X amount of dollars and if a 

construction job come onto Savannah River, they got 

almost a $5 an hour raise. So this is where a lot of 

this jumping would always come off to. And I don't 

think that they really changed a lot of those numbers 

in that. 

But that's neither here nor there. They're all 

considered construction trades. And it's just -- that 

picture that you're painting, it looks fairly good, but 

I think it's got some flaws to it.

Mr. Barton: Should we move on?

(No response.)

Mr. Barton: Sounds like it. Moving on to Observation 

2, and this -- just kind of reflected -- it's an 

observation -- that additional data was quoted from 

the law books just from subcontractors for 74, 83 and 

86, I believe. And all other evaluated data was based 

solely on the claimant records. And we did some 

rudimentary analysis of that to see if there's any bias, 

and there was some indications of that. Not huge. But 

we wanted to point it out and say, you know, if -- if 

the Work Group wants to sort of flesh this analysis 

out a little bit to get a better handle on it -- to actually 

go back and code that logbook data, then you'd be 

looking at the full population of workers rather than 

sort of mix and matching NOCTS data, which is -- it's 

only NOCTS for primes. And then subs is a 

combination for some years. So this is a question of 

whether it's worth it to go back and code those 

plutonium logbook raw results.

And one thing I'd point out here is that the coexposure model as currently formulated only uses 

the claimant results. Whereas, for plutonium -- and 

those are often censored. So when you have the 

censored values, you really have to -- you can just 

go back and pull the claimants out of the logbooks.
59

But I'd also point out that for -- for something like 

americium, NIOSH pulled all of the data for the entire 

site, coded it, and that's how they based their coexposure model. And we have those same logbooks 

for plutonium. In fact, often the -- they're the exact 

same logbooks as -- as the americium logbooks. So 

I guess I question why that data just wasn't coded 

because they're the raw results. They're -- they're 

better because you don't have the -- the censored 

results, and you have a full sample of the exposed 

population. But again, this is just -- it's an 

observation about how data was sort of selectively 

coded based on limitations of what was found in the 

actual claimant database for subcontractors.

Dr. Taulbee: Okay. Is that the completion of that 

one?

Mr. Barton: Yes.

Dr. Taulbee: Okay, alright. Alright, give me --

Member Beach: I am trying the hand-up method 

instead of interrupting.

Dr. Taulbee: Oh, I'm sorry. I can't see you right now.

(Laughter.)

Member Beach: Oh, well darn it.

Dr. Taulbee: There.

Member Beach: My question for you was, the 

suggestion of coding all the logbooks, can -- is -- I 

know I read several reports. Is that part of your 

report -- that you said it would take years? Or can 

you break out the logbooks -- do you have them in 

hand at NIOSH at this point? And what --

(Simultaneous speaking.)

Dr. Taulbee: Yes.

Member Beach: -- sort of effort are -- are we talking 

about for that?
60

Dr. Taulbee: We are talking years to try and code. 

We do have them in-house. That's how we've gotten 

-- and can go back and look at some of this data in 

more detail. We captured all of the logbooks for all of 

the radionuclides at the Savannah River Site. So 

those are all in the SRDB. You can go and look at 

them. So they're all out there.

But coding all of those handwritten logbooks, for the 

time period up through 1989 -- actually, through 

1990 -- is just a tremendous effort. What we've done 

is we've taken -- when we get a claimant -- when we 

get a claim, we take their bio-assay card that's part 

of the Savannah River documentation that they 

provide with us -- provide to us. And that gets 

entered electronically when we're doing the dose 

reconstructions. We took that database and that's 

what we used to develop the co-exposure models. 

Instead of going back and taking years to code all of 

the data.

And -- and you see how long that that's taken us to 

do -- taking that electronic data. So it -- this is a --

this -- that would be a multi-year effort.

Member Beach: Okay. And I've got one more followon, the hypothetical question. If the SEC does get 

granted through 90, does that change the -- the 

logbooks that would need to be coded if we decided 

we would like all those coded. Would you just go from 

'91 on? Or would you still have to do the full time 

frame?

(Simultaneous speaking.)

Dr. Taulbee: To come up with what? Go ahead.

Member Beach: The co-worker model.

Dr. Taulbee: Depending upon what you would define 

-- or how you would define that, what the dose 

reconstruction infeasibility was, we would likely still 

just use the claimant data that we have because we 

have a large amount of claimant data. We've been 

focusing here -- these numbers are specifically on 
61

subcontractor construction trades workers. When you 

look at the full population that comprises the coexposure model for non-construction trades workers, 

it is significant. It is much, much larger. So I don't -

- we don't have any plans to go through and code all 

of the -- the logbook data.

Member Beach: Okay, thank you.

Mr. Barton: And if I could just comment here. On the 

second bullet there -- the TIB-75, I'd just point out 

that that document is still technically under review. 

In fact, I checked the BRS yesterday in past 

discussions, and Finding 4 from that review of TIB70 -- I'm just going to read what Finding 4 was, which 

is still open. No analysis of uranium or plutonium 

exposure at SRS was possible because the available 

hard-copy data have not been reduced to electronic 

form. And then it goes on to talk about uranium and 

fission products being the same and that -- OTIB-75 

includes the comparison, essentially, for -- only for 

tritium and then only from 1991 to 2001.

So while it does say here that you can absolutely use 

claimant data sets to represent the fully exposed 

population, I think that's still under discussion. And 

Finding 4 we did include in our review of the coexposure models, I believe in Attachment B. So that's 

still open and under discussion. So -- and I am not 

sure that that's been settled yet. And that's my only 

comment on those response.

Dr. Taulbee: Okay. I would say that, keep in mind 

that OTIB-75 was designed to take current data sets 

that were already ready, that could be used to 

evaluate, and that was what was done. And you're 

absolutely right, Savannah River, we have not coded 

all of the plutonium. And we didn't have anything 

other than the tritium at that time period.

We have since obtained some of the electronic data 

from Savannah River from the post-1990 time 

period. But again, the OTIB-75 I believe is under the 

Procedures Work Group. But our current basis is that 

OTIB-75 provides the justification of why a claim --
62

work claimant population represents the same 

exposure potential as the non-claimant population. 

And until demonstrated it doesn't, that -- this is our 

basis.

The inverse is true -- that if we use the entire 

population, that that would represent any future 

claimants that we have. So that's our response to --

you know, your observation, you know, back here 

about a potential bias. We don't see any evidence of 

that when you look at it between a full data set and 

a claimant population. As long as the two are 

sufficiently large. Questions?

(No response.)

Dr. Taulbee: No? Okay. Bob?

Mr. Barton: Yes. Okay, so moving on to what's known 

as regression analysis. And just to kind of go through 

these -- there's a couple of slides and then we'll get 

to what the findings and observations were 

associated with that. So NIOSH noted in their 

analysis that the hard copy data often only contained 

an un-normalized bioassay result. And that would be 

activity per a disc, rather than a normalized result, 

which would be activity -- in dpm, not counts, but 

disintegrations per minute, personal volume --

usually per liter, or per 1.5 liter.

So what NIOSH did when it -- so, Tim, you can 

describe this a little bit better, is when you had this 

un-normalized disc result, they said alright, what 

we're going to do is we're going to fit a linear 

regression line through all of the results where we 

have an un-normalized result and a normalized result 

and come up with a linear formula that apply to 

bioassay that only has the un-normalized result to 

essentially calculate what the volumetric sample 

would be.

In any case, NIOSH assumed a linear relationship 

calculated for each year under analysis to convert 

any hard copy data that was un-normalized. And 

again, that would be a dpm per disc result. But we 
63

don't know what the actual concentration would have 

been back to urine results, because it just simply 

wasn't normalized. You can go to the next slide, 

please.

Actually -- let's see. Okay. This -- this also gets into 

a QA finding, but we actually notice situations where 

a dpm per disc result was actually listed as zero, or 

possibly even negative. But that normalized result 

came back as sometimes a positive result. We 

question whether the linear regression really has a 

meaningful numerical relationship to try to convert 

these un-normalized results to a normalized result in 

per volume of urine. What we found -- and that was 

Observation 3.

We also found that some of these data pairs that we 

looked at -- and they only piqued our interest 

because, again, this is either a negative or zero 

measurement on the disc, and somehow the site was 

coming up with a positive normalized result, which 

doesn't make a lot of sense but in those -- those 

specific situations we -- we followed up, went back to 

the logbooks and found that in some cases it was 

transcription errors into the electronic database. 

Sometimes they were just very difficult and hard to 

interpret. Sometimes there were legibility concerns 

that would explain why -- why we saw some of these, 

again, very unusual results.

So it was really two issues here. Can we use -- can 

we create a meaningful linear relationship between 

the activity on a certain disc measurement and what 

the normalized result would have been in cases when 

we only have disc measurement, and the site didn't 

actually provide a normalized result. And based on 

just looking at some of those -- again, some of the 

really abnormal ones, with the negative or zero 

measurement on the disc and coming up with a 

positive result, we wondered -- especially when some 

of them were discovered to be transcription errors, 

what quality assurance was applied to this data set 

as would be done -- because we're trying to figure 

out whether to stratify the co-exposure model. You 
64

know, what quality assurance do we have that the 

added logbook data that was transcribed here is 

entirely accurate to make a determination on 

whether we should stratify or not? And that was 

SC&A's Finding 3.

I mean there is -- one of those such linear 

regressions that was used. The orange dots are 

essentially what the linear relationship is calculated 

to be and the blue dots are the actual observed 

results where we have a dpm per disc and a 

normalized result. Now this is truncated, which is 

noted there at the bottom and was noted in the 

NIOSH response, but I would point out that this 

shows essentially 95 percent of the data, or if you 

include records from one chelated individual, it 

actually drops to about 92 percent of the data just 

because there is some not shown that were 

essentially higher than what we have here. Next 

slide? Okay. I think you're up, Tim.

Dr. Taulbee: Alright, let me go back up here. I think 

it's Finding 3, right? Okay. So I want to just quickly 

go back and -- you know, the goal here was to 

investigate whether there is any evidence that we 

should further stratify the population. So it's not 

intended to be a full co-exposure analysis. Again, we 

picked a few years so that was could do some 

comparisons and see, does this make a big difference 

that would warrant a stratification. We did not do full 

quality assurance tests that were performed on this, 

nor do we feel it was warranted. That would have 

added months and months of time to do this. So it 

was developed solely to evaluate whether further 

stratification was needed.

We recognize that the dpm per disc to dpm per 1.5 

linear regression is not perfect, okay? Some of the 

data deviate due to suspected miscalculation of the 

original data -- transcription errors, as Bob pointed 

out. Chelation, and -- but a lot of this is due to 

different aliquot sizes, okay? That's actually one of 

the bigger drivers that we believe is -- well, actually 

from what we can tell, is the cause of this. One of the 
65

things I want to point out with this dpm per disc to 

dpm per 1.5 regression is that we looked at a very 

large range here. And what SC&A expanded there --

and as Bob mentioned, it's truncated -- but that's the 

relative scale there, is that little red box that we see 

here.

Okay, so if we blow that up, this is what you see. This 

is the duplicate of Bob's graph. What we've added is 

we've twisted it -- or changed it a little. I shouldn't 

say twisted. Changed it to highlight who was 

chelated. And what you see -- that red line -- that 

whole separate regression going on is somebody who 

was chelated due to a different aliquot size --

different volume size. And so we didn't use that data 

at all in our regression, okay? Those were pulled out. 

The regression was done.

And so this is what the regression really looks like 

that was used. Now we could go through and we 

could change -- or, not change, correct all of the 

aliquot-sized variations and so forth. And many of 

these lines -- many of these points -- would end up 

down here on this line. But I really don't think -- and 

none of us believe at NIOSH and ORAU -- that this 

regression is going to change much by making those 

corrections. I mean, the regression is very strong 

from this region. And -- and so making these 

corrections and changes -- we just don't think it's 

going to change that regression line at all. And what 

we did was we applied this regression to those results 

where we only had the dpm per disc value. We 

applied the regression to get a dpm per 1.5 liter to 

substitute the less than 0.1 value that was recorded 

as their final result.

Okay, so this was to fill in for censored values. That's 

all that this was. We know the results were less than 

0.1. We were trying to, within those box plots, figure 

out where that 75th percentile, where that 85th 

percentile was. That was the only goal here -- was to 

try and reduce the amount of censored results that 

we've got to improve what the TWOPOS value was. 

So that's -- that's our response to this. Questions?
66

Mr. Barton: Well, I have two comments, if there 

aren't any direct questions right now. Again, I spoke 

with one of our statisticians in preparing for this 

meeting. And I think it's just the -- this is the first 

time I've ever seen this method used to take an unnormalized result and normalize it. And so it certainly 

gave us pause.

One of the things our statistician said that, you know 

-- you -- regression methods to essentially expand 

your sample size is you get a bigger sample size. And 

that's -- can be a valid statistical technique. However, 

this adds uncertainty when you do that. And so that 

-- any uncertainty associated with using this type of 

a method needs to be appropriately considered in any 

-- any modeled numbers that you get off that.

Also we note that in these regression analyses there 

was really no metric used, such as an R-squared 

value, to say how good of a fit these regressions 

actually were. They're essentially just a visual of how 

well it fits. And I am going to read this, not -- my 

statistics aren't that great, but I am going to read 

exactly what our statistician said. He's -- in response 

to my question about there being no R-squared 

values. And he said, if we really want to assess 

NIOSH's regressions, in addition to R-squared values, 

we want to look at regression coefficients and tests 

of significance for those from residual plots and 

information on leverage as it pertains to outliers. I 

can't say anything other than -- than quoting him 

here. And again, I apologize, he had a -- he was not 

able to join us today for -- he just had -- he had other 

obligations that were set well in advance and so he 

couldn't join us. But that -- that was his comment.

Now the other part of this -- they -- I tried to get a 

handle on how often was there an un-normalized disc 

result where we really had to use this regression 

analysis to fill in the normalized results so that we 

can add it to the sample size for evaluation here to 

determine whether things should be stratified or not? 

And so I went into the raw data provided by NIOSH. 

And I looked at all the individual disc results and 
67

normalized results, and I counted up -- literally 

counted up how many times I saw a disc result 

without a corresponding normalized result, in which 

you would -- NIOSH is proposing to use this 

regression formula to sort of fill in the blanks. And by 

my count, it was only 1.6 percent of the entire 

measurements had a dpm per disc result and then 

did not contain a normalized result.

So this entire discussion may be sort of moot here 

since we're only looking -- this was only applied to 

what we see as a very small portion of the data. But 

we do have those concerns, as I stated, from our 

statistician about using such a method as regression 

to sort of expand the data set if it's -- is intended to 

be used in any future situations. But like I said, I am 

a second point, it really doesn't appear to affect much 

here as it's only 1.6 percent of the -- of the data pairs 

-- or unpaired, essentially.

Dr. Taulbee: I guess my comment to that would be 

if, you know, that's the case, then why are these 

findings and not observations? If -- if you don't 

believe that it's going to have a major impact on the 

analysis?

(Simultaneous speaking.)

Mr. Barton: Well, arguably yes, we do have technical 

issues with using this type of approach to fill in the 

results. But really, it's a finding because we don't 

want to necessarily see it used going forward unless, 

you know, these additional uncertainties are 

considered and also a real discussion -- and 

quantifiable discussion of outliers that are viewed and 

what affect this has on the overall data set. Again, it 

was in prep for this meeting that I said, alright, let's 

see -- you know, how often do they actually use this? 

Because in the NIOSH report, it seems to indicate 

that they needed it for a lot of results. They said, well 

alright, are we talking about 50 percent of the 

results? You know, 60 percent of the results? And as 

it turns out, it's 1.6 percent of the results. And that 

was done, again, in preparation for this meeting, to 
68

try to get more perspective on it.

We did not do this in the original review. We were 

essentially going on what NIOSH said that -- and I 

think it's in your response there that, you know, a 

large portion was indicated. So we assumed it was a 

large portion. But upon review, it does not appear to 

us to be a large portion.

But we do have technical issues with using a 

regression method, if in fact it will ever be used in 

the future.

Dr. Taulbee: It might be, I don't know. Okay. Next 

finding?

Mr. Barton: We already -- we really already discussed 

finding four -- finding three and four, were again, 

about this regression analysis and whether it's 

technical justified to be used. And again, in this 

situation, as it turns out, it wasn't a large portion of 

the data set, as it was indicated. It's a very small 

portion of the data set, so it does not affect the 

results here. But again, we -- we have technical 

concerns if it's going to be used in the future, which 

I believe I -- I basically indicated this was the first 

time but you're unsure if it will be used in the future. 

So this is really a cautionary finding, or if it's used in 

the future, we've got to be really careful with it.

Dr. Taulbee: Okay, let me pull up my -- our response 

to that then.

(Simultaneous speaking.)

Dr. Taulbee: I said it greatly reduced the number of 

censored data for this analysis. I am actually 

questioning your one percent, but I'll -- I have not 

looked at the data in that detail, but I won't question 

it here. I do question internally, but -- I'll -- I'll take 

it that that's what it was.

But although not perfect, the vast majority of the 

data fell on a straight line over a very large range. 

And that's -- that's the driver for the regression. We 
69

-- like I said, we could make further corrections on 

aliquot size, but we don't believe it will significantly 

change the regression, so we don't believe that this 

is -- worth doing, effectively. It's just -- for this type 

of analysis, if this was going into a co-exposure 

model, absolutely. We would have done all of the QA, 

everything associated with it. But this was just a 

comparison of whether to stratify or not. That's it. 

Okay.

Mr. Barton: Okay, just to sort of sum up what our 

review was here is -- the refined analysis is limited to 

a handful of cases in five of the years in that DuPont 

era. Again, we talked about the payroll ID numbers 

and are they alone sufficient to designate a 

subcontractor versus a prime contractor. But we 

point out that quality assurance -- in the period -- at 

least, it's not discussed in the report and certainly, it 

doesn't appear to be the level of a co-exposure 

model. And I can understand NIOSH's position on 

that. This was -- this was meant to be an analysis on 

whether to stratify. But you know, when we're talking 

about essentially calculating co-exposure intakes, I 

mean, it seems to me that you need some assurance 

that the data set that you coded is -- is acceptable.

Again, we just talked about it that we question the 

use of linear regression to be able to convert those 

values that are un-normalized into a normalized 

result. And there -- there are certainly some -- two 

qualifiers there about what needs to be considered if 

you're going to use that in the future. And that 

includes the uncertainty added when you do that sort 

of thing.

And then, again, this goes back to the Finding 1 

where that -- the 95th percentile subcontractors had 

higher intakes by roughly 20 percent in that 79 to 87 

area. And that's the period when there was 

significantly more subcontractors being brought on 

site -- at least, when you look at the claimant 

population. A minimum of subcontractors there. And 

that's shown even in comparison to the total worker 

population. So you have more subcontractors on site, 
70

and at the 95th percentile, you have slightly higher 

intakes calculated again, in the -- that 1980s period.

So I -- in summary, we don't agree that this NIOSH 

analysis necessarily demonstrates that sub-CTWs 

and prime CTWs are definitely part of the same 

exposure strata and thus don't need to be considered 

for stratification. Again, it was in that 95th percentile 

comparison and also some drawbacks to the analysis, 

which I fully understand that to rectify some of these 

issues would take considerable work and it's certainly 

not SC&A's position to comment on resources and 

what's important to do, and what's not important to 

do. That's entirely, obviously up to the Work Group 

and, you know, NIOSH's own resources. So the fact 

that -- I think NIOSH agrees that were there time, 

they certainly would have done much more. But it 

sounds like the resources and time is costprohibitive.

So that's -- that's the summary of SC&A's review. Oh, 

and we did propose a path forward. And that was to 

expand the analysis to the remaining years in the 

SEC, at least for the DuPont era, which we're really 

looking at. And really the DuPont era alone because 

that's only where the co-exposure model covers. It 

ends in the late '80s for most things -- 1989. And it's 

suggested that co-exposure intakes from that DuPont 

era could be perhaps extrapolated forward and used 

for the 90s. I am not sure if that's still NIOSH's 

position. But again, our proposed path forward was 

to go in and get that logbook data and you'll have a 

much better idea -- you'll have more granularity to 

make a determination on whether stratification is 

necessary.

We felt that -- that report should document quality 

assurance that was done on the transcribed data set. 

And either correct -- correct those or, if there's an 

unacceptable number of transcription errors or what 

have you, that -- that you might have to go back and 

re-code, which -- not to give a preview of coming 

attractions, but when we get into the -- an americium 

discussion on the next item, that's exactly what 
71

NIOSH is going back and doing. Though -- and that's 

for a co-exposure assessment, which again, this is 

not necessarily a co-exposure assessment.

The exercise is to act as if it is to see if there is 

actually a difference between the two populations. 

And also, the issues that we found with identifying 

subcontractor designations -- and then the partially 

explained qualitatively in the NIOSH response. But 

again, DOL, when confirming these people's 

employment, considered them contractors. So the 

question is whether payroll ID alone is enough to be 

sure that all of these workers were actually 

subcontractors -- or subcontractors instead of prime 

contractors.

And we recommend not using regression methods for 

samples with un-normalized results. In this case, it 

really doesn't matter because even though -- at least 

our -- our count of the raw data provided by NIOSH 

when we looked -- unpaired measurements was quite 

low. Often you wouldn't have the dpm per disc, all 

you had was the normalized result. So -- and again, 

in preparing for this meeting, I had our statistician 

take another look at NIOSH's response and 

essentially he came out with -- again, this is Richard 

Griffiths who -- who I had do the first secondary 

review and check my work -- he said, well you can 

use regression, but there are a number of caveats 

including taking the -- count the uncertainty when 

you do that, and explain any variance in that result 

when you use a -- a linear -- linear model. And I 

believe that's my last slide, but -- okay, here are the 

references that I cited and then sort of the concluding 

slide here.

Dr. Taulbee: Okay. Alright -- yeah, that's pretty 

much it. I -- just from NIOSH's standpoint in 

wrapping up, the whole methodology was to do a 

simple comparison to see whether we should further 

sub-stratify and -- and that was the -- that was the 

goal with this particular analysis. So I'll leave it at 

that. Any questions?
72

Chair Clawson: Any Board members for the SRS have 

any questions?

(No response.)

Chair Clawson: Okay.

Member Lockey: Brad, I just have one comment. This 

is to Bob. Bob, I -- when I listened to your summary, 

I didn't get an idea of level of importance of your 

points. Some of them were, for me, were not really -

- reach a level that I would consider important. Are 

there any of those there that you feel were 

overwhelmingly important and have to be 

addressed?

Mr. Barton: I think it's -- it comes down to the 

question of whether we're going to stratify here. We 

have some current concerns with the method that 

came to these numbers. But at the end of the day, 

the method that was used and the -- the intakes that 

were calculated -- we saw that difference in the '80s 

for subcontractors. So that certainly raises the 

question of whether there are differences between 

the two populations for us. And again, that's a value 

judgment. But that's really the name of the game.

We saw higher intakes at the 95th percentile, and so 

we felt -- if we're going to conclude that they're the 

same population, we might want to do a little bit 

more work to have more granularity making that 

determination, including a more robust quality 

assurance method and going in, grabbing more of the 

logbook data and perhaps more years.

Member Lockey: Do you actually think that would 

influence and make it other than a value judgment at 

some point in the future? Do you think that would 

really have an important impact on that?

Mr. Barton: Well the only way I think that it wouldn't 

be a value judgment would be if you put in enough 

data and there was a statistical test that you could 

apply to it. I'm not sure what that would be. Again, 

I'm not a statistician and our statistician was not 
73

available for this call. But I mean it would be -- I think 

you'd have a better idea, certainly. But again I -- the 

numbers we see here -- it just -- it seems like there's 

-- there's reason to think that maybe there is a 

difference between the two populations and certainly 

more robust analysis, I think, would bear that out. Or 

at least, give us an idea of what's out there. And in 

turn, you then end up with a more robust -- robust 

plutonium co-exposure model. Because now you 

wouldn't be using just the NOCTS database, 

necessarily. You would be using the entire site 

population.

Member Lockey: Okay, thanks.

Dr. Taulbee: Can I -- if there's no questions, and 

before we move on, can I propose we do a break?

Chair Clawson: No, this is how we keep this wing 

down. Yes --

(Simultaneous speaking.)

Member Beach: I second that.

Chair Clawson: Hey --

(Simultaneous speaking.)

Chair Clawson: Let's take a ten-minute break, if that 

would be alright, Rashaun?

Member Lockey: Brad, let's make it 15.

Chair Clawson: Oh my goodness.

Dr. Roberts: Yes, I think a longer break is -- would 

be helpful. So 15 minutes. So we come back here at 

about 1:15 Eastern.

(Whereupon, the above-entitled matter went off the 

record at 1:01 p.m. and resumed at 1:15 p.m.)

(Roll call.)

Dr. Roberts: Okay. Very good. I think we've got 

everybody, so we can continue with the agenda.
74

Dr. Cardarelli: So Rashaun, I'm assuming that's my 

cue. Okay.

Dr. Roberts: Yes.

Issue 6: Status of Open Issues on SRS Co-Exposure 

Model (OTIB-0081 Rev. 4)

Dr. Cardarelli: Okay. This presentation is going to be, 

hopefully, fairly quick. I'm just going to be talking 

about the status of the various findings and 

observations that have been made by SC&A on OTIB0081, Revision 4.

This spans 13 concerns which -- we'll call them 

concerns. In September 2019, there were six 

findings. And then they were changed to five findings 

by March of 2020. And that one finding became an 

observation, which caused our observation numbers 

to go up by one.

As you can see in the sub-bullets, Finding 1 is the 

only kind of remaining open. And a presentation, 

detailed presentation will be following this talk.

Findings 2 and 3, we are recommending closing. And 

Findings 4 and 5 were closed in the last Work Group 

meeting last year.

Only Finding, Observation 7 was closed last year. But 

Observations 1, 2, 3, 4, 5, 6, and 8 we are 

recommending to close.

And I will start with Finding 1. And I'll just read this 

for the record.

Although SC&A recognizes that the incident-based 

sampling involving chelation is not considered in final 

coworker modeling, the removal of DTPA-influenced 

samples from consideration in the analysis of the high 

variability observed in the trivalent actinide bioassay 

results has not been justified sufficiently.

Evidence suggests that the variation among DTPA 

and non-DTPA samples is nearly identical. 

Furthermore, OTIB-0081 has not provided any 
75

reference to justify the assumption that DTPA causes 

heterogeneity among a single urinalysis voiding.

The status remains open, and we will have a 

presentation on this following this particular talk.

In summary, though, NIOSH agrees that the SC&A -

- with SC&A that chelation therapy is not a source of 

variability in repeated counts of a given planchette.

We do not agree with SC&A that the observed 

variability in the repeated counts prohibits the use of 

our -- of this bioassay data for developing coexposure models.

And there's no definition for high variability. We 

believe it's a subjective decision. Research on this is, 

on this issue is currently being performed by NIOSH. 

And Dr. Taulbee will be presenting what we have on 

that right after my presentation.

So Finding 2, this one, the status is recommending 

closing. But to read it into the record, use of imputed 

values that are less than one-half of the minimum 

detectable activity, the MDA, raises a fundamental 

fairness issue in that monitored workers who have 

bioassay results that are less than the MDA are 

assigned a missed dose in accordance with ORAUTOTIB-0060, which is the Internal Dose 

Reconstruction document.

Our response to that is SC&A's memo dated on June 

3, 2020, and it's in the SRDB database, the number 

is 182225 and it's entitled Review of the Multiple 

Imputation Methods Applied to Censored Bioassay 

Data Sets, concluded, quote, the use of the multiple 

imputation in evaluation of bioassay data sets with 

censored results is technically appropriate, 

scientifically defensible, and likely of small practical 

significance when considering its effect on resulting 

Probability of Causation calculations.

As a result of this -- that was unquote. Therefore, as 

a result, NIOSH is recommending closing this 

observation.
76

Finding 3, which is basically a discussion about 

claimant cutoff for data, it was originally Finding 4.

But to read it into the record, the coworker analysis 

uses the internal monitoring for claimants for which 

data were available to NIOSH in approximately 

August of 2011. There was around 4,000 claims.

Since that time, approximately 2,000 additional 

claims have been submitted that could be used to 

augment the coworker data set. Inclusion of these 

data would be especially important for the two 

contaminants that required a combination of multiple 

years for analysis due to lack of sufficient number of 

data points, specifically uranium and cesium.

NIOSH is recommending closing this particular 

finding based upon the transcripts of the December 

5, 2019 Work Group meeting, specifically on page 

165. It was decided not to pursue the inclusion of this 

additional data.

However, we could not find in the transcripts where 

an official vote was taken. Therefore, NIOSH believes 

this finding is closed. But we would like confirmation 

from the Work Group.

Finding 5, which was -- or Finding 4, which was 

originally Finding 5 in the September document, 

classification of a machinist as a non-CTW in OTIB0081 is inconsistent with its classification in OCASPER-014, which is entitled Construction Trade 

Workers.

This status has been closed by the Work Group as a 

result of the votes that were taken in the December 

5th Working Group. And I provide the pages 145 to 

146 in the particular transcripts there.

Finding 5, which is entitled Construction Trade 

Worker Misclassification Evaluation, this was 

originally Finding 6 in September of last year's 

document.

And it states, a target sampling comparing the OTIB-
77

0081 strata designation, construction trade worker or 

non-construction trade worker, against two alternate 

sources for identifying worker job classifications 

indicated that just over nine percent of the entries 

appear to be in conflict when comparing the NIOSH 

and SC&A analyses.

Again, this is a closed finding, same pages that we 

mentioned in the last one.

So, Observation 1, multiple imputation, to read it into 

the record, while the multiple imputation method is 

mathematically correct, it has the potential to result 

in biasing the simulated bioassay results 

unnecessarily low.

Alternate approaches, such as the maximum possible 

mean method, which replaces censored data with the 

actual censoring limit, or alternatively one-half the 

censoring limit, would solve the issues associated 

with data sets containing a large number of censored 

values in a claimant-favorable manner.

We recommend closing this particular finding based 

upon the SC&A memo, which is dated June 3, 2020, 

and the SRDB database, again, the number is 

182225, where they reviewed the use of multiple 

imputation methods applied to censored bioassay 

data, where they concluded that the use of this 

approach, multiple imputation, is technically 

appropriate.

For Observation 2, it's just a kind of continuation of 

this multiple imputation discussion. We recommend 

closing it.

But the observation itself states, a scoping 

assessment concluded that while intakes and doses 

are significantly higher using a missed dose approach 

in most of the sample calculations, the overall effect 

of the resulting Probability of Causation values was 

relatively minor.

And in most cases, the coworker derived Probability 

of Causation bounded the missed dose evaluation. 
78

This appears to be due to the effect the statistical 

distribution has on the resulting Probability of 

Causation values, mainly the use of the triangular 

distribution for missed dose evaluation versus a lognormal distribution for coworker data.

Our response in, or our justification in recommending 

closing this particular observation is SC&A noted that 

the calculated intakes and doses differed between the 

multiple imputation method versus the limit of 

detection divided by 2 method, but concluded that 

the overall effect of the Probability of Causation was 

relatively minor. And in most cases, the co-exposure 

derived Probability of Causation bounded the missed 

dose evaluation.

And again, we referenced the SC&A memo, which 

was dated June 3, 2020, as the justification for 

closing out this particular -- or recommending closing 

this observation for the Work Group.

Observation 3, multiple imputation specifically 

targeted on uranium, it was originally a finding. This 

was the one that was changed to an observation.

It states, the sample comparison of co-exposure 

intakes to a missed dose method for uranium showed 

that the co-exposure model derived intakes were a 

factor of 4 or more higher than the missed dose 

approach.

This illustrates the potential for inequity between the 

treatment of unmonitored workers assigned 

coworker intakes and monitored workers with results 

less than the detection limit in some situations.

We recommend that this be closed, and it's based 

upon these justifications. We acknowledge that using 

the multiple imputation method the censored values 

can be higher or lower depending on the uncensored 

or the -- uncensored data. These would be the results 

that are above the detection limits.

Further, in the case of uranium, there are multiple 

censoring levels over time, and that the relatively 
79

high censoring level for some data explain the 

increase in intake results.

In contrast, missed doses is based exclusively on 

data that are less than the minimum detectable 

activity. The resulting intakes use a triangular 

distribution encompassing the full range of possible 

missed intakes from zero to the minimum detectable 

activity.

And we point to the same SC&A memo dated June 

3rd that helps justify the recommendation we feel 

warrants closing out this particular observation.

For Observation 4, which is the difference in the 

number of trivalent samples, this was originally 

Observation 3 in September of 2019.

It states, available trivalent logbook data show 

notable differences with the number of reported 

samples taken in 1980 and 1982. These years and 

any changes in operations are not discussed 

specifically in OTIB-0081.

However, it is noted that a future NIOSH report on 

americium exposure potential at SRS is pending that 

may address the apparent gaps in the data.

We recommend closing this based upon our NIOSH 

response, which is in the SRDB. The number is 

182704. And we provided this to the Working Group 

on August 11, 2020 regarding the completeness of 

the trivalent logbooks.

This particular slide provides an example showing the 

difference of two separate measures of the same type 

of data coming from two different sources -- I'm 

sorry. One comes from the HPS Summary Reports, 

and the other one comes directly from the americium 

logbooks themselves over the 15 years where the 

concern has been raised.

And the real metric here is the cumulative number of 

samples that have been reported over those 15 

years. Ranges, it's up over 11,000. And the 
80

difference between that is 140 of the two different 

metrics.

We feel that this sufficiently explains the 

completeness of the trivalent samples for americium.

Observation 5, a statistical comparison of stratified 

groups, this was originally Observation 3 in 

September 2019.

So Observation 5, OTIB-0081, does not provide a 

statistical comparison of the two stratified groups as 

prescribed in the coworker implementation guide. 

The various coworker models were stratified based 

upon a priori assumption that exposure potential 

between construction trade workers and nonconstruction trade workers was different.

We recommend closing this. Based upon the 

transcripts from the December 11th meeting on page 

129, it shows that SC&A states, quote, and so the 

status of this is there's really no action required. It's 

just, it's there to note the fact that the coworker 

guidelines say that you should perform a statistical 

analysis after you stratify the groups to see if they're 

truly different.

For Observation 6, quantitative assessment of the job 

plans, it was originally Observation 5 in September 

2019.

And it states, SC&A acknowledges that there are 

inherent difficulties in correctly associated workers, 

individual workers with the correct construction trade 

worker versus, or slash, non-construction trade 

worker strata.

This is particularly true for job titles that could 

potentially be included in either stratum.

SC&A suggests a scoping analysis in which such 

broader -- such borderline job titles are removed to 

ascertain the effect of the resulting distributions. 

Such an analysis would help determine whether 

current strata designations are sufficient or a more 
81

rigorous approach to individual job classification is 

warranted.

We're recommending closing this particular 

observation based upon a White Paper that we've 

entitled The Savannah River Site Plutonium 

Construction Trade Worker Stratification Refinement, 

which was dated May 28, 2019. This was the 

presentation that Dr. Taulbee just gave prior to this 

presentation.

NIOSH, it states, quote, NIOSH believes it's 

reasonable to combine all construction trade workers 

into a single strata for assignment of intakes in the 

SRS internal dose coworker study, unquote.

Of course, SC&A disagreed in their review, which was 

dated November 12, 2019, and suggested additional 

analyses.

NIOSH's responses to SC&A comments dated in 

March 4, 2020, which were in the SRDB, and that 

number is 179903, concluded that the -- that, quote, 

the final conclusions that substratification is not 

necessary remains unchanged, unquote, and notes 

that additional coding and analysis would take many 

months, if not years, to complete.

So we're recommending that that one be closed 

based upon on that information.

Observation 7 has already been closed. And I will 

save us the time for reading all of this information in 

there. But it had to do with the sensitivity analysis of 

misclassification. And it was voted to close in 

December 5, 2019 on pages 145 to 146.

Observation 8 is based on the error rates dependent 

on payroll ID, which was originally Observation 7 in 

2019.

And it states, the results shown in Attachment A of 

OTIB-0081 demonstrate a high degree of confidence 

that the acceptable error rates are within the goals 

established for each test.
82

However, this conclusion is dependent upon the 

assumption that the payroll ID issues identified would 

not affect the resulting coworker distributions.

And it's referring to Section 6.5 in that report.

We're recommending that this particular finding, or 

observation be closed. This was a data validation 

issue. The payroll prefix issues have no effect on the 

construction trade worker or non-construction trade 

worker coworker distributions.

The Work Group did discuss this on pages 146 

through 150, and the December 5th transcripts 

indicated that there was agreement by all. This was 

a non-issue, although no vote was taken.

Page 134 of the December 11th transcript has SC&A 

considering this observation closed, and therefore, 

NIOSH recommends closing this observation just for 

-- to clear the record.

That would conclude the summary of the 13 concerns 

raised for OTIB-0081. Any questions? Okay. Well, 

what I might do now is turn this over to you, Tim.

Dr. Taulbee: Actually, no, we'll go to Bob next, 

because he's going to give a summary of the --

Dr. Cardarelli: Okay.

Dr. Taulbee: -- I believe the trivalent. You are next 

on the agenda, correct, Bob?

Mr. Barton: That's correct.

Dr. Taulbee: Okay. Before we move on, though, I 

would ask Brad and Dr. Anderson, actually I guess 

more Dr. Anderson, Henry, on this because this is 

more from an SEC issues Work Group with regards to 

the OTIB-0081. It was primarily your Work Group 

that was closing out those particular findings and 

issues on OTIB-0081.

We noted several locations here in John's 

presentation where a vote wasn't taken. My question 
83

to the Work Group is, do you agree with our 

recommendations of closing the ones that were 

identified and leaving the variability issue open, or do 

you want discussion on those issues?

Chair Anderson: Committee members, what do you 

feel?

Chair Clawson: Well, first of all, this is Brad. So this 

seemed like to me that this was all SRS issues.

Chair Anderson: Yeah.

Chair Clawson: So this really comes down to the 

Savannah River Work Group, Tim. I don't think it's 

the SEC people. I think this all pertained as -- am I 

correct in that?

Dr. Taulbee: You may be. I was trying to recall the 

actual discussions of the issues that were closed, like 

multiple imputation were a global type of issue, not 

just a Savannah River.

While it pertained to the OTIB-0081, you're 

absolutely right. But I believe that was an open one 

that was for the SEC issues. But either Work Group, 

I don't care. I apologize.

Chair Clawson: Well, I understand what you're 

saying. But I do not, as a Savannah River Work 

Group, want to close something that is an 

overarching issue that the SEC group is working on.

I can for SRS but not for the Work Group. So that 

being said, I'd like to be able to go through each one 

of them and the SRS Work Group close them or 

remain open, whatever.

Dr. Cardarelli: Excellent. Bob, can you let me share 

my screen? I'll bring them back up.

Mr. Barton: Sure. If I may just comment here, 

though, in SC&A's view there's really only three 

issues that came out of this. There's trivalent 

variability, which is the next item discussion. There's 

multiple imputation, which is a global issue, which is 
84

the item after trivalent variability.

And the only other one that I, in my understanding, 

that wasn't actually closed out, I guess we don't have 

the language that it was closed out, related to 

differences by year that we saw in the number of 

reported americium results versus the number we 

had in hand.

Now, that was partially resolved during the Work 

Group. If you go back to the transcript, there was an 

indication by [identifying information 

redacted] that she received a note from the ORAU 

team that they had documentation to explain why 

there might have been a little bit of variation by year 

where you had a section where it seemed (audio 

interference) less data points and then subsequent 

years had significantly more data points and that 

there was, I think they were trying to hunt down the 

documentation of why that happened.

But the explanation was essentially that the bioassay 

lab just had a, for whatever reason, a really large 

backlog of these samples. So, even though the 

samples were taken in a given year, they actually 

weren't analyzed until a couple years later.

And it looks like the documentation wasn't found. 

However, the analysis that NIOSH presents here, I 

mean, I think it's pretty compelling. So I wouldn't 

have a problem closing Observation 4.

And like I said, the only other two open issues 

regarding the TIB-0081 review is bioassay variability 

and multiple imputation, which are the next two 

items on the agenda.

(Simultaneous speaking.)

Chair Anderson: Just for me, just to comment 

further, I thought in December the focus was on the 

Savannah River determination. I think NIOSH and, 

Tim, you were interested in expanding it out into kind 

of a global overall thing, but I think our group was 

not yet prepared to say it is an overarching issue.
85

I think we were using this as an example of could a 

coworker model be developed as opposed to since it 

could be done for SRS that that then became a global 

application to all sites. I think we're focusing on 

individual sites right now.

Dr. Taulbee: Okay. Then, I'll leave it to Brad then as 

to --

Member Ziemer: Brad, could I comment? This is 

Ziemer. Brad, could I comment? Ziemer here.

Chair Clawson: Sure, Paul.

Member Ziemer: Yeah. And these are all findings or 

observations on OTIB-0081, which I think is an SRS 

document. So it seems to me it's appropriate for the 

SRS Work Group to deal with these particular issues.

Chair Clawson: Okay. I just wanted to make sure, 

Paul, because that's why we actually brought you 

guys in is because of the implementation of this, it 

would become a site-wide profile.

Member Ziemer: Right.

Chair Clawson: So, Bob, what I have to ask from you 

is your recommendation on which, if there's any at 

all that need to be -- remain open, if SC&A has not 

got a satisfied response back, which at this point I'm 

not seeing any.

So you're proposing to the SRS Work Group to be 

able to have 1 through 5, is that correct, closed?

Mr. Barton: I think it's, again, there's three open 

issues. The one concerning the sort of discrepancy 

between the number of americium bioassay results 

taken or reported and the number we have in hand, 

I think SC&A is satisfied with that answer. I'm not 

sure we need to go into it further.

(Audio interference) shows that there's not a huge 

necessary gap. And it can be explained by a backlog. 

There's a -- there's what appeared to be a gap. And 

we have significantly less samples in hand, and then 
86

subsequent years from that what could have been a 

gap, where we have significantly more samples in 

hand than what was reported by the Health and 

Safety Department.

NIOSH was looking for documentation of that. But, 

instead, the quantitative analysis I think satisfies 

that.

And again, the only other two open issues from 

SC&A's view, from that TIB-0081 review, are the next 

items on the agenda. So --

(Simultaneous speaking.)

Dr. Cardarelli: We can go through these.

Chair Clawson: For Findings 2 and 3, we're 

recommending closing.

Mr. Barton: Well, the imputation findings, sure, we 

can close them because, unless, you know, we have 

a presentation on that very issue, which is global. So 

it would involve the SEC Issues Work Group, too. And 

again, that's a discussion item for this agenda sort of 

outside of this summary presentation that John --

Chair Clawson: Bob, I understand that. But what I'm 

looking at is strictly for SRS and how it implements 

into the SRS group.

Now, if it becomes a global issue, which many of 

these things end up into, that is still open for 

discussion. But my understanding for Savannah River 

Work Group, that this one is closed.

Mr. Barton: I think that's accurate. And again, it's --

Chair Clawson: I think 2 and 3 were closed, correct?

Mr. Barton: I'm sorry. I was speaking over you. Can 

you say that again, Brad?

Chair Clawson: Findings 2 and 3, we're 

recommending closing. Bob, you're on mute.
87

Mr. Barton: NIOSH is recommending closing based 

on a memorandum that SC&A produced from earlier 

this year that has not been discussed yet.

So, ultimately, again, not to preview coming 

attractions too much, we can -- we're fine with 

closing them if the Work Group is satisfied with 

SC&A's conclusions regarding multiple imputation, 

because essentially we come out and (audio 

interference) as saying, you know, we think it's a 

valid statistical method to deal with what's a very 

difficult situation when (audio interference) number 

of censored bioassay results.

So I think NIOSH is saying, look, SC&A did their 

review (audio interference) 96, which is the global 

issue for using multiple imputation across the entire 

program. And SC&A came out favorably in its review 

of that. So that would mean that we're in favor of 

using it at SRS under the context of the OTIB-0081.

So, I mean, we're fine with closing it out to save time 

since we've already burned up a lot of the day here. 

We're fine with that, or we can go through that report 

and what we found to satisfy both SEC and SRS Work 

Groups that multiple imputation is okay.

Now, trivalent variability, that's Finding 1, is really 

tied directly to SRS and SRS-specific data, which is 

the next agenda item --

Dr. Cardarelli: Right. And that one's remaining open.

Member Lockey: Brad, Jim Lockey.

Chair Clawson: Yeah.

Member Lockey: There were some, I think John also 

said there were some other either findings or 

observations that he felt were closed, but there was 

no vote. So do you want to go back and just briefly 

have him go through it and pick out those ones we 

need to have a vote on so we can get past it?
88

Chair Clawson: Yeah, I don't see any other way of 

doing it. But let's just go back --

(Simultaneous speaking.)

Dr. Cardarelli: Alright. So multiple imputation we just 

talked about. And we recommended closing for that 

statement. And, obviously, we'll have a presentation 

on it later. So we're recommending closing based 

upon SC&A's context that was just described.

Chair Clawson: Okay. SC&A, your feelings?

Mr. Barton: I have no objection. Like I said, our 

review of the imputation method in general was 

favorable, and that we agree with NIOSH that it's 

appropriate to use absent any other better method 

that could be employed on this, so I am okay with 

closing it. 

The question is do you all want to see the 

presentation on how we got to the same place and 

essentially agree with the NIOSH methodology? 

Again, that's the -- it's one of the items on the 

agenda, but I mean, the fact that we come out in the 

same place, maybe it's worth saving time to simply 

close it or we can have that discussion.

Member Lockey: Brad, I'm going to recommend that 

we close this as it applies to SRS.

Chair Clawson: Okay, I still want the discussion. I still 

want Bob to be able to do it, for Board Members of 

SRS to be able to close this. I recommend closing it. 

Jim, you recommend. Phil?

Member Schofield: Yeah, I'll go with closing.

Chair Clawson: Okay, let's go to the next one.

Dr. Cardarelli: This one is simply -- there was no 

official vote taken, but there was an understanding 

that this was likely to be closed.

Chair Clawson: Okay, NIOSH, your feelings on this? 

Were you satisfied with this?
89

Member Beach: You mean SC&A?

Chair Clawson: SC&A, I'm sorry.

Mr. Barton: Yeah, I'm kind of surprised that it wasn't 

officially closed back in December because, you 

know, the Work Group, I mean, they acknowledged 

the fact that these additional claims exist, but did not 

feel that it was worth the time and effort to go and 

capture that data to supplement their co-exposure 

models and the Work Group agreed, and so I'm 

surprised it's not closed as it states here.

Chair Clawson: Well, and they said that in there, they 

couldn't find an official one.

Mr. Barton: Right.

Chair Clawson: So, today we'll make it official from 

the SRS Work Group. I recommend that we close 

Finding 3.

Member Lockey: I agree.

Chair Clawson: Phil?

Member Schofield: I agree.

Chair Clawson: Okay, Finding 3 is closed.

Dr. Cardarelli: Finding 4 has already been closed. 

Finding 5 has been closed. Observation 1, we 

recommend closing this one because it's associated 

with multiple imputation and we're referencing the 

SC&A memo.

Mr. Barton: And this ties directly into Finding 2 that 

we just discussed if we want to close this out as the 

SRS --

Chair Clawson: SC&A, you're good with this?

Mr. Barton: Yes, SC&A does recommend closing.

Chair Clawson: With the understanding that it's 

pertaining to SRS Work Group, I recommend that we 

close this issue. Jim?
90

Member Lockey: I concur, Brad.

Chair Clawson: Okay, Phil?

Member Schofield: I'm for closing it.

Chair Clawson: Okay, let's go on.

Dr. Cardarelli: This one is just a continuation of 

multiple imputation and we recommended closing it 

for these reasons.

Chair Clawson: Okay, and this is the one, Bob, we 

have a presentation for you on later on?

Mr. Barton: That's correct, this and the previous, 

yeah.

Chair Clawson: For SRS, I recommend closing this 

Observation 2. Jim?

Member Lockey: I concur.

Chair Clawson: Phil?

Member Schofield: I agree.

Chair Clawson: Okay, Observation 2 is closed.

Dr. Cardarelli: Observation 3 is multiple imputation 

again applied to uranium. We are recommending 

closing again based upon the same type of SC&A 

memo June 3, 2020.

Chair Clawson: Okay, for Savannah River, I 

recommend that we close too. Jim?

Member Lockey: I concur.

Chair Clawson: Phil?

Member Schofield: I concur with that.

Chair Clawson: Okay, the next one?

Dr. Cardarelli: Observation 4, we believe this was 

closed, but we're recommending closing it based 

upon the information that we provided and the 
91

information that was just described by SC&A.

Chair Clawson: Okay, Bob, you're good with that?

Mr. Barton: Yeah, SC&A concurs, the only comment 

being that we were kind of hoping for documentation 

that was indicated to exist, but we think that the 

additional analysis provided by NIOSH, and that if 

we're missing anything, it's less than a one percent 

difference. We're satisfied.

Chair Clawson: Okay, I understand. Thank you, Bob. 

Okay, I recommend closing this. Jim?

Member Lockey: I also agree.

Chair Clawson: Phil?

Member Schofield: Yeah, I agree.

Chair Clawson: Okay, that one's closed.

Dr. Cardarelli: Observation 5, statistical comparison 

of stratified groups, we're recommending closing 

based upon the discussion on December 12, and I've 

read it into the record, so you can look at the screen 

here and discuss how you want to do it.

Chair Clawson: Bob?

Mr. Barton: Yeah, I believe that's my quote there at 

the bottom, the third bullet. There was no action that 

SC&A recommended as a result of this. It was really 

for informative purposes. It's an observation for the 

Work Group just to point out that SRS stratified a 

priori without any sort of statistical verification (audio 

interference) this being not subcontractors versus 

prime contractors, but just operational workers 

versus construction workers as a whole were 

stratified a priori. 

There was no statistical analysis done as is indicated 

should be done in the co-exposure guidelines, but 

NIOSH basically elected to stratify it anyway. 

So, that was just for informational purposes and it 
92

has my quote there at the bottom. I'm pretty sure 

that's me.

Dr. Cardarelli: Yes.

Mr. Barton: No action required, so we are okay with 

closing that.

Chair Clawson: Okay, I recommend closing this. Jim?

Member Lockey: I agree.

Chair Clawson: Phil?

Member Schofield: I agree.

Chair Clawson: Okay, that one's closed. 

Dr. Cardarelli: Observation 6 is the quantitative 

assessment of job plans. We do recommend closing 

this based upon the White Paper that Dr. Taulbee just 

presented to us earlier. This is coding additional data.

Chair Clawson: Bob, what's your --

Mr. Barton: I mean, this is really subsumed under the 

discussions we just had this morning, which is sort of 

an ongoing thing. 

So, I think for the purposes of the OTIB-0081 review, 

I think it's okay to close it because the issue is sort 

of being carried forth and will be dealt with after any 

SEC determinations are made, so it's really been sort 

of pulled out of the OTIB-0081 review and is its own 

separate item.

Chair Clawson: I understand. So, I can close this 

without creating a problem later on? This was my 

issue. You know, it's ongoing somewhat, but it's out 

of the, like you say, it's out of the 81 profile. Is that 

true, Bob?

Mr. Barton: Yeah, I think we can safely close it in the 

guise of 81 because it's still being dealt with under 

the plutonium stratification subcontractor discussion 

we just had.
93

Chair Clawson: Okay, with that being said, I 

recommend we close that. Jim?

Member Lockey: I agree.

Chair Clawson: Phil?

Member Schofield: I agree.

Chair Clawson: Okay, closed.

Dr. Cardarelli: Observation 7 has already been 

closed, so we'll move to the, I think the final one, 

error rates dependent on payroll ID, which is 

Observation number 8, and we have recommended 

closing this.

Chair Clawson: And SC&A's feelings?

Mr. Barton: Well, again, this is another one that I'm 

surprised that it wasn't formally closed, but as it said 

there in the third bullet, the Work Group discussed it 

pretty thoroughly back last December, and as it 

indicates and this is my recollection as well is that it 

was a nonissue, so I think it's okay to close.

Chair Clawson: Well, that was my thought too, but if 

you remember, that was a pretty big discussion and 

it went on for quite a while, so I recommend to the 

SRS Work Group that we close this. Jim?

Member Lockey: I agree.

Chair Clawson: Phil?

Member Schofield: I agree.

Chair Clawson: Okay.

Dr. Cardarelli: Thank you, Work Group. That 

concludes this presentation, and I appreciate the 

vote for closures. Just a brief summary, I'll go back 

to this first slide here.

 Everything that we had recommended closing did 

get official votes for closing, and the only remaining 

open item was Finding number 1 which we will move 
94

to the next topic on the agenda for Dr. Taulbee to 

present and discuss that particular finding.

Chair Clawson: Thank you, John. I appreciate that, 

and Bob too for weighing in on this. One of the things 

I want to always remind everybody is there's a lot of 

times that we do not sometimes come to complete 

closure, and it's good to sit down like this and be able 

to take them one by one and discuss them and close 

them. I appreciate that. So, with that being said, we'll 

turn it over to Tim.

Dr. Taulbee: Actually, it goes to Bob.

Dr. Cardarelli: Oh, Bob, sorry.

(Simultaneous speaking.)

Chair Clawson: You guys directed me the wrong way.

Dr. Cardarelli: I did that. That was my fault, sorry.

Chair Clawson: Okay, Bob?

Mr. Barton: Let me just get the presentation up here. 

Okay, I know I saw Joyce had joined us. Patrick Kelly, 

are you still on the line? You may be on mute. Patrick 

Kelly? Alright, well, hopefully he will join us later on.

I'll be giving this presentation, but really sort of just 

really drove the car to get our subject matter experts 

here, which is Patrick Kelly, our guru on 

radiochemistry, and Joyce Lipsztein, who is our 

expert on internal dosimetry.

So, I'm going to go through this presentation and 

then I'll let any questions the Work Group has be 

handled by them, hopefully. If I need to make a side 

call to get Patrick back on the line, I'll certainly do 

that.

Alright, so let's go through this issue, a little bit of 

background on the implementation guidelines, and 

these were really what were voted on by the Board 

last December as appropriate, so they went out of 

draft form and it is now the procedure on how you 
95

evaluate co-exposure guidelines.

And one of the core tenets of that is data adequacy 

which really asks the question does the monitoring 

method being applied, so the measurement 

techniques, chemical recovery, the numbers we get 

from a monitoring result, does that adequately reflect 

the exposure that we're trying to reconstruct? 

And this comes actually right out of the co-exposure 

guidelines and it's directly applicable to this issue of 

trivalent variability, which we'll obviously get into 

what our concerns are there.

But this comes from the co-exposure (audio 

interference) that says when paired measurements 

are available, the prevision between measurements 

should be examined. If widely different results from 

the same aliquot are observed, the effect this might 

have on the usefulness of (audio interference). Is 

someone trying to speak?

Chair Clawson: Hey, Bob, hold on one second. 

Somebody needs -- there are several people that 

need to mute their phones. We are hearing them. 

Check to make sure that you are muted. We're 

hearing a lot of background noise. Thank you.

Mr. Barton: Okay, thanks, Brad. So, as far as this 

specific issue at SRS in the guise of the co-exposure 

guidelines and what needs to be evaluated, SC&A 

first brought up a concern over what we saw as 

significant variability among disks of the same 

sample. 

And what this really means is often at SRS, 

specifically for americium, but sometimes for 

plutonium, they would basically take a sample, a 

single voiding, and break it out onto a number of 

disks, at least this is our understanding to date, and 

then measure those disks.

And you'd expect that when they normalize those 

results to get back to an activity per volume in that 

urine sample that was split up and measured several 
96

times, that you'd have reasonably close values, 

especially when looking at results that were well 

above the detection limits.

So, we first brought this up in September of 2013. It 

was part of the review of Addendum 3 of the SEC 

Evaluation Report. In February of 2014, we released 

a memorandum, and all of these documents are 

available on the website, by the way.

And we dove into it a little bit more and we provided 

188 individual examples from among those samples 

that were above the detection limit that we saw 

variability that we felt was concerning, so that was 

all the way back in 2013 and 2014.

And so we fast forward and this was brought up 

again, and this was Finding 1 that we just discussed 

as part of the OTIB-0081 review, and at that time, 

the joint Work Groups requested that SC&A really 

formalize our position and sort of distill down all of 

the issues we had with it, including the longer memo 

in February of 2014, and really just summarize where 

we're at from a technical standpoint and really just 

get at what are our concerns, and there were also 

some specific questions asked by the Work Group at 

that time to investigate, which we'll get into.

So, one of the things we did in putting together our 

most recent memorandum, which is available on the 

website under the SRS work site -- I don't believe it's 

posted necessarily on the meetings page, but if you 

go to the SRS specific page, you can find it there 

along with all the reports that John had mentioned 

and given the SRDB numbers in the previous 

presentation. 

I checked. Those are all available online, so you don't 

necessarily need to have access to the CDC system 

to see all of these different documents.

So, we revisited the 188 examples from our 2014 

analysis basically to assure the sample was taken 

from a single voiding. In other words, it wasn't a 

separate voiding possibly taken later that day or 
97

possibly the next morning and combined with the 

previous sample the night before, that sort of thing.

We wanted to make sure there was no indication on 

the logbooks that the sample was actually 

invalidated, either it got contaminated or lost in 

process. Those are certain examples of why you 

would think that the sample is just not good and 

shouldn't be included in any case.

And so that 188 got reduced to 145 that we feel are 

still valid examples of these multiple measurements 

above the detection limit where we see significant 

variability, and what we found was on average on 

these samples, you see plus or minus 50 percent on 

the different aliquot measurements when you 

compare it to the average of all of the ones for that 

single voiding.

So, one of the big questions is what does this really 

matter in terms of dose? Well, if you go back to that 

SC&A 2014 report, there's a Table 12 which provides 

a scoping calculation of potential doses at some 

example bioassay levels of doses to the critical organ, 

which for americium is the bone surface.

So, you can refer to the extracts, or we're going to 

refer to the extract from that 2014 report on the next 

slide. It's important to note here that these dose 

calculations were based on an assumed chronic 

intake over one year and that the urinalysis result, 

which again is hypothetical, is performed at the end 

of the intake period.

So, this is just to get a sense of how much does the 

dose really vary when you're talking about plus or 

minus 50 percent on these individual measurements? 

So, what does this variability really mean?

So, this is a fairly simple table, but to the far left, you 

have some examples of bioassay levels, so consider 

these perhaps -- you know, consider these as 

examples of what a claimant might have in their file 

based on measurements that can vary significantly in 

our opinion.
98

So, you can see at 0.3 dpm per 1.5 liters, which is 

essentially dpm per day, the 30-year committed dose 

is about 20 rem to the bone surface. Now, if you 

double that, it's proportional. If you double that, you 

really double the dose.

So, what we're just trying to show here is the 

significance of this variability when you take these 

individual measurements by themselves without 

averaging them, and the effect that would have if you 

just took a single measurement and evaluated that 

versus taking multiple measurements and averaging 

them, how much of an effect this could have 

potentially on calculated doses, and again, this is 

based on a one-year assumed chronic exposure to 

americium with a bioassay result submitted at the 

end of that year.

So, back in 2019, in December, NIOSH provided us 

with two technical reports to hopefully assuage some 

of our concerns here about the variability that we 

were witnessing in these samples above the 

detection limit.

One of them was Determination of Actinides in 

Biological Samples with Bidentate Organophosphorus 

Extractant, and the other one was an article about 

two californium inhalation cases.

So, we reviewed those documents, both Joyce 

Lipsztein and Patrick Kelly, who hopefully is on the 

line now, and basically the conclusion when they 

reviewed those documents was that there's general 

methodologies and they are illustrative of the 

process, but the technical questions we have still 

regarding this dataset and the variability we see were 

not really adequately answered without some 

additional information provided beyond those two 

documents.

So, we have several specific technical questions here 

and I'll read these into the record. These are the 

types of questions that, you know, we hope to get 

some answers on to give us a little more confidence 
99

that what we're observing is, in fact, okay, and that 

the data is adequate to reflect exposures to 

americium.

So, are the multiple aliquots actually from the same 

sample and a specific void? That's our understanding 

right now, but the question might be is that actually 

the case, because that might explain some of the 

variability.

Were the aliquots taken from what appeared to be 

different fractions of a sample based on the 

observable attributes or was there chemical 

difficulties attributed to nonhomogeneous samples in 

the interest of representativeness?

If the multiple aliquots were taken, can we assume 

that they are the same or equivalent volumes? So, 

you know, what are the volumes out there and how 

does that factor into what we're seeing?

What is the technical basis for this analytical 

approach that was used at SRS and where is this 

necessarily referenced in the analytical protocol, 

which for SRS, we found one for 1987. I believe there 

was one in 1993. However, I don't think we were 

actually able to find that level of detail prior to that.

And sort of regardless of what the multiple counts 

represent, is this approach adequately represented in 

the method's uncertainty, the uncertainty in the 

measurement? When we're averaging all of these 

things, is that uncertainty really accounted for?

And the real question is how much variation between 

the disks is actually acceptable such that a simple 

average of all of the values from what we believe is 

a single voiding is actually representative of the 

exposure?

And what was the acceptance criteria for the degree 

of variability at SRS? Was it, you know, don't exceed 

50 percent at a given level? 

These are the types of questions that would really 
100

help us understand what we're seeing, what we 

observe as anomalies, to give us confidence that 

these samples that we have are adequate, not only 

for use in a co-exposure modeling setting, but also 

for individual dose reconstructions.

So, some examples of additional information that 

would really seek to answer some of those technical 

questions on the previous slide would be formal SRS 

or laboratory calculations with all of the terms 

identified, the volume, the counting efficiency.

So, that would be counts per disintegration because, 

you know, when you measure something, you'll get 

counts per minute, but you have to convert that 

based on various efficiencies, chemical recovery, to 

get to disintegrations per minute, and so that's 

information we would certainly be interested in, the 

chemical yield, the target analyte percent recovery 

based on the tracer or some other technical reason, 

and the overall measurement uncertainty that was 

considered by the site.

Also, you could have possibly written instructions

explaining the technical basis, practice, and 

procedural controls regarding when you do multiple 

counts, how many times you do multiple counts, and 

why you might not do multiple counts of the same 

sample.

Prospectively determined acceptance criteria for the 

performance samples analyzed with each batch, you 

know, what were the performance criteria for blanks 

and spikes?

And also objective evidence that the laboratory had 

predetermined acceptance criteria for the 

performance of the samples and that these criteria 

were technically appropriate and were, in fact, 

applied to these routine bioassays.

There's also some concerns that arose when Patrick 

Kelly did his review of some of these logbook 

examples and they are about batch spike samples. 
101

Spike samples are a routine aspect of the analytical 

process and we'd like some evidence of the 

laboratory ability to quantify that target analyte and 

identify any potential systemic biases in the 

analytical protocols based on these spike samples.

Because what we observed is that sometimes the 

spike sample recovery would range from six percent 

to over 100 percent, and in some cases, you got zero 

percent recovery in the spike sample, but these were 

deemed by SRS as still okay to report. Somebody did 

sign off on the page, but we're really not sure what 

the criteria was for accepting these measurements.

One of the questions that the Work Group asked us 

back in December was, well, you observed variability, 

but how does that actually -- I mean, how much is 

too much? What comparisons can be made to other 

facilities and such?

So, we did some literature search, and so we only 

found that, the precision at SRS, we could only find 

documentation in 1987 and they had it at plus or 

minus 19 percent, and that's at a level of 13.6 dpm 

per day. 

In our review in 2014, we identified aliquot examples 

that were above the 13 dpm per day range, and they 

could range anywhere from 16 to 246 percent of what 

the average value was that was reported.

We got some NCRP documentation. Again, this is 

much later in the period than in the period that we're 

talking about. This is in 2009 from Report 164.

And they had 18 individual laboratories perform 

actinide analyses and the optimum conditions were 

determined to be less than 25 percent, and the 

purpose was really to see if you're above that level, 

what can we do to improve the precision at your 

individual laboratory? 

And again, this is based on the 2009 report, but also 

that 25 percent is at a value much lower, about a 

factor of five lower than what the MDA was for SRS, 
102

so it was a precision of 25 percent at much lower 

levels, which would be expected as measurement 

methods improve into the 2000s. 

And again, I remind that the SRS detection limit was 

0.3, so you can compare that to 0.06, which were the 

optimum conditions, and one thing that report 

actually found was that on average for those 18 

laboratories, it was plus or minus 30 percent, and 

that's at a level of 0.006 dpm per day, so now you're 

a factor of essentially 50 below the SRS detection 

limit we're talking about.

There's also a quote from that report and I'll read 

that into the record. In addition to the normal 

counting uncertainties due to counting time, detector 

efficiency, and counting background, the parameters 

influencing the uncertainty of the results include 

heterogeneity of the material being analyzed, 

reagent blanks, and chemical yield, tracer recovery. 

The uncertainty due to the heterogeneity of the 

material is calculated as the standard deviation of the 

results from repetitive measurements of several 

subsamples randomly taken from the bottle and 

analyzed under the same experimental conditions. It 

has been estimated to be equal to five percent.

So, again, this is you take one sample. You break it 

out into several different planchettes or disks as it 

were at SRS, and they found that heterogeneity of 

the urine itself was around five percent. Again, that's 

from the National Council on Radiation Protection and 

Measurements.

So, what do we conclude from this? We still don't feel 

that a sufficient explanation has been provided to 

explain why we're seeing some of this variability at 

levels far above the detection limit when measuring 

the different aliquots of the same sample.

Again, in comparison to what we could find in the 

literature about other acceptable or observed 

variability, at SRS, they reported a precision level of 

plus or minus 19, and then there were the other 
103

analytical laboratories which I just discussed that 

were part of that NCRP report.

And what we still find is that we simply don't have 

the documentation or other objective evidence to 

really understand what those trivalent bioassay 

represent and verify that they are, in fact, technically 

accurate to be able to use in both the co-exposure 

model, but also for individual dose reconstructions.

In our memo sort of summarizing all of this, we did 

suggest a path forward, and this would be to try to 

capture any benchtop procedures that were in use at 

SRS in the analytical laboratory. This might really 

help us understand what they did. You know, what 

were the procedures? What was the acceptance 

criteria and things of that nature?

And also documentation about the quality assurance 

criteria that was in place at SRS, this would be just 

additional information to give us confidence that even 

though we see variability, that the results are 

essentially okay. They are adequate to quantify, 

again, the exposures we're trying to reconstruct, 

which is mainly americium in this case.

And the third bullet, I don't think I did this one yet, 

but the third bullet was, and this one might be very 

helpful, is to interview some of those workers who 

worked in analytical chemistry so that they can 

explain, you know, what it is they did and what the 

procedures were beyond sort of the general methods 

that are described in the documents provided by 

NIOSH back in December.

And so since our 2020 memo, I actually went into the 

claimant database and looked through CATI reports 

for potential candidates who might have worked in 

the analytical laboratory and I was able to find at 

least 11. 

Now, some of them started work very early in the 

'50s and worked, you know, through the period we're 

interested in, but they might simply not remember 

what's going on or might not be available for 
104

interview.

But some of the candidates that we identified actually 

started work in the 1970s and 1980s and might still 

have that institutional knowledge about how these 

bioassay samples were analyzed, what performance 

criteria there was, and basically how we got from a 

urine result to the reported results in these logbook 

samples which are currently being proposed to form 

the basis of dose reconstruction in co-exposure.

These are the references. I cited a bunch of stuff in 

there, so I wanted to make it easy to be able to look 

back. As you can see, many of these reports are 

available right on the website and you can see them 

just through that link, so that's just for people's 

edification.

So, with that, I'd certainly like to accept any 

questions which -- let me just ask again. Patrick 

Kelly, are you with us right now? Patrick, are you on 

the line? Okay, well, maybe I should give him a call, 

but we do have Joyce Lipsztein on the line, and we 

can either take questions now, or I know NIOSH has 

a response. We could wait and have the discussion 

then, whichever. 

It might be beneficial to do it that way just because 

I can give Patrick a calright now and I hope I can get 

him on the line for that discussion if that's amenable 

to everybody.

Chair Clawson: Bob, this is Brad. I'd like to have

NIOSH give their response if that would be alright. I 

agree with you.

Mr. Barton: Okay, very good.

Dr. Taulbee: Okay, this is Tim and I'm going to share 

my screen now or share that box. Can everybody see 

the presentation okay?

Chair Clawson: I can.

Dr. Taulbee: Okay, great. Alright, so this is our 
105

response to the SC&A memorandum from earlier this 

year, and I certainly want to acknowledge my 

coauthor on this presentation, and she'll actually be 

presenting some of these slides, and that's Dr. Nancy 

Chalmers from MJW Companies working under the 

ORAU contract.

And so one of the things that we noted here, kind of 

an overview, is SC&A kind of has two primary issues. 

One is dealing with the high variability context and 

then the other is dealing with the procedures, and 

that's how we've kind of broken this presentation into 

our parts.

And then Dr. Chalmers is going to talk about the 

metric to define variability, and then we're going to 

wrap up with a conclusion. Then I'm going to 

introduce a new data issue with regards to the 

americium and our path forward with regards to that, 

so that's an overview.

So, Bob had gone through a lot of this, the 

background and the discussion timeline. What I want 

to kind of focus on is more of the recent time periods 

of the NIOSH response back in November of 2019, 

about the americium results are averaged, you know, 

four times in the co-exposure modeling. 

So, even though there's this variability, we do have 

where we developed the time-weighted OPOS and 

then we do the intake modeling. 

So, you know, doing the dose calculations directly off 

of the bioassay is done for individual claims, which 

Bob did bring up, and actually the context that this 

was originally raised I felt was more under the coexposure, and so that's where we had focused in the 

past, but the variability does apply, you know, to the 

individual claims from that standpoint.

But we're still taking an average, and so, you know, 

the overall variability of the original data when you 

take an average of an average, you know, it's much 

smaller than what you're considering here, and when 

you're doing a bioassay fitting for an intake type of 
106

modeling, you know, you're looking at more than just 

one individual sample. 

Especially for many of these that are large, these are 

intakes. These are multiple samples from multiple 

people, or not multiple people, same person, multiple 

samples, and several of them are in the same day 

type of scenario, and so for the co-exposure models, 

we will sometimes combine them into that timeweighted OPOS. 

Alright, SC&A's response in June implies that the 

acceptability of variation should be judged without 

consideration of its use, and this is something that 

we disagree with and I'll get into that in a moment.

And so what I'm presenting today is the additional

response that we released late last month, October 

21, so that's the focus of this presentation.

So, with regards to the high variability, the context is 

crucial, okay, for an example, variability in 

emergency monitoring after an incident versus 

variability in routine monitoring.

The measurement's acceptable variability is tied to 

its use, okay, and so that's something that I wanted 

to point out here, and along those lines, I want to 

consider the seven examples in SC&A's Table 1 of 

their report.

Okay, five of those seven examples are for one 

worker involved in a single incident, alright, and the 

five examples were all small aliquots of 10 to 30 

milliliters compared to 300 milliliters, which is what 

the routine sample typically is, so these are much 

smaller, and so you're comparing a small aliquot 

versus a large aliquot. 

We don't have evidence of this, but I can tell you from 

personal experience of running a counting laboratory 

that samples can be counted for different times, 

especially under an accident or incident scenario 

where the health physicist and the medical officer or 

doctor are trying to decide whether to chelate 
107

somebody. 

They're going to be getting results as quick as they 

possibly can to try and make those decisions, and so 

were they really all counted for the same amount of 

time? My best guess is the smaller aliquots probably 

were not, and given the activity levels, I can almost 

guarantee that they were not.

But one of Bob's indicators was perhaps we should do 

some interviews and talk to people, you know, was 

that the case? That is something that could be 

clarified from that standpoint.

From the standpoint of a co-exposure model, these 

five examples would contribute to one TWOPOS 

result in the co-exposure model, and if you're using 

these five examples to fit a bioassay to estimate the 

dose for that individual person, you're going to be 

looking at all of them and trying to do an excretion 

curve or looking at what that is and back 

extrapolating to what that maximum intake is. So it's 

not an individual sample that is contributing to what 

we would assign for an intake and subsequent dose, 

okay.

Furthermore, this particular worker where these 

examples were brought up by SC&A was chelated, so 

the data weren't used in our current americium coexposure model at all, alright. They were taken out 

from that analysis.

So, let's look at the remaining two in the table of 

examples from SC&A. One of the workers was also 

involved in an incident and was chelated. We actually 

found this was an error in our co-exposure model in 

that they should have been removed from our coexposure model. 

They did a payroll ID change that resulted in an 

inadvertent exclusion of pulling them out, and so 

they ended up with this chelated data in our coexposure model. We have since fixed that coding 

error. 
108

But again, this was a sample that was nonstandard 

as well. It was not 300 milliliters. It was 210 

milliliters, much closer than the much smaller 

samples I previously discussed.

So, let's look at the final example that was provided. 

The remaining variable example was actually flagged 

by the radiochemist in the logbook and there's a note 

in the margin that this sample, this particular result 

is suspect and they requested a subsequent followup sample. 

A subsequent sample was collected and analyzed for 

that person. We went to a different, actually the 

same logbook, but several weeks, I think maybe a 

month or so later, and the follow-up result was below 

the reporting level.

So, it was flagged by the laboratory, this high 

variable sample. It was also much higher than the 

others and they flagged it for follow up. So, to us, 

this demonstrates that they were paying attention to 

what the samples were, and what the results were, 

and doing appropriate follow up.

I would like to emphasize that those examples we 

don't feel are representative of the americium coexposure model, and so to draw conclusions based 

upon that, we don't think are appropriate.

So, now I want to get to the next kind of component 

of this is the response from SC&A attempts to define 

excessive variability and they use two reports 

basically, the 2003 optimization of monitoring for 

internal exposures, the OMINEX bioassay survey, and 

then the 1987 SRS DuPont standard operating log, 

DPSOL, 47-206.

Both of these documents were reviewed in detail in 

Appendix A and B of the memo. I'm just going to give 

some brief details here. If you want more details, 

please go and read that, and if you have questions, 

let us know and we'll be happy to go further on that. 

And SC&A referenced the NCRP Report 164. Well, the 
109

full OMINEX report notes that this is optimal condition 

of less than 25 percent uncertainty for a sample 

containing one millibecquerel. In our opinion, this is 

an arbitrary value established by the authors for 

state of the art methods in 2003.

Less than half the labs were able to meet this 

standard using alpha spectrometry in the 2003 era. 

So, we don't really feel this is appropriate to try and 

apply to the americium monitoring that was done in 

the 1970s and '80s, and '90s at SRS.

In DPSOL 47-206, they do provide some precision 

levels, precision criteria of plus or minus 19 percent 

at the six picocurie level per 1.5 liter, which is 13 dpm 

as Bob pointed out. This is at the 95th percent 

confidence. 

Now, this is a minimum quantifiable value. This is not 

a minimum detectable value. This is a measure of 

what the process capability is, and the process was 

capable of analyzing americium at a level of 13.3 dpm 

per 1.5 liter with a coefficient of variation of ten 

percent.

 This is not inappropriate as a QA criterion for 

individual analytical results. There is many other 

things that are going into this. This is what the 

process generally was.

 As I pointed out earlier, if you change that sample 

volume size, if you change the counting time, if you 

change these other variables, that value of plus or 

minus 19 percent will go up. Of course, if you 

increase counting time, it will go down.

So, this is what their standard process was, okay? 

This wasn't something that they graded against. Go 

back to my initial discussion of emergency situation 

for many of these samples versus your routine 

counting.

If you look at ANSI N13.30, the current performance 

criteria for radiobioassay, it defines acceptable 

variability only for high-level testing samples used in 
110

the DOELAP accreditation, and there they give, you 

know, a minimum testing level which is actually much 

higher than what that SRS MDA is.

So, again, the variability is really not defined for 

these lower samples. It doesn't apply to sample 

specifics. 

So, our conclusion is there's no generally applicable 

quality criteria for variability that can be applied to 

an individual analytical result generated in an 

occupational radiobioassay program, and if there's no 

criteria that can be applied today, then we really can't 

apply criteria in earlier time periods.

So, now let me talk a little bit about procedures here 

and this is the -- going back to our implementation 

guide where one of the things Bob quoted there was 

that we should be, there should be a review of the 

sample collection methods, any chemical processes 

employed, and the radiation counting equipment 

used, and we've done that, okay.

Have we done an extensive review and documented 

absolutely everything? No, we did a summary though 

in OTIB-0081 where we described these different 

things. We feel the level of review of historical 

documents referenced in OTIB-0081 fulfills this 

requirement.

SC&A appears to suggest a much higher level of 

scrutiny is required and that the level of review 

performed to date is inadequate. SC&A listed some 

documentation they would like to see. We 

acknowledge these documents would be helpful, but 

we don't know that they're necessary. We don't 

believe they're necessary.

There is difficulty in obtaining the information, 

locating it, vetting it, properly interpreting it, locating 

and properly interpreting all of the relevant 

procedures and QA records, especially in the preDOELAP area.

The radiochemist reviewed the logbooks as a mean 
111

of sample-specific criteria where the variability is 

included, that they were met, that these are good 

results, and so that's what we really rely upon here.

One other topic I'd like to briefly touch on is 

americium recovery because SC&A pointed out that 

the recovery values were quite, well, they said they 

were too variable, implying that the data appeared to 

be too variable for use because it ranged from zero 

to 116 percent.

Well, we went back to SC&A's source of one of the 

logbooks that they listed from 1981 to 1986 and we 

pulled out all of the spikes within that logbook, and 

there were 263 batches that were done within that 

logbook and we looked at all of the spikes and what 

the recoveries were.

And this is a histogram off to the right of what those 

recoveries were with percent across the bottom, and 

yes, it does range from zero to 116 percent, actually 

up to near -- I want to say there was one that was 

120, but the typical range for recovery is about 25 to 

120 percent.

And in this particular case, 255 of the 263 recoveries 

were within that range, so 97 percent of the data are 

within the range of 25 to 120 percent recovery, each 

batch this recovery is applied to.

So, really if it's ten percent, that means those results 

are going to be biased higher, much higher than, you 

know, it could be in reality for the individual samples.

Only three of the batches had a recovery of zero 

percent, okay. That's one percent of that dataset 

within that time period, and this is just one logbook 

within that time period. 

There are several logbooks. Some of them are 

plutonium and americium combined analysis. Some 

of them are just americium logbooks. Others are 

americium -- I'm sorry, americium, plutonium, and 

neptunium, so this is just one logbook.
112

Our general conclusion is that the original bioassay 

results of record at the site that are used to 

demonstrate the compliance with the regulations that 

appear in the individual records for the individual 

claimant that we used for dose reconstruction are the 

best available data that can be used for the coexposure models.

A limited review of that data is performed as a 

confirmatory measure. We don't go into the details of 

every single one. This would be incredibly time 

consuming to do.

And with that, I'm going to pass it over to Dr. 

Chalmers to discuss the metric to define variability.

Member Lockey: Tim, this is Dr. Lockey. Can I ask 

you a question before you do that?

Dr. Taulbee: Yes, sir.

Member Lockey: Go back two slides for me, would 

you? Right there. So, the typical recovery range was 

25 to 120 percent, and 97 percent were in a typical 

range. Is that --

Dr. Taulbee: That's correct, 97 percent of the 

recoveries were within that range of 25 percent to 

120.

Member Lockey: And there was three with one or 

zero percent recovery?

Dr. Taulbee: There was -- three of the batches 

indicated a zero percent recovery, yes, three of the 

263 batches.

Member Lockey: So, I'm going to go back and ask 

Bob. Bob, was this data available to you also in this 

way?

Mr. Barton: Well, I guess I also had a comment on 

the presentation. This response was provided about 

a month ago, I think it was late October. 

Unfortunately, it did not clear with DOE to the point 

where I could share that response with our technical 
113

experts, Joyce Lipsztein, and I hope -- Patrick Kelly, 

I think I see your number there, so hopefully you're 

on. I do have --

Mr. Kelly: Yes, I'm on.

Mr. Barton: Oh, great, great. So, they've only had 

basically a little over one week to really look at this, 

so we haven't had much of a chance to digest it, but 

one question I have on this slide is it says typical 

recovery range is 25 percent to 120 percent. 

Tim, are you saying that that's what was typically 

observed in the logbook data? Is that correct or are 

you citing industry wide standards on what the 

typical recovery would have been?

Dr. Taulbee: I don't know that I'm citing any 

standards from that standpoint. It more has to do 

with this is kind of an acceptable or generally thought 

about range when you're doing recoveries in a 

chemical laboratory for an analyte. 

At least that's my experience. I mean, others may 

have different, I don't know. I mean, Patrick may 

have a lot more experience along that line than what 

I do from that standpoint.

But what I'm trying to say is that 97 percent of those 

batches fell within that range and you can see on the 

histogram there that, you know, it kind of peaks at 

around 75 to 80 percent recovery. It's kind of a 

normal distribution as one would expect with 

recoveries.

Member Lockey: So, I guess the question I'm asking 

when I look at SC&A's comment above that, when I 

see zero to 116 percent, that's alarming to me, but 

when I see what you're saying, that's not alarming to 

me, and I just want to know, Bob, was that data 

available to you when you came up with this slide 

originally?

Mr. Barton: Well this is NIOSH's slide based on their 

response, which again was -- a preview DOE version 
114

of it was available late last month, but again, our 

technical people have only had it for about a week. 

I don't know if we want to have Patrick comment on 

this right now or we can wait until the end of the 

presentation and circle back, or we can handle this -

-

Dr. Taulbee: I would like to interject that this is from 

the SRDB, so this is an SRDB logbook, so, yes, in my 

opinion, the data was available to SC&A. 

Now, we had not analyzed it in this manner. We 

analyzed it to respond to your comment because 

from our looking at the typical recoveries when we 

went through, I was like no, I'm typically seeing 

somewhere between 60 and 80 percent, and so we 

went through and we pulled those batches out, which 

SC&A could have done. 

This logbook was captured multiple years ago, and so 

that was what we did to address SC&A's comment 

about zero to 116 percent because like you, Dr. 

Lockey, that caused me some concern as well and it 

just didn't jive with what I had recalled seeing in 

those logbooks.

Member Lockey: Well, I guess my question is that if 

this was readily available in a logbook, this is data I 

also would have expected SC&A to present, okay. 

I would have liked to have known that one percent 

represented three out of 264 because that would 

have given me a different perspective on this range.

Mr. Barton: And we're talking about the spike 

recoveries here. I'm not sure, Patrick, do you want to 

comment on this?

Mr. Kelly: Sure, I would be happy to.

(Simultaneous speaking.)

Mr. Kelly: Yes, I'd like to point out that we're talking 

about data, in this case from one logbook of many. 

The examples I chose which were then worked up 
115

were examples I chose just at random by looking 

through a logbook.

We did, I think, have accessible all of the information 

that everybody else had. I don't know offhand how 

much that is, but there were many logbooks. This 

was one example I chose at random.

It was not intended to be comprehensive, but rather 

to indicate a degree of variability that I was trying to 

understand for my own edification to assess the 

validity of the method.

And this is, the data that were worked up in this one, 

the histogram of the SRS spike recoveries from 1981 

to 1986, those are values from one logbook of which 

I believe there are 13 or 14.

Again, I chose a group of values because they 

represented a fair amount of variability. It was not 

intended to be comprehensive or to be representative 

of every value that was included in all of those 

logbooks.

Member Lockey: But you looked at the same logbook 

that Tim's group looked at, right?

Mr. Kelly: I assume so, yes. I did look at this logbook. 

I looked at a good many logbooks. I can't tell you 

exactly how many, but, yeah, I was, I assume -- I'm 

just looking over my notes. I assume I did, yes.

Dr. Taulbee: This logbook selection was the one that 

was referenced in SC&A's memo with that range of 

zero to 116 percent.

Member Lockey: And so the data you looked at is how 

you came up with the range of zero to 116 percent, 

but what you didn't do, or at least didn't present to 

us, that that zero percent was three out of 263.

Mr. Kelly: I did not present that. You are correct. 

Again, I was looking to understand the degree of 

variability in the method. This is one logbook. 

There are other logbooks that I believe have 
116

examples of variability as well, and I would caution 

us from assigning the values that are in this one 

histogram to the entire dataset over all of the 

logbooks. I mean, it may be correct. I just don't think 

that we have done that yet.

Member Lockey: No, and I understand that different 

logbooks will present different data, but I think for 

completeness sake, it would have been extremely 

helpful for me is that when you present the zero to 

116 percent, you would have put some kind of 

degrees of how was that zero to 116 percent 

distributed.

Because zero to 116 percent is very disturbing, but 

when I look at the lower part of the slide for this 

particular logbook, for this particular analysis, it's not

disturbing.

Mr. Kelly: Well, I take the point and I think it's a fair 

one. I would say the logbooks presented with me or 

presented to me with some degree of difficulty in 

terms of understanding exactly what I was seeing. 

As Bob said earlier, we got this response, and upon 

seeing this, I thought, oh, this is worth taking 

another look at. I have not done that between when 

we got this response from NIOSH and now.

Member Lockey: Okay, well, thank you.

Member Schofield: This is Phil Schofield. I got a 

question. You said this, the samples were put on a 

disk of some type and some of these could wait. If I 

understand correctly, it could be up to two years 

before they actually process some of these samples. 

Is that correct?

Dr. Taulbee: Two years is a bit long. I don't believe 

that that's the case. I have seen six to nine months 

and maybe a year. I'm not for sure on that, but yes. 

But what they would do is not necessarily put them 

on a disk, is they would save the sample, save the 

urine sample and, you know, there would be a 
117

backlog and then they would process through. Does 

that make sense, Phil?

Member Schofield: Okay, yeah, because I was going 

to say if you put them on a disk and, you know, 

depending on a number of factors, you could actually 

lose part of that sample and then your numbers 

would change than if you had processed it in the first, 

you know, few days or something, or weeks even 

after they received it.

In other words, they took a sample and then they, 

instead of just putting it on one of these disks or 

whatever they particularly did, and then it just sits 

there for a long time. So, yeah, that gives me a little 

more confidence.

Dr. Taulbee: Okay, thank you. Alright, are there 

other questions? Hearing none, Nancy, go ahead.

Dr. Chalmers: Alright, thanks, Tim. So, what we're 

trying to do here is define a metric to assess 

variability. We've talked about high variability and 

excessive variability, those sorts of things, but 

ultimately we need to settle on a statistic to define 

variability.

And so what I'm going to walk you through sort of is 

the history of what's been proposed over the years, 

I guess, back and forth between SC&A and NIOSH, 

so we'll start with the February 2014 SC&A response 

to OTIB-0081. 

SC&A had a table with 188 values called out that 

were chosen subjectively and they highlighted some 

of those results in green and called them inconsistent 

disk results, which I don't think the inconsistent part 

was ever defined there, and so that's obviously 

subjective, so that one is not a metric necessarily the 

way they initially called these values out.

So then November 22, 2016, ORAUT-OTIB-0081 Rev 

3, this is the previous version, NIOSH proposed the 

use of CV, also known as the coefficient of variation. 

I believe we've talked about it a little bit.
118

Some folks in other fields may know this as the 

relative standard deviation, but it's basically just a 

standard deviation of those multiple counts divided 

by the absolute value of the average of those counts.

And one way to look at that, which is in Rev 3 of 

OTIB-0081, is to plot the coefficient of variation 

versus the absolute value of the average, and you 

can sort of look at those values and they have a 

distribution as you would expect, so you take a look 

at that plot in OTIB-0081, Rev 3.

And then in September of 2019, SC&A response --

oh, can you flip the slide, Tim? Sorry. September 

2019, SC&A review of OTIB-0081 included a log-log 

plot of the coefficient of variation versus the average, 

so what we were just talking about except it's on a 

log-log scale and they did it for average values of 

0.32 dpm per 1.5 liter or greater.

You know, we would say that for proper assessment, 

we should use all average values, but SC&A also 

mentions in the text of that document that the 

coefficient of variation is commonly used as a 

measure of variability.

And then in November of 2019, the NIOSH response, 

we didn't propose a new metric then because we 

believed the CV that we previously proposed is the 

appropriate way to look at this.

And then in the most recent June 2020 SC&A memo, 

there is sort of another type of metric they included 

there. One of the examples was 145 samples had a 

range greater than plus or minus 20 percent of the 

average value.

From my experience, that's not a well-known metric 

and they didn't reference sort of what that is, what 

it's called, why consider it in that way instead of the 

coefficient of variation. So, can you flip the slide, 

Tim?

So, what we're proposing is to use this coefficient of 

variation versus the mean plot. We initially proposed 
119

it back in 2016. SC&A used a very similar version of 

that same plot in 2019. It's a common, well-known 

metric. You can go Google it and learn all about CV if 

you'd like.

And we can use that plot that I described to assess 

variability and it eliminates for us the use of these 

subjective, and unjustifiable, and not referenced, and 

all of these other things which we've talked about 

these other metrics, so that's it for the metric. Back 

to you, Tim.

Dr. Taulbee: Are there any questions for Dr. 

Chalmers? Okay, hearing none, I'm just going to 

wrap up here with the conclusion.

So, with regards to the high variability, we don't feel 

there's any general, no generally applicable criteria 

for variability that can be applied to individual results 

today.

If there's no such criteria today, then there really 

wasn't any in the previous years that these logbooks 

are covering.

Generally, the bioassay result of record is used to 

demonstrate the compliance and are considered our 

best available data. We do a limited review of the 

data when we go through and do these co-exposure 

models as a confirmatory measure.

It's not rigorous, but as Nancy pointed out, the 

coefficient of variation is the proper variability metric 

and we will be using it moving forward. Are there any 

questions there before I go on to the new data issue? 

Because this one's kind of important.

Okay, well, one of the benefits of the SC&A review 

here is that during our review and answering their 

questions, we did identify several new concerns that 

prompted some further evaluation, and so this got us 

to go back and look more closely at this data.

Our evaluation found that many of the high variable 

results were not necessarily variable, but had some 
120

undesirable characteristics that unfortunately could 

impact our co-exposure model, and I'm going to talk 

about three of the examples here that we came into. 

Well, I guess before I go into these three examples, 

keep in mind that one of the things that hasn't been 

considered, but I believe made it into SC&A's list of 

potential topics that can affect variability is sample 

aliquot size.

And as I noted earlier, many of these aliquots, many 

of these samples or flags that SC&A had pointed out 

certainly have smaller sample sizes.

So, they're not following the standard method that 

one would consider, and so one would expect the 

variability to be higher than your typical routine 

result within those samples.

But some of the examples that we found when we 

went through and investigated some of these caused 

us some pause, and so I want to talk about those.

The first one is that we noted that some of our 

samples in the database, that spike samples were 

inadvertently included with the individual results, and 

this was a surprise to us because what ended up 

getting coded here -- and I'm going to try and zoom 

in. Actually, let me leave it out here for just a second. 

Can you all see a pointer or the crosshairs on your 

screen?

Member Lockey: Yes, I see it.

Member Beach: Yes.

Dr. Taulbee: Okay, so what you'll see here is this is 

an individual sample, and you'll see this little bracket 

that indicates that it's the same sample, and you go 

across here and it's all the same sample. 

This is the bottle date, the date it was received, the 

area. This would be that person's payroll number, 

and you go through here and this is the dpm per disk 

and the dpm per 1.5 liter, and then this is the 
121

reported result. Okay, and so for this one here it 

would be less than 0.3 dpm per 1.5 liter. 

Now, if I zoom in here, and hopefully you can see 

when I zoom in, you'll see that the result here was 

zero and then the lower result was 3.536 for 

americium. We coded these together inadvertently 

because they were the same sample, same date.

And what wasn't noted was this note down here at 

the bottom that samples three, seven, and 11 were 

spiked with plutonium. The plutonium spike was 1.25 

dpm and later spiked with americium at 4.96.

So, all three of these particular samples here were 

spiked, and so they were included in our database 

and they should not have been, and so when you're 

looking at zero versus three, we're going to come up 

with an average of around 1.7, something like that 

because it was 3.5, and you're going to compare that. 

Well, one sample was zero and one sample was 3.5. 

This has a high variability associated with it. Well, the 

one should never have been included, and we 

certainly recognize this.

So, including this in the database, this would have a 

biased high-type of result because we included spikes 

where they shouldn't have been, and so by removing 

them, the overall co-exposure model would decrease. 

We don't know how many of these are in there. We 

don't think there's very many. This was a very rare 

scenario. Typically down here, samples 18, 19, and 

20 would be the blank and then two spikes. That was 

what we typically saw, so this was an unusual entry.

The other example error I'm going to say is an 

extreme-typo example, and here is an example 

where the values were entered into the database as 

3.59, 92.07, and 4.3. When you look at this, this is 

3.59 running diagonal, 2.07, and 4.3. 

So, clearly when you average these and look at the 

variability amongst this sample, you would come to 
122

a conclusion this is incredibly variable and there's 

something wrong when, in fact, there isn't. This is 

another example of the co-exposure model would be 

biased high inadvertently.

The third example is an example of misinterpretation 

of the data. Occasionally, not always, when the 

aliquot size was non-standard, an additional 

multiplication factor was needed to obtain the 

reported dpm per 1.5 liter.

The top of this column was actually labeled as dpm 

and this column over here was the dpm per 1.5 liter. 

So, to get to 31, you had to take the average of 18.2 

and 23 and multiply it by 1.5 to get to 31, so this is 

that sample volume difference.

So, in this case, the coded result underreports the 

true value, okay? This would be -- we would be 

including 18.2 and 23 into the database when we 

should have been including 18.2 -- yeah, 18.2 times 

1.5 into the database, 23.0 times 1.5 into the 

database.

So, the bottom line here is we've got some issues 

that were discovered in August of this year, the 

extent and the bias. The bias is in both directions. 

We're seeing some cases where it's higher, some 

cases where it's lower. There's no clear impact on the 

result for the co-exposure model.

So, what we've done is we've started recoding all of 

the americium-241 data. That was initiated in late 

August. What we've added to this is an additional 

Health Physicist Quality Assurance step to each result 

to ensure the appropriate interpretation of the data.

Now, this HP Quality Assurance step is done when we 

do dose reconstructions, okay. That's the health 

physicist's job is to look at that data and make sure 

that the data was coded properly and that the results 

are there.

So, this really isn't a problem with doing dose 
123

reconstruction at all. The health physicist checks this. 

It wasn't checked when we coded the americium 

data, putting it into the co-exposure model. 

We did checks of the data, and if I can go back up 

two here to this particular one, what was checked 

was this dpm per 1.5 liter. Yes, that value is zero. 

Yes, that value is 3.536, not how it was interpreted 

and put into the co-exposure model. 

This is why we've gone back, recoded the data, and 

are having that health physicist make sure that that 

result should appear, and so this is a case where the 

data coder enters the data and a health physicist 

checks each result.

The good news is we've been working on this since 

August, and as of the beginning of this month, 12 of 

the 13 logbooks have been recoded, and I can 

happily report that earlier this week, the coding was 

complete on the aspect of the data entry side. 

The health physicist's QA of that data is finishing up 

and is expected to be completed early next month. 

Once we do this, we will rerun all of the americium241 analysis and update the co-exposure models in 

OTIB-0081. 

At that point, we can evaluate this coefficient of 

variation. We can reevaluate it, plot the curve of the 

coefficient of variation that Dr. Chalmers was talking 

about, and do the comparisons at that point.

So, I wanted to give this update. This is our path 

forward with regards to this based upon the further 

investigation of it. So, with that, are there any 

questions?

Dr. Lipsztein: I would like to make some observations 

if possible. Can you hear me?

Dr. Taulbee: Yes, we can hear you.

Dr. Lipsztein: Alright, I would like to make some 

observations. I think the ultimate objective of the 
124

coworker model is not to average results so they 

don't have a meaning.

I think the ultimate result you want to get from this 

work is the dose, and for me, the dose is the most 

important thing, and also, of course, the analytical 

chemistry procedure that was applied.

And it makes me very nervous that you gave an 

example of seven points when your coworker, I don't 

remember her name, Nancy, was talking just about 

one of the table that you presented with 188 results 

in 2014 when about at least one-third of them had 

very different results on the disks. 

So, you have results on disk and then you have 

reported result, and those results on disk, whether 

they were routine results with very low activity, or 

whether they were follow-up results with very high 

activity, or whether they were labeled with special 

and had very high results, or whether they didn't 

have any label, many of them have activities -- the 

same urine samples as I understand, and you are 

going to see that they are all from the same urine 

samples. 

They have disk results that have results that are 

three times one of the disks, four times one of the 

disks, five times one of the disks, and this implies in 

the dose that is two times, three times, five times 

higher from one disk to the other.

So, you have to remember that respectively, what 

you think we have now as variability, we cannot have 

something that we have to be objective of calculating 

the dose for the worker, and we have for the same 

worker and the same sample these results that have 

a difference of five times, three times, two times, 

one.

So, I don't think it's fair for the worker, and they have 

been waiting for this for 70 years. That's when we 

first talked about this variability. It's not the 

variability that you are going to do it through a graph 

or something. Just think in terms of the dose. Is it 
125

fair for the worker? 

Again, we have results that are, from one disk to the 

other, five times with those results, five times, four 

times, three times because you know that the dose 

is proportional to the urine sample results. Now, of 

course we will have to wait until you review all the 

disks and see what is happening. 

And the second thing is that when we ask you for 

what chemical procedure was done, what was the 

analytical procedure, you gave us a paper with some 

methods that required 250 ml. So, I don't think you 

can do something with ten ml or 50 ml. It's not good 

chemistry.

So, I think we have to go back and see what's 

happening. You cannot, you know, average disks that 

would give five times the dose with something than 

the other disks. That's it.

Dr. Taulbee: If I could respond to that, I understand 

what you're saying if you think about it from a single 

dose or a single sample, but many, many, many of 

these results are from the same individual from a 

single incident, okay.

When I go through these logbooks and I see, you 

know, a whole listing of the samples together, they're 

from the same individual and they're repeated 

samples for that individual, but even on the individual 

ones where you talked about that ten milliliters, that 

you don't think you could do that, I disagree. 

When you're in an accident type of scenario and 

you're trying to get a quick result, those were 

included in there, and so they would take a small 

sample and they would put it on a planchette, and 

some of these are very high activity because they got 

a very large intake.

And so you're going to see some of this variability 

that is in excess of what your routine sample would 

be. Using just a single routine, yes, they're 

disproportional, but most of these that I'm seeing are 
126

from an incident type of scenario.

Dr. Lipsztein: May I? I saw this on routine samples, 

on samples -- and we gave you a list of the routine 

samples already in that paper, samples that have 

very low activity, and we saw this in samples that 

were a little bit high, but the person was not 

considered an accident and didn't have a follow-up.

So, we see this in every kind of sample, and even 

routine, even the ones that are written routine and 

have small activities, you can't say, oh, I have a 50 

percent variability, it's okay, but 500 percent, 400 

percent, that's too much. 

There is something wrong with the methodology. I 

have run also a laboratory for many years, and of 

course I'm an internal dosimetrist, so what I want to 

know, the result is in those.

 So, and I think the objective here is to give a dose, 

to see if the worker can have compensation or not, 

and if you cannot say if the dose was 100 millirem, 

or if it was 50, or if it was 200, then you cannot use 

this data for compensation analysis.

Dr. Taulbee: Okay.

Dr. Lipsztein: I don't think it's claimant-favorable to 

do this, no, and not fair.

Dr. Taulbee: I believe that you're pulling out some of 

the extremes, but I can't demonstrate that at this 

time until we look at the most current recoded data 

here.

Because I'm concerned we're running back into the 

same discussion that we had with the recovery, that 

this affects an extremely small portion of the data, 

but I just can't demonstrate that at this moment --

Dr. Lipsztein: Okay.

Dr. Taulbee: -- but we hopefully can.

Dr. Lipsztein: Okay, we'll wait for it.
127

Dr. Taulbee: Thank you. Other questions?

(Simultaneous speaking.)

Dr. Lipsztein: -- the dose in relation to the question.

Dr. Taulbee: Are there other questions?

Mr. Barton: I have a comment if this is a time to bring 

it up or the Work Group can ask questions.

Mr. Kelly: Bob, may I make a comment?

Mr. Barton: Sure, I guess. It's not my meeting, but 

go ahead.

Mr. Kelly: So, I had a tremendous amount of difficulty 

understanding what I was looking at in terms of the 

logbooks to see this additional issue that comes up, 

you know, the previously unidentified one, I can 

understand that.

So, I'm a radiochemist and when I look at this, I don't 

really know what they did. I don't know where they 

get a chemical recovery for americium. 

You know, there's no spectrometry, so there's no 

tracer. Do they assign a chemical recovery based on 

the spike, or batch, or whatever? I don't see any 

indication of that.

So, what I try to do is I try to find information that 

will convince me one way or another about what 

happened, and when I see this -- and perhaps I'm 

not being comprehensive at looking at everything, 

that's entirely possible, but based on everything I did 

look at, I still don't know what they did. It's not clear 

to me what kind of a radiometric instrument was 

used, what procedures, et cetera.

So, I have less concern with the issue of the 

variability, although I acknowledge that and it's 

interesting to hear that discussed, but my concerns 

go more to whether as a whole this body of 

information is adequate as objective evidence of a 

controlled process to produce something that was 
128

quantifiable.

Chair Clawson: Who was that that was just speaking? 

I'm sorry. I didn't recognize the name.

Mr. Kelly: Sure, I'm Patrick Kelly. I work with Bob and 

Joyce for SC&A.

Chair Clawson: Okay, I just wanted to know who was 

talking.

Mr. Kelly: Sure.

Chair Clawson: Because to tell you the truth, I'm 

pretty well confused myself right now of what we've 

actually got here.

Mr. Kelly: Right, and that's my main point. I don't --

and the fact that there were logbook pages and that 

they're signed by someone, or that they had a 

program, that's all fine. 

I mean, I think that's certainly good, but for me, in 

all candor, I'm probably applying a slightly higher 

degree of technical rigor to it, which may not be 

appropriate for this program, and if so, then, you 

know, that's fine, but that's just a little bit of 

orientation as to where I am coming from with some 

of the things I have written, or that I might have said 

or will be saying.

Member Ziemer: Could I comment? This is Ziemer. I 

assume those logbooks don't include all of the 

procedures. Wouldn't that be correct, Tim?

Dr. Taulbee: That is correct. They do not.

Member Ziemer: If the radiochemical procedures are 

elsewhere, maybe they should be made available to 

-- Bob Barton, to your colleagues. I think SC&A has 

access to what those radiochemical procedures were, 

so that would be useful for the radiochemists to look 

at if they hadn't.

Mr. Kelly: Yes, I saw that there were procedures. It's 

not clear what procedures were used exactly when, 
129

over what time periods, nor is it clear how the 

recoveries are determined for the specific analytical 

protocols. 

I don't -- you know, there is no spectrometry, and of 

course as you all know, you know, this is not just 

americium. It's all trivalent actinides, so that's fine, 

you know, americium, curium, californium, perhaps 

some thorium if that should be in there.

You know, so I look at this and I saw the procedures, 

but I still don't have a good sense based on this what 

we are really looking at in terms of the data.

Dr. Taulbee: Okay, I'm not sure. I mean, we can have 

our folks, you know, go over this more. You know, if 

you want to do, you know, the interviews of the 

radiochemists at the time, I mean, that's up to you 

all.

I can tell you from my read on the logbooks in 

particular, to me, the spikes and blanks were fairly 

understandable of what they were doing from that 

standpoint.

Now, the actual chemical process going through to do 

all of that, no, I don't -- I'm not a chemist from that 

standpoint, but it sounds like that you are and 

somebody who could understand all of that.

And again, that's where I would point to those papers 

that describe the process because they were more 

inclined to publish things like that versus having 

written down procedures on their methods within the 

radiochemistry laboratory. Those are things that they 

left to the radiochemists to do, so that's really all I 

can answer at this point.

But again, this is an open issue, Brad, just to circle 

back here. We just finished coding the data. Let us 

get it into a better quality review and in a form that 

we can present back to the Work Group, is kind of 

where I would like to go from this point forward with 

regard to the numbers.
130

Chair Clawson: That would be nice because right 

now, I'm really questioning even what we have, and 

it sounds like I'm not the only one that's questioning 

that. So, we'll have to address that and go from 

there, but we'll wait for your report to be able to be 

issued to SC&A on this then. Is that correct?

Dr. Taulbee: That's my standpoint, yes.

Chair Clawson: Okay.

Ms. Naylor: And this is Jenny. I'm also wondering if 

this is something that SC&A just needs to sort of have

an internal pow-wow and sort of understanding or 

articulate to us what additional resources that you 

need to sort of help you understand what goes on in 

these logbooks and what other procedures or 

documents that you need.

Mr. Barton: Well, this is Bob. I think that a technical 

call, as we usually refer to them, could be warranted 

where we can send in writing certainly some 

clarifying questions and see what the NIOSH staff 

knows directly about our specific questions. 

That might be helpful. I do agree we need to allow 

NIOSH to go in and recode the data and see how that 

affects this issue of variability.

I will say that, you know, we didn't do our own 

independent compilation of the logbook data. We 

were operating off of an electronic database that 

NIOSH had produced themselves. So, when we show 

this variability, we were working off of what was 

transcribed, which it sounds like there might be some 

errors in it. 

So, to the extent that this coding effort and review 

by a health physicist of each new coded data point 

clears up some of our concerns, we simply don't know 

until that work is done.

One other comment I had, because one of the major 

points made in NIOSH's response is that, well, listen, 

at least in the guise of a co-exposure model, you're 
131

taking these disk results. You average them. You 

might average more than one sample in a day for the 

worker.

 Then you come up with a TWOPOS value for that 

worker for that year and you fit that to a distribution, 

and then you plug that into, you know, IMBA to come 

up with an intake rate over a range of a number of 

years, and so in the end, this variability essentially 

gets washed out, which I'm not arguing against that 

point, but I sort of disagree with that as an 

explanation.

I think you have to be able to explain sort of these 

variances that we point out and the adequacy of the 

data as a whole at the outset before it goes into the 

co-exposure machine.

We all agree on the machine, but I guess the old 

adage is that if it's, you know, bad or questionable 

going in, then what you get out is still bad and 

questionable.

So, I do disagree with the point that none of it really 

matters because it's all getting averaged so many 

times and fit to distributions that the end results, the 

variability will not affect. Obviously, that will not 

apply to an individual necessarily, but I think we need 

to get a better feel. 

I mean, we're not saying that this data is necessarily 

bad. Our point is that we have several concerns that 

we don't feel that we have sufficient explanation or 

documentation of to give us confidence that it's 

adequate for these purposes.

And frankly, we point back to the co-exposure 

guidelines. This was the issue of why Dr. Neton put 

in that line about if you have multiple measurements 

of the same aliquot and they're showing widely 

different results, then their use in dose 

reconstruction really needs to be considered.

I understand that samples involved in chelation 

would be removed from the co-exposure guidelines, 
132

but that does not necessarily explain the 

measurement method itself, and as NIOSH has 

agreed at the last meeting in December, that 

chelation is not a factor and does not explain the 

variability.

So, while I understand they're not using the coexposure model, we still don't feel that we have had 

sufficient explanation for what we're seeing and what 

gives us pause here.

Again, we're not saying the data is necessarily bad, 

but if there's more information out there, we want to 

understand, as Patrick Kelly put it, what did they do 

and is that actually acceptable under the auspices of 

this program, and that's where we came up with our 

path forward which would involve some form of data 

capture at the site.

Now, Tim, you'd know much better than I what's 

potentially out there, and I think in the actual written 

response from NIOSH, you indicate that that 

information simply is not available, that perhaps 

you've already looked for it, looked through the 

EDWS system for evidence that that information is 

out there that might be beneficial to us.

So, I don't know what's out at the site. It could be 

that that information is simply gone forever, in which 

case we're left with what we have.

And again, the other aspect of that would be maybe 

we can talk to a few of these radiochemists and find 

out, and get real perspective on what they were 

doing out there.

Dr. Taulbee: The latter one there is probably your 

best path from that standpoint to try and locate 

additional procedures. We have located some 

procedures. They're in the Site Research Database 

and we've looked at those, and some of them are 

cited in the OTIB.

But further details on what it is they're doing, I 

believe, to answer some of Patrick's questions, your 
133

potential path would be then to talk to some of the 

radiochemists from that standpoint -- from an 

interview-type of standpoint, and then they could 

also point you to what methods that they used.

I know, out at INL we learned from that particular 

site that the early procedures were -- not where they 

were actually documented in individual notebooks 

that they used, and there'd be one radiochemist that 

specialized in alpha, and one that specialized in 

others.

And so, they were the ones that did the analyst. 

These were typically doctoral-level radiochemists, 

and so they analyzed the samples themselves.

So, that might be the best path to go fastest forward 

from that standpoint. But I would urge you to look 

at, when we get the data, how it's been checked, as 

to is this still a significant issue with you all. Okay.

Mr. Barton: I agree completely. We need to see what 

you all produce.

Dr. Taulbee: Okay. And we will do that.

(Simultaneous speaking.)

Member Ziemer: Tim, this is Paul. I think Dr. 

Chalmers' suggestion on use of the coefficient of 

variability is really a good one, and that the SC&A 

also should take a look at that as they find that, as 

we think of the variability in these samples.

I'm sure that we have sort of an agreed-on -- in 

looking at that. I know that that variability has been 

bothering everybody. I like that suggestion that Dr. 

Chalmers had.

Dr. Taulbee: Excellent. Okay, and that's what we plan 

on doing in our response back to you, Brad, and the 

Work Group and SC&A with regards to this dataset.

Chair Clawson: I understand. As we get into this a 

little bit, though, I think that we do need to have a 

technical call with SC&A so that we're all on the same 
134

page of what we're requesting to be done, what's 

going to help us. That's always helped out in the past. 

That good, Bob?

Mr. Barton: Yes, actually. Well, on Wednesday when 

we were discussing this internally at SC&A -- and that 

was the point where we weren't even sure if this was 

going to be still on the agenda -- but that was the 

sense of what our path forward was going to be, if it 

wasn't going to be on the agenda, was to formulate 

clarifying question, and we can see what we do know, 

what we don't know, and how that does it does not 

meet some of the concerns that we've laid out.

Dr. Taulbee: If you could forward us those questions 

ahead of time before the call, that would be much 

appreciated.

Mr. Barton: Absolutely.

Chair Clawson: Okay, that sounds good. I'd also like 

to just be able to listen to it, because I'm going to be 

right honest: you guys have got me pretty confused 

right now.

Dr. Taulbee: Sorry.

Chair Clawson: So, is this a path forward that we 

have to go now?

Dr. Taulbee: Yes, sir.

Chair Clawson: And NIOSH and SC&A both know 

what is required of them in this path?

Dr. Taulbee: NIOSH is clear.

Mr. Barton: Well, I guess from SC&A, we could use 

clear directions for the path forward, but it involved 

data capture. We're trying to find benchtop 

procedures, or the notebooks that can give you 

examples at Idaho.

I don't think we're going to do that yet. Or even if it's 

worth tracking down radiochemists yet until we see 

what that does in the recording of the data and the 
135

review by health physicists, modification of the coexposure model, and what they could do statistically 

with the coefficient of variation.

And then, I think SC&A core action item is to put 

together a list of clarifying questions for use in a 

future technical call, so that later on what we know, 

we know. And what we really should know, or what 

information is truly unavailable to us.

COURT REPORTER: Can I interrupt? There are two 

telephone callers, area code 314 and 817, who need 

to mute their lines. They are interfering with the 

conduct of the meeting.

Chair Clawson: I appreciate that. I was going to bring 

that up myself there.

Okay, so it sounds like the bulk of this is actually on 

NIOSH. And once NIOSH gets a little bit there, I 

guess the only thing I don't want, Tim, is to go 

through this whole process and then end up with 

more questions.

As we get a baseline here for it, I would like SC&A to 

be able to see the reports of this data that we've got, 

so that they can get their questions put in there too.

Maybe a technical call, one or two technical calls, may 

be in store for this. But I think that would be the best 

path forward.

Dr. Taulbee: I concur. I would ask that, instead of 

SC&A waiting until we get our report out, if you've 

got questions now about the bioassay and how to 

interpret the logbooks, if you could write those now 

to us, that would give us a little extra time to be 

working on it while we're doing the other.

So, that would be helpful to us, to work them in 

parallel, to join to a technical call that would be a little 

more productive, if that's okay.

Mr. Barton: I agree with you, Tim. We'll put those 

together. That's our action item going forward. We 
136

won't wait for your report to put that together.

Dr. Taulbee: Okay, great. Thank you. Alright, are we 

ready to go onto the last topic?

Chair Clawson: Sure.

Dr. Taulbee: Okay. If I'm reading the agenda 

correctly, I'm going to give a brief primer, which is 

really just a reminder of the multiple imputation 

method, and then turn it over to Bob for the 

remainder.

So, this is just a brief primer on the multiple 

imputation process that we go through. And as we've 

been talking about earlier, the multiple imputation 

method is a better and more statistically appropriate 

method for estimating censored data, compared to 

the traditional LOD over two that we've used at the 

beginning of this program.

As the Dose Reconstruction Program has evolved, 

new, more robust statistical methods we believe can 

and should be expected to replace the initial methods 

and assumptions.

It's well-known that external dosimetry and bioassay 

data tend to follow log-normal distributions, and so 

we're trying to take use, or trying to apply those 

types of methods to improve our dose reconstruction 

methods.

One of them here is the multiple imputation method, 

so I'm going to go through this. There's basically five 

steps here. 

And the first step that we do when we're doing 

multiple imputation, is a regression on ordered 

statistics of all of the bioassay data for a given year. 

Okay?

So, we lump it all together. And sometimes we do 

combine years, but generally, it's on a individual 

year. So, to keep it simple, that's how I'm going to 

talk about this.
137

And so, this effectively -- this regression creates an 

imputation model. We then use the imputation model 

to estimate and replace the censored data for an 

individual's data that are censored. Okay?

So, we take this imputation model, and let's say a 

person has a total of five bioassay results and three 

of them are censored. Okay? So, we will use this 

model -- this regression -- to estimate what those 

three results are that are censored.

We'll take that then, those five results, and we'll 

calculate a time-weighted one-person, one-statistic -

- a TWOPOS value -- using the individual's data, 

which can be a combination of the uncensored data 

and the imputed data. Alright?

So, we repeat this process for each individual in that 

particular year, to obtain the first TWOPOS 

imputation. So, we're repeating steps two through 

four. We're doing this for each individual.

Then, we repeat steps two through four for the 

second imputation and the subsequent TWOPOS 

imputation. So, graphically, what this looks like is 

here.

This is the SRS imputation model for 1969. And what 

you'll see here on the left is the imputation model. 

This is all of the positive bioassay data for plutonium 

in that particular year. This is the regression on the 

ordered statistics.

So, for the first TWOPOS imputation -- that's the 

graph here now on the right -- this is all of the people. 

On the left was all of bioassay in 1969. On the right 

is all of the individual workers, all of the individual 

people. And so, you see that the end total goes from 

892 down to 295.

And the color coding here is more for your benefit to 

understand this. The black dots up here at the top --

the upper end -- these are people who all of their 

bioassay was not censored. Okay? One hundred 

percent of their data was not censored.
138

The red dots here is where an individual worker has 

between -- well, less than 50 percent of their data 

was censored. So, somewhere between zero and 50 

percent of their data was not censored.

And then the orange dots, as you're working down 

through here, is where between half to 99 percent, 

let's say, of their data was censored. Okay? We got 

50 percent to 100 percent.

And then, the yellow is the people who all of their 

data was censored. So, all of these yellow dots is 

where their data was censored. We went to the 

imputation model and estimated what their result 

was. Okay? In the calculation of the TWOPOS.

So, this was the first imputation that came out. We 

then repeated that whole process, that step 5, over 

and over and over again, to where this is what we 

end up with. And this is all of the gray.

All of these are the multiple imputations, so you 

begin to see the banding or the spreading of the data 

at the ends. And we fit then the regression here to 

come up with that TWOPOS value for that particular 

year. So, this is what the imputation model's doing.

Now, keep in mind, this is TWOPOS. So, we're using 

the 50th percentile -- this is the 1969 data that I'm 

showing you here for non-construction trades 

workers -- this would be the DPM per-day value -- is 

.036.

And if you go up here, you see the geometric mean 

is .036; that's the 50th percentile. And the GSD is 

3.142, from this particular model. And there's the 

GSD. So, the 84th percentile here is .1136. This 

would be 296 individuals.

But that's just for the TWOPOS data. But what I want 

to remind you of is that we take that particular data 

-- the TWOPOS data -- we use the 50th and the 84th 

percentile, and then we do the intake modeling for 

that co-exposure model. And in this particular case, 

the value we're looking at here is this 1969 datapoint 
139

up here at .36.

And so, we're lumping the -- this would be '69, '68, 

'67 and 1970 -- into one band and we fit an intake to 

that to come up with our intake model. Alright?

And the intake model is what we use for the dose 

reconstruction. Alright? Not the TWOPOS values. 

TWOPOS values are used to come up with the intake 

model.

And in this time period of 1967 to 1970, we do this 

for the 50th percentile, we do this for the 84th 

percentile TWOPOS data, and then we calculate what 

the GSD is. In this case, it's 3.49.

And from this 3.49 then, we calculate the 95th 

percentile, which we were talking about much earlier 

with the stratification discussion, that is then one of 

the parameters or choices that we could use for a 

dose reconstruction. If this GSD is less than three, 

we round it up to 3.0, because that's our minimum 

uncertainty that we use from these TWOPOS values. 

And you see that for the 1955 to 1966 data, in order 

to get that 95th percentile.

So, this is just how this multiple imputation is used. 

Normally, the 50th percentile, with the full lognormal distribution, will be assigned to workers 

who've been exposed to greater than environmental, 

but less than a typical operations or considered highpotential exposure.

Workers considered to have the high potential for 

exposure may be assigned that 95th percentile. And 

again, this is all determined on a case-by-case basis 

by the dose reconstructor, using professional 

judgment.

So, with that, is there any questions on the multiple 

imputation, before Bob takes off, of the method?

Okay. Hearing none, Bob, it's all yours. And we can't 

hear you. There you go.
140

Mr. Barton: Yeah, just took myself off mute. Let me 

get my presentation up here.

Okay, I'm going to give just a little bit of a back story 

of the discussions back in December and how that's 

evolved over the past year. A lot of the heavy lifting 

was done by Dr. Carl Gogolak, who is on the call. And 

I'll give the presentation, but he's available to answer 

any questions you might have.

So, again, background, Kimberly just went through 

it, so that this is going to be really quick. But a lot of 

these datasets we deal have a large number of 

censored results. That's results of less than some 

detection limit, decision level, or some other 

predetermined threshold levels. All you'll have for 

bioassay result is less than some value.

So, the true value of that bioassay datapoint is 

somewhere between zero and that censoring level.

Co-exposure modeling -- again, I refer to it as the 

machine in the previous presentation, which we all 

agreed on -- but it requires statistical interpretation 

so you can fit these data in different distributions. 

Typically, it's the log-normal. We point out that some 

datasets have very large portions of censored data.

So again, the multiple imputation. Again, this is really 

just summarizing what Tim went through. NIOSH 

developed the methods to impute or infer censored 

data based on the positive results in the dataset, and 

this is all documented in Report 96, Multiple 

Imputation Applied to Bioassay Coworker Models, 

from 2019.

And the first application they actually saw of it in coexposure models was for SRS, and that's the TIB-81 

discussion that we had previously this afternoon.

So initially, our concerns when we saw it in the SRS 

co-exposure, we had one finding and one observation 

directly related to the use of that.

And what we saw was that when imputation was 
141

used, we were getting estimates of the co-exposure 

bioassay results that would be modeled for the coexposure intakes that were often much less than onehalf of the minimum detectable activity.

And we noted that, well, you know, missed dose 

approaches, if you had a worker who was monitored 

and had a censored result, we treat it differently. You 

assume one-half the MDA.

Again, this was Finding 2. So, that raised our 

eyebrows. We hadn't reviewed Report 96 in its 

entirety yet, but we're looking at the results and 

saying, wow, you're getting some really small results, 

again compared to that one-half the minimum 

detectable activity, which immediately raised flags 

with us.

There's Observation 1, the method is mathematically 

correct but has the potential to bias results low, and 

we note that in that observation.

The previous method, called the maximum possible 

mean method, may be preferable. And that 

maximum possible mean method would essentially 

replace or substitute that censored result at the 

censoring level.

So, if the bioassay result was less than three, we 

were just going to assume that it's three. That was 

the previous method prior to imputation.

So, again, we discussed those findings and 

observations related to the SRS co-exposure models 

back in December of 2019 at the joint meeting of SRS 

and SEC Issues. And at that time, we were tasked 

with performing a broader technical evaluation, 

essentially tasked with reviewing Report 96, which 

details how this entire process is done.

So, this past June when we delivered a technical 

memorandum, Review of Multiple Imputation 

Methods Applied to Censored Bioassay Datasets --

and that was in June, and again, Dr. Carl Gogolak 

who is on the call -- thankfully, all the technical 
142

review and literature review of this method as a 

statistician, as I would fall woefully short myself.

So, some relevant literature that we found related to 

this -- and I really love the title, the Helsel 2009, 

Much Ado About Next To Nothing, Incorporating NonDetects In Science -- and these are two quotes from 

that work. The first one I'll read into the record.

In general, do not use substitution. Substitution is 

not imputation, which implies using a model, such as 

the relationship with a correlated variable, to impute 

or estimate the value.

The second one is, method of valuations for 

estimating a mean do not necessarily carry over to 

the more difficult issues of how to compute interval 

estimates, upper percentiles, correlation coefficient, 

regression, slope and intercept.

There's more work from Helsel, and this is actually 

from 2020. And it's actually titled, Why Not 

Substitute 1/2 Detection Limit for Non-Detects. And 

Helsel 2020 notes, this creates problems of what he 

terms invasive data.

For example, the artificial lowering of the standard 

deviation, which of course is going to affect your 

upper percentiles and how the data is ultimately used 

in co-exposure modeling.

And he also noted that using substitution, such as 

one-half the detection limit, may create artificial 

trends in the data that do not actually exist.

And these problems are especially problematic for 

datasets with multiple censoring levels. For example, 

if you have bioassay methods, the limit of detection 

improved over time.

So, some general technical comments that we have. 

Again, it's in a technical memorandum, which is 

available under the SRS website. I don't believe it's 

posted on the Meetings page, but if you go to SRS, 

you'll find it towards the bottom on all these White 
143

Papers and discussion papers related to SRS.

So, just three real technical comments here, based 

on our review, is that multiple imputation uses 

information in the detected data, if that's in the 

positive results, to generate values below the 

detection limit.

Co-exposure modeling generally assumes a common 

log-normal distribution. Detected and non-detect 

data come from the same distribution.

Therefore, imputation uses more of the available 

information in the dataset than a substitution 

approach would. So, in that sense, imputation is 

statistically preferable.

Unquestioned is, when you get to a point where you 

just have so much censored data that imputation 

really doesn't make much sense.

I'm probably going to mess up this name but I'll try 

it. Krishnamoorthy, et al., a 2009 document, 

suggests that the performance of any imputation 

model is really more dependent on the total 

numbered of censored results you have, than on the 

proportion or percentage of censored results in a 

given dataset.

And SC&A agrees with that. And we don't recommend 

any universal upper limit on the percentage of 

censored results for that reason. It's really about the 

total number, not the percentage.

So, it's sort of variable on the size of the dataset 

there. And so, each time we use this imputation 

method, you've really got to evaluate it individually, 

again with the emphasis on the total number of 

uncensored results when applying imputation. And 

we provide one very basic example.

In other words, if you have a dataset of ten and only

one of those ten results was above your detection 

limit and the other nine were censored, using 

imputation may be problematic. Whereas, if you had 
144

a dataset of 100, and ten of them are positive above 

the detection limit and the other 90 are censored, 

then it may be okay to use imputation. So, we just 

offer that caution there.

There were several cases -- and this is a topic for 

NIOSH's consideration. It's not a finding. I guess you 

could consider it an observation, although we didn't 

put it in as such in our review, we just basically had 

a conclusion.

But NIOSH may want to consider this because we 

noted that several examples in Report 96 indicate a 

mix of positive and negative values, i.e., some values 

do not have a log with which to use.

A document from way back in 1957, Aitchison and 

Brown, discuss an alternate distribution called a 

delta, which is really a mixture of log-normal and a 

discrete probability at zero.

Dr. Carl Gogolak, who did this review, in 1986, 

actually describes methods for estimating the three 

key parameters, the delta, the mu and the sigma.

So, we offer this up, that NIOSH may want to 

consider the delta distribution when using imputation 

methods when a large portion of unexposed workers 

are mixed with a much smaller proportion of exposed 

workers.

Alright, what does this really mean in terms of dose 

reconstruction? So, SC&A 2020A, which was a 

revision to our coworker model review, which was 

discussed last December, the change was basically to 

change one finding to an observation and add some 

clarifying language as a result of December, so 

there's not a real big change.

But essentially, in our original review as well, we did 

some scoping calculations to compare how does 

these imputed values, which I noted earlier, could be 

much less than one-half the MDA?

If we do some sample calculations and compare that 
145

method using imputation to a standard missed dose 

approach, what do we see? So, we did evaluations for 

strontium-90, cobalt-60, neptunium-237, plutonium239, and uranium-234.

And what we found when we did those comparisons 

of this imputation method and the missed dose 

method, is that intakes and doses calculated are 

much higher from this -- well, not much higher, but 

appreciably higher, when you use the missed dose 

method.

But actually, when you carry the calculation all the 

way through to a resulting Probability of Causation, 

there's really very, very little difference that we 

observe. And that was Observation 2.

Also, we found that, specific to uranium when we did 

this, the imputed co-exposure values were actually -

- and when I say values, I mean the doses here, were 

actually a factor of four higher than the missed dose 

approach.

So, you can only see smaller values, compared to the 

MDA or one-half the MDA, using imputation. The 

doses are higher from this but the imputed coexposure doses are either very comparable or, in the 

case of uranium, were actually bounding on the 

missed dose approach.

And you say, well, how can that be? If the intakes 

and doses are so much higher from missed dose, why 

is there no follow-through effect on the Probability of 

Causation?

And what we hypothesize here is that it's really the 

effect of the uncertainty on Probability of Causation 

when you're applying a co-exposure assignment.

So, in essence, when you look at missed dose, you 

calculate a dose, but then when you calculate the 

Probability of Causation, you're using a triangular 

distribution, with the dose being the mode, the 

minimum at zero and the maximum at censoring 

level itself.
146

And this is just a note on 95th percentile. I don't want 

to get into this too much because we talked a lot 

about that this morning.

But what I note here is that if you use the 95th 

percentile of these co-exposure models, you're likely 

looking at the uncensored portion. That is the actual 

positive results feeding into that upper percentile.

I mean, if you had just an incredible number of 

censored results, that may not be the case, but it 

would be logical that if they're going to apply the 95th 

percentile, imputation wouldn't even really factor in 

all that much.

So, just to summarize our technical evaluation of the 

method multiple imputation as a whole, because this 

was first applied at SRS, but it's really to be applied 

at all of co-exposure models going forward. And so, 

we have a number of bullets here and I'll read them 

in.

Multiple imputation is mathematically correct, an 

accurate method for assessing censored bioassay 

data in the absence of other information. For 

example, raw data measurements, if they're 

available, which was the case at SRS for at least 

plutonium and americium.

We also note that the total number of uncensored 

results, rather than the percentage, should be used 

when you evaluate what the appropriate statistical 

method is and whether multiple imputation should 

really be used.

So, again, it's the total number that you have that 

are positive to infer the ones that are censored, 

rather than just the percentage. And that was that 

really simplified example I gave earlier, where you 

have one out of ten uncensored results versus ten 

out of 100 uncensored results.

One out of ten may be problematic, ten out of 100 

might not be, even though that's the same 

percentage. It's the total number.
147

I mentioned the delta distribution as one option they 

may want to consider as integrating into their 

methods for co-exposure when you have large 

proportions of unexposed workers mixed with a much 

smaller proportion of exposed workers.

And that would be the situation when sometimes the 

log-normal fits are really less than ideal.

And we noted, again, substitution as described in 

Helsel in comparison to imputation has many 

analytical drawbacks, such as the artificial lowering 

of the uncertainty of the geometric standard 

deviation.

And then, finally, what is the effect on actual dose 

reconstruction? And as I just went through, scoping 

calculations indicate very little practical difference in 

Probability of Causation values, when evaluated at 

50th percentile.

And I described that that's really because of the 

effect of applying uncertainty to co-exposure 

assignment, whereas it's a completely different 

approach for missed dose.

And then, that note about unmonitored workers can 

be assigned the 95th percentile, which is likely 

reflective of the actual monitored results. Imputation 

would have less of an effect on that.

So, in summary, conclusion, SC&A finds that the use 

of multiple imputation and evaluation of bioassay 

datasets, the censored results, is technically 

appropriate, scientifically defensible, and likely of 

small practical significance when considering its 

effect on the resulting PoC calculations. And here's all 

the references that are provided in there, including 

the Helsel papers.

Some of them are available online. And again, the 

NIOSH co-exposure model, which is going to be 

revised at least for americium, but Rev 4 is available 

on the website. And then, our actual technical review 

of Report 96 is there as well on the SRS page, but 
148

you have the link right there. That's that last bullet.

So, if there are any questions, I know Dr. Gogolak is 

on the line. I don't know if he wants to add anything 

to the presentation I just gave, or we can field any 

questions that the Work Group might have.

I'm glad this one went last, because it's lot easier 

one. There's very little disagreement, just the one 

technical suggestion for using that delta distribution 

in certain situations.

Chair Clawson: Are there any questions from 

members of the Work Group?

Member Lockey: Well, it sounds like there's 

agreement.

Member Beach: Yeah, Brad, this is Josie. I don't have 

any now.

Chair Anderson: Just a quick question. For some of 

these elements, you'll have a background exposure 

level. So, with imputation, you put assigned values 

that are going to be -- you will assign at least some 

values that are going to be lower than what is the 

background exposure levels for individuals.

Dr. Taulbee: Is there a question?

Chair Anderson: Well, I was wondering if, then, one 

could assume you would not impute below what 

background level exposure is.

Dr. Taulbee: Yes. I believe that's correct.

Chair Anderson: I don't think that's in your model. I 

mean, now we're really getting into -- I mean, don't 

spend time, I'm just saying that. It seems to be one 

thing I would --

(Audio interference.)

Chair Anderson: -- where there is a background level.

Dr. Taulbee: Right.
149

Chair Anderson: And you're assuming their work 

environments are cleaner than what the background 

is, and for some of these, that may not be true.

So it's just a word of caution more than build it in, 

but you need to be aware in some instances for some 

datasets that may be where the imputation, when it 

seems to be very low compared to the other 

methods.

Dr. Taulbee: Okay. Understand. Thank you.

Member Ziemer: Question, Tim, or Bob, I guess, or 

both. This is Paul. I assume this is sort of universal. 

This is not just Savannah River. This is an approach 

you're planning to use throughout the whole system. 

Isn't that correct?

Dr. Taulbee: That is correct. Yes.

Member Ziemer: Yes.

Dr. Taulbee: For both internal and external.

Member Ziemer: I don't know if we have to approve 

it at all. I think it sounds fine and looks like both 

SC&A and NIOSH are in agreement that it's an 

appropriate approach. I'm certainly good with it.

Dr. Taulbee: Thank you, Paul.

Chair Clawson: Well, that brings up a question, 

though. Is there anything, as the SRS Work Group 

meeting, because this is kind of the difficulty that I've 

been having, is some of these are overarching issues, 

but they pertain only to SRS too, so I just want to 

make sure, is there an action that we need to take 

on this, Tim or Bob, as the SRS Work Group?

Dr. Taulbee: This is Tim. I don't believe so, because 

I believe from the SRS standpoint, you close them 

out earlier, as you went through with John, on the 

OTIB-81. And so, really, I believe everything's closed 

from SRS. Do you concur with that, Bob?

Mr. Barton: Yeah, that's what we went through with 
150

John's presentation. And we sort of perfunctorily 

closed them out. Just based on this presentation that 

we're about to give, there really isn't any 

disagreement on the use of imputation.

So, while we had some concerns about the results we 

were seeing in that co-exposure model, we decided, 

you know, it just really doesn't have a practical 

difference compared to missed dose when you're 

talking about Probability of Causation, and we find it 

to be technically accurate.

When Dr. Gogolak did the research, to really look at 

the model as a whole, not just for SRS, but the entire 

complex, the entire program, we're in agreement 

with NIOSH that we think that this, while not perfect, 

it's the best currently proposed method for dealing 

with this, and we don't have a better suggestion, and 

we think it's probably an improvement over what was 

there before from a scientific standpoint.

(Simultaneous speaking.)

Mr. Barton: I'm not sure if that answers your 

question, but we did close those out earlier in the day 

when John was going through the update on TIB-81, 

because of this session that we're about to have.

We closed them then, but I think we would have 

come back and closed them anyway, just based on 

our agreement with Report 96.

Chair Clawson: And I appreciate that. I just wanted 

to make sure that we captured them all, because 

there were some questions at the beginning of this 

that you thought we'd -- NIOSH thought we'd closed 

them, but there wasn't anything, and so I just wanted 

to make sure that we're correct on all of these.

Chair Anderson: The only other thing I would add, 

you did raise the question and there isn't -- no one 

agreed on cut point, and that's on the number of 

detectable occurrences in a dataset before you can 

use imputed values.
151

I'm just going back to my environmental health days 

with VCVs, and then wanting to sum up all of the 

conjoiners. In some of the conjoiners, you won't 

detect them all.

And so, how you can end up with an exposure that's 

all made up, basically, because you're adopting onehalf the limit of detection, some imputation there, it 

might be very helpful.

So, I think we just have to, again, not routinely do 

things without thinking about the datasets that we're 

applying this to, would, I think, like here you have so 

many in the dataset that are really -- it's not an issue, 

other than to comment how many there are that are 

detectable in the system.

Dr. Taulbee: I agree and understand.

Chair Anderson: And that would also be an argument 

for not going into the subgroups, because you can 

end up with small detect numbers in all the 

subgroups.

Mr. Barton: Yeah, Dr. Anderson, in response to that, 

I think, as we pointed out in our paper and in the 

presentation, I think these have to be looked at on a 

case-by-case basis whether imputation is truly 

appropriate.

In a situation where there's all censored results, I'm 

not sure how you would impute anything anyway. But 

yeah, I think that's a case-by-case basis.

So, this is going to be used in other sites. So, I guess 

(Audio interference) your evaluation, the appropriate 

--

Chair Anderson: The problem you want to avoid is 

you don't want to think about it, make a decision, 

rather than simply you push the button like you do 

now in running your SAS statistics and you get 75 

pages of numbers and statistics, and really are 

irrelevant to what you're doing. You just have to be 

sure you're thinking about what you're doing.
152

Mr. Barton: Point taken.

Dr. Taulbee: Agreed.

Chair Clawson: Okay, do we have anything else that 

needs to come before the Work Group, or for the 

overarching work? Do we have anything, Paul? Is 

your group fine?

Chair Anderson: We're going to leave it all up to you, 

Brad.

Chair Clawson: Well, I appreciate that, Andy. I've still 

got a lot of questions, but I will wait for those reports 

to come out.

I was going to -- if we have no more further business, 

I was going to see if the petitioners had anything that 

they'd like to be able to say, but I want to make sure 

we had covered everything that we need to in this 

session.

Issue 7: Petitioner Comments

Tim? Bob? Joe? Is there anything more that needs to 

come?

Dr. Taulbee: The only thing that I have is, what do 

you need from NIOSH, or what do you plan for the 

Board meeting coming up in December? And I don't 

know if you wanted to wait until after the petitioners 

talk about that, or what is it that you are requesting 

from NIOSH to support that meeting?

Chair Clawson: Well, right now, and I appreciate -- I 

do have out to the Work Group right now some 

wording for the SEC for Savannah River. But I wanted 

to get their input before we send it to you.

I think it's going to come down to that, in this Board 

meeting -- and, Rashaun, you're going to have to 

chime in on this one -- we've got to bring the full 

Board up to kind of where we're at, where and why 

we've gotten where we did.

So, I figure that there'd be about a 30-minute 
153

presentation from each side explaining where we're 

at, what we're doing, and why we've come to this 

point. And so, that's kind of what I'm expecting from 

both sides.

Issue 8: Work Group Discussion

Member Lockey: Hey, Brad?

Chair Clawson: What, Jim?

Member Lockey: I agree with you, but I don't think 

30 minutes is going to be an adequate amount of 

time.

Chair Clawson: Well, we could put another two or 

three days on to it if you'd like.

Member Lockey: No, I don't want to do that, Brad. I 

just don't think 30 minutes. I would say this is 

probably going to take the afternoon at the meeting. 

So, I don't think 30 minutes in discussion is going to 

do it because I think there's a lot of different issues 

here that I think we need to bring in front of the 

Board that we need to allow time for people to really 

understand our process and our thinking as 

thoroughly as they can in the time limit we have.

Dr. Roberts: I'm sorry to interrupt. Right now there's 

an hour-and-a-half allocated on the agenda for SRS. 

How much more time would be appropriate?

Member Lockey: I would say a total of three hours. 

That's what I would say. And if we get through it 

faster than that, that'd be great.

Dr. Roberts: Okay. And while I'm on, there's 

someone with the phone number ending in 661, I 

keep hearing typing, or some interference in the 

background. So, if you could mute, please.

Member Ziemer: I don't know. Three hours seems 

like a long time for this.

Member Lockey: Well, Paul, you were saying that 

some of the presentations we heard on Tuesday, 
154

you'd like to see presented also at the Board meeting. 

So, that's why I was saying that. But if you don't 

think that's important, I'm okay with that.

Member Ziemer: Well, my thought is, at this point we 

still have, I believe, SC&A needs to cover why they 

believe dose reconstruction cannot be done, and 

NIOSH should explain why they believe it can be 

done, because that's really the decision, aside from 

the issue that this has gone on a long time.

You can end the time issue either way. But in 

fairness, the Board needs to hear both viewpoints. 

And then, to have the recommendation from the 

Work Group as to why they have determined that 

they recommend an SEC. But the Board needs to 

hear both sides of the issue, certainly.

Chair Clawson: And Paul, when I made the comment 

about time, time, you're right, doesn't play into it. 

But I just want to remind everybody that it has been 

a long time --

Member Ziemer: Oh yeah. Yeah. Well, I understand 

that, Brad.

Chair Clawson: And it comes down to --

Member Ziemer: And we're not going to make the 

decision on that basis. I want to make sure in the 

record that the Board -- and I think Jenny has 

stressed this -- the Board has to make it very clear 

why they are going in a particular direction, because 

that has to go through the Secretary and on up 

through the White House.

Chair Clawson: And I understand that. I will tell you, 

Jim, what I was looking at. And that's why I said at 

least a half-hour.

Hit the high points on this. We could go into -- well, 

let me ask both sides of it. How much time does SC&A 

and NIOSH feel that they'd need to do a brief 

description of where they're at?
155

Because this comes down to data completeness, 

period. And so, all the other stuff we'll have to deal 

with. But it comes down to that one issue.

And that's why I feel that we really don't need that 

much more time. But I also want to make sure that 

each side has adequate time to express what they 

want to.

Member Lockey: Well, Brad, I agree with you about 

we're not going to -- spending another year on this 

is not going to be helpful. But I just want to make 

sure that there's enough time with the Board that 

everybody understands that this is as far as we can 

go with this in regard to data completeness, and this 

is how our subgroup made the decision.

Chair Clawson: I understand. I do.

Member Lockey: Okay.

Chair Clawson: Josie?

Member Beach: Yeah, I was just going to say the 

Board members need to have enough time to ask 

questions and have their questions answered so the 

Board members feel comfortable.

Because they're not enmeshed in this data like the 

SRS Work Group and maybe the SEC Work Group is. 

So, you need enough time for that.

Dr. Taulbee: I would like to propose, back to what 

Dr. Lockey was saying, of setting aside a three-hour 

interval. And I know that sounds long, but keep in 

mind we've been going at this now for almost six 

hours.

But the reason I would propose this is if you look at 

the current agenda, on the second day we run from 

1:00 to 4:15, which is three hours, to where the other 

agenda items could be moved up to Day 1, and then 

SRS could be all day on Wednesday, December 9, 

and it would fit within that and it would be the last 

agenda item, and we run as long as we can, or as 
156

long as needed for the Board.

I can certainly wrap up our presentation. Thirty 

minutes is pushing it, but I can certainly do it 30 to 

45 minutes, not a problem. I don't need to go longer 

than 45 minutes. Just thinking of some of my past 

presentations.

And if SC&A was the same, that would be an hourand-a-half, and give the Board an hour-and-a-half to 

decide. And if they finish up early, then the meeting's 

over. There's nothing else to wait. There's no dead 

time. Just my suggestion.

Chair Clawson: And that's a good suggestion, too. 

Well, I figure we almost have more than half the 

Board members right now on this phone call.

Member Ziemer: I was going to say that. Fortunately, 

a good fraction of the Board is already up to speed 

on it.

Chair Clawson: Up to speed on it. So, I agree with 

that. Let's do that and put it there. It's just I want 

the petitioners to be able to have an opportunity to 

have their voice heard on this too, especially where 

we're coming into it like this.

And so, that's why I was just holding for a more 

precise time. But that's fine with me. Would that work 

with you, Rashaun?

Dr. Roberts: Yeah, I think that's an easy enough 

adjustment to make. I'll work on moving some items 

and things around. But it should be possible to 

accommodate what's being recommended for at least 

the three-hour block.

Mr. Fitzgerald: Yeah. Brad, we'll aim to -- quite 

independent of what Jim just said, I thought 45 

minutes was probably about right. So, that's an hourand-a-half.

That's half the time would be presentations, and the 

other half would be discussion and questions.
157

Chair Clawson: Okay.

Member Lockey: Plus, Brad, the other thing is, I 

suspect the wordsmithing on this letter is really going 

to have to be very precise. So, I think that's going to 

take us a little time.

A lot will be done before the meeting, but I suspect 

there will be additional suggestions or changes 

actually at the Board meeting too.

Chair Clawson: Okay. You kind of blurred out there 

at the end of it. I think I fell asleep. But no, my feed 

was going out of there.

Member Lockey: That's because you're in Idaho.

Chair Clawson: You're absolutely right. And if you've 

read it, it is pretty precise.

Member Lockey: It is very precise. I have more 

comments to send you now after today. And I think 

as I drive to Utah, I'll have even more.

Chair Clawson: Oh, great. Okay. Well, I want to tell 

everybody how much I appreciate all of the work. I 

know that we laugh and joke and we poke a lot of 

fun, but this has been a lot of work.

And I know that everybody here has the best 

intentions for the petitioners, for our government, for 

everything. And I just wanted to say thank you. And 

I appreciate the professionalism in which all of you 

have demonstrated in this.

And it's been a long time and I really, truly appreciate 

it. Rashaun, she's only got the last few hours, not the 

last 13 years that we've been doing this. So, it is what 

it is.

But is there anything that needs to come before the 

SRS Work Group as a path forward? Does NIOSH 

need any further direction on what is requested of 

you on the sampling?

Dr. Taulbee: No. We are clear as to what we need to 
158

do and we will prepare a presentation of why we 

believe dose reconstruction's feasible, and look 

forward to presenting.

Chair Clawson: Okay. That being said too though, a 

little bit earlier there was, on the sampling of -- what 

was that? I think, was it with 81 that we had the 

discussion on the -- yeah, whatever.

You guys will figure it out. We'll go on from there. So, 

is there anything that needs to come before the Work 

Group? If you would get in to me your comments on 

the wording for this so I can get it sent up to NIOSH 

and other members, I would appreciate it. With that 

being said, is there any -- do we need to bring 

anything else up at this time? That being said --

what? Go ahead.

Member Ziemer: Enjoy Thanksgiving.

Dr. Roberts: Brad, I just wondered about the 

petitioners, offering the petitioners comment -- time 

to comment now.

Chair Clawson: If any petitioners are the phone would 

like to make a comment, now is your opportunity.

Not hearing any, Rashaun, I would say that this 

meeting's adjourned for the day.

Adjournment

Dr. Roberts: Yes, I agree. And I would just like to 

echo the appreciation that you expressed for NIOSH 

and SC&A and the Work Group members for working 

hard in this meeting, and for all the materials and the 

work that SC&A and NIOSH worked to develop.

Just much appreciation for that. And I'd like to send 

a special thinks to Dr. Cardarelli, who really played a 

crucial role in making sure that we had all the 

materials and presentations together.

So, I really do appreciate his coordination. So, with 

that said, happy Thanksgiving everybody, and thanks 

a million.
159

Chair Clawson: Okay.

(Whereupon, the above-entitled matter went off the 

record at 4:08 p.m.)