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Centers for Disease Control

National Institute for Occupational Safety and 

Health

Advisory Board on Radiation and Worker Health

Savannah River Site (SRS) and SEC Issues Work 

Groups Joint Meeting

Thursday, December 5, 2019 

The Work Group convened in the Cincinnati Airport 

Marriott Frankfurt Room, 2395 Progress Drive, 

Hebron, Kentucky, at 8:30 a.m., Eastern Time, 

Henry Anderson and Bradley Clawson, Work Group 

Chairs, presiding.
2 

Present: 

Henry Anderson, SEC Issues Work Group 

Chair

Bradley P. Clawson, SRS Work Group Chair

Josie Beach, Member

James E. Lockey, Member

Genevieve S. Roessler, Member*

Phillip Schofield, Member*

Paul L. Ziemer, Member

Also Present: 

Ted Katz, Designated Federal Official

Matt Arno, ORAU Team*

Bob Barton, SC&A 

Liz Brackett, ORAU Team

Ron Buchanan, SC&A*

Grady Calhoun, DCAS

John Cardarelli, DCAS

Nancy Chalmers, ORAU Team

Harry Chmelynski, SC&A*

Paul Demopoulos, ORAU Team*

Joe Fitzgerald, SC&A

Jenny Naylor, HHS*

Joyce Lipsztein, SC&A*

Mike Mahathy, ORAU Team*

Lavon Rutherford, DCAS*

Mutty Sharfi, ORAU Team*

Dan Stempfley, ORAU Team*

Tim Taulbee, DCAS

Chris Tornes, ORAU Team

* Participating by phone
3 

Contents

Centers for Disease Control National Institute for 

Occupational Safety and Health Advisory Board on 

Radiation and Worker Health Savannah River Site 

(SRS) and SEC Issues Work Groups Joint Meeting 

Thursday, December 5, 2019 1

Roll Call/Welcome 4

Coworker Modeling Guidelines Review Internal 

Coworker Dosimetry Data for SRS (OTIB-81) 

by Tim Taulbee 9

SC&A Review of OTIB-81, by Bob Barton 54

Path Forward on Coworker Guidelines 155

Adjourn 227
4

Proceedings

(8:33 a.m.)

Roll Call/Welcome

Mr. Katz: So, good morning, everyone. This is the 

Advisory Board on Radiation and Worker Health. It 

is a joint meeting of the Savannah River Site Work 

Group and the SEC Issues Work Group, and I'll tell 

you a little bit more about that in a little bit in the 

plan for the day.

But, to get started, just a few logistical matters. For 

people on the line, the materials, the agenda for 

today, and the materials that can be made available 

today because they've cleared DOE clearance and 

so on, are on the NIOSH website under schedule of 

meetings, today's date. You can go there and you 

can pull up -- so, many of the documents and 

background documents, in other words NIOSH 

documents and the SC&A reviews for the Board, are 

there and available for you to read through as you 

might.

For the Board Members that are not here but are 

joining us remotely, Skype should be up and 

running. And although there shouldn't be anything 

shown there yet, I think we will have -- Tim, at 

least, is planning to put up maybe some of his 

presentations, if that all works well. So you'll be 

able to get them there, and you should have gotten 

them by other means by me directly, as well.

So, roll call. Let's just, I guess, start there. It's 

easiest. 

(Roll call.)

Mr. Katz: Okay. So that takes care of roll call. We 

did that. So just let me -- although we don't have 

any members of the public to benefit from this right 

now, or they're not saying, let me just say a couple 

things about this somewhat unusual format meeting 

going forward and then ahead with that.
5

So, this is a meeting of two Work Groups and in a 

sense they have sort of interrelated but different 

tasks. The SEC Issues Work Group is here because 

it has the charge of reviewing the guidelines for 

coworker datasets, for use of coworker datasets 

that the Board has already tentatively approved and 

wanted to see it demonstrated using a site, and SRS 

was a handy one, and a complicated, difficult one, 

probably, but useful for that demonstration. So that 

is, in a sense, why SEC Issues Work Group is part of 

this meeting.

And then, of course, SRS is because the models that 

are being used are for SRS, and SRS is dealing with 

an SEC petition and those models are relevant to 

the petition. And they are getting ready to move 

forward on SRS SEC petition and make some 

progress on that and get that a disposition. So 

there's been a lot of work done that's obviously 

relevant for them.

So, and with that in mind, just let me note, then, 

how something will work here with respect to the 

Work Groups. So, we do hope, and we'll see how it 

works out, that we could possibly have action on the 

guidelines to complete those, since we've had all 

this work on SRS now and we have a sense of how 

those guidelines work. And that would be the SEC 

Issues Work Group's job to do. So if there's a 

motion on that, that would come from that Work 

Group. It would be that Work Group that votes on 

that motion. 

And then, for SRS, this is, as it is on the agenda and 

also on the agenda for next week, almost 

immediately, this is an update on the SRS SEC 

petition. And it's an update because there's a vast 

amount of work that's been done and it's going to 

take some doing to get through it, both by the 

Members -- and we have quite a large membership 

here of the Board -- but then also the other 

Members that will be at the meeting tomorrow for 

them to get sort of -- begin to get their heads 

around the SRS issues.
6

So, there isn't expected to be action on the SRS 

petition at this, or at the meeting next week, but I 

think the idea is that we have this large meeting to 

get ourselves well started on that matter. And then 

we have January, February, March to have more 

Work Group meetings as we may need them. And 

they may just be SRS Work Group meetings. They 

may be joint depending on whether there's a reason 

to go have more joint meetings. But, so, we have 

quite a bit of time ahead of us to be able to work 

through this matter, and we hope have action -- in 

April we have a Board meeting and we could 

potentially have action on the petition in April and 

get this behind us.

So that's sort of the general scheme, and that's 

about the last I have to say. Except logistically, 

please, folks on the phone, please keep your phones 

muted except when you're speaking. And if you 

don't have a mute phone, *6 will mute your phone 

and *6 again will take your phone off of mute. But 

please keep your phones muted and please don't 

put the call on hold ever. Just hang up and dial back 

in if you have to, because hold creates problems for 

everyone.

And let me just check and see -- folks on the phone, 

can you hear me well? Am I clear?

Participant: Yes, Ted, you're fine.

Mr. Katz: Okay. Good. I just thought --

Member Roessler: I can hear well.

Mr. Katz: Super.

Member Schofield: I can hear fine.

Mr. Katz: Great. So I'll just say for people in the 

room, then, as this is a long skinny table, we may 

need to move this phone about a bit because we 

want people on the line, Board Members and 

members of the public and other staff that are not 

here, to be able to hear what's going on, too. We 
7

only have one phone thing. I don't know how much 

-- how many degrees of freedom we have here. We 

have some. We have some cord here, so we'll try to 

move it about. And, people on the line, if at some 

point you can't hear someone, just pipe up. Let us 

know and we'll try to remedy that because it's 

important that people can hear. 

And, with that, then I have my two Chairs. If you 

want to, do you have remarks you want to make to 

start with?

Co-Chair Clawson: No, that's fine.

Member Beach: I have a question before we get 

started.

Mr. Katz: Yeah, absolutely.

Member Beach: So, on the coworker modeling, 

because you made a distinction between the two 

separate groups.

Mr. Katz: Yeah.

Member Beach: The coworker criteria came out in 

March and then again in July based on new --

Mr. Katz: In 2015.

Member Beach: -- 2015, based on comments from 

petitioners. So, where are we as a Work Group? We 

haven't looked at that. I don't know that --

Mr. Katz: It was really not -- there wasn't 

substantial change from -- there were some 

comments that Jim addressed, but they were more 

about -- descriptive than they were substantive.

Member Beach: But it came out, and I don't think 

we've ever really had a discussion and --

Mr. Katz: But we had -- the Board already 

tentatively approved those, so they're really --

Member Beach: Okay. So that was in 2015?
8

Mr. Katz: There wasn't any substantial change since 

the Board's approved the guidelines.

Member Beach: Okay. So we have approved it? 

Okay.

Mr. Katz: So, really, the question now is just, is this 

demonstration illustrative enough to say these 

guidelines are functional as guidelines, we can go 

forward. And the reason that's important is because 

this is not the only site that needs coworker 

modeling.

Member Beach: I understand.

Mr. Katz: And there's a whole lot of other stuff 

that's going to just sit backed up waiting for this to 

get done. So it is important to get it done.

Member Beach: Okay. So then I was wondering if, 

before you jump in on your slides, if you would give 

like the brief history of that, because it's been since 

2015, and where we're at with that criteria, if you 

can --

Dr. Taulbee: I actually have it as part of my 

presentation.

Mr. Katz: Okay. Great. Super. No, that's for that 

question. And, Brad, you'd said you --

Co-Chair Clawson: I'm good with it. I'd just like to 

start in with it and go from there.

Mr. Katz: Yeah. Henry?

Co-Chair Anderson: Yeah, I'm fine as well. And it 

would be nice if we could change and rather be 

tentative have it be an accepted and not a draft. 

Mr. Katz: Yes.

Co-Chair Anderson: It's been draft for almost 10 

years now. So that is my intention here, is to have 

the Committee look at it and say we're comfortable 

with it and we can just report back to the Board. 
9

Mr. Katz: Okay. So it sounds like -- Tim, are you the 

first up?

Dr. Taulbee: I am. And it works.

Mr. Katz: And it's working. 

Dr. Taulbee: For now. Can people see it?

Mr. Katz: Right. There are not that many people on 

Skype, but the people that are on Skype, someone, 

can you say is it showing the presentation?

(No response.)

Mr. Katz: Maybe no one's looking at Skype. It's 

possible.

Member Beach: I think Phil was going to be on.

Dr. Buchanan: Yes, it's showing the presentation.

Mr. Katz: Okay. Thank you, Ron. Thanks. 

Okay. Go ahead.

Coworker Modeling Guidelines Review Internal 

Coworker Dosimetry Data for SRS (OTIB-81) by Tim 

Taulbee

Dr. Taulbee: Okay. Well, thank you all. The title of 

this talk is the Coworker Model Implementation 

Guide, and the SRS coworker model is the example 

from here. And before I get started, I really want to 

recognize the team that did this work, and that is 

led by Liz Brackett and Chris Tornes here, that went 

through and -- Chris was more responsible for Rev. 

3 of this example and Liz for Rev. 4. And as Dr. 

Ziemer noted when he came in, there will be a new 

person who will be leading coworker next, and that 

will be Tim Kirkham.

The statistical support on this is Dr. Nancy 

Chalmers, sitting next to Liz here. And so a lot of 

this development and the stuff behind the scenes 

and all is led by her. So, and there's a whole team 
10

that did this type of work, or did this work here.

So -- oh, one other thing. John Cardarelli, sitting to 

the right of Grady here, is going to be taking over 

for Savannah River from me. As you know, I've 

moved to Associate Director for Science now, and 

having a site as well as doing that job is too much. 

So I've been double duty for a while. And so John 

will be taking that over, but Grady wouldn't let me 

throw him to the wolves on this meeting.

(Laughter.)

Member Beach: Best way to learn.

Co-Chair Clawson: Baptism by fire.

(Laughter.)

Dr. Taulbee: So, but he will be taking over and I 

gave him a -- him attending here, I said any notes, 

action items that would come out of here, they're 

yours. But I will still be involved, just to let you all 

know.

Mr. Katz: And just to remind people on the phone, 

please mute your phones. So, *6 for those of you --

there's at least one phone that's not muted. Thanks.

Dr. Taulbee: And I don't know why, but the slide is 

auto-advancing on me. But it is. 

Co-Chair Anderson: Just to keep you on time. 

Dr. Taulbee: It is. It is. 

(Simultaneous speaking.)

Dr. Taulbee: Okay. So my goal here is, an overview, 

is to give a little bit of background that you were 

talking about there, Josie, and to go over the draft 

criteria for the evaluation and use of coworker 

datasets. And then go through the SRS coworker 

model as the example of where we went through 

and implemented this particular guide. And then 

wrap up with a summary.
11

So, background leading to this development. If you 

go back to 2003, kind of the first original coworker 

was a bounding approach, and that would be OTIB1. And we don't use this one anymore, but this is 

kind of the beginnings of coworker, if you will. 

And in 2010, during discussions, there was some 

concern of coworker models using just the raw 

bioassay data that they would be dominated by a 

few individuals. This lead to RPRT-53, which was the 

one person/one statistic report. 

In 2012, there was a series of reports that we did 

comparing different types or strata of coworkers. 

And then, in 2013, the Advisory Board, SC&A 

reviewed those comparison methodology. There 

were multiple SEC Issues Work Group meetings 

discussing one person/one statistic, stratification, 

statistical comparison methodology. And these 

discussions promulgated the development of the 

draft criteria for the evaluation of coworker 

datasets. At that point, around 2014, is when Jim 

kind of said, okay, let's set the criteria. And so he 

went back and he developed these.

So, this is kind of the timeline of its development 

and presentation. June 2014 was Rev. 1. And then 

it goes all the way to March 1015, which you were 

talking about earlier, Josie. And then July 6th was 

Rev. 4.1.1, and that's the information that Ted was 

indicating was minor cosmetic-type changes. I 

shouldn't say cosmetic. That's wrong. It was minor 

changes; didn't change the methodology at all.

Okay. And so at that point is when the SEC Issues 

Work Group requested a demonstration or a pilot of 

our implementation of this implementation guide. 

How do we do this?

And so we did this first with the Savannah River 

group. And we have three datasets that were 

readily available -- or I shouldn't say readily. They 

weren't as close as we thought at the time, let me 

put it that way, to meet the QA standards that Jim 

set forth in that draft guide were more difficult, and 
12

so it took us time to go through and do that work. 

But in November of 2016 we presented this to the 

Work Group. And at that time the general 

consensus was -- and I believe this was from Dr. 

Melius -- that he wanted to see the full model in 

order to do an evaluation, or to fully evaluate all of 

the aspects.

And so we did implement -- completed all the other 

radionuclides. It took us more time. It took us to 

March of this year. And this was the first 

introduction of the full coworker model. And I do 

have a hard copy of it here, it's 258 pages, so it is 

huge, the volume of work that Liz and her team did 

here.

So, let's go over the criteria of what was set forth in 

there, just to refresh everybody's memory, and 

then we'll go through the actual coworker.

So, the main elements of it are data adequacy, and 

this is -- I kind of shortened it into the point of, 

where the methods correct? Were they doing 

sensitive enough measurements to actually measure 

the radionuclides of interest? Is the data complete 

or reasonable from the standpoint of representative 

of the work of the people who were unmonitored? 

And then I've added the word validation. This is the 

validation of the datasets. Do they contain all of the 

data that we think they do?

Then the applicability to the unmonitored workers.

And here's where we go through a discussion to 

indicate why we feel that this data applies to the 

unmonitored workers. And that's then the analysis 

and application. Then I've got here the time interval 

of the model data. And this is kind of where, in the 

past, we've used multiple years sometimes when we 

had limited data. And in this particular guideline it 

indicates that we should do a one-year interval with 

no more than three years without some additional 

justification. And then the final section of this guide 

is the evaluation and stratification.
13

So, let's talk about the data adequacy. This is where 

we review the sampling methods, the laboratory 

analysis. And, according to the criteria, 

consideration should be given to the 

representativeness of the bioassay collection 

methods. Were the workers with the highest 

exposure potential monitored and what was their 

frequency? Radiochemical recovery. Was this 

considered or -- the counting efficiency, selfabsorption, did they consider this in the 

measurement methods? And then the reliability of 

the overall measurement method. 

Under data completeness, the draft criteria indicates 

we should evaluate whether the data are either 

sufficiently representative or bounding of the 

exposure potential. And it recommends a minimum 

of 30 person measurements per year. This is where 

the one person/one statistic comes into play, that 

we -- or through the Work Group discussions 

changed to the time-weighted one person/one 

statistic methodology. Under data completeness 

we're to assess temporal trends and gaps. We're to 

assess the data quality, the accuracy of the data 

transcription. Those were all things that were 

discussed in the Work Group meetings. And we are 

to evaluate the potentially missed data -- or 

censored data is what I should have said there. And 

for some of this evaluation they compared to the 

claimant files and the NOCTS data for completeness.

Okay. Next is the applicability to unmonitored 

workers, and the criteria sets out a hierarchy in 

there of routine representative sampling, routine 

measurement of the highest exposure potential. So, 

it does actually indicate in there that you don't 

necessarily have to have representative sampling if 

you've got measurement of the highest exposed 

group.

And then the collection of samples after the 

identification of incidents, if you've got just incident 

data, as long as your unmonitored population is also 

represented by those data. And that's the critical 
14

part here. So, representative sample of the exposed 

population or workers with the highest potential for 

exposure.

Okay. The next is the analysis and application to the 

unmonitored population. And here is there sufficient 

data to construct a representative coworker model? 

And the criteria recommends the use of 30 workers 

per interval; however, less data can be used if the 

data fit a distribution reasonably well. In other 

words, if you've got 20, 25 workers and you put 

them on a log probability plot and it's following a 

straight line, well, do you really have to have 30? Is 

it really going to change a great deal if you had 10 

additional? Probably not. So the 30 is a guideline, 

which is what this draft guidance is, is just that, a 

guideline. We try to stick to that 30 as best we can. 

And, in fact, we will expand the time period first 

before we will reduce that.

And can the data be reasonably represented by a 

statistical distribution? We use the time-weighted 

one person/one statistic. And this is emphasized 

within the draft criteria, and this was the result of 

multiple discussions of the SEC Issues Work Group 

and from that discussion. And this is when multiple 

bioassay samples are present during a monitoring 

period -- a year -- for a given individual. It is 

appropriate to average the values so that a single 

statistic can be computed for that individual, 

because in some cases you've got a person who's 

involved in an incident and they might have 10 

bioassay samples. And so, if you only have 30 for 

the entire population, or let's say 40, and one 

person has 10 of them and the others each have 

one, well, then that person is going to be 

dominating your coworker distribution. So, by using 

the one person/one statistic you've weighted each 

person, each coworker equally going across that 

distribution.

The time-weighted gives some credit for when that 

exposure occurred. Was it uniform across the year 

or was it halfway through the year or something 
15

along those lines? And that's where all those 

discussions with the SEC Issues Work Group came 

from, and we settled on the time-weighted one 

person/one statistic.

And this is part of why I believe the SEC Issues 

Work Group wanted to see this implemented, 

because these were a lot of concepts that were 

discussed at the time -- and we would give 

examples -- but how does this all play out? And so 

that's what was wanting to be observed, is the 

implementation.

And, finally, the evaluation of stratification should 

be conducted. Where there's accurate job categories 

or descriptions that can be obtained for all workers, 

there's reason to believe that one job category are 

is more highly exposed than the others, and there 

are unmonitored workers in that job category. 

And this actually begins to play a role more in some 

of the external coworker models that we're 

beginning to try and look at and develop where 

we've got a high exposure going on, and we go 

through and we start looking at the job categories. 

Should this group be pulled out? And we begin to 

see things like everybody in that group was actually 

monitored on the external side for neutron 

exposures, for example. And you go through and 

even the secretaries in that group were monitored. 

So, from that standpoint, do you need to stratify? 

Well, no, everybody in that high group is monitored. 

They can be moved out of the actual coworker.

I put a note in here that stratification by individual 

job categories was never our intention, never Jim's 

intention, nor mine, or anybody's intention from the 

standpoint of coworker models. We never intended 

to get down to that fine of a grain of having a 

coworker model for pipefitters or a coworker model 

for operators, along those lines. It was more of 

general categories.

Co-Chair Anderson: Is that text in here?
16

Dr. Taulbee: It's not.

Co-Chair Anderson: No, I didn't --

Dr. Taulbee: No, it's not. But that's why I put it in 

here as a note, that it was never our intention 

whenever Jim wrote this evaluation criteria. He 

mentions it in here, job categories, but it was never 

intended to do a coworker model for every job 

category there is and to evaluate every job 

category. It was more groups, along those lines. 

Does that make sense?

Co-Chair Anderson: Yeah, but some of the job 

categories, that's a group of people. I mean, when 

you say group, if you're saying this -- I mean, the 

group can be pipefitters, it could be all construction 

workers, which has a much broader job 

classification.

Dr. Taulbee: Right. In the context here -- what I 

recall Jim meaning, and we're meaning, was 

construction trades, like you were talking about, 

would be a group, but not within that group of going 

much further than that. Now, maybe there is a 

group in there that we know about or somebody has 

some knowledge about, but to go through and 

evaluate every single construction trades category, 

that wasn't the intention.

Co-Chair Anderson: Not everyone?

Dr. Taulbee: Exactly. It was more of --

Co-Chair Anderson: But you could have some of 

them, because if there's knowledge that this group 

did some kind of unusual things where others did 

not --

Dr. Taulbee: Exactly. Yes. Yes, and --

(Simultaneous speaking.)

Dr. Taulbee: That is right. We did recognize -- the 

whole purpose of the stratification was along that 

lines of there could be groups of workers who are 
17

different. And if we had some knowledge of that, 

evaluate and look at that. That was the intent. 

Okay. All right. So, any questions before I go on 

here with the actual implementation?

(No response.)

Dr. Taulbee: All right. So, with the SRS coworker 

models, a priori we decided to stratify all 

construction trades workers versus non-construction 

trades workers, which is effectively all other 

workers. Okay? Everybody we could identify as a 

construction trades worker we put into one group 

and everybody else went into the other. 

As I indicated, there was a lot of discussion about 

differences in monitoring methods, frequency, the 

exposure potential, high versus low, duration of the 

exposures. This was all done during previous Work 

Group meetings along this line. You can recall that 

we presented RPRT-53 in the past and there was no 

real consensus on a quantitative approach. Okay? 

We showed different comparisons and there wasn't 

any agreement, really at, that time.

So, instead of getting bogged down into that, that's 

why NIOSH -- we settled on the qualitative 

exposure potential differences as the basis for 

stratification. So this is effectively professional 

judgment. And the construction trades/nonconstruction trades centers around, from our 

standpoint, routine operations and non-routine 

operations, that that's the actual strata, in a sense. 

Okay?

And the reason was we found it was difficult to 

make an argument the exposure potential was 

similar for these two types of workers. If you think 

about it from a practical standpoint of a plutonium 

glove box line, you've got operators who are 

working on that line, they're putting their hands in 

the gloves. They're protected from the plutonium 

due to the glove box line, due to the engineered 

controls. 
18

They're not wearing respirators. They might have an 

escape respirator around their neck, but they're not 

wearing it. They're relying on that engineered 

control, that glove box, to protect them from the 

plutonium. That's what we call a routine operation, 

people who are doing that type of work.

Conversely, we need to do a maintenance job within 

the glove box. So, the operations folks leave the 

room, construction trades workers come in, they 

take the face off of the glove box. They build a hut 

around it first, obviously. But they're in there with 

full-face respirators on, their bubble suits, their fullline respirators. So now that barrier of protection 

between that worker and the plutonium is 

something PPE. It's more personal protective 

equipment. It's not an engineered control.

So they're breaching that particular glove box. This 

is a non-routine operation. This doesn't happen all 

of the time, but this is the type of work that 

construction trades workers would be doing. They'd 

be going into these rooms and doing this type of 

work. So, this was our basis for the stratification for 

-- and it's not just pipefitters that would be doing 

this. It would be sheet metal workers, it would be 

carpenters. Laborers come in. 

We did an interview -- I think Brad might remember 

that -- down at Savannah River, going into the 

building trades to talk about the tritium work. And 

they walked us through taking apart one of these 

glove boxes. There were 14 different trades that 

went into that hut in order to do different work, 

from boilermakers who had to do work with 

pressure lines -- and so they're all working together 

at different times here into this hut. Okay? That's 

the non-routine operations that we're talking about. 

Does that make sense?

Co-Chair Anderson: Yes.

Dr. Taulbee: Okay. So, previously we tried to 

quantitatively compare these two strata, 

construction trades and non-construction trades, 
19

based upon this routine/non-routine work. And this 

was critiqued by the Advisory Board and SC&A. And 

Dr. Melius had mentioned at the time that he 

thought it was going to be generalize on that 

because there are so many different situations that 

might change the evaluation of that statistical 

analysis. And the reasoning why I think he was 

coming to that is because there's more that needed 

to be considered than just that quantitative 

analysis. 

So we couldn't come up with a single statistical 

analysis to be identified a priori, so we left it for 

professional judgment. Now, here's the reality and 

the practical side of things. The initial construction 

trades worker and non-construction trades 

stratification of the coworker model, that was the 

hard part. Okay? Before we had been doing small 

little groups and trying to compare, but when we did 

this on a global scale, this was the hard part. This 

was going through all of the individual models and 

separating construction trades and non-construction 

trades. Okay? 

But once we did this -- and part of why we did this -

- is if the SEC Issues Work Group and the SRS Work 

Group disagreed with our stratification 

methodology, it's fairly easy for us to put it back 

together. It's much easier to take two strata and 

combine than it is to go through and actually 

stratify them. So, if further stratification is needed, 

a lot of the work's already done. But if we need to 

put them back together, that's really fairly easy.

Now, putting them back together could result in a 

better statistical analysis if the two groups are truly 

the same; or worse if they're different. So, it could 

go either way. All right? We don't really know. I will 

that, from looking at the intake models that we've 

got, I don't think that they will get worse. The 

intake models at the end that are in OTIB-81 are 

quite similar between the two groups.

What remains unclear to us -- and this is based 
20

upon some of the mixed comments we've gotten --

is the recommendation of these respective Work 

Groups as to whether we need to stratify, as to 

whether no stratification is needed, whether we 

should stratify on construction trades and nonconstruction trades, or now possibly subcontractors 

or non-subcontractors; meaning people who work 

for DuPont as only one group and people who didn't 

work for DuPont in another, at least at SRS. 

So we've demonstrated, I think, in this coworker 

model that we can stratify, that the data is sufficient 

to do it. The question is whether we should. Do we 

need to? And please note our preference is to not 

stratify. It makes it more simple from a dose 

reconstruction standpoint and there's less 

professional judgment. 

And here is a question that I have for the two 

respective Work Groups, Dr. Anderson and Mr. 

Clawson, is the issue of stratification is a dicey one. 

It's one that is difficult. And we're going to be 

struggling with that somewhat today. Can we 

postpone it for the rest of this discussion of the 

coworker model implementation and address it 

during the comment resolution component and 

RPRT-92? Which will be later today and all day 

tomorrow, or the half-day tomorrow. 

And the reason I wanted to do that is for the 

following. Let's see if we can agree on the elements 

of the rest of the draft implementation guide and 

the evaluation methodology that we've implemented 

on a non-controversial population; say, nonconstruction trade workers, all the operations 

workers at Savannah River. Did we take that group, 

implement the coworker model appropriately, is 

what I would ask if we could do that. Then we'll get 

to the stratification issue last. Is that acceptable?

Member Ziemer: Can I ask a question before we 

decide that? So, at this point, can you identify sort 

of a primary issue that would be the downside of 

stratification? Or are there multiple issues that are 
21

of sort of equal weight?

When you say you would rather not stratify, that, in 

a sense, is because there are some issues on 

stratification that are sort of downside issues. I 

think that's sort of inherent in what you're saying. 

Is there a single downside issue on stratification or 

are there just a lot of different multiple issues and 

they'd be different in every case?

Dr. Taulbee: It would be different in every case. 

And let me give some examples as to why. The 

biggest one is the time that it takes to go through 

and identify --

Member Ziemer: That it takes? Okay. Well, that's 

always a downside, yes. 

Dr. Taulbee: And the second one is at Savannah 

River we have the ability to stratify of construction 

trades, non-construction trades, subcontractors, et 

cetera, because we have work history cards for 

everybody. At other sites we don't necessarily have 

that.

Member Ziemer: Yeah. See, I would rule out the 

time issue if, in fact, stratification gave you a better 

answer. But if it doesn't make much difference, you 

know, then go with the non-stratification.

Dr. Taulbee: Right. And that's where I want to show 

toward the end of this presentation. All of the end 

result of this is going to show that -- or I hope show 

-- that the coworker model is claimant-favorable in 

the way that we've applied it and the way that 

we've developed it, to where when we get into the 

stratification, how much is this really going to 

change? And it --

Member Ziemer: Yeah, I mean, that's clear with 

your final issue, but I'm trying to be clear whether 

or not you gained anything to speak of by 

stratifying. Is it like in most cases we don't or 

occasionally we do?
22

Dr. Taulbee: Well, this is the first one we've done 

from the standpoint of trying to stratify.

Member Ziemer: Okay.

Dr. Taulbee: This is the first one that's been done 

this way.

Member Ziemer: Yeah.

Dr. Taulbee: But I can see at other sites where this 

is going to be much more difficult, which is part of 

why our preference is to not do it, because it 

involves a lot of data capture. And this took us --

this took about a year longer than I thought it was 

going to take, the development of this coworker 

model, to be quite honest. I thought we would have 

this too you all late 2017-type of timeframe, and it 

wasn't until March of 2019 that we actually got it 

out. So that's the biggest downside, to answer your 

question.

Member Beach: So, Tim, I have a question, too. You 

talked about professional judgment and if you 

stratify you would be using more professional 

judgment.

Dr. Taulbee: Yes.

Member Beach: Can you give us a little more on the 

professional judgment that would be required and 

how you would be documenting that, because I 

know we're --

Dr. Taulbee: It would be on the individual dose 

reconstructions of whether this person met this 

particular -- the strata for this criteria. And so that's 

where the dose reconstructor would be looking at 

this particular claim of, let's say that they were a --

I believe SC&A is going to use an example of 

general services operator. And so, this person, was 

it an operator, was it an construction trades worker? 

And you got to look at more information about that. 

So that's an example of professional judgment of 

when we get an individual claim to apply to this.
23

Member Beach: Okay. And speaking of this, so, 

when you're stratifying you may be using more 

professional judgment than if you're not stratifying. 

And you felt that that was a downside to 

stratification? Reading your slides that's kind of 

what I took out of it. Maybe I'm wrong.

Dr. Taulbee: No, I'm not sure I would say that 

there's more professional judgment in the initial 

development. I mean, there's going to be some, 

obviously. The health physicist who's developing the 

coworker models and developing the strata is going 

to be -- they're going to have to have some basis 

and they're going to have to document as to why --

Member Beach: You just mentioned that a couple 

times and we're kind of sensitive to that 

professional judgment and trying to track it more, 

moving forward, than we have. 

Dr. Taulbee: Sure. I understand.

Member Beach: So I wanted a little more info.

Dr. Taulbee: Okay.

Member Beach: Thanks.

Co-Chair Clawson: And this is Brad speaking. And 

so stratification on this, now, you're wanting us to 

say to just kind of push this to the side right now. 

This is just for this presentation?

Dr. Taulbee: Yes.

Co-Chair Clawson: Because one of my things was is 

this part of our issue from the very get-go of how 

we were going to do this.

Dr. Taulbee: Yes.

Co-Chair Clawson: I have no problem of proceeding 

on with that, but I'm not agreeing to not -- or to go 

-- but for this presentation we put this to the side, 

correct?
24

Dr. Taulbee: This is just for the other components of 

that draft criteria that we talked about. Just so that 

you can see the full coworker model being 

developed and how we do it without thinking of this 

strata and that strata.

Co-Chair Anderson: Yeah, I just don't want to push 

it off --

Dr. Taulbee: No, no, no.

Co-Chair Anderson: -- and then we run out of time 

for what's really the critical part, is the discussion.

Dr. Taulbee: No, no, no. 

Co-Chair Clawson: Because this was one of the 

issues from the very get-go. 

Dr. Taulbee: Yes. 

Co-Chair Clawson: So I just want to make sure --

Dr. Taulbee: Yes, absolutely. So, for the next 

however many slides here, just the operations, the 

mechanics of us implementing the draft criteria.

Co-Chair Anderson: Because there's always the 

possibility you can't do a coworker model as 

opposed to now we've got -- because you're saying 

it takes too long and it's been years and years. The 

alternative is it's not feasible --

Dr. Taulbee: That is correct

Co-Chair Anderson: -- to make the people wait for 

this.

Dr. Taulbee: That is correct. 

Co-Chair Anderson: Savannah River is one of these 

that's been --

Dr. Taulbee: And we very may well run into that 

from the standpoint of a particular radionuclide for 

one of the construction trades strata, that we can't 

implement this guide sufficiently. But what I've 
25

picked here is operations workers, and I'm going to 

go through the plutonium example, which is where 

we have lots of data so that you can see the 

mechanics of how we implemented the coworker 

model and see if we can gain agreement based 

upon that. And then we can start looking at the 

other things under the strata standpoint. Does that 

make sense?

Co-Chair Clawson: Yes.

Dr. Taulbee: Okay. And as I believe Ted mentioned 

earlier, another reason why we wanted to do this 

was we do have several coworker models under 

development: Idaho National Lab, Fernald. And then 

there's other sites where we need to update this 

TWOPOS methodology. So if we can get the draft 

criteria accepted, finalized, then we can continue 

work on it. We're working on them now, but it's 

under draft guidance. And so we'd like to implement 

this.

It doesn't mean we can be successful in all these 

cases, to answer what Brad's talking about, but this 

is the baseline, this is the target.

Okay. The other goal, the final point, is if we need 

to change something here as I go through it, 

something that you all don't like in how we 

implemented it, please let us know now so that we 

can make that change and move forward.

All right. So, for Savannah River -- and if we just 

think of the non-construction trades workers here --

OTIB-18 is a bounding approach and it actually 

takes care of a large number of the claimants who 

would need a coworker model. At Savannah River, 

from an operations standpoint, a large number of 

the workers are monitored. I mean, you're looking 

at over 80 percent of the actual workforce is being 

monitored. And so OTIB-18 for those who are not 

monitored we tend to use as a bounding estimate. 

But our goal for a coworker model for SRS is to 

supplement OTIB-18 as a best estimate for 
26

coworker models. Okay? There are some cases 

where we need a best estimate. But we never know 

which radionuclide we need it for, so we developed 

a coworker model for all the major radionuclides at 

Savannah River. And that is these nine. The 

trivalents: americium, curium, californium. At some 

sites they're called exotics. Savannah River it runs 

both ways as to whether they're exotic or whether 

they're production. Tritium, plutonium, uranium 

fission products, strontium, cobalt-60, cesium-137, 

neptunium, and thorium. Okay? 

So this was what we needed to develop. And that is 

what is in OTIB-81, the 258 pages, is a model for 

each of those radionuclides. And just to throw it in, 

OTIB-81 is a model -- a non-construction trades 

worker model and a construction trades worker 

model for each of those radionuclides. Okay. 

Because we did do that strata. All right. But we're 

just talking about the non-construction trades right 

now.

And what you'll find going through OTIB-81 is that 

the format closely follows the Coworker 

Implementation Guide, or that draft criteria, of data 

adequacy where there's a discussion of the personal 

monitoring, the applicability to unmonitored 

workers, the bioassay analysis techniques. Under 

data validation the data completeness and quality, 

how we interpreted the data and what data we 

excluded. Okay?

So I'm going to go through -- oh, and this next --

the other steps are statistical analysis, the 

development of the TWOPOS, time-weighted, one 

person/one statistic values, and then the intake 

model. So I plan on taking you through all five of 

these with plutonium. Okay? 

So with plutonium monitoring who is monitored? 

Bioassay control procedures were started -- we 

have those documented from 1968 going forward 

and they identify the types of workers and the 

frequency of monitoring within the specific areas. 
27

For operations there's a lot more detail. For 

construction trades, it was monitored every one --

one plutonium sample every three years. Okay? 

The applicability to the unmonitored workers. The 

number of workers monitored was relatively 

constant over time. There weren't any temporal 

gaps that we noted and the workers with the 

highest potential for exposure were monitored. 

Well, how do I know that? And that is looking at this 

particular table, Attachment C, which I see now you 

can't look at or can't see very closely, but what 

you'll see is at the very top, first block is minimal 

potential. And this is for people working in tritium 

facilities and 723, the 305-M areas, like 100 areas 

and like 773-A areas. 

For plutonium -- these were non-plutonium areas, 

by the way -- they were monitored once every three 

years. If you go down to 221-F and H areas for 

Savannah River, this would be -- these would be the 

canyons. And so this is where the depleted uranium 

targets that had been irradiated were separated, 

fission products were separated out and the waste 

was sent to the waste tanks. Uranium product went 

to the A-Line; the plutonium product went to the BLine. Okay? So this is the main canyon. High levels 

of gamma radioactivity in there. People didn't go 

into canyons. They would draw samples, but they 

were less exposed than the next group that I'm 

going to talk about. 

And these are the actual plutonium workers. These 

are the people on 221-HB Line, FB-Line and JBLines. These are the plutonium lines. This is where 

they separated out the plutonium, purified it, made 

the plutonium buttons. And so this is the final 

product lines coming off. 

There's three other areas there: 235-F, 773, the 

main research divisions. These workers were 

monitored for plutonium four times a year. So right 

now we've got three different frequencies of 

monitoring. People working on the plutonium lines 
28

were monitored four times a year, people who 

worked intermittently in the canyon areas where 

they might draw a sample, where there's lots of 

fission products associated with plutonium are 

monitored once a year, and people who are more 

office-type workers who might go through the areas 

occasionally are monitored once every three years. 

Okay? So this is why we feel we know that the 

highest exposed workers were monitored.

Member Beach: So, Tim, quick question. This says 

1976. Do you have '72 through '76 samples or just -

-

Dr. Taulbee: We have 19 -- we have these tables 

from 1968 through -- well, when they actually 

changed the criteria in the 1990s. And they're all 

here in the appendix, Appendix -- Attachment C of 

the bioassay, of the coworker model. Okay? So this 

is why we feel we have a good handle on it. 

And if we also think about the whole discussion of 

the time-weighted OPOS as well, one person/one 

statistic, here you've got the highest exposed 

workers four times a year. Their samples are going 

to dominate, especially if we didn't take it to one 

person/one statistic, from that standpoint. Okay? 

All right. So next we looked at the bioassay analysis 

techniques. And from 1954 they did a bismuth 

phosphate lanthanum fluoride coprecipitation. And 

then in 1959 there's a nitric acid/hydrogen peroxide 

dissolution and ion exchange. 1966 started the 

TIOA; because I am not going to try and pronounce 

that part, liquid extraction. And then in 1981 there's 

a coprecipitation technique with alpha spectrometry.

We looked at the reporting level. The reporting level 

or censoring level was 0.1 dpm per day. And I got a 

bolded note here that this is a reporting level not 

necessarily the limit of detection or the minimum 

detectable activity. This is what the site reported. 

So if a value was less than that on their bioassay 

card, they wrote less than 0.1. Okay? And here is a 

snapshot of plutonium logbooks. I believe this is 
29

1980 here. And, gosh, that is hard for you all to 

see. I apologize. 

Mr. Calhoun: You can maximize it. It's pretty easy 

to see.

Dr. Taulbee: Oh, I do this?

Mr. Calhoun: Yes.

Dr. Taulbee: Awesome. Thank you, Grady.

Mr. Calhoun: Oh, it did that, too? Oh, I wasn't even 

looking at that. I was just looking at my screen. 

(Laughter.)

Mr. Calhoun: That's why I get paid the big -- it 

worked for me.

Dr. Taulbee: Awesome.

Mr. Calhoun: My work is done here.

(Laughter.)

Dr. Taulbee: Thank you. And what you'll see here is 

this is a blowup of this area in this upper region 

right here. And you can see this value here is 0.029, 

zero point -- is that another 0.029, 0.023, 0.064. 

They're all less than 0.1. So on the bioassay cards 

we see less than 0.1, but in the logbooks we see the 

uncensored results. 

Member Lockey: What was the standards then for 

that, exposure standard for that? Do you know? 

Dr. Taulbee: There really --

Member Lockey: Well, wait. In the -- the idea was 

to keep it at as low a level as possible. So what 

would have been the guidelines in that time period 

for internal dose, what were you -- the 1980s?

Ms. Brackett: You mean like the limits for intakes? 

'90s?
30

Member Lockey: 1980. '80.

Ms. Brackett: '80 it was 50 percent a maximum 

permissible body burden. 

Member Lockey: '80?

Ms. Brackett: '80. Yes.

Dr. Taulbee: No, 1980.

Member Lockey: 1980.

Ms. Brackett: Nineteen --

Member Lockey: 1980.

Ms. Brackett: 1980. Yes.

Dr. Taulbee: So they used the maximum 

permissible body --

Ms. Brackett: It would be maximum permissible 

body burden it was based on then. It wasn't based 

on a dose. It was based on body burden. 

Member Lockey: And what was that? Do you know 

what that would have been? 

Ms. Brackett: The specific value for plutonium? I 

don't know that off the top of my head because it's 

different for every --

Member Lockey: Right.

Ms. Brackett: -- nuclide.

Dr. Taulbee: I believe it -- I'm not sure I --

Ms. Brackett: And that would be a body burden, not 

a urine sample, so --

Dr. Taulbee: Yes.

Ms. Brackett: -- it's dependent on when the intake 

was. There's -- which is why they converted to dose 

at some point because it was difficult to --
31

Dr. Taulbee: To do at that time.

Ms. Brackett: -- to compare this to say external 

dose, to give you a good value.

Member Lockey: What I'm trying to get a handle on 

at this level, how do you relate this level to a safety 

issue?

Ms. Brackett: I mean we don't. I mean we take a 

result and then make some assumptions and come 

up with a dose at this point. At that point there 

would have been a value that the site -- they would 

have done something to say, okay, 50 percent of a 

maximum permissible body burden would give me 

this much in a urine sample. Like at ONRL there was 

something that called an excretion index, and so 

that would be related to the body burden. But I 

don't know that value off the top of my head. But 

there were --

(Simultaneous speaking.)

Member Ziemer: Well, typically it would be much 

higher than this. 

Dr. Taulbee: Yes, it --

(Simultaneous speaking.)

Member Ziemer: This was like a lower limit of 

detection or -- but you're saying it really isn't that. 

It's --

Dr. Taulbee: Exactly. This would be their action 

level, effectively where they would go and begin to 

assess the dose for an individual. So if it was less 

than 0.1, they didn't worry about it.

Member Lockey: So the action levels are set to a 

factor of 10 to 100 to 1,000 lower than --

(Simultaneous speaking.)

Dr. Taulbee: I believe this is probably at least a 

factor of 10 lower than what that 50 percent level 
32

is, but I don't know that it's that's translatable.

The other thing I wanted to point out here is if you 

look over here into the remarks, you can see that --

where the zero had been changed to 0.046. Okay? 

And what they're doing here is a low-volume 

correction, because off to the left you'll see the 300 

milliliter was their standard volume, and this one 

here was only 175. So they ended up taking this 

dpm per disc, converting it up where they would do 

this -- they did this correction to it and came up 

with this value. So this was still less than 0.1, and 

so that's what they reported on the person's 

bioassay. Okay?

So this will become important for the next 

discussion, the next part here. Actually I want to 

make sure that -- yes, coming up is where that's 

going to become important. Most of the 

measurements were gross alpha for plutonium. 

During the 1980s they did report both plutonium238 and 239. Those were reported separately. Liz's 

team merged them into a gross alpha, a single 

gross alpha measurement assuming -- assumed to 

be 12 percent of 10-year aged plutonium. And this 

was chosen to be claimant-favorable. 

Following along, the draft criteria is talking about 

data exclusions, what were taken out of the dataset. 

Well, we took out all of the chelation samples, 

people who underwent chelation or had an 

indication of DTPA use, because for one thing that's 

going to really mess with the biokinetic models that 

I'm going to be working with, or showing you 

shortly here. So those samples were removed.

Co-Chair Anderson: Those are likely to be the 

highest?

Dr. Taulbee: They are, but it -- I mean it depends. 

It depends. But what ends up happening is the 

bioassay results become --

Co-Chair Anderson: Yes. No, I understand, but I'm 

just saying there -- potentially is there a bias, and 
33

do you exclude these -- if you were chelated --

Dr. Taulbee: Right, but if you --

Co-Chair Anderson: -- there was likely a reason 

more so that --

Dr. Taulbee: But you might also think of it from the 

standpoint of these are extreme accidents, from 

that standpoint, extreme exposure scenarios that --

keep in mind the unmonitored worker would be 

somebody who there's no indication they were ever 

involved in one, that there was -- that 

contamination control was --

(Simultaneous speaking.)

Co-Chair Anderson: So it was only when there was 

an accident that they were chelated? 

(Simultaneous speaking.)

Dr. Taulbee: I can't think of a reason you would 

ever do that.

Co-Chair Clawson: If they chelate somebody, that's 

a kind of last resort.

Dr. Taulbee: Yes. So it's -- yes, it's not done 

normally. 

(Laughter.)

Co-Chair Anderson: No. No, I know.

Dr. Taulbee: There were some that we didn't have 

sufficient identifying information, and so those were 

excluded. Some samples within the datasets were 

given in units of mass. And these were likely fecal 

samples, not urine, so again, that changes the 

bioassay monitoring or the intake modeling 

methods. So those were excluded as well. And for 

each radionuclide in OTIB-81 we went through and 

explained what was excluded, what was in there. So 

this is where we feel we're following the draft 

criteria and meeting the intent.
34

Data validation. And this was one of the things that 

took a lot more work than what we had initially 

thought it was going to take. And we used the 

NOCTS in vitro dataset. And it contained plutonium, 

uranium, enriched uranium and fission products. 

And early on, we had to set the criteria of what are 

our acceptance rates. And so for critical fields we 

decided on one percent, all other fields five percent. 

These would be basically transcription errors is what 

we're looking at here. 

And keep in mind that these initial datasets that we 

used from NOCTS, these were coded to assist the 

dose reconstructor. They weren't coded for 

coworker models initially. Okay? And so the dose 

reconstructor is who does the final QA typically on 

these data when they're doing the dose 

reconstruction, but there's times when a dose 

reconstructor has a lot of external dose. They don't 

even look at the internal dose because the case is 

compensable on the external, and so it's never 

reviewed or looked at, or maybe not even entered. 

And so we had to go back to these -- to multiple 

claims and enter the data for the first time. Okay? 

And so we did that.

Critical fields were determined to be the isotope. Did 

we get the isotope right? The less-than symbol, 

which is really just part of the result. Those were 

the three critical fields that we felt that needed to 

be correct from this standpoint.

And so in order to do this -- and this is where Nancy 

and her team came up with how to do this, the 

procedures to do it, and wrote some reports about 

it. And because we have a large dataset, we needed 

to check 4,386 samples. And there were 11 errors. 

So are these datasets perfect? No. We didn't expect 

them to be perfect, but this is pretty darn good 

from that standpoint. And so the actual error rate of 

the critical field was 0.25 percent with a confidence 

interval of 0.13 to 0.45. And that confidence interval 

is a 95th percentile confidence interval. 
35

All other fields: last name, first name, middle initial, 

payroll ID, date, units, area, there we checked less 

because we were requiring a five percent error -- or 

we were allowing up to a five percent error. And 

here we checked 874 and we found four errors 

within that.

So we did this for each radionuclide that we did a 

coworker model on, or actually not for each 

radionuclide. For each dataset that we used. Each 

dataset. Okay.

The next step was the time-weighted one 

person/one statistic methodology. Again, this 

followed RPRT-53, which is the analysis of stratified 

coworker datasets. This is where all the background 

came up. Started out as OPOS, one person/one 

statistic. We changed it to time-weighted one 

person/one statistic based upon SC&A's comments. 

And these time-weighted one person/one statistic 

data are fitted to a log-normal distribution during 

this analysis.

Most of the bioassay data is censored. Data is 

reported as less than some value. In the case of 

plutonium it's less than that 0.1. Most of it is less 

than 0.1. 

Member Lockey: Most meaning what?

Dr. Taulbee: I'll show you that in just a second. 

Analysis method that we used to -- for the censored 

data was we used a multiple imputation, and this is 

RPRT-96. And we'll talk more about that this 

afternoon -- or not this afternoon, later this morning 

when we get to the comment -- our responses to 

SC&A's comments about multiple imputation. But 

this is it in a nutshell. Okay? And to answer your 

question there, Dr. Lockey, this is for 1969 

plutonium. There are 892 plutonium bioassays for 

the non-construction trades workers, the operations 

folks. 

Dr. Chalmers: No, this is all workers.
36

Dr. Taulbee: This is all --

Dr. Chalmers: All workers.

Dr. Taulbee: Okay. I'm sorry. This is all workers. 

Okay. Thank you, Nancy. 

Eight hundred and ninety-two. And we have 217 

that are uncensored. So as you can see, there's a 

large number that is censored within this dataset.

What we do is for multiple imputation, we'll fit the 

upper tail of the data; and as you see it fits fairly 

reasonable, and assume that all of the censored 

data follows along that line. In the calculation of 

OPOS -- or TWOPOS, I'm sorry, what we will do is if 

an individual has all uncensored data -- and now 

moving to the plot on the right, to where they have 

no censored data, that's those black dots up there 

at the top, which you'll see up here. Okay? These 

are people that don't have any censored data. 

People who have less than 50 percent of their data 

as censored -- in the calculation of OPOS remember 

some of these workers are monitored four times a 

year. They might have two of them that are positive 

and two of them that are not. Okay? And so in that 

case those two censored values, we would go to the 

plot on the left and draw samples, less than 0.1, 

somewhere along that log-normal, and plug them 

in. 

So now let's say they have four samples: two 

censored; two not. We draw two values from that 

imputation model and calculate the TWOPOS value 

for that person. We go to the next person, do the 

same thing. Some of these people, the yellow dots, 

have 100 percent censored results. Now, most --

many of those are a single result and we'll grab one 

value from that left plot to calculate their TWOPOS 

value.

The plot on the right is run Number 1. We do this 

over and over and over again imputing these 

values, imputing the censored values, calculating 
37

the TWOPOS value over and over to where you end 

up with the plot there on the left and you get this 

scatter about them. And this is the values that we 

fit, or this is the TWOPOS plutonium plot for 1969 

for non-construction trades workers. These are the 

parameters that we will use to develop the intake 

model. Does that make sense?

Member Beach: Yes.

Dr. Taulbee: Okay. I've put 1970 off to the right so 

that you can see how these look. And as you can 

see down here at the bottom the spread gets larger, 

as one would expect. Okay?

All right. So now that we've got these TWOPOS 

values and we've fit this log-normal distribution to 

each year, we have -- in this particular case this 

table I've just taken 1967 through 1970. You'll see 

that the non-construction trades 50th percentile is 

0.036 dpm per day. And by the way, that's coming 

from right up here, 0.036, and GSD is 3.14, or 3.42. 

Yes. Okay?

And we -- so off of that plot we pull the 50th 

percentile and the 84th percentile. And why do we 

do those two? A) it makes the math really easy, for 

one, to calculate a geometric standard deviation. 

Okay? And so for 1969, if you take the 84th, divide 

it by the 50th, you get 3.14 for our GSD. And this 

was 296 individuals. Okay? So this is our TWOPOS 

values. What we'll do next is we'll model the intake 

based upon somebody being exposed here at the 

50th percentile and somebody exposed at the 84th 

percentile. Okay? All right.

So the next step, once we get these TWOPOS 

models, these TWOPOS distributions is we model 

the intake for each of the nine radionuclide 

categories. We do the 50th and 84th percentiles for 

each year, and we'll re-look at in a group year. We 

look at it by solubility type and then we'll look for 

similarities of time intervals of similar results. And 

this is where the internal dosimetrist makes their 

money. This is where they earn their pay, I should 
38

say. There's a lot -- there's professional judgment in 

here. This is a skill. It is a rare skill in this country, 

but it is a very valuable one and one that is 

necessary.

We assume a chronic intake scenario for each time 

interval to determine the intake. So let me walk you 

through this. 

Time interval Number 1 is those blue dots, those 

first six years where we're doing this. And this is --

these are those individual TWOPOS values, the 50th 

percentile of those TWOPOS distribution. That 

distribution that I showed you previously -- well, it 

was actually 1969. It's this one right here. But what 

you have here for this particular year is a whole 

'nother plot. You have one of these plots and we're 

taking the 50th percentile here. Okay? That's what 

this plot is, or that's what that data point is. That's 

what that represents. 

So we take these six years. We look at this as if this 

was an individual's bioassay and what intake would 

they receive of plutonium to get that result, 

assuming chronic intake. This would be type M 

plutonium, all right, down here. The line here is 

what their excretion would be if they started on day 

one with that intake and stopped in the last data 

point. This is what their bioassay would look like. 

Okay?

Now we move in time to the next interval that 

seems to fit. This is data that seems to be lower 

than the previous six results, and so we fit a 

different intake model, or a different intake rate. It's 

got a different dpm per day intake rate. We do this 

again, because in this time period starting in about 

1967 there appears to be an increase based upon 

what we see from those TWOPOS results. And so we 

fit this one. And then in 1971 to 1981 we fit this. 

Then in the latter years, 1982 to 1900, it's low and 

it fits this. Okay?

Now put it all together of somebody who's exposed 

over this whole time period. Oh, I'm sorry. Back up. 
39

Got ahead of myself. Apologize here.

The -- so this is the intake tables that we come up 

with. These start and end years are the different 

time intervals I just through with you. The 50th 

percentile is the 50th percentile intake. So 3.265 

dpm per day, that is the intake for each of those 

that I just showed you going down. A high one, the 

67 to 70, is 5.778 dpm per day. We repeated all of 

these steps. 

These plots here were all for the 50th percentile. We 

did this for the 84th percentile data as well. We 

went back to that plot, picked off the 84th. There 

you see the 84th percentiles alongside. And you can 

see the 84th for that 1967 is a 20. We again 

calculated the geometric standard deviation of the 

intake rate because this is -- the intake is what we 

assign when we do dose reconstruction.

The adjusted geometric standard deviation, if one of 

these geometric standard deviations is less than 

three, we increase it to three. That's the minimum 

that we use based upon guidance from the ICRP for 

internal dosimetry, which is where you see 2.98. 

That changed to three. Based upon the 50th 

percentile and this geometric standard deviation, we 

calculated the 95th percentile. Okay? So this is how 

we developed the intake table.

Now if you plug in this intakes -- these intakes for 

somebody who started in January 1955 and ended 

in December of 1990, this is what their urine 

excretion would look like, the green line, summing 

them all up. If they started in a later time period, 

say, out here around 1971 time period, yes, this 

line won't be up here. It will be somewhere 

encompassing these data points. That's the goal is 

that that excretion will encompass these data 

points. 

Is it always? No, but it's close. Okay? So our goal is 

to get an intake to the unmonitored worker, keep in 

mind. This is an unmonitored worker. We're taking 

all the monitored workers, putting all of the 
40

bioassay together to come up with an intake model 

to assign. 

This is the 84th percentile that I'm showing you 

right now. This is what this curve looks like, and you 

can see it's above those data point as well. That 

censoring level of 0.1 is right there. So if somebody 

started in 1955 and went through the end to 1990, 

of an unmonitored worker, and we assigned this 

intake rate, if they had in fact been monitored, they 

would be showing positive bioassay every year from 

like 1967 or so time period. That's -- it's claimantfavorable built in here, but it's not unreasonable. 

Okay? We believe this is favorable but sufficiently 

accurate. It's high, yes, but it's acceptable. 

Member Lockey: The green line is what again? 

That's 84th percentile, right?

Dr. Taulbee: Yes, this is 84th percentile. This would 

be the -- if you go back up to these plots here, this 

would be where the standard normal quantile is 

one. It's this value here.

Member Lockey: Yes.

Dr. Taulbee: Okay? 

All right. Now let me get back to where I was. So 

here's another way of looking at this one: the box 

plot here, the lower error bar here, is the 5th 

percentile, 25th percentile, 50th percentile, 75th --

oh, wait a minute. I'm sorry. It's 10th, isn't it? 

Dr. Chalmers: No, I think we did 5th and 95th.

Dr. Taulbee: Fifth and ninety-fifth? Okay. And then 

the 95th percentile. Okay? And so the red dots are 

the geometric mean of our intake overlaid with all of 

the TWOPOS values, the TWOPOS distributions. So 

you can see even our 50th percentile is still high but 

not unreasonable. This line here is the 84th 

percentile which I was pointing out. It's just -- it's 

this plot with taking these individual dots and 

turning them into the actual box plots that they 
41

represent. Okay?

So we did this -- oh, one of the things I wanted to 

point out is these are the plutonium intakes from 

SRS over this time period. These are all of the 

positive dose assessments that they've done. 

This is hopefully to help answer your question, Dr. 

Lockey, of if you take people who are all positive 

and they went through and they calculated dose, 

here's these dose values. And you can see that 

there's clusters: 1955, around 1960, around 1969-

1970, this time period, and out here around 1987. 

The general trend though is the dose is going down. 

Most of these doses are between 100 millirem and 

10 rem. And these are all of those positive ones that 

you see up there at the upper tails of these 

distributions. And this is committed effective dose 

equivalent. All right?

And by the way, just for -- can you tell when the 

Cold War ended and production stopped?

(Laughter.)

Dr. Taulbee: When they stopped making plutonium? 

It's pretty obvious.

So we did that for plutonium. We did this for 

americium. And here's this plot. For tritium. And 

tritium is of interest in that this is 100 millirem. So 

you get beyond 1980 and people aren't even getting 

100 millirem. 

Uranium. The reason I put all of these on here is 

this is type F uranium, solubility type, type M and 

type S. And what you'll notice here, type F, because 

it clears the body so quickly, the actual curves 

follow very closely to the actual -- to the bioassay. 

When you get into the slow solubility category is 

when you begin to see more of these buildups. The 

intakes are higher because that's how we end up 

doing the modeling of the intakes. All right?

This is cesium. 
42

Neptunium. This one looks really interesting, which 

is why I put it in here. Up to 1970 we have 

urinalysis data up here. After 1970 we have very 

little urinalysis data. That's what this is right in 

here. We got big gaps. What we have is whole body 

county data. We went through, did the whole 

TWOPOS methodology that we just discussed using 

whole body count data and it results in a much 

higher intake. Whole body counting is not as 

sensitive as the urinalysis. So what ends up 

happening in the coworker model is we kind of have 

a step function happening here, but it's due to a 

change in the methodology.

I asked ORAU to go ahead and plot the urinalysis 

that we had here that was limited in this time period 

to see how does it fall with our model. And you can 

see the limited urinalysis we have is well below it. 

And so we believe that this is sufficiently accurate 

and claimant-favorable.

So to go through the major steps here we've got --

we start with the individual bioassay. We then 

calculate an individual TWOPOS value, individual --

each person there's a TWOPOS value that's 

calculated. We substitute that censored data using 

multiple imputation. We take all the TWOPOS 

values, we fit a log-normal distribution to them. We 

then go through the modeling, the 50th percentile, 

the 84th percentile. That gives us the intake that we 

assigned and then we apply it to the unmonitored 

worker. 

So in words here it is just that: individual results are 

averages, so there's a lot of averaging going on 

here. Bioassay results from the individual worker 

are averaged into a single time-weighted OPOS. The 

TWOPOS results are fit to a log-normal distribution, 

so another type of averaging. The TWOPOS 50th 

and 84th percentiles are fit in IMBA to develop the 

intake rates. We look at the uncertainty of that 

intake rate, at 50th and 84th percentile, calculate a 

GSD. If it's less than three, we bump it up to three. 

And so that is how we come up with our intakes to 
43

assign to workers.

Normally the 50th percentile with a full log-normal 

distribution will be assigned to workers who may be 

exposed to greater than environmental levels, but 

less than a typical operations worker. Okay? So 

these would be your intermittent unmonitored 

workers going into an area. 

If a worker is considered to have a high potential for 

exposure for whatever reason -- their records are 

missing, it appears that this person may be at the 

end -- we can assign an upper bound of this in the 

95th percentile on a case-by-case basis. This would 

be determined by a dose reconstructor. Does a 

regular log-normal distribution fit, which is most of 

the cases?

Keep in mind that at least for the operations 

workers at Savannah River about 85 percent of 

them have data themselves and we don't even need 

a coworker model for them. Okay? We use their 

individual data. This is for people who don't have 

any monitoring data or a big gap in their data and 

we're trying to supplement it. That's where this is 

used.

Member Beach: Have you figured out the highest 

group of non-data at Savannah River, which group 

of workers has no monitoring data?

Dr. Taulbee: From the operations side, none. None. 

I mean there's -- I mean that table that you saw 

there at the beginning of the people who worked on 

the B Lines and 235-F, they were being sampled for 

plutonium four times a year. 

Member Beach: How about the subcontractor?

Dr. Taulbee: And that we'll get into with the 

stratification and the subcontractors. Okay? And this 

is part of why I wanted to keep it separated, is to 

go through the method --

Member Beach: Sure.
44

Dr. Taulbee: -- and see if this method is acceptable. 

And I think you just pointed out the area of where 

we need to have a lot of discussion is back into that 

applicability: were these people monitored? For 

operations? Yes. And we can demonstrate this. For 

others, maybe not. Okay?

So in summary, this example coworker model 

demonstrates how the draft criteria for the 

evaluation and use of coworker datasets would be 

implemented. We believe the intent of the draft 

criteria for evaluation and use of coworker datasets 

has been met and we believe the coworker models 

presented are claimant-favorable, claimant-friendly, 

reasonable and adequately bound potential doses 

for compensation purposes.

And with that I'll be happy to answer any questions.

Co-Chair Clawson: I think you wore us out.

(Laughter.)

Mr. Calhoun: It's way too early to be worn out, 

Brad.

Co-Chair Clawson: I know it. Well, actually a lot of 

the questions are from the other part.

Mr. Calhoun: The next part? Part 2.

Co-Chair Clawson: Yes.

Member Ziemer: Now let me ask SC&A, are you 

going to comment on this today or you'll do that 

later?

Mr. Barton: Right off the bat, I don't really have any 

comments. I mean we just saw this yesterday, but 

the mechanics of the coworker model have been in 

place for quite a while. I think the question here is -

- and again, the next step of our review of OTIB-81, 

which parts of it may affect the implementation of 

the --

(Simultaneous speaking.)
45

Member Ziemer: Bob, do -- you did your comments 

before you've seen this, is that correct??

Mr. Barton: Yes.

Member Ziemer: Okay. 

Dr. Taulbee: Well, they had OTIB-81.

Member Ziemer: Well, yes.

Dr. Taulbee: And they've written comments on that, 

which is --

Member Ziemer: Right.

Dr. Taulbee: I think the next phase is for Bob to go 

through his comments on this --

Member Ziemer: Right.

Dr. Taulbee: -- methodology.

Mr. Barton: And I think we're trying to keep two 

separate facets. A lot of our comments are specific 

to SRS, not necessarily how the coworker 

implementation guideline is -- were implemented. 

So a lot of our comments are going to be sitespecific. A couple of them will apply if you were 

going to use the implementation guide at other 

sites, but only a couple of them.

Dr. Taulbee: Multiple imputation is the big one.

Mr. Barton: Yes.

Member Lockey: Let me ask you a question. If I was 

sitting in a data analysis room and I had this type of 

exposure data, it's elegant data and I --

Mr. Barton: It's massive amounts of data.

Member Lockey: I mean it's really eloquently 

presented. This is data that I could use to assign 

doses to people. Do you have any -- would you 

have any problems with how the model was 

designed?
46

Mr. Barton: No, not with the design, but again 

there's a lot of questions of representativeness; i.e., 

monitoring the correct people. And the unmonitored 

people, do we have information on their exposure 

such that we can apply this elegant data approach 

to that group? That's sort of a later discussion.

Member Lockey: That I understand, but in an ideal 

population this is a good approach to take.

Mr. Barton: I believe so, yes, except for -- and 

we're going to talk about the multiple imputation.

Co-Chair Anderson: Everybody was monitored.

Member Lockey: Well --

(Simultaneous speaking.)

Member Lockey: -- percent of them. Nobody 

monitors 100 percent. You can't do it. But if you 

monitor 50 percent or 60 percent, this is the type of 

data you would use. That's just reality. So I think 

from your perspective we have to reach a point 

where we say, yes, this -- sign off to this or you can 

use this someplace else. But your question is still a 

valid question. That's what I'm getting at.

Mr. Barton: Like I say, I think we're -- as we said at 

the outset we're trying to separate what the 

Implementation Guide says 

and --

Member Lockey: Right, I understand.

Mr. Barton: -- what you'll be using at other sites, 

and the SRS-specific issues that wouldn't 

necessarily apply anywhere else.

Co-Chair Anderson: The guidelines are appropriate 

and they can be --

Member Lockey: That's what I'm saying. Yes.

Co-Chair Anderson: It can be 
47

applied --

Member Lockey: It's been a draft for 10 years. 

Co-Chair Anderson: Yes, exactly.

Member Lockey: I think this is the time, not make it 

a bad thing.

Co-Chair Anderson: Yes.

Member Lockey: That's what I'm saying.

Mr. Fitzgerald: Yes, I guess what I add to comment; 

this is going back to your line of questioning, I think 

we're always going to have the question of 

interpretation and application of the overall model 

to a specific site.

Member Lockey: Right. Oh, yes.

Mr. Fitzgerald: And actually SRS is offering a 

microcosm of that, but if we're comfortable -- and I 

think we are -- with the overall model --

Member Lockey: Yes.

Mr. Fitzgerald: -- we'll probably have that other 

discussion when it's applying to Idaho. And that's

just part of the game. 

(Simultaneous speaking.)

Member Lockey: But the general model is a good 

model. That's what I'm hearing.

Member Ziemer: It's really an elegant model.

Member Lockey: Yes, that's what I said. It's an 

elegant model.

Member Ziemer: I thought you said good.

(Laughter.)

Mr. Calhoun: Way to go, group. 

Member Lockey: I guess good isn't good enough.
48

(Laughter.)

(Simultaneous speaking.)

Dr. Taulbee: Is there any -- I mean, SC&A is going 

to talk some about the multiple imputation 

component of it, but is there any other areas of this 

where -- that you see that there's something else 

we need, because I think we've implemented the 

draft criteria as requested and demonstrated that 

we can do this.

Member Lockey: One thing I'd like is that there is 

some judgment, is you don't use it to fit 50 

percentile, but you use 95 percentile based on 

detailed data on that individual. That's what they're 

saying.

Dr. Taulbee: Eighty-fourth.

Member Lockey: Eighty-fourth percentile.

Ms. Brackett: Ninety-fifth.

Member Lockey: No, 95th.

Dr. Taulbee: On this particular part here, yes, we 

can. 

Member Lockey: You can, sure. 

And I think that gets -- that gives the evaluator 

some wiggle room --

Dr. Taulbee: Yes.

Member Lockey: -- because there's something 

unique about this particular worker which it has to 

take under advisement.

Mr. Barton: And as I point out there's sort of almost 

three tiers. There's the environmental level. That 

would be someone -- an administrative if they need 

to enter radiological areas, but they're on the site. 

There's ambient exposures. And you've got a 50th 

percentile. Normally we would consider that to be 
49

moderately exposed, for lack of a better term, not 

necessarily your chemical operators and things like 

that. 

But if you had an operations worker out there who 

again was either missing their data or wasn't 

monitored for whatever reason, most likely they 

were missing their data, then you do have that 

option of the 95th percentile. And that is applied as 

a constant, right? I believe.

Member Lockey: I think that's important, yes. 

Co-Chair Anderson: And it took a couple -- I mean, 

it's a massive amount of work.

(Simultaneous speaking.)

Co-Chair Anderson: Well, but I mean a couple of 

years, so kind of the question is if you get to a --

and this was one of the bigger sites. When you get 

to some of the smaller sites, now that you have 

this, are we going to be looking at an additional two 

years? I mean you're working on a number of these. 

It's one thing if it's a --

Member Lockey: Trick question.

Co-Chair Anderson: Well, I mean it's a good --

Member Lockey: Trick question.

Co-Chair Anderson: -- design and it's very 

comprehensive. On the other hand -- and it could 

be very practical to use when you have lots of 

unmonitored workers at a -- not lots, but relatively 

needed as opposed to a smaller site to -- at what 

point is it inefficient to try to develop a coworker 

model?

Dr. Taulbee: Yes, and that we don't -- but I mean 

for us the critical part is trying to get the draft 

criteria turned into a guide, and then we can start 

looking at the other ones, other sites and where we 

would apply this. 
50

We do have what, eight or so sites that -- I'm 

thinking of the 49 for Super-S.

Ms. Brackett: Right, that's going in.

Dr. Taulbee: Separate.

Ms. Brackett: Yes.

Dr. Taulbee: Right, going ahead without this?

Ms. Brackett: Yes.

Dr. Taulbee: But that have large -- or that have 

large coworker models that need to be updated 

TWOPOS.

Ms. Brackett: Oh, yes. More than eight.

Dr. Taulbee: More than eight that need this really 

bad, and we haven't done it.

Co-Chair Anderson: So the rest of your coworker is 

going to be --

(Laughter.)

Dr. Taulbee: Beyond ours --

(Laughter.)

Dr. Taulbee: -- to be quite honest.

(Laughter.)

Co-Chair Anderson: Oh, I mean that just -- I mean, 

having done it, is there -- it looked like there was a 

lot of hand work. I mean is it -- to the extent more 

of this could be automated. 

Dr. Taulbee: Nancy's done a really good job of 

automating a lot of this in our -- with having 

standard codes set up.

Co-Chair Anderson: Yes.

Ms. Brackett: Yes, so we've been working on Idaho 

since this, and that's much more automated than 
51

the Savannah River data were. So we have made 

progress on that. 

Co-Chair Anderson: So hopefully we get -- in other 

words, what I'm asking is will it come to us timeintensive and stressing to the staff?

Co-Chair Clawson: But, Henry, this is -- goes to my 

caveat in the beginning of this, and that is data 

adequacy.

Co-Chair Anderson: Yes.

Co-Chair Clawson: If we don't have that, then --

Co-Chair Anderson: Yes.

Co-Chair Clawson: -- it's no good. And that's --

Co-Chair Anderson: And that's part of the criteria.

Co-Chair Clawson: And that's what I -- that's one of 

the things to remember, that when you start getting 

smaller sites, you were asking and so forth like that, 

if we do our due diligence up front --

Co-Chair Anderson: Up front.

Co-Chair Clawson: -- and get that taken care of --

Co-Chair Anderson: Yes.

Co-Chair Clawson: -- we may not even be able to 

use this. And so that's where the SEC comes in.

Mr. Katz: Gen and I don't know if, David 

Richardson, if you've joined us, and Phil, do you 

have any comments, questions at this point?

Member Roessler: Yes, this is Gen. Am I off mute?

Mr. Katz: You are. We hear you perfectly.

Member Roessler: Okay. Good. Well, that was a 

fascinating and very good presentation, Tim. I think 

I need to know a little more about plutonium 

biology to understand the bioassay, but I think your 
52

representation was very good.

I will comment on the -- how well we can hear. I 

could certainly hear Tim very well through the 

presentation and I can hear Paul and I can hear 

Bob. And I don't know about anybody else on the 

line, but I was not able to hear Josie and Brad and 

Henry very well, particularly Josie, and she -- I 

know she asked a lot of really good questions, so 

I'm wondering if maybe people, those people could 

get a little closer to the mic.

Co-Chair Clawson: Yes, hey, Gen, they pushed us 

out to the outer edges of the table.

(Laughter.)

Member Lockey: We muted --

(Simultaneous speaking.)

Mr. Katz: Brad, you're just one over from me, but --

no, but, yes, Gen, thank you. That's exactly what 

we wanted to hear about. I think Josie was talking 

just at a normal voice level, too, and --

Member Beach: I'll speak up.

Mr. Katz: -- she's at the far end of the table. But so 

she'll speak up and if need we'll move this down a 

little.

Member Beach: Gen, can you hear me at this level?

Member Roessler: Yes, that sounds good, Josie.

Member Beach: So I'll just yell at everybody here.

(Simultaneous speaking.)

Member Roessler: -- when one person is talking, it's 

very clear, but if more than one person is talking at 

a time, it tends to get all kind of mumbled, from 

what I hear.

Co-Chair Clawson: Okay. 
53

Member Schofield: I have to agree with Gen. I had 

a hard time hearing particularly Josie, and Brad 

would seem to fade in and out.

Co-Chair Clawson: I'm so soft-spoken.

(Laughter.)

Mr. Katz: He's a lightweight, right.

Okay. Thanks, Phil. And, yes, we'll try to do better. 

Member Beach: Can you hear this? Is it time for a 

break?

(Laughter.)

Mr. Katz: So that's the second person who's asked 

me about a break, so I think it is time for a break. 

So it's about 10:10 right now. Why don't we take a 

-- is 10 minutes enough?

Member Beach: Yes.

Mr. Katz: Ten minutes? Why don't we take a 10 --

so at 10:20 we'll come back on. I'll just leave the 

phone on and put it on mute. (Whereupon, the 

above-entitled matter went off the record at 10:09 

a.m. and resumed at 10:27 a.m.)

Mr. Katz: Okay, sorry for folks on the line, we're a 

few minutes late, but it's -- always works this way. 

So we're back on and ready to continue discussions.

Member Schofield: I'm back on, Ted.

Mr. Katz: Great. And, Gen, you're back on?

Member Roessler: I'm on.

Mr. Katz: Super.

Member Lockey: Hey, Gen, can you hear us?

Mr. Katz: Stop that. Go ahead, Tim.

Dr. Taulbee: I just want to make a quick comment, 
54

and that is to thank everyone for letting me put the 

stratification on hold, and now that hold's off, and 

so, SC&A, go. I just wanted to -- get through that 

coworker model without those issues --

Mr. Katz: It was super helpful.

Dr. Taulbee: -- and so now we can go.

SC&A Review of OTIB-81, by Bob Barton

Mr. Barton: Okay. This is Bob Barton with SC&A, 

and before I get started, can I just ask do I still 

have Ron, Harry, and Joyce on the line?

Dr. Buchanan: Yes, this is Ron. I'm on the line.

Dr. Lipsztein: Yes.

Dr. Chmelynski: I'm here too, Bob.

Mr. Barton: All right, great. All right, the 

presentation that we're about to give is actually the 

same exact presentation that one can find online for 

you folks on the phone who don't have the Skype 

connection.

As you can see from this title slide, we had quite the 

SC&A team working on this project. That's Ron 

Buchanan, Harry Chmelynski, Rose Gogliotti, and 

Joyce Lipsztein. We were going to add Joe's name, 

but it looked too cluttered.

(Laughter.)

So this is the review of OTIB-81 which is the 

internal coworker model for Savannah River Site, 

and it's again sort of two-fold. One is how does it fit 

in with the implementation guidelines that we just 

discussed. And then there's going to be some actual 

site-specific issues. Really more site-specific issues 

than the coworker guidelines as you'll see.

So I'll just go through these really quick just to 

recap what the purpose of OTIB-81 was. And you 

see there's a couple quotes up here. But essentially, 
55

it's repeating what Tim's presentation said.

You have workers out there who weren't monitored 

or their records are missing, and that's why we 

have coworker models. And the guidelines were 

developed for that purpose to assure that these 

coworker models, when we develop them, are going 

to cover the workers that need to be covered.

So I'm going to skip right ahead to SC&A's review 

focus. And, again, it's adherence to the principles 

and guidance in the Draft Criteria for that Evaluation 

and Use of Coworker Datasets.

And there are basically four main facets, the data 

adequacy, and that -- basically asking the question 

does the available data and monitoring methods 

accurately reflect the exposure that's intended to be 

reconstructed. The data we have, can we actually 

use that in a sufficiently accurate sense to 

reconstruct the exposure.

There's data completeness, and that's essentially 

how well does the data we have actually represent 

the worker population, and are there gaps? Is there 

a group of workers out there who's not essentially 

covered by the data that we do have.

And that kind of bleeds into the next one which is 

the evaluation of the monitoring program. This talks 

about the procedures in place, but also the actual 

execution of those procedures. You could have a 

procedure in place, but you really have to go in and 

check to make sure that it was put in practice and 

not just a piece of paper.

And then the last item, which is back on the table, 

as Tim said, is stratification. Is there a 

subpopulation of workers who had a distinctly 

different exposure potential than the full group, and 

do we have the data available to develop a separate 

exposure profile for that subpopulation?

So, again, one, coworker data adequacy. Again, this 

is talking about the instrumentation and the 
56

measurement techniques. It's also -- we're going to 

be talking about the treatment of the censored 

data, particularly the imputation methods that are 

being employed currently.

And, again, when we say censored data, that is the 

biological result itself is just less than some 

predetermined value. It's not -- it could be 

anywhere essentially between zero and less than 

that number.

And, again, it's sort of the same thing as the next 

bullet. How do we use and interpret data that is less 

than the minimum detectable activity?

Completeness in this context for SRS, there's 

actually two different main data sources. One was 

the claimant data that was used to construct the

plutonium, uranium, fission products, and tritium 

coworker models.

And then the second source was actually laboratory 

logbook, and that's what's covering the trivalent 

actinides which is americium, curium, californium. 

That method also caught thorium and neptunium.

And the reason that it went into the laboratory 

logbook data is I believe simply because there 

wasn't enough data in NOCTS to simply use the 

claimant data. We had to go and try to get the full 

dataset available from SRS.

Now, the evaluation of the monitoring program, 

again, that's who was monitored and is there a 

group out there that maybe wasn't monitored 

adequately. But that's really going to get addressed 

later today and possibly into tomorrow with RPRT92, and that's really focused on the job-specific 

monitoring at SRS.

More on just what our evaluation focus was, moving 

to stratification as was talked about briefly before. 

That a OTIB-81 coworker model was stratified into 

construction trade workers and essentially all other 

monitored workers or non-construction-trade 
57

workers.

And what we're really talking about there is the 

group that's routinely exposed and the group that is 

in more non-routine exposure scenarios such as 

maintenance or work inside of a glove box rather 

than outside of the glove box.

We're going to talk about how we evaluate whether 

workers are accurately identified with the 

appropriate strata. In addition to those four main 

facets of the draft criteria, the TIB-81, the 

Savannah River coworker model performed a pretty 

extensive quality assurance assessment which 

included the completeness of the claims tracking 

system. So that's the claimant data that was used 

for several of the individual radionuclide coworker 

models.

And also the completeness of the logbook data that 

was used for getting neptunium, the trivalent 

actinides, and thorium.

We're also going to talk about that QA also looked 

at the construction worker classification and the 

construction worker determination QA summaries, 

so we'll get to that at the end.

Okay, the first item is coworker data adequacy. And 

our main issue here is bioassay variability. SC&A 

has expressed concern about that for several years, 

and it's the observed variability in those transuranic 

bioassay measurements. That's the americium, 

curium, californium.

And this has been brought up in several previous 

SRS Work Group discussions and SRS-related 

reviews. And the key question here is is the 

measurement technique sufficiently accurate to 

reflect the exposure potential it is intended to 

quantify.

OTIB-81, the SRS coworker model concluded that a 

small percentage of the identified samples that were 

unaffected by chelation showed high variability, 4 of 
58

52, and concluded the aliquot variability has an 

insignificant effect on the overall results.

And now if I could hand it over to Harry Chmelynski 

who took a look statistically at the variability in 

those samples in our review, and we'll move right 

into finding 1. So, Harry, if you're there, could you 

talk a little bit and gently guide us through how we 

took a look at this issue.

And, again, what the main issue is for these 

trivalent actinides, they would take a sample, one 

single voiding, and they would split it up sometimes 

as many as 10 times and measure it 10 separate 

times. Sometimes it was only once; sometimes it 

was five times. I think the max we saw was 10 

times.

And what we observed was that those 

measurements of the exact same sample had very 

high variability. As I said, we'll go back one, OTIB81 took a look at SC&A's previous findings on that 

subject and concluded that the aliquot variability 

has an insignificant effect on the overall results.

Member Lockey: Bob, can I ask you a question?

Mr. Barton: Sure.

Member Lockey: The 4 out of 52 it means -- 4 out 

of 52 were unaffected by chelation, and 4 of those 

52 showed high variability. Is that what that figure 

is?

Mr. Barton: We believe it's much more than that.

Member Lockey: The 4 out of 52, that was meant to 

mean 4 have high variability of the 52, is that 

correct?

Mr. Barton: I would turn it over to NIOSH to 

describe what they did on that subject.

Member Lockey: I'm just questioning the 4 out of 

52.
59

Dr. Taulbee: The 52 was the original I believe; 

correct me if I'm wrong here, folks. But the 52 were 

the original ones that were identified by SC&A that 

had high variability.

Member Lockey: 52 had high variability?

Dr. Taulbee: 52 had high variability. When we 

excluded all of the chelation folks which we hadn't 

done in the past, but we did for this coworker 

model, we were down to 4 that showed high 

variability that we really can't explain.

Member Lockey: Oh, I see.

Dr. Taulbee: But if there's -- but there were 

hundreds of results.

Member Lockey: So 52 had high variability, but you 

take out all chelation, there were 4 that still had 

high variability that you couldn't explain.

Dr. Taulbee: Yeah.

Member Lockey: I'd looked at that and -- now I 

understand.

Mr. Barton: And I remind everybody that we're 

really focused on the highest results there, the 

results that are well above the MDA where you 

shouldn't really be seeing that much variability. Now 

if you're well below the MDA, there might be some 

noise in there that might cause that.

But we were really focused on those high results 

because those would be the best to look at to figure 

this thing out. So, Harry, are you on the line?

Dr. Chmelynski: Yes, I am, Bob. So I've looked at 

all the data that was in SC&A's original letter report 

sent to the group in February of 2014. And that 

report had three tables in its appendix that listed all 

the readings that were conducted upon -- the 

multiple readings on each of the samples.

What I did was I took all that data in those three 
60

tables. There were three tables that were divided 

into low exposure, medium exposure, and high 

exposure cases.

So I combined all the data together into one table, 

and I calculated -- I removed everything that had 

only one observation. In other words, there wasn't 

any multiple recordings done. Everything that had 

two or more, I could calculate a coefficient of 

variation, and I calculated that and the mean for all 

the samples, and the results are shown on figure 2 

which is on page 18 of our report.

And this was a plot of the coefficient of variation 

versus the mean level for these results that had the 

multiple aliquot measurements.

And what I see here is that almost all of them are 

below 100 percent coefficient of variability. I don't 

know if that's an achievement or not. There are four 

or five that are above that level which may be the 

four that survived to be the four out of the 52 that 

NIOSH reported.

There was a question about how the variability of 

the DTPA-influenced data compares to the 

variability of the other ones. And when I look at the 

figure that I drew, they seem to have the same 

variability all the way across from low 

measurements up to high measurements.

So variability in itself isn't a good reason to exclude 

DTPA data. They do tend to be on the high end, so 

excluding them does reduce the level of exposure 

that you get out.

I guess that's about all I can say about this. The 

question about whether they should -- the DTPA 

data should be used seems to have already been 

resolved and that they're not going to be.

That still doesn't explain what the high variability of 

the other ones are due to. And, in fact, the highest 

ones are not associated with the DTPA.
61

Mr. Barton: Okay, thanks, Harry. So that led us to 

finding 1 in our review, and I'll read that into the 

record.

Although SC&A recognizes that incident-based 

sampling involving chelation is not considered in 

final coworker modeling, the removal of DTPAinfluenced samples from consideration in the 

analysis of high variability observed in trivalent 

actinides bioassay results has not been justified 

sufficiently.

Evidence suggests the variation among DTPA and 

non-DTPA samples is nearly identical. Furthermore, 

OTIB-81 has not provided any reference to justify 

the assumption that DTPA causes heterogeneity 

among a single urinalysis voiding.

So basically what we're saying is the DTPA doesn't 

affect the variability that we're actually observing in 

these samples. We're still seeing it, and so while 

they may not be used in the coworker model, the 

indications of that variability go back to the counting 

method and the adequacy of the data.

So while we don't use them in the coworker model, 

they still do reflect the measurement and whether 

the data is adequate to actually reflect the exposure 

that we're trying to reconstruct.

Further on with the trivalent actinides data --

Member Ziemer: Question, Bob. So -- or maybe 

him, too. Are we talking about the variability in a 

sample that has been split into --

Mr. Barton: Yes.

Member Ziemer: -- not from sample to sample.

Mr. Barton: No, it's a single sample that's been 

split.

Member Ziemer: And do you have an established 

criteria as to -- this is sort of a judgment, but what 

is an acceptable variability? Like plus or minus some 
62

value?

Dr. Taulbee: I don't think we have established -- an 

established criteria for acceptance into the coworker 

model.

Member Ziemer: I'm asking about the word high 

variability. Bob, do you have a value that -- high --

what does high mean?

Mr. Barton: No, no I don't.

Member Ziemer: So I don't have a feeling for 

whether we're talking about an order of magnitude, 

plus or minus 50 percent.

Dr. Taulbee: It was actually, I was just reminded, 

that was one of our questions back to SC&A, what 

are they defining as high variability. Because when 

we looked at the variability, and I've got a plot that 

I was planning to show, we don't see that this is a 

major issue when you look at the dataset as a 

whole, I mean a large --

(Simultaneous speaking.)

Co-Chair Anderson: -- outliers?

Dr. Taulbee: There's always outliers.

Co-Chair Anderson: Yeah, I know. That's what I 

mean.

(Simultaneous speaking.)

Dr. Taulbee: But if you think how I went through 

the coworker model and how this would play a role, 

if you get a few outliers that are showing high 

variability, when you consider we're taking the 50th 

percentile and the 84th percentile and then 

averaging that --

(Simultaneous speaking.)

Member Ziemer: It kind of disappears.

Dr. Taulbee: Exactly.
63

Member Ziemer: But the other part of it is, I think 

you suggested, I don't know if you said this 

specifically, that probably the ones that were 

influenced by DTPA probably wouldn't have made 

much difference in the final model anyway. Or did 

you actually test that?

Dr. Taulbee: I didn't say that, nor did we test that. 

The problem is the DTPA changes the biokinetic 

model, and so you can't really use the bioassay.

Member Ziemer: Well, yeah, inherently, but even if 

you did --

Dr. Taulbee: Even if you did, yes.

Member Ziemer: -- there weren't that many DTPA 

samples compared to the total, were there? For the 

--

Dr. Taulbee: There's a fair number. I believe it's --

we've got a table here.

Member Ziemer: Was it a small percent of the total 

if you're looking at the --

Dr. Taulbee: It's a small percentage of the total.

Member Beach: It was kind of an interesting 

comparison between the two models, the chelation 

versus non --

(Simultaneous speaking.)

Dr. Taulbee: But there's a fair number of people 

that had DTPA from the second one.

Member Ziemer: And then you had --

Mr. Barton: I don't want to lose focus here though 

because we're not talking about the effect it has on 

the final coworker model result. What we're talking 

about is the --

(Simultaneous speaking.)

Member Ziemer: -- about individual?
64

Mr. Barton: We're talking about the data we start 

with before we go through the averaging process 

and all that. The data we start with, is it sufficiently 

accurate.

When we see variation, even among samples that 

are high and above the censoring limit or MDA, 

we're seeing variability among the same bioassay --

before we even get to doing averaging and TWOPOS 

and all that, all that coworker modeling to get 

eventually to a coworker intake.

We're still at that first stage where we're evaluating 

the data. Is the measurement technique actually 

measuring what we want with sufficient accuracy.

Member Ziemer: That goes back to the original 

question, what's high variability.

Member Lockey: What was the specificity,

sensitivity of the test? Did you go back and look at 

that timeframe?

Mr. Barton: I don't know offhand what necessarily 

the magnitude was between the --

Member Lockey: What was the accepted variability 

during that timeframe for that particular test?

Mr. Barton: Let me pose it to our internal 

dosimetrist. Joyce, in your opinion, I mean this goes 

back a number of years, when we looked at that 

data and we're seeing a number of samples --

again, I don't remember offhand --

Member Lockey: We're talking about split samples, 

right.

Mr. Barton: Split samples.

Member Lockey: Not one sample analyzed against 

another, but split samples.

Mr. Barton: It's a single voiding.

Member Lockey: Yes.
65

Mr. Barton: One person, single-voiding split onto a 

number of discs and then measured. Ideally, you 

should be in the same range in values, and that's 

what these are --

Member Lockey: So what was the acceptable range? 

That's what we're asking.

Mr. Barton: I'm not sure there is a necessary 

standard.

Dr. Lipsztein: I think if you take a sample and 

repeat it, because when you take five aliquots of the 

same sample, you wouldn't expect a variation more 

than 10 to 20 percent. And sometimes here you 

have even double or 60 percent. I don't remember 

the exact number because this was a long time ago, 

but we had two problems. Not only this variation, 

but also the limits of detection for until 90 -- the 

'90s because they were much lower than the 

detection limits that were reported by the National 

Commission of Radiation Protection, the NCRP, that 

should be taken at the time.

And also we saw the other installations that we 

were looking at. So we have serious doubts on the 

validity of those results. One, because they had that 

high variability; second, because the limits of 

detection were too low for gross alpha samples.

Member Lockey: It may be that the high variability 

was due to the very low limit of detection that they 

thought they could achieve.

Member Ziemer: Well, I think the concern we raised 

was for higher samples.

Mr. Barton: We were only looking at the higher 

samples.

Dr. Lipsztein: Yeah. So we had the two things. One, 

I don't think the limit of detection could be so low 

because it's different from everything today that 

was established in other very good laboratories in 

the world, actually not only in the U.S. but in the 
66

world; and second, because these, the variability 

was much higher than someone would expect.

For example, if you use other radionuclides that 

were analyzed at the same chelation, and you have 

multiple samples of the same urine samples, then 

they weren't so big. So I'm trying to find out what 

was the variability, but --

Dr. Taulbee: If I can point people to --

Dr. Lipsztein: -- it's a long time ago.

Dr. Taulbee: -- attachment D of the OTIB-81 is 

where we did the evaluation of SC&A's previous 

comment. And here is a plot of the coefficient of 

variation of all of the samples. Coefficient of 

variation versus the absolute value of the mean.

And you can see the bulk of the data is below where 

we consider it an acceptable curve here. Are there 

counts above it? Sure, there's going to be some. 

But we feel that the bulk of the americium data --

Mr. Katz: One sec, Joyce, can you mute your phone 

because you have a lot of background noise coming 

through your phone.

Dr. Lipsztein: Oh, I'm sorry.

Mr. Katz: No, it's all right. It's all right. I just -- I'm 

just concerned about the other people on the 

phone. Thank you very much.

Dr. Taulbee: And so if you look at that attachment 

D, and -- what page number is that, Dr. Lockey? It's 

up at the top.

Member Lockey: It's page 153.

Dr. Taulbee: Page 153. The CR plot of the 

coefficient of variation and what we consider how it 

changes with the absolute mean value. So obviously 

as you get to lower values, the coefficient of 

variation will go up. And --
67

(Simultaneous speaking.)

Member Ziemer: -- have a chance to look at the 

finding. I just wanted to get a feel for what the high 

variability meant, and apparently there's some of 

the higher samples that have quite a high 

variability.

Dr. Taulbee: But there's not many higher samples.

Member Ziemer: I understand. I think the 

question's been --

(Simultaneous speaking.)

Mr. Barton: I would add that the higher samples are 

the one that you would expect if there's something 

there and it's above your detection limit, that you're 

going to have reasonable measurements of it as 

opposed to lower samples.

(Simultaneous speaking.)

Member Ziemer: Thank you.

Mr. Barton: As Joyce was saying, this is on the next 

slide which goes into the actual reported MDA for 

that trivalent actinide data. They report .3 dpm per 

day. And we compared that with some other 

standards. It's a factor of 3 less than, as Joyce said, 

reported by the ICRP as late as 1989.

It's a factor of 3 less than what was at Rocky Flats 

in 1977, and a factor of 10 less than what was being 

reported by Los Alamos National Laboratory.

And so what we concluded from that is that such a 

low MDA is probably only achievable by alpha 

spectrometry which really wasn't, I don't think, 

around until the mid '90s, maybe 1995 around 

there.

Dr. Chalmers: Late '80s.

Mr. Barton: Late '80s, okay. And that's really the 

time period that we were looking at with this data 
68

because that's the time period that the coworker 

model was built for.

And so another facet of this data that gives us great 

concern, so we have two things so far. We have 

what we feel is, what's the right word, unusual, 

unusually low dpm. And we just said compared to 

some other state of the art at the time, we have the 

variability among the higher samples that are above 

the detection level where you wouldn't expect it.

And the next thing we're going to talk about is for 

trivalent actinides, it's unique in that we were 

talking about data that was below the MDA in the 

previous presentation. For the trivalents we actually 

have -- or NIOSH coded the raw data for the 

trivalent actinides, and what we find is the data is 

even much more less than that .3 which we already 

feel is a little unusual compared to the state of the 

art at the time, and I think the next chart is going 

to really demonstrate that.

So what we're looking at here, again the two red 

lines, the top one is the MDA at .3, and then half 

the MDA there. And what's on the body there is the 

50th percentile coworker values based on that raw 

data.

And as you can see for some years, and again we're 

only looking at that SEC period, I mean we're below 

.05, so less than one-sixth of the MDA that we 

already feel might be too low.

So what we -- I mean we kicked this around a lot as 

a team. We felt once you're getting that low, even 

lower than an MDA we already think is too low, are 

those really meaningful results anymore, or are we 

just seeing noise in the instrument.

And so that's another one of our concerns, is do 

those data points that are so far below the MDA, 

and these aren't imputed, these were reported 

values, are we just seeing noise in the instrument 

or are we actually seeing something that can be 

relatable to dose.
69

And so that's our major concern we feel just looking 

at this chart here that we're so below the MDA that 

we feel the values have actually lost their meaning 

in relation to dose.

Member Lockey: Do you think the values are real or 

not?

Mr. Barton: I think that they were measured, 

recorded by the instrument. Do they actually reflect 

americium? We don't think so.

Dr. Taulbee: So you're saying that they could not 

detect an americium intake. Is this what you're 

saying?

Mr. Barton: At that low of a value.

Dr. Taulbee: Because this data is uncensored, the 

data that we used. We did not use any of the MDA. 

We used the raw data coming out of the logbooks, 

that's what we used.

And from that, whenever there were people who 

were high, they would then do a dose assessment. 

And so this method detected when people received 

an exposure. Okay? That's well-documented.

Now we used all of the data whether it was -- I 

mean, it's not censored. We used the raw data. So a 

lot of this is what you would call background. No 

dose, zero. But this is what we used.

We used the low data here, and they had the ability 

to detect when an intake occurred. So this is a 

coworker model that is dominated by --

Member Lockey: This is uncensored data?

Dr. Taulbee: This is uncensored data. It's low, yes. 

The site controlled the exposures.

Mr. Barton: I understand that as a screening tool, 

sure. But we're using this data for a dose 

reconstruction.
70

Dr. Taulbee: We're using it for dose reconstruction 

now. When people have positive doses, we're using 

that in assigning doses.

Ms. Brackett: I would clarify that. We're using it for 

the coworker models. But individual dose 

reconstruction, these values are not used.

(Simultaneous speaking.)

Dr. Taulbee: -- we're using an MDA, yes, when we 

do the dose reconstruction.

Mr. Barton: But, again, should this data actually 

reflect any sort of meaningful connection to an 

americium exposure?

Member Lockey: That's like -- I'm not sure I 

understand your question. If there's no exposure, 

then you're not going to measure any in urine. I 

mean, I --

Mr. Barton: But see what our feeling is, as you look 

at this data, it's so far below what the detection 

limits were that it's not actually a meaningful 

measurement anymore. It's just noise, background 

--

Member Ziemer: You could call it nondetectable.

Member Lockey: Just call it nondetectable. I mean 

that's what we would do. It's -- you're right, it's so 

low we don't have to worry about it.

Mr. Barton: That kind of moves us into the next 

point of what do we do with this data that's so low 

below the detection limit, how do we deal with that. 

Do we use it as a real data point? Does this reflect a 

real exposure potential?

Member Lockey: Well, we deal with it that at -- all 

the time. If it's below the level of detection we 

assign a value to it. If one-half of the limit or 

whatever, we -- yeah, you assign a value to it.

Mr. Barton: This is the third part of our concern with 
71

the americium data. Again, it's the variability. It's 

the low detection limit which we think is too low --

in relation to the state of the art. And now we have 

values that are so low that we feel they have 

actually lost their meaning.

Dr. Taulbee: I would like to go back up to your 

comment there of the previous one of comparing it 

to the other sites. Rocky Flats, okay. Savannah 

River may be americium, so that's where it went. 

And Los Alamos as well. I mean, Savannah River, 

this was one of their production items that they 

made, americium, curium, and californium.

So comparing it to the other sites, well this is way 

lower than the other sites. It should be. They're the 

ones who developed the technology --

(Simultaneous speaking.)

Mr. Barton: It's comparable to an MDA that we 

believe is only achievable with the technology alpha 

spectrometry --

(Simultaneous speaking.)

Mr. Barton: -- the late '80s or mid-'90s.

Mr. Calhoun: So is the concern that these levels are 

so low that when you put them into a model it's 

driving everything down too far?

Mr. Barton: We're concerned that we're using data 

that doesn't actually reflect exposures to the 

trivalent actinides.

Dr. Taulbee: Okay. I can't believe that because, I 

mean, and the reason I say that is because there 

are people who this method showed they got an 

intake and they did follow-up, and they assessed 

dose based upon it. So it clearly works at some 

level. It clearly works.

Mr. Barton: In that situation, you would have 

someone that had a value above the censoring limit, 

right?
72

Dr. Taulbee: Yes.

(Simultaneous speaking.)

Dr. Taulbee: But it's the same, and they're part of 

the distribution, and they're part of this whole 

method.

Mr. Barton: We question those values above the 

distribution because of the variability we see. Again, 

the whole question is does this system of 

measurement, is it sufficiently accurate enough to 

reflect the exposures we're trying to reconstruct?

Mr. Calhoun: So you're bringing up just the fact that 

you believe that the whole analysis is invalid?

Mr. Barton: Well not the -- I'm saying -- yeah. The 

data that we're looking at --

Mr. Calhoun: For all the data.

Mr. Barton: -- the starting point. Well, what we see 

here -- hey.

Mr. Calhoun: Sorry.

Dr. Taulbee: This is a published paper, a published 

method in Analytical Chemistry. It's been analyzed. 

It's been reviewed. I mean we even had Dr. Glover 

go through it to make sure that the thorium would 

come through in this particular analysis. I mean, 

and now you're throwing out the whole analysis. I'm 

finding that --

Co-Chair Clawson: Questioning it.

Dr. Taulbee: -- incredible.

Member Lockey: I would go back to -- you proved 

to us that the system is not reflective of what it 

should be measuring.

Mr. Barton: That's what --

Member Lockey: That's what your --
73

(Simultaneous speaking.) 

Member Lockey: I'm not going to -- I guess NIOSH 

should go down prove their system is right, and you 

prove it's not working right, okay. Go back, look at 

the system that was utilized, your sensitivity, 

specificity, define it, bring the documentation that is 

not adequately reflecting what was supposed to be -

-

Mr. Barton: When you see the measurements is 

what we're saying.

Member Lockey: I want to look at the technique 

that was used, and tell me whether it was a valid 

technique during that timeframe and supply the 

documentation that goes with that, one way or the 

other.

Mr. Barton: Understood. Before moving on, Joyce, 

do you have anything you want to add before we 

move onto this?

Dr. Lipsztein: I think everything was talked about. 

But I really, with the practice I have -- for many 

years in laboratory, I don't believe that the 

americium method was reliable enough so that we 

can believe that the data they had on americium 

was valid.

I think that the limit of detection is very -- that they 

reported was very low. Even if it was the main 

guide, still it was gross alpha counting. And I think 

that when you had the samples -- I'm sorry I didn't 

-- don't have the data with myself. It's because my 

CDC computer is not working. So I only have what 

is available for everybody so I don't have the raw 

data with me.

But there was a lot of variation on repeated samples 

which what makes you think there is something 

wrong with the method so we had two things 

pointing that you cannot believe on the results. It's 

the limit of detection and the variability on samples 

taking -- aliquots taken from the same urine 
74

samples.

So that's it. I don't have anything more to add.

Member Lockey: It's Jim Lockey. I guess as a Board 

member, I appreciate what you're saying, and I'd 

like to have you provide a really -- a White Paper 

review and your position on this with all the 

appropriate published literature that supports your 

view one way or the other.

Dr. Lipsztein: I think we had that before, but we can 

repeat it. It's just that I -- as I told you, my CDC 

computer is not working so I don't have the data 

with me.

Mr. Barton: Either way, we need to fully document 

why we would -- not just on the observations and 

the data but with sources.

Okay, moving along. And this kind of leads into our 

next discussion which is the multiple imputation. 

This goes beyond the raw data that we were just 

talking about with americium.

This is what we have, a dataset with a large number 

of censored data. That is a large proportion --

sometimes the vast majority of the data is less than 

some number. And the question is what do we do 

with that data in the context of a dose 

reconstruction.

As Tim outlined in his presentation, TIB-81 adopts a 

method to infer a numerical result below the MDA, 

and this is known as multiple imputation.

And, again, we had Harry Chmelynski look at that 

method of multiple imputation to see how it affects 

the actual dose reconstruction. When we employed 

this method which is admittedly heavy on the 

statistics and far beyond my individual abilities, you 

know, what are the implications? What is this 

multiple imputation method doing, and what are the 

effects on the results that ultimately are calculated 

from it?
75

Harry, if you're there, could you talk about the 

analysis you did related to this multiple imputation 

method that has been employed in this coworker 

model?

Dr. Chmelynski: Okay. I began life as a physicist. 

And what I learned there was all models are wrong. 

Some are useful within the proper domain, and that 

sometimes the edge of those domains have been 

leading to new discoveries in science.

Well, when I became a statistician, I learned in Stat 

101 all about regression. And the first thing you 

learn is it's very risky to extrapolate any regression 

outside the range of the data.

And the first thing I see when I look at these 

imputation method, is we're extrapolating all the 

way down the line. Sometimes we only have 30 

percent of the data. But we're going to extrapolate 

all the way down to zero. And this bothered me 

right from the start.

Now, how do you usually do regression and 

prediction? By the way, we started out using the 

lognormal model because it's a convenient 

parametric model that gives us a mean and a 

geometric standard deviation, or a median and a 

85th or a 84th or a 95th percentile. All these on the 

upper end where we have data. It also is useful to 

find an estimate of the mu and sigma for the 

lognormal.

Now one of the things that NIOSH never addresses 

is what are the uncertainties in those estimates of 

mu and sigma. And when you're only using it to 

characterize the upper percentiles, I don't have too 

much of a problem with that because I think we're 

going to be okay.

However, we're doing something completely 

different here. Now, we're not using parameter 

estimates in the same way. Now what we're saying 

is we're going to predict individual values down in 

the lower tail of the lognormal where we have no 
76

data, and we're far far below where there is any 

data in some cases.

I don't know any way of doing that except -- well 

let's back up. If we're doing real regression, we 

know what we would do. We would calculate the 

predictive interval.

And we've all seen these graphs that show the 

hyperbolic shape of the predictive interval. It gets 

broader and broader and broader as you get away 

from where the data is. In fact, sometimes it gets 

so broad as to be almost infinite for the predictive 

distribution.

Dr. Taulbee: If I could --

Dr. Chmelynski: I'm sorry, let me finish.

Dr. Taulbee: Yes, sir.

Dr. Chmelynski: Now, what we're doing here is 

predicting individual values, and what we're saying 

is, as quoted earlier, we're just going to pick them 

randomly on that line as you extend the regression 

line down into the region where there's no data.

That isn't the way you do prediction intervals. As a 

matter of fact, it ignores uncertainty completely. So 

now, question.

Dr. Taulbee: Well, it was one of the things that you 

said of using the data down in that lower region. 

And what we're doing is we're imputing the values 

to get to the lower region in order to calculate 

another value, the time-weighted OPOS value, and 

those we are not using the lower tails. We are using 

the 50th and 84th percentile to develop the intake 

models.

So, to me, your characterization of saying we're 

using all this low-end data, we're really not in the 

final coworker model.

Dr. Chmelynski: Then why did you bother doing this 

if you're not using it? That doesn't make any sense 
77

to me. You're saying oh, I can predict stuff down 

here that doesn't make any sense, but it won't 

make any difference, so it's okay.

Dr. Taulbee: Well, because there's some of the 

datasets that may be, say, 75 percent censored, 

okay. So we take all of that data, and we're 

developing what the underlying distribution is, and 

that's that lognormal model, okay.

Then when we're taking that, we're substituting the 

individual values for our TWOPOS calculation. That's 

how we're using this.

Dr. Chmelynski: Yeah, you're imputing or rather, 

say, predicting what values should occur down in 

the lower tail.

Dr. Taulbee: No.

Mr. Barton: Well those values in the lower tail are 

used --

Dr. Chmelynski: And as you said sometimes you 

only have 30 percent of the data, so the tail goes 

from the 70th percentile all the way down to zero. 

It's a huge tail. It's three times bigger than where 

you have data.

Now we've all seen those regression models that 

show you how the predictive interval behaves as 

you get outside the range of the data. Why doesn't 

somebody think about that here?

I know why. Because when you do on a list, you 

don't know what the uncertainty in mu and sigma 

are.

Dr. Taulbee: In our RPRT-96, we go through the 

multiple imputation method and we compare it to --

or we compare -- the method was developed using 

uncensored data, picking censored values, and 

seeing how we could model this and does it work. 

Okay? This wasn't done blindly --

Dr. Chmelynski: Which dataset are you talking 
78

about?

Dr. Taulbee: This would be the Y-12 dataset, I 

believe, and Fernald datasets, that we used to 

develop this is RPRT-96.

Dr. Chmelynski: And what year were those datasets 

done in?

Dr. Taulbee: I don't know off the top of my head. 

But my --

Dr. Chmelynski: Usually what happens -- what I 

have seen -- the only time I've ever seen the lower 

tail of a lognormal actually plotted on real data is in 

more recent stuff where we have good precision and 

very low MDAs.

I hardly ever have run into a case where I can plot 

the lower tail of a lognormal.

Dr. Taulbee: Well I would urge you to look at RPRT96. In fact, I think you guys did in here. I mean I 

remember -- it's part of my presentation to 

comment on that. But it's -- yes it's a new method 

that we've got.

We've been doing multiple imputation on the 

external side for at least the last four years. And 

this, well, it's the first coworker model we've done 

in five years or so, and so we've employed that 

same methodology here.

Dr. Chmelynski: So you don't agree that the 

predictive interval is the right answer?

Dr. Chalmers: What predictive interval?

Dr. Taulbee: What predictive interval?

Dr. Chmelynski: The predictive interval for the 

values you're imputing down there in the lower tail. 

How uncertain is that line first off? That's the first 

question.

Then the second question is, now that we know how 
79

uncertain the line is, what if we take individual 

predictions? Now that's a much broader predictive 

interval of uncertainty.

Dr. Taulbee: But that's not how we're using this.

Dr. Chmelynski: Let me just finish this discussion 

and move on to one more topic here. Let's assume 

that you do this and ignore all the complications 

that we just talked about, and what do you end up 

with?

Well, I did a simulation on this, and it's reported on 

page 30, figure 9 of our review. And what I did here 

was I looked at -- well, let's say we have a 

lognormal, and what we're going to do is below 

some censoring level we're going to predict 

numbers in the lower tail.

And then what I did was I did it over and over, and 

I took the mean value of all of them. And here on 

figure 9, I looked at four different censoring levels. 

The median is the same always, and everything 

scales since the horizontal axis is the GSD and the 

vertical axis is the mean of the imputed values 

expressed as a percent of the censoring level.

And on each draft there's a line called a censoring 

level over 2, which gives you a perspective as to 

where we are here. We're right down in the range 

below the censoring level and down to zero is where 

we're talking about.

Now, when the PSE is low, the slope is low, and we 

impute a lot of values that are up near the 

censoring level, and you can see that in all these 

graphs. So on the left when the GSD is 1.5, we get 

somewhere around 80 or 90 percent of the 

censoring level as the expected value.

When we get out to GSDs over 5 or 4, depends on 

which one you're looking at, now we start getting 

expected values that are down below the half of the 

censoring level, getting down close to about 20 

percent of the censoring level for the high GSDs.
80

And this, by the way, is just simulated. But what it

points out is when you have high GSDs, you end up 

imputing much lower values down here in these 

lower tails. Now why is that? Well, that's because of 

this idea that the lognormal applies both on the 

upper end and on the lower end.

And if I have some people that are 10 times above 

the median in this group, now I have to start 

thinking well there must be some people down 10 

times below the median. Well, I don't -- as a 

physicist, I don't see any sense in that statement.

Why would there be lower ones if now I have higher 

worker exposures? It just doesn't make sense.

Dr. Taulbee: Okay. My response to that is you have 

some workers who aren't exposed, okay? In that --

if you actually look at much of the -- or some of the 

data, you'll see there's negative values in the raw 

results that we end up truncating to zero.

Dr. Chmelynski: Well, that's exactly my point is you 

can't fit the lower tail of lognormal because of what 

you just said.

Dr. Taulbee: But we can make it claimant favorable 

by moving it up to zero --

Dr. Chmelynski: It's all hypothetical. It's all 

hypothetical what's going on down there in the 

lower tail. You make up some numbers, you write 

them down, say ah, just on the line, good. I don't 

believe in these made-up numbers.

Member Lockey: I'm sorry, what numbers are being 

made up?

Dr. Chmelynski: Your imputed number that you're 

sampling along that line down in the lower tail.

Mr. Barton: It's a non-detectable result that's being 

--

Co-Chair Anderson: And those bring the mean down 

--
81

Dr. Chmelynski: The imputed value for a nondetect, yes, let's leave it at that.

Member Lockey: Okay. So if we go back and say, I 

guess we can go back and say the analytical 

technique at the low end of the tail are -- because 

the analytical technique is valid, those values that 

were measured are non-valid.

In other words, they don't really reflect what the 

exposure was because the technique was incorrect. 

But if we accept that the technique was correct and 

all those values -- those actually are -- been 

measured values, then what do we do at that point?

Mr. Barton: I think this is different than the trivalent 

discussion. In the trivalent discussion, we had raw 

data points written down in a logbook. What we're 

talking about now is all we have is a big cohort of 

less-than-detectable. All it says is less than some 

number.

And so we're taking that number that's less than, 

and we're assigning it essentially a simulated real 

number, and then treating that simulated real 

number and putting a distribution to it. So these 

aren't real measurements.

Member Lockey: So you think that's influencing the 

higher exposure values?

Co-Chair Anderson: No, the median.

Member Lockey: The median? Do you think to a 

significant degree, you think it's biasing the 

median?

Mr. Barton: Well, Harry, correct me if I'm wrong, 

but I think that's what your analysis shows, does it 

not?

Member Lockey: I'm just trying to understand --

Dr. Chmelynski: I'm not carrying it through at this 

point to the final step of getting the medians. I just 

wonder why we want to make up these numbers. 
82

The argument is that it won't affect the median 

much. I know I've heard that before, but why do we 

make them up if that's the case?

I guess because we have no other alternative 

maybe, because you don't like the CO over 2, you 

don't like the maximum possible mean, you don't 

like -- I would even take a uniform between zero 

and CO. Who knows? It could be anything down 

there.

Dr. Taulbee: But any of those values, any of those 

methods --

Dr. Chmelynski: All of them are wrong. All of them 

are wrong.

Dr. Taulbee: Sure, that's my point. Is all of them 

you're coming up with a value of what you think is 

down in that lower region. Based upon our review of 

multiple uncensored datasets, they tend to follow a 

lognormal distribution, and therefore we are trying 

to use this to simulate the best possible method 

that we can, and that's how we came up with this 

multiple imputation methodology. Okay?

That's why we're doing it this way. We could do it 

one time, okay. It's multiple imputation. We could 

impute it one time and go and run with that. But 

we're trying to make sure we're not biasing around 

that mean in some unknown way so we do it 

multiple times, the simulation multiple times to 

make sure that we're coming up with something 

that is reasonable.

We then take those values, the 50th and the 84th --

Dr. Chmelynski: That's why these, that's why these 

--

Mr. Katz: One at a time.

Dr. Chmelynski: That's why these plots are plots of 

the expected value because you are doing it 

multiple times. The expected values keep tapering 
83

lower and lower as the exposures get higher and 

higher. I just don't believe it, I'm sorry.

Now maybe other people who are statisticians will 

believe the lower tail of the lognormal is something 

meaningful, but as a physicist, I don't, I can't 

connect the upper exposures with the lower 

exposures. It's like those entangled particles that no 

one can understand in physics.

In this case, why do the lower ones have anything 

to do with the upper ones? I don't know. But here, 

we're saying it has to be symmetric.

Dr. Taulbee: Nancy?

Dr. Chalmers: Okay. All I want to say is that we 

discussed this in RPRT-96 in detail for bioassay 

results where we had a completely uncensored 

dataset. We censored it at a certain level, maybe 70 

percent censored, you know, whatever you guys 

were talking about.

And we used this technique to basically show that 

it's superior to any technique that we've ever used 

to do coworker. You can compare the multiple 

imputation technique results to the completely 

uncensored dataset. If you fit it, you have it. And in 

those examples, we did have it.

So we did a comparison. It's clearly superior I think 

if you look at RPRT-96 to all these other methods 

that we've ever used in the past. This LOD over two 

they're talking about. The ROS we used to use, all 

those things.

And this is just a specific use of it, and I think SC&A 

has already said that we implemented it correctly. 

They just sort of had an issue with the technique 

which I think there's references all through RPRT-96 

of textbooks.

Review journal articles that say, you know, this is a 

great thing to do. Dr. Richardson has a paper where 

they use the imputation technique to do these sorts 
84

of things. And so I think we really need to discuss 

96 probably in that setting instead of trying to get 

into the gory details here. That would be my 

suggestion.

Mr. Barton: Well, currently we have responses from 

NIOSH on this issue in the section written on the 

statistical basis and the effect of multiple 

imputation.

Harry, I think you maybe got those on Tuesday 

from us. I don't know if you've really had a chance 

to really digest it. And if you have any comments or 

does NIOSH want to explain essentially the rebuttal 

to what our review says about multiple imputations.

Dr. Taulbee: I actually have a little bit of -- that was 

part of my kind of response to this, but I wasn't 

sure whether we wanted to bounce back and forth 

or wait until Bob was done. Any preference, because 

I can bring it up now if you want.

Member Beach: Might as well go for it.

Dr. Taulbee: Go for it, all right. Can you give me --

(Pause)

(Off-microphone comments)

Mr. Barton: I guess, Tim, while you're working on 

that, I guess taking a big picture view. The multiple 

imputation method isn't actually in the coworker 

implementation guide. In fact, the implementation 

guide is silent on the exact treatment of less-thanMDA values.

I was simply pointing out that this multiple 

imputation method that's contained in RPRT-96, it's 

actually not in the implementation guide. And the 

implementation guide doesn't really address what 

you do with highly sensitive datasets. So, I mean, 

while this effect would affect all other sites for 

coworker model, it is not directly related to the 

implementation guide. But it is how you deal with 
85

less-than-MDA results. So it is universal in that it's 

sort of complementary to what's in the 

implementation guide, but it's not necessarily in 

that, nor is any other method.

Dr. Taulbee: I'm not sure it was completely -- oh, 

it's on yours, never mind. Well, yeah.

Okay, so I mean, our response to this is multiple 

imputation is a better, more statistically appropriate 

method for estimating the sensor data compared to 

MDA over two. And this is pointed out in RPRT-96.

And I just want to point out that, you know, the 

program's going to change over time. We've started 

using MDA over two and other methods, LOD over 

two for external. And as we get better methods, we 

plan to continue to use them. 

Like I said, it's been well known that we've been 

using this in the external dosimetry side. And, but 

both the external dosimetry and the bioassay data 

are tending to fall log-normal distributions.

RPRT-71 covers this methodology for external dose 

OTIB -- I'm sorry, that should be RPRT-86, 

shouldn't it? Oh, RPRT-96, it's a -- oh, I'm sorry, 

never mind. Pantex. Sorry. RPRT-71 was the 

external dose methodology using multiple 

imputation. We implemented this in OTIB-86, which 

was the Pantex external coworker model. 

SC&A reviewed this at that time, but it, the 

methodology hasn't been critically commented on. 

They agreed with the coworker model, but didn't 

see, going back through transcripts and so forth, 

that there was any major discussion about the 

imputation method. 

But this table here kind of shows the similarities 

here, the external dose and internal dose, all right. 

When we're dealing with dose reconstruction, we're 

using a log-normal -- these would be for the sensor 

values, the nondetects, when we're physically doing 

the dose reconstruction. 
86

And we assume a log-normal distribution for the 

missed does, with a geometric mean of N times the 

LOD over two and a GSD of 1.52, which comes out 

to the 95th percentile, is N times the LOD.

On the internal side, we use a triangular distribution 

with a min of zero, a mode of NDA over two, and 

max of the MDA. This is for dose reconstruction 

because we have individual data for that person. 

The older method of doing coworker was we would 

use that LOD over two methodology for external, 

and then we used a PROC-95 method, which is a 

ranking method, to estimate the values below the 

censoring level for the internal.

So we've been effectively imputing or substituting 

those censored values all along in coworkers 

models, okay.

Now, the new coworker model for external side, and 

we're using the multiple imputation, which is very 

similar to what we're doing here, there we're going 

through the nondetect values, following the lognormal, and we're substituting them in for an 

individual worker, and then that gets put in together 

to develop that coworker model as a whole for that 

particular year.

What we're proposing is doing the same thing here 

for the internal. In fact, not proposing, this is what 

we're doing. But we're doing the imputation, there's 

an additional step here. 

Instead of going directly imputation to dose, we're 

doing the imputation to the TWOPOS values. The 

TWOPOS values are then the fitted value that goes 

into the intake that I went through today. All of 

these additional steps follow this particular method 

with the multiple imputation, okay.

So from our standpoint is we intend to continue to 

use multiple imputation as a primary method for the 

analysis of censored datasets. We believe it to the 

be the statistically superior method, as pointed out 
87

in, is it RPRT or OTIB?

Dr. Chalmers: RPRT-71 is the external and RPRT-96 

--

Dr. Taulbee: RPRT-96, RPRT-96. 

Mr. Barton: Harry, if you're still there, any 

comments on that explanation?

Dr. Chmelynski: I looked briefly at RPRT-96, 

although we weren't tasked to look at that report. 

And I, it has a lot to do with this. And a handful of 

datasets doesn't really answer the question. We're, 

but several of those datasets were very recent, 

actually, too, which also disturbed me.

But going back to the 80s and trying to guess what 

those numbers are down below the censoring level, 

however you want to call it, I still think it's a 

hopeless task. You can make one assumption, like 

we did, that the log-normal tale is the right answer. 

There's a lot of other assumptions. None of them 

are right. That's about all I can say.

Co-Chair Anderson: Just a question for you. I mean, 

we're dealing with censored data. At what point, if 

the censored data, the percent of censored data 

makes your results less -- I mean you're assuming 

the censored data is a log-normal. But if your actual 

measured data is at ten percent of all of the 

samples versus the others, is there a point at which 

you'd say the dataset is, because of the low levels -

-

Dr. Taulbee: This is where I'm going to ask our 

statistician to jump in.

Dr. Chalmers: We've actually developed a loguniform method that we can use even if it's all 

censored. And that's probably since 96 was 

published, I think. So when we revise it, we're going 

to put that into RPRT-96.

Member Lockey: Say it again now, you're going to 
88

develop what now?

Dr. Chalmers: If all data are censored, we can use a 

log-uniform that's another distribution, a loguniform imputation model.

Member Lockey: So the assumption is that nobody 

had uncensored data; it's all censored.

Dr. Chalmers: Yes, yes.

Co-Chair Anderson: So the --

Member Lockey: I'm sorry?

Co-Chair Anderson: They're assigning values when 

there was nothing there.

Member Lockey: Well, there's two ways I would look 

at that. They should have been measuring it when 

they weren't, or exposure levels were so low that it 

wasn't worth measuring.

Dr. Chalmers: So basically it's all noise and --

Member Lockey: It's all noise, yeah.

Dr. Chalmers: What you do with it.

Member Lockey: And if it's all noise, you're about, 

it's about technique. If they should have been 

measuring when they weren't, then that's ours.

Co-Chair Anderson: Or if the limit of detection is so 

high --

Dr. Chalmers: But that's a data adequacy --

(Simultaneous speaking.)

Co-Chair Anderson: Not current, but of the older 

data. It's like here are the --

Member Lockey: Yeah, you have to back and look, 

you got to look back --

(Simultaneous speaking.)
89

Co-Chair Anderson: Assumed to be is actually a 

higher value than what they use. So if the 

confidence of the laboratory technique --

Member Lockey: Right.

Co-Chair Anderson: If you're confident in the 

technique, which should have detected things in the 

range you wanted to detect.

Member Lockey: Right.

Co-Chair Anderson: Then you're assuming the 

nondetects. But if you want to do the -- it's like 

you're, if it's a minus number, you --

Dr. Taulbee: We make it zero.

Co-Chair Anderson: Zero to be claimant-favorable. 

Then MDL over two is even more claimantfavorable. So.

Member Lockey: Then the question is do you start 

throwing data out or not, you know?

Co-Chair Anderson: Oh, it's --

Member Lockey: I'm always hesitant to throw data 

out.

Co-Chair Anderson: Well, it's like most of the 

human data studies for other chemicals, you can't 

get it published if you try to use something other 

than the limit of detection over two or the square 

root of two. 

I mean, that's a, you're looking at all the chemical 

data that the CDC has adopted. They may 

eventually move to this kind of a model. But at this 

point, we're telling you this is what, for PCBs, for 

instance.

Member Lockey: Yeah, and that's usually based on 

sensitivity lab technique.

Co-Chair Anderson: Yeah, yeah.
90

Member Lockey: That's what it's based on, how 

much resources you spend on your lab technique to 

get down to parts per billion.

Co-Chair Anderson: Yeah.

Member Lockey: It's -- just may not be 

economically feasible to go to that level, so you do, 

divide it by two.

Co-Chair Anderson: That's why looking at 

comparison, if it doesn't make much of a difference, 

then how you assign those nondetect values with 

the MDL is so low already. You know, it isn't going 

to change the ultimate decision, so.

Dr. Taulbee: And that's where I'd like to try and 

reel, or come back to that initial presentation of how 

this is used. The censored values are used as 

imputed values to calculate that TWOPOS, that 

time-weighted OPOS for each person, and then we 

fit that to get the 50th and 84th percentile, and 

that's what we carry forward.

Dr. Chalmers: We fit that multiple times.

Dr. Taulbee: Multiple times.

Dr. Chalmers: One time for each imputation.

Dr. Taulbee: One, exactly. So.

Mr. Barton: Well, I'll say for my own, and again, I 

admittedly am short on the statistics, but what 

really caught our eye when we saw this new method 

was the output from it. You know, we have, all of 

these censored data say less than one. 

And this method is coming up with coworker values 

that are ten percent of that value. Which to us, you 

know, seems very low, especially when compared to 

previous methods.

Now, the next thing -- well, first, Harry, any final 

thoughts on where we are right now?
91

Dr. Chmelynski: No, I'm not sure where we are. We 

seem to be at loggerheads. That's about all I can 

say. Again, if it really doesn't make a whole lot of 

difference what numbers you pick -- well, let me 

take that back. 

When you have 70 percent of the data that's down 

there in the nondetect range, are you still doing 

this?

Dr. Chalmers: Yes.

Dr. Chmelynski: Yes, okay, no, that doesn't make 

sense to me. It's way too, too much extrapolation. 

Now, maybe if, you know, if 20 percent of the data 

were nondetects, well, you got a fair amount of data 

to fit your regression line. 

And even though it's a phony regression line and we 

don't know what the uncertainties are, et cetera, it 

still seems to be okay. But the higher percent of the 

nondetects was, the worse I like this.

Dr. Taulbee: I guess I would encourage, or you've 

said you've already looked at RPRT-96. And maybe, 

I mean, that's where we did this analysis where we 

censored it at different levels and showed that we 

are able to predict the uncensored dataset fairly 

well. 

Perfect? No. But for the purposes of this, we feel 

that this is reasonable in the way we're proposing to 

go forward.

Mr. Katz: So just a side, just a side note then on 

this. I think, Harry, you said that SC&A was never 

tasked with reviewing that report, is that correct?

Dr. Taulbee: That's correct, yes.

Mr. Katz: Okay, so then it seems like it'd be 

reasonable to have them review it, right?

Dr. Taulbee: Sure.

Mr. Katz: Given this discussion.
92

Dr. Taulbee: Yeah.

Mr. Katz: Yeah, so why don't we just go ahead and 

note that for the records, that let's have you review 

that report since it matters.

Dr. Taulbee: Just a bit of procedural here. Can I ask 

that the SEC Issues Work Group take that on, or 

maybe Procedural Work Group?

Mr. Katz: I mean, since this has been, I'm --

Dr. Taulbee: One of the authors on that is Tom 

LaBone, and so he is conflicted with Savannah 

River.

Mr. Katz: Right.

Dr. Taulbee: And so --

Mr. Katz: He's conflicted on Savannah River, but 

he's not, this is a issue of general applicability, so 

he's not conflicted there. The fact that it's Savannah 

River data is not going to --

Dr. Taulbee: It's not Savannah River data.

Mr. Katz: Oh, okay. So there --

(Simultaneous speaking.)

Mr. Katz: His conflict has no bearing.

Dr. Taulbee: If the next discussion comes up during 

an SRS Work Group meeting.

Mr. Katz: No, no, so anyway, that's a generic issue, 

and that would be handled either by SEC issues, 

only because they've already started on it, or 

Procedures, or the two. But anyway, so it wouldn't 

come under the SRS, right, correct. Good, so it's 

good to establish that. Right.

But right now, it's going to take them a while to 

review that. So SC&A too.

Dr. Taulbee: Absolutely.
93

Member Lockey: This is Jim Lockey. Just maybe I'm 

missing something here, but let me just raise this 

issue. If we take the center value and divide it in 

half, versus doing your imputed modeling value, at 

these exposure levels, the biological plausibility of 

an injurious injury is virtually nonexistent. 

So from a medical perspective, it makes no 

difference to me, all right.

Co-Chair Anderson: It's within the rounding error.

Member Lockey: What's that?

Co-Chair Anderson: It's within the rounding error.

(Simultaneous speaking.)

Member Lockey: Plausibility at these exposure 

levels is any injurious impact. I mean, it's just 

beyond comprehension.

Dr. Taulbee: Yes. 

Mr. Barton: Injurious, I agree because the levels are 

low. But we're still trying to reconstruct the doses 

for people, so it's important to know how we're 

going to handle those sensitive datasets if they get 

a coworker --

Member Lockey: But that has an impact on how 

long we're going to allow this to continue, you 

know. We're dealing with something here that --

Dr. Taulbee: I think due to the averaging and 

everything that we do going to the intakes, it 

doesn't really matter.

Well, actually, Bob, if you could give me some data 

here. I'm jumping ahead, but maybe this will help.

Well, let's continue on, I'm sorry. The thing is that 

one of the next findings shows that this method will 

result in some values that are higher. And so we've 

got some that are showing higher, some that are 

showing lower. 
94

And they'll end up doing a Probability of Causation 

analysis that comes out to where it shows about the 

same. So that's, sorry, that's our interpretation of 

it. But Bob needs to go through it, not me. Sorry, 

Bob.

Mr. Barton: The next thing that we're really going to 

look at, as I said, the thing that really stood out to 

us was the magnitude of what this method was 

producing. Again, we're talking about taking a value 

that's less than some number and what the 

coworker comes out as is a very small fraction of 

that number, often ten percent, sometimes lower 

than ten percent.

So the next question, logical for us and we just, we 

already discussed finding two, so what does it really 

mean in the context of a dose reconstruction. So 

the first thing that we thought of was, well, if it was 

an individual worker with a less-than result, there's 

a missed dose methodology. 

And that's where you use one half of the dose, 

triangular distribution from zero to one half to the 

full dose, based on the MDA. Which is a completely 

different method than what we're using for 

essentially the coworker who would be assigned 

missed dose. 

So we have two workers, you have the monitored 

worker who's getting missed dose, you have the 

coworker who's getting missed dose, but there's two 

different methods being employed here.

Dr. Taulbee: And that'll bring you back to the 

external side of things, how we do the missed dose 

for dose reconstruction versus the coworker model, 

which SC&A has already reviewed and said it was 

okay.

Mr. Barton: You're talking about the imputation and 

the external.

Dr. Taulbee: Yes.
95

Mr. Barton: Well, you also said that we didn't 

specifically critically review that.

Dr. Taulbee: You reviewed the coworker component 

of it, the coworker model. I mean, all of those 

findings were closed out.

Mr. Barton: I guess this is the, I mean this is the 

first time I've seen it because it's the first time it's 

been used in an internal coworker modeling.

Dr. Taulbee: That's right, that's correct.

Mr. Barton: And I mean, this RPRT-96, right?

Dr. Taulbee: Yes.

Mr. Barton: RPRT-96, I think that came out in 

January?

Dr. Taulbee: January, yeah, about two months 

before the coworker did.

Member Ziemer: Could I ask, Bob, are you asking 

about the fairness of an unmonitored worker ending 

up with a higher value than the monitored worker?

Mr. Barton: That's what we're about to get to. We 

did some scoping calculations to see how it --

Member Ziemer: But I think that can often happen.

Mr. Barton: Yeah.

Member Ziemer: Because we're, you're being 

conservative. Here's one person where I have this 

actual data and I know what he got. Here's another, 

I don't know what he got, so I'm sort of giving him 

a --

Member Lockey: Benefit of the doubt.

Dr. Taulbee: Benefit of the doubt.

Member Ziemer: A benefit of the doubt and he ends 

up higher. It happens all the time.
96

Mr. Barton: I guess our point was they're both 

missed doses. And so --

Member Ziemer: Oh, both missed.

Mr. Barton: Well, they're really both --

(Simultaneous speaking.)

Mr. Barton: Yeah, one has actual values that, yeah. 

Member Ziemer: It happens a lot in the external 

world, and I know that. We've got it at many 

different sites.

Mr. Barton: I guess if I could offer just my simplistic 

view of it. In the simplest sense, if coworker 

modeling was let's get everybody who had 

monitoring records in a line from smallest to largest, 

and the person right in the middle at the 50th 

percentile has a censored value, a value that's less 

than the MDA, now enter the unmonitored worker 

and say go find that coworker at the 50th 

percentile.

So they go to the middle of the line, they shake 

hands, and they say oh, I guess you're my 

coworker. Yes, I'm your coworker, I guess we're 

going to get the same dose reconstruction. And it's 

not true.

Dr. Taulbee: No, because there's built-in the 

conservatisms, the unknowns.

Mr. Calhoun: I mean, the whole program is built on 

being claimant-favorable. That's not even arguable, 

you know, I mean we got to be claimant-favorable. 

And it doesn't make sense to me, sorry.

Member Ziemer: Yeah, I know a lot of members of 

the public think the coworker's the person they 

work with. And it's not, it's a theoretical person who 

worked maybe somewhere else or could have 

worked somewhere else.

Dr. Taulbee: Could have worked somewhere else.
97

Member Ziemer: So anyway.

Mr. Barton: Well, what we did, and again, the point 

was, as we're looking at these very low values, the 

question really is, what's the effect. So we went and 

we did some scoping analysis where we assumed 

the coworker-computed values, which again are a 

small fraction. 

And the missed dose for a monitored worker, which 

is again, evaluated half the MDA, and so the obvious 

thing to me before we even started out -- can 

people see this?

(Simultaneous speaking.)

Mr. Barton: All right, so we performed the scoping 

analysis, because the obvious thing is if you're 

going to evaluate dose at half the MDA and evaluate 

the dose at one-tenth of the MDA, the doses are 

going to be higher for the missed-dose monitored 

worker, right. And this is what we see here.

And this, these IMBA runs were actually performed 

by Ron Buchanan and Rose Gogliotti.

Ron, if you're there, you want to walk us through 

what the results were?

Dr. Buchanan: Yeah, this is Ron. For this discussion, 

what we did this exercise for was to step back, say 

does it make a difference, and if so, what are some 

of the areas, pitfalls we might want to look at.

So what we did was we said, okay -- and there's a 

lot of parameters go in dose reconstruction. We just 

tried to get a scoping view of it to get some 

examples. What we looked at was at two workers 

working side by side at the same period had the 

same latent period, and then the same cancer. 

And what was the intake ratios, what was the dose 

ratios, what was the PoCs, which is the final 

number, of course, we're interested in.

And so we did this for several radionuclides and 
98

several major organs. And we seen that we used 

the beta, the gamma, and alpha emitters. And so 

we looked at what the final PoCs would be in the 

ratios using these two workers.

And we see that we used strontium-90, a beta 

emitter. We have seen that the missed dose for the 

person that had records had low, less than MDA, 

was about two and a half times the dose as the 

coworker dose. 

However, the PoCs was about very similar. 

Strontium-90 is not a big dose delivered, and so 

PoCs are fairly low MDA levels. And so they're less 

than one percent, so not a real good isotope that 

would give us a lot of information. So we wanted to 

look at a beta. 

We looked at the gamma, at cobalt-60. Again, the 

missed dose using MDA values is about five times 

greater than coworker dose. However, the PoCs, the 

missed dose, led to PoCs about twice that of the 

coworker dose. However, the PoCs, again, were 

very much less than one percent. So not a good 

isotope that gives us much information.

Now, as we move to the alpha emitters, of course, 

there are bigger dose delivered. So we looked at 

that, neptunium-237, plutonium-239, and also 

uranium. 

And in this case, we've seen that the missed dose, 

using MDA values, was about 1.4 times higher than 

coworker dose. However, the coworker PoCs were 

slightly larger than the missed dose. And we'll 

explain why in a little bit.

And the plutonium-239, similar situation, the 

missed dose was one and a half to about 1.9 times 

higher than the coworker dose. However, the PoCs 

for the coworker were slightly greater than the 

missed dose PoC. Okay, you want to go to the next 

slide?

Okay, so this observation two was simply that: an 
99

observation. We're saying that we see that in this 

case, although the intake and the doses were 

greater, usually for the missed dose because a lot of 

the values were for Savannah River Site, this is all 

data taken from RPRT-92, that the PoCs came out 

relatively the same. 

And that is because the missed dose is assigned 

using the triangular distribution, whereas the 

coworker dose is assigned a log-normal distribution. 

And so this distribution difference, even though the 

intake and the doses were higher for missed dose, 

this distribution difference about equalizes PoCs.

And we did this, like I say, for strontium, cobalt, 

neptunium, plutonium, to four or five major organs. 

However, we did also for uranium, and we did not 

see the same patterns. Go to the next slide.

We found that when we had used uranium, we 

found that the intakes were greater for uranium. 

Bioassays were greater because you had more 

workers with positive bioassays, had greater 

intakes. And so you had the PoC for the coworkers, 

which is about four times higher than the misseddose approach.

Now, this really probably should be an observation. 

It's not really a finding, it's more informative. 

And so what we wanted to illustrate was and make, 

you know, everyone aware that using the coworker 

approach as compared to missed dose sometimes 

gives the worker a higher dose, the unmonitored 

worker. Sometimes they get a lower dose and the 

PoCs sometimes are different. Next slide.

So now we want to make sure and say an important 

caveat is that we realize there's a lot of things going 

into dose reconstruction. Our main purpose in this 

exercise was we had all this discussion we've seen 

this morning on which to use and what you 

shouldn't use. 

But we found out that the coworker model is usually 
100

assigned as a log-normal. Missed is defined as a 

triangular distribution, so the PoCs come out similar 

for the cases and organs that we did.

And we just want to emphasize care needs to be 

used in using these two different approaches, and 

that the unmonitored worker isn't shortchanged and 

doesn't get a lower dose than a coworker. On the 

other hand, the monitored worker that has records 

sometimes is penalized for having MDA values 

reported as -- instead of being assigned a coworker 

dose.

I think that was the end of my part.

Member Ziemer: I understand what you're saying, 

Ron. But in most of these cases, we're quibbling 

about whether you should give somebody one 

millirem or five millirem. Either way, it doesn't make 

any difference on the PoC to speak of. And if we're 

talking about the censored data. 

So you know, I think we've already talked in other 

venues about the issue of what we worry about at 

the low end of the dose spectrum. Obviously we'd 

worry about a factor of four if we're up in the 

hundreds of millirem or up in the rads or rems or 

sieverts or millisieverts. 

But whatever units you want to use, if you're up in 

high dose, a factor of four is a significant difference. 

If you're down here in the couple millirem, it doesn't 

make any difference.

Dr. Taulbee: If I could interject. If you go back to 

that one plot that I showed of the coworker of once 

we got done with the intakes and the overlay of the 

bioassay, that 84th percentile. 

If somebody had been exposed to that, this is using 

the multiple imputation method, if somebody had 

been exposed to that level for that long, all of their 

bioassay would then be showing positive. And it 

would still be a relatively low dose that you're 

talking about.
101

So I think we've got enough conservatisms built into 

the coworker models that they are claimantfavorable, as they need to be. And this method is 

scientifically superior, RPRT-96 demonstrates that. I 

know SC&A's going to review it. But I don't see any 

reason to change what we're doing from that 

standpoint.

Mr. Barton: And just to add a little perspective, why 

are we even talking about this? Again, this is the 

first time we had seen it for an internal coworker 

model. And again, the output, the output, we looked 

at the output. It seems so low, especially compared 

to what you do for missed dose, and that's why we 

performed the sample calculations.

Now, what's the real, I guess to wrap it up, we think 

the Board needs to know this kind of information 

about when, what is going on with these less than 

MDA results, both in missed monitored dose and for 

an unmonitored worker. 

And that's why we went through this exercise, to 

really show that, so that we're all informed about 

how the program works.

Ms. Brackett: I would point out, though, that you 

know, the analysis showed that the PoCs weren't as 

different as -- and that is the bottom line, that's the 

important thing. And keep in mind that when you 

have assigned a missed dose, it's a triangular 

distribution. 

And coworker is a full distribution. You can't 

compare them directly, you have to look at the PoC, 

because you know, you're, it's different in the 

outcome. So that's an important comparison, right.

Mr. Barton: Right, and that was actually one of our 

observations, that, while the doses can be very 

different, the missed monitored dose can be much 

higher. It can be a factor of five higher, I think, for 

when we evaluated cobalt. A factor of five higher on 

the dose, yet the log-normal --
102

Dr. Taulbee: But what I think Liz is coming up with 

is you're saying it's a factor of five higher, but 

you're comparing the two geometric, the two central 

tendencies, the 50th percentiles. And you're 

comparing two different distributions that don't 

compare that way.

Mr. Barton: That was the observation that we said. 

It's what's really driving the PoC actually isn't so 

much the magnitude of the dose, it's about what 

statistical distribution you can put on it. 

And so for the coworker model we're using a lognormal with the GSD minimum of three. But 

whereas for the actual monitored worker, it's a 

triangular distribution.

Dr. Taulbee: Right.

Mr. Barton: So even though the doses are higher for 

the monitored worker, the effect on the PoC is really 

driven by that statistical distribution. And that's 

something that we wanted the Board to understand.

Dr. Taulbee: Okay.

Member Ziemer: Did Ron say you were changing 

that finding to an observation, or was that --

Mr. Barton: I think he did say, yeah, this is even as 

we were preparing for this meeting. It really fits in 

more as, again, with the intention of informing the 

Board so that they have all the information possible 

about these less-than-MDA results are being 

interpreted. It is really more of an observation.

Dr. Buchanan: Yes, I agree that this is mainly 

informative for us. But then when we -- we knew 

the question was going to come up, well how does 

this impact the end results of PoC. And so that's the 

reason we did the exercise and the reason we 

wanted to present it today is that it's informative 

observations, so I think that finding should be an 

observation.
103

Co-Chair Anderson: I guess I think one solution 

around this would be, when you say coworker, most 

of the workers think about individuals near them. 

This is really what you're doing is the rest of the 

workforce. 

So it's really a workforce general exposure 

assessment versus coworker, because you're 

including a lot of workers that the person you're 

reconstructing has no idea who they are or where 

they are. 

As opposed to, I want to know, he's sitting next to 

me, we have the same exposure down that end of 

the room. It's different, so it could be just a 

terminology issue might be one way to think about 

it.

Dr. Chalmers: I think in other fields they call it a coexposure model.

Co-Chair Anderson: Yeah, exactly.

Dr. Chalmers: They were exposed the same or 

assigned sort of the same.

Co-Chair Anderson: There's a lot of confusion in the 

workforce that we're really not doing -- we're doing 

coworker. So co-exposure, you might want to think 

about where the missed dose is a different. You're 

taking into account the data you already have on an 

individual.

Mr. Barton: Right.

Co-Chair Anderson: And that since they already, 

most of them will have measured.

Dr. Taulbee: And we can certainly change the 

names of this is that helps the public.

Co-Chair Anderson: Going back to when we have 

public comment, you know, you're going to hear 

much the same when in fact the understanding is 

different.
104

Member Ziemer: No, we've heard that a number of 

times where someone says they used a coworker 

model, and I checked with so and so, and they 

never interviewed him.

Co-Chair Anderson: Yeah, exactly. So co-exposure, 

I think that's a, right.

Mr. Barton: And we understand that. And really, 

again, the point was if we're sitting here asking this 

question about the new imputation method coming 

back with a lot lower bioassay results than what was 

used for missed dose before, it's like, well, I thought 

we used half. 

Well, it's like now we're down around ten percent. If 

we're asking the question, somebody out there 

might be asking the question. So the Board really 

needs to know the mechanisms of what's happening 

for any effect.

So we can move on? 

Mr. Katz: Yeah.

Mr. Barton: All right, moving on to completeness.

Mr. Katz: Can I just, well, before you move on, 

though, just let me just get a sense what, how 

much time do we have left. Is this another half an 

hour?

Mr. Barton: I think this is going to go a lot quicker 

because, based on the responses we got from 

NIOSH, I am not prepared to fully respond on some 

of these issues, especially related to the --

Dr. Taulbee: I think the next few are when we get 

to stratification I think that's a good --

Mr. Katz: Because I just want to know when we 

should take our break, that's all. But that's good.

Mr. Barton: Yeah, I think that this is fine. I guess 

we're going to follow the format of we'll provide our 

finding and then respond.
105

Dr. Taulbee: Sure.

Mr. Barton: Because that seems like, it's been 

working. Okay, so this is moving on to data 

completeness. And one thing we noticed is of course 

several of the coworker models for SRS are based 

on claimant data, not the full logbook data. And 

basically what happened is NIOSH used the first of 

4,000 claims. 

And the reason for that is you have to put a cutoff 

somewhere and then start analyzing the data. You 

can't just keep, you know, refining the numbers 

every time you get new claims. And so the cutoff 

date was, you know, based on when the files were 

transmitted by DOE sometime in the fall of 2001.

Since that time, so we started with 4,000 claims to 

evaluate. Since that time, there have been another 

2,000 clams submitted that could be used to 

augment this coworker model. So essentially it's a 

50% increase. Now, why would you do that? 

Well, notice that for at least uranium and cesium, 

data had to be combined for multiple years because 

you didn't have enough workers to evaluate. So 

that's why we brought this up that you have 

additional claims out there. 

And as it's written in OTIB-81, it's already in 

electronic format. So we -- do you want to modify 

that? That is what it says in OTIB-81, easily 

retrievable electronic format.

Dr. Taulbee: There's easily retrievable and then 

getting through the QA verification and 

completeness testing is two different things. 

Actually, it's the same thing, but getting through 

that step took us a lot longer than we thought. 

Mr. Barton: Okay.

Dr. Taulbee: To clean up the data and part of the 

reason is, as I tried to allude to earlier, not every 

dose reconstruction will go through and do a heavy 
106

QA on internal data that's been coded, because it 

may not be needed for the dose reconstruction. 

Okay. 

So on those particular cases, we go back and we 

look more closely at the data to make sure that it's 

been coded correctly along those lines. Because we 

don't do double blind entry up front. It's a single 

entry by the data entry clerks, it goes to the dose 

reconstructor. 

They're responsible for looking at that individual 

data whenever it goes before they do an intake 

model or anything with that data. They're the ones 

that are responsible for that.

So yes, it's been coded. It has not been massaged 

or looked at by anybody else at that point, and 

that's what takes us a little longer to do. So yes, it 

is readily available, but it's not oh, let's just go do 

it. It's not that.

Mr. Barton: Not that simple, okay.

Dr. Taulbee: No. By the way, going through this, we 

learned that, okay.

Mr. Barton: Well, I mean, and this chart just shows 

what we're plotting here is the monitored claims 

from the -- so the top line is the original dataset. 

And it's basically the percentage of monitored 

claims that you could add if you had added this 

data. 

And obviously once you get to the SEC period, it 

starts climbing up to where those additional data 

that represent a little over, or about 45 percent, 

somewhere below 40 percent of what the available 

internal monitoring you could have for analysis. 

So that was the point of that one. There's more data 

out there. I guess it sounds like it's much more of 

an effort than simply plugging and chugging.

Dr. Taulbee: It is more of an effort. And but the 
107

thing that I want to try and emphasize is if you 

think back to the intake modeling that I did, or not I 

did, Matt Arno is the one who did that work, and 

Liz's team checking it and all. We end up taking 

groups of years.

So yes, it would fill in some of those years where 

we've grouped, but is there a big change within that 

time period? Generally not in these particular 

coworker models. So is adding this additional 2,000 

data points, is that going to move the mean in the 

84th percentile? I don't think so, I don't think it'll 

move it hardly at all.

And so to do this additional --

Mr. Barton: You can't be quite sure, though, right?

Dr. Taulbee: When we have two, three hundred 

data points, I mean.

Mr. Barton: Yeah, part of it is --

Dr. Taulbee: You add another hundred data points, 

unless they're all at one end or the other end, it's 

not going to move much. If it's evenly distributed, 

there's no reason it's going to move.

Mr. Barton: One comment on that is there's a 

similar chart to this one in the report that shows the 

actual employment periods. And obviously these 

later claims are going to be in the SEC period more 

so than in -- or in the post '72 period more so than 

in the earlier. 

So the addition of their data might have more of an 

effect on the actual SEC period. I don't know that, 

but you wouldn't know until you do it.

Dr. Taulbee: Right. I --

Mr. Barton: I guess the other, you know, just kind 

of thinking through this too, you'd also have to 

stratify them, right.

Dr. Taulbee: Yeah.
108

Mr. Barton: So it would be a lot of work. Well, 

anyway, the point was there's a lot more data out 

there to fill in some of these years where we had to 

group, because we didn't have the 30 workers 

recommended in the implementation guide. But the 

implementation guide does also say that if you have 

a reasonably consistent distribution, then it's okay.

Now, this is the completeness of those trivalent 

logbook data. And again, it's an observation. And 

we notice that especially 1982, or 1980-1982, there 

were significantly lower, I believe it was around 70 

percent of the data we have in hand was reflected in 

what, the totals that were put forth by the Health 

Physics Department.

In other words, Health Physics said they put out 100 

bioassays in that year, but yet we only have 70. 

And so this goes to the completeness issue. 

And the observation here is, you know, if you have

these years where it appears you might be missing 

some of the data points, it's sort of important to 

discuss if there was any changes in those specific 

years that would make those operations important 

from the fact that you might be missing data. And 

that's what, that's the observation in here.

We had an update because we thought, and we 

wrote in the report that perhaps you can taper on 

americium source terms would address those years 

and any changes. But from our or my reading, it 

doesn't look like it does that.

Dr. Taulbee: The one thing I'd like to point out is 

that year-by-year comparisons are really difficult to 

try and do, because you've got some samples that 

were collected in one year and they're analyzed in 

the following year.

It's not always together. And if you look over the 

entire time period of like 1963 to 1987, there are 

18,293 samples for americium. Okay, so almost 

18,000 samples, 18,293. Over the same period, 

what they reported in those summaries that you're 
109

looking at is 18,153. 

So over this large period, there's a difference of 140 

samples. So does this really matter?

Mr. Barton: I'm not all that comfortable just 

averaging it over that kind of a period and saying, 

well, overall we had it. Because you want to see the 

temporal changes in it. And if there's a period where 

data might be incomplete around, I mean, I'm 

looking at 70 percent.

Dr. Taulbee: You're saying that, how did they make 

that bioassay summary table? What data did they 

use, do you know?

Mr. Barton: Are you talking about the source 

documents?

Dr. Taulbee: Yeah. What were they counting?

Mr. Barton: I assume the number of bioassays they 

issued.

Dr. Taulbee: See, we don't know, you assume. We 

don't know that either. We don't know if it's the 

number the lab counted, we don't know if it's the 

number the Health Physics Group submitted or 

requested. We don't know what that number is.

Mr. Barton: Okay.

Dr. Taulbee: So to try and say it's definite in this 

year, you can't. You got to look at over a larger 

period of time. Is there a bunch of data missing? 

The answer is no.

Mr. Barton: Well, you have some years in here that 

are 180 percent.

Dr. Taulbee: Exactly, because you don't know what 

that, that source document, that summary, when 

those were counted, okay. So because we have the 

logbook, so we know what the bottle date was. We 

know when the worker left the bioassay sample. 

That's listed in the logbook. 
110

But we don't know how Health Physics or the people 

generating that bioassay summary table, when they 

were counting what samples or what their criteria 

was.

Mr. Barton: So if we can't make the comparison, 

how do we judge completeness? How do we judge if 

we have --

Dr. Taulbee: Look at it over a larger period. Does it 

look like a large number of samples are missing? 

No.

Mr. Barton: Well, you know, the theory that some 

were taken in December and then measured in the 

next year, that would apply to each successive year, 

you know what I mean?

Dr. Taulbee: Maybe, maybe not. Not necessarily.

Co-Chair Clawson: So what are we doing? Are we 

just rolling the dice?

Dr. Taulbee: We have 18,293 americium samples.

Co-Chair Clawson: I understand that, that's 

wonderful. But we also have a responsibility too. 

And this is where our completeness and stuff starts 

to come into a lot of this. I know a lot of this, we're 

doing the best job we can. But there are some areas 

that I think we could do a little better.

Dr. Taulbee: I don't see where we're missing a large 

quantity of the trivalent samples. I just, I'm not 

seeing that.

Mr. Barton: I would add that this, the data --

Dr. Taulbee: I mean, does SC&A have evidence that 

we were missing?

Mr. Barton: Well, we were actually just comparing 

the percentages that were provided in OTIB-81 as 

part of the completeness analysis.

Dr. Taulbee: Okay.
111

Mr. Barton: That data comes right out of it.

Dr. Taulbee: Right, and we're looking at it from, we 

looked at it from a larger perspective of when were 

they counting these samples. And our comparison --

Mr. Calhoun: It's less than one percent.

Dr. Taulbee: Yeah.

Mr. Calhoun: It's less than point one percent.

Mr. Barton: So basically what you're saying is we 

can -- over what, how many years? I mean, 20 or 

30?

Mr. Calhoun: Exactly.

Mr. Barton: But there's fluctuations in there. But 

basically what you're saying is we can't actually use 

this data that's presented in OTIB-81.

Dr. Taulbee: No, I'm saying you could use it. But 

you're saying that there was a large number of data 

missing, and I don't see it. I don't see it missing.

Mr. Barton: Again, let's go back. This observation's -

-

Co-Chair Anderson: Is it assigned to the right year, 

though?

Dr. Taulbee: It may not be assigned to the right 

year. But is it significant?

Co-Chair Anderson: -- retired in '83. Their sample 

may have been analyzed in '85, but it's somebody 

that's no longer there.

Member Ziemer: But does it matter?

Dr. Taulbee: Right, but does it matter? No. I mean, 

because when we're doing the coworker, we're 

actually averaging an intake time period. 

Member Lockey: So if you had 200 samples overall, 

and you're missing, it doesn't matter if you 18,000.
112

Dr. Taulbee: If you're assigning the same dose 

every year.

Member Lockey: That's right.

Co-Chair Anderson: The worker, coworker. But if 

they're only there for one year over your period.

Dr. Taulbee: But we would be assigning that 

coworker model over that time, to that year.

Member Lockey: You're still going to assign the 

level.

Dr. Taulbee: I mean, most of these intakes, I mean, 

we rarely do an intake for one year.

Co-Chair Anderson: I understand, I'm just --

Dr. Taulbee: Averages.

Mr. Barton: And what does this observation actually 

say for a path forward? All we wanted to hear was a 

description of the americium operations. If it's not a 

gap, it's not a gap. 

But if it is a gap, it would be nice to know what was 

going on at the site. And if it was no different, then 

the exposure potential's no different. It doesn't 

matter if, you know, that 70 percent is 70 percent 

or 80 percent.

Dr. Taulbee: Where was that, by the way?

Member Ziemer: Well, I think you're asking them to 

clarify if it looks like a gap and is not a gap, why 

isn't it a gap? 

Mr. Barton: Or what were the operations going on in 

those years that can make it, you know, that it 

really doesn't matter. Because the surrounding 

years are fine.

(Off-the-record comments.)

Dr. Taulbee: And you're looking at the Table 4-1?
113

Mr. Barton: It doesn't say here, but I --

Dr. Taulbee: It said 1985.

Mr. Barton: And that is years from 1963-1987.

Dr. Taulbee: In 1985, the number of, the summary 

says they only did 244 samples, and the logbooks 

we have 435 samples. So we still have a large 

quantity of data. That's not a small percentage 

there being monitored. To our knowledge, nothing 

changed from an operations standpoint in those 

time periods.

Mr. Barton: Yeah.

Dr. Taulbee: I can say I've seen from the logbooks 

where there will be time periods where equipment 

went down and they held onto samples for three, 

four, five months, and then pushed through a whole 

bunch. And so is that the case here? I don't know.

Mr. Katz: One sec, one sec. There's someone on the 

line who's not muted. Can you press *6 to mute 

your phone, or a mute button if you have that? 

We're hearing you, and that means other people on 

the line trying to listen are hearing you, too.

Mr. Barton: And again, this is just to add 

information for where if there's a perception of a 

gap, can we do something to obligate that --

Co-Chair Anderson: You could look at that peak in 

'85. Pull some of those and see when were they 

collected versus when were they analyzed. Because 

they may have had a problem, and then that would 

explain why there appears to be a gap. That they 

did a catch-up period because inspectors were 

coming in.

Dr. Taulbee: Are you wanting, so you're wanting to 

us --

Co-Chair Anderson: Well, I'm just saying you 

wouldn't have to do much, but that would explain it. 

I mean, three months isn't going to get you from 
114

'83 to '85. We just said there were 240 extras. 

Something going on in '85 that was a catch-up or 

the samples came from somebody else.

Member Lockey: Well how does '85 figure?

Co-Chair Anderson: Yeah, I mean --

(Simultaneous speaking.)

Co-Chair Anderson: Clearly they did the analyses, 

so --

(Simultaneous speaking.)

Member Ziemer: What determined the 100 percent 

value? Is that where you have one-for-one samples 

collected?

Dr. Taulbee: Yeah.

Member Ziemer: Versus samples --

Dr. Taulbee: Versus what they reported. But see, 

the bioassay summaries, every month at Savannah 

River, they would go through and report from the 

bioassay lab how many samples they analyzed. 

But we're not sure, we're not 100 percent sure that 

it's the number of samples analyzed, the number 

that was requested. It's all under the Health Physics 

Department. And so that's what that first column is.

Member Ziemer: If they did split samples, does that 

count as two?

Dr. Taulbee: We don't know, or were they recounts 

in addition to?

Member Ziemer: We don't know that.

Dr. Taulbee: We don't know that. So I mean, the 

logbook values are the real values, because that's 

what we have. I mean, we collected all of those 

logbooks. So we have those, those are complete. 

It's more of how did they report those summaries, 

and --
115

Member Lockey: That was a waste of time.

Dr. Taulbee: Yeah.

Ms. Brackett: The next slide shows the graph, right? 

So.

Member Lockey: That's what I'm referring to.

Ms. Brackett: And that's somewhat misleading when 

you first look at it, because the bottom line is 60 

percent, not zero. So the lowest value is 70 percent, 

it's not ten percent. It's not what it would appear to 

be.

Dr. Taulbee: And so yeah. And so my guess is is 

that there is, and this is a guess, 1980, 1982 type 

of timeframe, those larger time periods where 

there's less samples, got caught up in '83, probably 

the 80s into '83 or '84. And it just carried over, and 

they got them all completed there in 1985.

Member Ziemer: 1963 you don't know what's --

(Simultaneous speaking.)

Dr. Taulbee: Sixty-three is 19 samples.

Member Ziemer: You're looking at percentages.

Dr. Taulbee: Exactly. So you know, 19 samples 

there, and then 1985 is 435.

Ms. Brackett: And I got a note that says we know 

that they got behind in '82 and '83, and that in '84 

and '85, they caught up. So that's why.

Mr. Barton: That's exactly the information that --

Co-Chair Anderson: Yeah, that's, we're looking at 

nothing.

Dr. Taulbee: So based upon that, can we close this 

one? We haven't tried to close any observations or 

findings or anything yet.

Member Ziemer: No, even these, it says 90-some 
116

percent.

Member Lockey: Where's that note?

Ms. Brackett: This is just I got a message from 

someone, so I'm asking if we have documentation. I 

don't know for sure what, how they know that, so.

Member Lockey: There's documentation that backs 

it up.

Co-Chair Anderson: That's exactly what we're 

looking for.

Member Lockey: We can just look at a couple of the 

lab's reports, and then we'll say whether that 

collection, when it was analyzed. That's probably 

what somebody did.

Member Ziemer: Well, she just said they were 

behind in '83 and four. And both of those, well.

Mr. Barton: The next item is stratification, so.

Mr. Katz: Okay, that's good because I sure could 

use a break. Are we okay at this point? We're 

getting to stratification next. Are we okay taking a 

break now lunch?

Consolidate your thinking about what's been said so 

far. And then for lunch, this place is super slow, this 

place here. So, and then if you're going to go 

somewhere else, it's going to take time to get there 

and get back. But so I think we need an hour.

Mr. Calhoun: So Ted brought lunch for all of us is 

what he's talking --

Mr. Katz: I did, I brought lunch for everyone.

(Simultaneous speaking.)

Mr. Katz: So it's about 12:20 now, so let's break for 

an hour. And folks on the phone, if we're not on 

right at an hour, just be patient, we'll be back.

Dr. Lipsztein: May I speak one second before we 
117

close, because I can't be here after lunch. I was 

asked to do a memo on the americium uncertainties 

and measurements. But we did it already. There is a 

memo from SC&A from February 2014, so I don't 

see why I should do something again, the same 

thing.

Dr. Taulbee: This is when you asked for a more 

detailed analysis of why you felt that the 

americium?

Dr. Lipsztein: Yeah, but we did this in February 

2004. 

So please look at it, and then if you still want 

something that I do, I'll do it. Okay?

Mr. Katz: Okay, Bob, can you shoot that to me so I 

can, or shoot it directly to actually Jim Lockey. And 

copy, just copy everyone else. Thank you for 

following up on that, Joyce.

And okay, so let's break. And we'll, back in at 

approximately 1:20.

(Whereupon, the above-entitled matter went off the 

record at 12:19 p.m. and resumed at 1:29 p.m.) 

Mr. Katz: All right, off we go. 

Mr. Barton: All right, we're picking up with the sort 

of last facet here, which is coworker stratification. 

And then there's going to be one last slide about 

quality assurance. 

Okay, so we're going to go through, like I said, the 

rest of the coworker stratification slides from 

SC&A's review of OTIB-81. And then there's a very 

brief slide about the quality assurance review that 

NIOSH did that we looked at. And then we'll have 

the comment/resolution on the stratification. 

So start off with Observation 4. And that's that 

OTIB-81 does now provide a statistical comparison 

of the two stratified groups as prescribed in the 

Coworker Implementation Guide. The various 
118

coworker models were stratified based on the a 

priori assumption that the exposure potential 

between construction trade workers and nonconstruction trade workers was different. And as it 

sort of says in the observation, this is in 

contradiction to the coworker criteria where it says 

once the dataset has been stratified based on job 

category, a statistical analysis should be conducted 

to determine if the two datasets should be modeled 

separately. 

The next observation, and this is another one that's 

sort of just pointing towards later discussions at 

RPRT-92, and it's that SC&A believes the 

quantitative assessment of available job plans, 

rather than the qualitative basis that was in OTIB81, is appropriate to determine that prime 

contractor and subcontractor construction trade 

workers are part of the same exposure strata. Such 

an assessment has been performed by NIOSH and a 

report of their findings has recently been issued. 

And it says this issue was discussed in RPRT-92. 

And also the issue of stratification comparison 

between prime contractors and subcontractors is 

contained in a separate White Paper, "Savannah 

River Site Plutonium Construction Trade Worker 

Stratification Refinement." SC&A has subsequently 

reviewed both documents. 

Finding 5, this is specific to the job title "machinist". 

In OTIB-81, a machinist is listed as a nonconstruction trade worker. We noticed that its 

classification in at least one other document, which 

is the Program Evaluation Report 14, that job title of 

"machinist" was classified as a construction trade 

worker. So sort of a -- somewhat of a discrepancy 

there that we need to sort out. 

And Finding 6, this is where we kind of sat down 

and said what can we do as far as, you know, some 

sort of analysis to sort of get our heads around, you 

know, not only how difficult it is to determine who's 

a construction trade worker and non-construction 

trade worker -- and what we're really talking about 
119

is routinely exposed versus non-routinely exposed. 

SC&A took two alternative sources and compared 

them to the designations provided in the 

spreadsheet files that NIOSH provided us, along 

with the White Paper. 

Now, this was a targeted review going into some 

detail in the actual review paper about what exactly 

we're looking for. And the result of that was that we 

found that 9 percent of the entries appeared to be 

in conflict. And when I say it was a focused or 

targeted evaluation or sampling -- and sampling 

may not be the correct word here -- but we looked 

specifically at what I would call the gray area job 

titles, where it's not obvious whether the exposure 

potential would be routine or non-routine. You 

know, if you have a chemical operator who is an 

outside routinely exposed worker, non-construction 

trade worker. You know, welder is a construction 

trade worker. Now you have these sort of three 

categories in the middle up here on this slide. And 

it's really the line managers and the foremen. 

Because you could be a foreman who's in his office 

all day and walks through the job site just to check 

on his crew. It could be the one up in the gallery 

watching. Or it could be a foreman of a small crew 

actually down doing hands-on work. That's the sort 

of grey area I'm talking about. 

The assistants and helpers were also were also 

classified in OTIB-81 as non-construction workers. 

And if you think about the title of an assistant or a 

helper, you could be a lab assistant or something 

along that lines, or you could be a pipefitter 

assistant. Same thing with a helper. You know, you 

could be a chemical operator helper or you could be 

a carpenter helper. And so it's tough when you have 

these gray areas, which bin do you really put them 

in? Because if we're going to assume that there's 

different exposure potential, if they're in the wrong 

group, this could sort of muddy the results.

And the last one is operator. Now, operator we 

really generally consider with the chemical operator 
120

category. They're out there doing routine work in 

production. Specific to Savannah River, what we 

found is that a lot of times these operators were 

actually classified as general service operators. 

Which a lot of times we interpret it as more of a sort 

of entry level position that could mean really 

anything. It could be a janitor, which would 

probably be considered a non-construction worker. 

But it also be a truck driver, packaging and driving 

waste to the burial grounds. It also could be just a 

laborer category, which is generally considered a 

construction worker. 

So when we took a look at those, we found that was 

actually less than 10 percent, surprisingly, that in 

those gray areas we found evidence that maybe 

they were in the wrong bin, so to speak. Now, 

again, the sort of question that I try to focus in on 

is, okay, so what? Is there any sort of path forward 

on this? And that leads us to Observation 6. 

And Observation 6 reads, "SC&A acknowledges that 

there are inherent difficulties in correctly associating 

individual workers with the correct CTW/non-CTW 

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 

borderline job titles" -- and, again, we're talking 

about assistants, helpers, you know, the general 

service operators, and positions like foreman where 

they could be doing either type of exposure. Those 

are sort of the gray areas we're talking about. 

What if -- and I feel like this is not that difficult --

what if we just pulled them out, re-ran the R code, 

and let's take a look at the two distributions again 

and see what effect it actually had on it. Because if 

these gray area jobs aren't changing anything, then 

it's not an issue. However, if we suddenly see -- we 

take these gray area jobs out and suddenly there's 

a significant difference, we may have to perform a 

more rigorous approach to classifying these gray 

area job titles. 
121

Dr. Taulbee: Can I ask a question? 

Mr. Barton: Sure.

Dr. Taulbee: What would you consider a significant 

difference in the sensitivity analysis? What is 

significant? 

Mr. Barton: Well, that would certainly have to be 

worked out. I mean, I'm not going to come up with 

the entire plan. That would have to be -- as you 

said in the response, it would have to be worked out 

between both parties, what are the results that 

we're looking for and what they mean. 

Dr. Taulbee: Well, yeah, and that's where I'm 

getting at with this. You know, this is something 

that can be done. It's going to take significant time 

to do so, to --

Mr. Barton: Just to pull those jobs --

Dr. Taulbee: Well, to work out the details of how 

you're going to do this sensitivity analysis. And from 

our position, especially along -- the goal here is, do 

we have a significant misclassification issue? Yes or 

no? I mean, that's the goal. And when we looked at 

our -- we did a misclassification evaluation through

our QA analysis. We looked at the datasets. And, 

well, may I share? 

Mr. Barton: Sure. 

Dr. Taulbee: Okay. 

Mr. Katz: Do you need the cable? 

Dr. Taulbee: Yeah. By the way if I said "I", it's not I, 

it's we. 

Mr. Katz: No, you said we. You said we. 

(Laughter.)

Dr. Taulbee: Okay, all right. Let's see here. Okay, 

when we did misclassification evaluation, we did 
122

probability sampling to quantify the misclassification 

rate of the coworker models. And for the SRS in 

vivo dataset, in vitro dataset, the neptunium log 

books, and the tritium. Those are the dataset sizes, 

the number of fields checked, and the number of 

errors, along with classification error rate with 95th 

percent confidence internal. And all of these are less 

than 5 percent, which was our acceptance criteria. 

So, we looked at a targeted group. And you came 

up with a central point estimate of 9 percent. And 

your targeted group was the gray area. We looked 

at the entire dataset as to misclassification and 

we're less than 5 percent. Is this something that we 

should be doing a sensitivity analysis further on? I 

mean, we don't really see the value of doing this. 

You know, none of the other -- I mean, SC&A 

presented the general service operators, 

supervisors, foremens as examples where they 

could be on either side. And we agree that that's 

there. None of these are listed in OCAS-PER-14. We 

looked at CPWR document, Bingham as well for the 

Oak Ridge area. They don't list, like, machinists in 

that particular category. 

We use this information to develop our master 

occupational table for all operators as to how we 

categorize the people. And we went through and we 

reviewed the seven examples presented by SC&A in 

Table 17 and we found no discrepancies in the 

original CTW versus non-CTW designation.

So, kind of our questions to you is, because of this, 

I mean, is this really a finding or is this an 

observation from this standpoint? I don't know what 

the -- what's the conclusion that SC&A has here 

that our coworker model is invalid or not 

appropriate? 

Mr. Barton: Well, we performed the analysis and 

this is what we found. I think there's a lot of 

attention being put on the fact that I put 9 percent 

in the finding. And it wasn't meant to address the 

quality assurance criteria. In fact, I never 
123

mentioned the quality assurance criteria. You know, 

when we're going to go and test to see if there are 

any problem areas, we're not going to recreate the 

wheel and do the same thing you guys did. We're 

going to go and look at those job titles and see, oh, 

yeah, some of them fall into Column A and some of 

them fall into Column B. 

Dr. Taulbee: Okay. 

Mr. Barton: We get to that point and then we say, 

okay, what's the conclusion? And that's Observation 

6 where we're saying, well, something we could do 

that I thought would be pretty simple is just pull out 

those job titles and then run the code and then we'll 

see what the difference is. And it may very likely 

turn out that there are no differences. And then, in 

which case, we're done. You know? 

Dr. Taulbee: And like I said, this can be done. I 

mean, it's not that it's impossible to do. It's just, 

you know, we don't really see the value that it's 

added. We already know from our evaluation of the 

job titles we have less than 5 percent 

misclassification rate. I mean, do you want more 

details than that? I mean --

Co-Chair Anderson: Well, it sort of depends on your 

approaching it as a population.

Dr. Taulbee: Yes. 

Co-Chair Anderson: The program deals with 

individuals. So if you happen to be an individual in 

one of these groups -- if you're in one or the other 

for a coworker model, it might make a difference. 

So I think that's why the focus on gray area things 

from an individual standpoint, as you want to know 

which dataset was used in your individual case. 

And, I mean, that would be the way I would look at 

it. Now, overall, yes, you want to do that. And datawise, since most of the workers not in a gray area, 

you can say, well, we're just -- good luck to you 

guys. You know? 
124

Dr. Taulbee: No, no, no, that's not what we're 

saying. I believe what you're talking about, Dr. 

Anderson, is, from an individual dose reconstruction 

we're assigning, you absolutely need to go and look 

as to which category they are, whether they are 

construction trades or non-construction trades in 

this particular case. But I think in the development 

of the coworker model we know we have less than a 

5 percent misclassification rate amongst these gray 

areas. 

So whether they're in one versus the other does 

not really change those two intake models. And I 

don't believe that it does. Absolutely, what you're 

talking about, when we go to apply it, yes, that 

matters a great deal. And that's where I think the 

Dose Reconstruction Subcommittee review would 

catch that, was it being applied properly? But in the 

development of the coworker, I don't see where this 

is going to change the two groups significantly. 

Does that make sense? 

Co-Chair Anderson: I was just thinking you -- you're 

saying that an individual then can be categorized 

differently in different datasets. So you've made a 

determination that this person -- this job title is 

amiss. And when your risk assessor goes to do it, 

my assumption is they would see that this person's 

already been classified as XYZ. 

(Simultaneous speaking.)

Mr. Katz: No, they wouldn't see that. They wouldn't 

see that. 

Co-Chair Anderson: Okay. 

Member Lockey: Tim, you said something just now 

and I want to understand. You said less than --

when you did your analysis, there was less than 5 

percent misclassification in gray area. Is that what 

you said? 

Dr. Taulbee: No, not in the gray area. Overall. 
125

Member Lockey: You said gray area, I just wanted 

to be --

Dr. Taulbee: I apologize, no. 

Co-Chair Anderson: Total.

Member Lockey: Just to clarify because I think --

Dr. Taulbee: Yes. This is our actual misclassification 

rates amongst -- for the datasets that you've got 

there. So, I mean, you can see that they passed 

each of them, the 95th percentile, was less than 5 

percent. And I mean, SC&A can go through and 

they can find, there are errors. I mean, there are 

some that when we developed the model, they're 

not perfect. Now, when we went through and did 

this, we corrected those errors, obviously. 

Member Lockey: If you took the gray areas out of 

this, this is probably going to drop to less than 1 

percent, I suspect. 

Dr. Taulbee: It's going to drop, I would think so, 

yes. 

Co-Chair Anderson: But, again, there's a probability 

sample of 28,000. You looked at 800.

Dr. Taulbee: Yes. 

Co-Chair Anderson: So you're --

Dr. Taulbee: But randomly looked at 800. 

Co-Chair Anderson: Yeah. 

Dr. Taulbee: Randomly. 

Co-Chair Anderson: Okay. 

Mr. Barton: I guess one of the things that I wanted 

to check on this during lunch -- at least in the in 

vitro dataset -- and I can point you to exactly where 

there is. These are in the files that were provided to 

SC&A. And the name of the file is "SRS Combined In 

Vitro Data 91818 with construction tradeworkers." 
126

And it has a column in it that says "OTIB-81, Rev 4, 

construction trade worker." And then it has the job 

title. 

Okay, so we're looking at these two columns here. 

And if you look at -- we're looking at assistant, and 

this is the classification, assistant. No, no, no, no, 

no, no, no, no. I mean, is this --

Member Ziemer: What does the "no" mean in this 

case? 

Mr. Barton: Not a construction trades worker. 

Member Ziemer: Got it. 

Dr. Taulbee: Some of them could be construction 

trades in there. 

Mr. Barton: And then the same thing for helper and 

then foreman. They're all classified as non.

Dr. Taulbee: When you look at the dataset as a 

whole, less than 5 percent is misclassified. 

Mr. Barton: I understand what you're saying. And 

when I did my analysis, I wasn't trying to recreate 

that quality assurance. Again, I wanted -- it is 

focused. It is targeted. And when we saw something 

like that, that's what prompted us to go in and look 

at those files and say, well, you know, how many --

are these really all just non-construction workers? 

And I can go down to helper --

Member Ziemer: So where did you put those, then?

Co-Chair Anderson: They all went into non.

Mr. Barton: Non-construction workers. See, now 

we're on boilermakers --

(Simultaneous speaking.)

Dr. Taulbee: Carpenters are all yes. 

Mr. Barton: No, I understand. I understand. I'm just 

saying this is why we took a closer look at those, 
127

because all of the assistants and all of the helpers 

were classified as non in this in vitro database. And 

had the most recent timestamp on there. 

Dr. Taulbee: Okay. So, again, I say what is the 

conclusion of this? You found there's approximately 

10 percent, 9 percent, whatever, which you would 

consider in the wrong category. 

Mr. Barton: The solution I put forth, or potential 

solution, was to take these out and re-run the R 

code and take a look. That was all that -- that was 

Observation 6. 

Member Ziemer: And do the distribution without 

these --

Mr. Barton: Exactly. 

Member Ziemer: -- and see if it made any difference 

either way. 

Mr. Barton: Yes, either way. 

Member Lockey: Do you think that's worth it with 

their sensitivity analysis at 3 percent or less? 

Mr. Barton: Well, I guess the point is, I figured it 

wasn't going to take a lot of work to do. But it 

seems the answers that I keep getting is that it's 

going to take a whole lot more than I thought. 

Dr. Taulbee: Well, it's going to take more work than 

you thought, I think. But, I mean, I went through 

the steps here earlier. What this would do would be 

we'd go back and we'd have to redo all of the 

TWOPOS values. We could separate them into the 

two groups, or take them out rather. And then 

you're going through and you're analyzing the 

TWOPOS values, doing those fits again. Then you're 

coming up with the 50th and 84th percentile. You're 

redoing all of the intake calculations. 

Mr. Barton: I don't think you'd have to do the intake 

calculations. 
128

Dr. Taulbee: Why not? 

Mr. Barton: Because you can just take a look at the 

relative magnitude of the annual TWOPOS values.

Dr. Taulbee: But that's not what we use for the final 

model, for the final coworker model -- coexposure 

model. We're using the intake values. It all gets 

rolled in together at the end here. And that's where 

I'm asking about, you know, what are your 

parameters for the sensitivity? What is a large 

difference? Because you're going to get different. I 

mean, but how much is too much at that point? 

Co-Chair Anderson: Well, you had said 5 percent. 

Dr. Taulbee: Well, I had said 5 percent -- our 

misclassification rate is less than 5 percent. 

Co-Chair Anderson: Yeah, but that's your target. It 

could've have been more than 5 percent --

Dr. Taulbee: If it's more than 5 percent --

(Simultaneous speaking.)

Co-Chair Anderson: -- and then you would've 

stratified it and looked to see where the excess was. 

And the excess is in --

Dr. Taulbee: Sure. 

Co-Chair Anderson: -- 9 percent of this group, 

calling them all non.

Dr. Taulbee: If it had been more than 5 percent, we 

would have done more investigation to try and find 

out what was causing it and where it was. 

Absolutely, we would have done that. 

Dr. Chalmers: Well, and even if we would have 

noticed a systematic pattern in the errors we found, 

we would have tried to do something. 

Dr. Taulbee: You're right, because we did do that on 

some of them. 
129

Dr. Chalmers: Yeah. 

Dr. Taulbee: We found the systematic problem. 

Member Lockey: So, a priori, you had a 5 percent 

figure set? 

Dr. Taulbee: Yes. 

Member Lockey: Okay, so, a priori, use that 5 

percent figure. It's found 3 percent or less, so you 

just move one. That's basically --

Dr. Taulbee: That's what we did, yes. Member 

Lockey: So we need -- I guess from you we need --

Mr. Barton: We keep talking about the quality 

assurance criteria and the 5 percent. What I'm 

talking about is the effect on the actual distribution 

of bioassay values. When you take these out, how is 

it different? Is it even different? You know, it's very 

likely, because it's a gray area and some should be 

construction trade workers and some shouldn't be, 

it might just wash itself out. 

Member Ziemer: Well, is that the equivalent of 

mislabeling, in a sense?

(simultaneous speaking)

Dr. Taulbee: That's my interpretation, but maybe 

it's not. I don't know. 

Member Ziemer: Is the 5 percent --

Dr. Taulbee: Our 5 percent is, were they 

categorized properly? And our standard from that 

standpoint in going back is, you know, you're 

looking at a bioassay table here. We go through and 

we pull that particular worker --

Member Ziemer: If you pulled one of these, would it 

show up as misclassified? 

Dr. Taulbee: Yes, and we have some that do. 

Member Ziemer: Some do and some don't. 
130

Dr. Taulbee: Right. Most of them -- 95 -- well, less 

than 5, more like 97 percent are correct. And we 

looked at 847 of those. So, yes, maybe some of the 

gray areas can go either way. 

Co-Chair Anderson: What was the total count for 

the gray areas? 

Dr. Taulbee: How many did you look at? 

Mr. Barton: I think it was 14,000, somewhere 

around there, data points. 

Dr. Taulbee: You had 14,000 gray area data points? 

Mr. Barton: That I evaluated -- they weren't part of 

that 9 percent, but the total was about 14,000. 

Dr. Taulbee: Okay, what was the part -- what was 

in for your 9 percent figure? 

Mr. Barton: Nine percent of that. Hold on. 

Co-Chair Anderson: Because that's kind of getting 

to, if you're in a worker group title class that's 

proportionally small, but they are disproportionally 

higher miscalculation rate, it will be across-theboard missed when they've got 28,000 and there's 

all these different worker groups. So your sampling 

frame -- I mean, not that it -- I'm just saying --

Member Ziemer: So you're not saying the gray area 

is 9 percent. You're saying --

Mr. Barton: No, when I looked specifically at those 

gray area jobs. And you can see up here, the first 

one is conflict with both NOCTS and employment 

history, conflict with employment history only, 

conflict with NOCTS only, conflict with both. And the 

adjusted versus the unadjusted. Unadjusted counts 

each conflict as its own thing. The adjusted takes 

into account that you only have one conflict as 

actually two of them agree, one of them doesn't. 

Because they have three independent sources. 

OTIB-81 was one source. Then there was the 

NOCTS files, which can be the CATI, it can be DOL -
131

- the whole file. And then there's the work history 

cards, which actually have a lot of temporal 

information.

I guess the way we approached it is, how we do 

something to take another look at the issue of 

stratification? Now, I don't think 9 percent is a huge 

issue. But the question was, if it's easy to do, is it 

worth doing? Now, it may not make a difference. It 

may make a difference, in which case maybe we 

have to take a harder look at those classifications 

for those specific workers because those are the 

toughest one to make a call. 

Co-Chair Anderson: I mean, the assumption would 

be, on the other one, misclassification is a random 

event. 

Dr. Taulbee: Right. 

Co-Chair Anderson: This would suggest, given these 

-- it's not particularly random unless you were to 

look at it and -- it's almost nine times the rate of 

your overall one. 

Dr. Taulbee: Well, it's nine times the rate, but it's 

still pretty low. And that's the --

Co-Chair Anderson: Low in the eye of the beholder. 

Dr. Taulbee: But the application of it, I don't think 

that's a problem. I mean, I think we look at enough 

details when we go to apply this coworker model. 

Mr. Katz: Hold on a second. Excuse me. Someone 

on the line, is there a line open? I can hear talking. 

Can you mute your phone? Press *6 to mute your 

phone if you don't have a mute button. 

Go ahead. 

Dr. Taulbee: But, I mean, from this standpoint, 

we're looking at population-type data to develop the 

coworker model. Okay? So, I mean, we can look at 

this. It's going to take a lot of time -- not a lot. I 

mean, at least six months, I mean, if you think 
132

about it, at least. Mr. Barton: I was thinking a few 

days. Well, you just go through and you delete -- I 

mean, if we're going to do full intake modeling, I 

understand that. I was thinking a more 

rudimentary, just calculate the TWOPOS with those 

groups not included.

Dr. Taulbee: No. No, we'd have to --

Mr. Barton: Okay, it sounded like we had an R code 

that had kind of streamlined the process. So that's 

sort of where I had gotten that idea from. But if it's 

six months then certainly I'd --

(simultaneous speaking)

Dr. Taulbee: -- and get it reviewed and get it to you 

all for you guys to comment on. I mean, we're 

looking at at least something like that for the 

sensitivity analysis. And we've got to agree on the 

parameters first. How different is too different? And 

is this worth it? 

Member Ziemer: I'm still confused. Maybe not a 

surprise. Bob, on your chart where you listed all the 

aides and there were non --

Mr. Barton: This right here? 

Member Ziemer: Yeah, if you scroll up to the no's. 

The assistants, let's say, for example, you're not 

saying that that's a misclassification. 

Mr. Barton: This isn't my spreadsheet. This is 

provided by NIOSH. 

Member Ziemer: Yeah. But you wouldn't say that's 

a misclassification because it says no, necessarily. 

Co-Chair Anderson: No, nine percent of them. 

Member Ziemer: Is that what we're talking about? 

(simultaneous speaking)

Member Ziemer: Regardless of where it is. 
133

Co-Chair Anderson: Well, I mean, it's a systematic -

- I mean, you've got to do that. I mean, you could 

use your random assignment like you do with the --

but that's not what you want to do. 

(Simultaneous speaking.)

Member Ziemer: So presumably 9 percent of these 

no's might be misclassified. And 9 percent of 

everything else might be. So that's the 9 percent. 

Right? 

(Simultaneous speaking,.)

Mr. Katz: Focus on those gray job titles.

Member Ziemer: I just want to make sure we're not 

saying these are all misclassified. 

Dr. Taulbee: No. I mean if you go down -- what is 

this dataset? This is the in vivo one? 

Mr. Barton: That's the in vitro model. 

Dr. Taulbee: In vitro? How many -- This has got like 

200,000 --

Mr. Barton: I think it's 100,000. I don't know why 

it's not filling the full screen. Yeah, just over 

100,000. 

Dr. Taulbee: Okay, so 100,000 data points in there. 

And there are a few job categories that SC&A has 

pointed out that, when they went to look at them, 

they found that nine out of ten are okay and one of 

them may not be, may not have been classified 

properly, of these job categories that are in the gray 

area. And what we did was we looked at the whole 

dataset, all of them, and came up with what is that 

misclassification rate across all 100,000.

Member Lockey: How many are in the gray area? 

Mr. Barton: I don't know that offhand. 

Member Lockey: How many in total? 
134

Dr. Taulbee: 100,000.

Member Lockey: 100,000 total? 

Dr. Taulbee: Yes. 

Member Lockey: And do you have any idea how 

many are in the gray area? 

Mr. Barton: Well, I looked at about 14,000.

Co-Chair Anderson: Was it 13,000 and something? 

Mr. Barton: Well, it's 13,000 was the one with no 

conflicts. But the total I looked at was a little closer 

to 14,000 or 15,000. So, say 15 percent just for the 

sake of argument. 

Member Lockey: So 15,000? 

Mr. Barton: Yeah. 

Member Lockey: So 15,000 in gray area, total 

100,000, 9 percent and 15,000 is -- 1,500 over 

100,000?

Mr. Barton: Yeah. 

Member Lockey: That's 1.5 percent. 

Co-Chair Anderson: Of the overall group, yeah. 

Member Lockey: Yeah, I don't think it's worth --

Mr. Barton: Again, what I wanted to do was present 

some options --

(simultaneous speaking)

Member Lockey: Well, if it's going to take six 

months --

Mr. Barton: I agree with that. 

Member Lockey: I think the chance of you getting 

something that's going to be significant is remote in 

comparison. 
135

Mr. Barton: And I would add that the ones I wrote 

there is not all the workers that fell into those three 

gray categories. 

Member Lockey: I understand. Well, I think you did 

the right thing. I mean, when we get job histories 

and we get -- we have gray areas. And it's hard for 

us sometimes to classify them into one group or 

another and it's a struggle. But, yeah, since there 

are 100,000 people and this is 15,000, 9 percent of 

that -- that's pretty good for gray area data. 

Co-Chair Clawson: Why are we using NOCTS on this 

again, NOCTS data? Is there a comparison between 

or is this -- I'm just trying to understand why we're 

using this. 

Dr. Taulbee: NOCTS data, primarily because it's the 

most readily available dataset we have that's 

electronic now. We do have all of the plutonium 

data from the site in log books. It's hard copy. In 

order to use it all, we would have to code it all. And 

you're looking at multi-year project. We rely on 

OTIB-75 where we had compared electronic 

datasets to the NOCTS dataset. And we found that 

there was no statistical difference between the two. 

And so we use that methodology to use the NOCTS 

data as a random sampling, basically, of the total 

population that's out there. That's our basis. 

Co-Chair Clawson: Okay. Because I know we're just 

-- this is just dealing with plutonium. Correct? 

Dr. Taulbee: No. 

Mr. Barton: Plutonium, uranium, fission products. 

Everything except for trivalents, thorium, and 

neptunium.

Dr. Taulbee: The trivalents and the neptunium --

well, the trivalents we coded all of that data several 

years ago because the NOCTS data was limited. So 

we went to the log books and coded all 18,000 

exotic radionuclides -- the trivalents, all 18,000 

americium results. 
136

Co-Chair Clawson: Okay, thank you. 

Mr. Barton: So, that was the last on stratification. It 

sounds like it's way too much of a bother to yield a 

meaningful result. 

Dr. Taulbee: Can we consider that observation, 

then, closed? 

Mr. Katz: That's up to the Work Group. 

Dr. Taulbee: Yeah, that's what I'm asking the Work 

Group, the Board. 

Member Lockey: My perception is yes, that's fine. 

Jim Lockey.

Co-Chair Clawson: I do. With one caveat, though, 

because I'm looking at this from another standpoint, 

from the dose reconstruction. Because I hear so 

many times, well, their classification was this, so 

they wouldn't have that type of a radioactive dose. 

We use classifications for a lot of different things. 

Now, I'm not saying that in this sense, it is that 

way. But you guys do use classification and their job 

class in dose reconstruction. 

Dr. Taulbee: Yes, sir. We do.

Co-Chair Clawson: That's where I have problems 

with this. I'm not saying with what Bob has been 

saying. I just want to go on record. We use 

classification for a lot in there. And so we better 

make sure that it is the best we can. I do agree with 

Dr. Lockey, and if the other Board Members, that 

with this one, it is not. But this is one of the 

positions where I want to make sure we realize that 

when we use a classification like that, it does affect 

people. And right down to the level of their doses. 

Member Lockey: Do we need to hear from the other 

--

Mr. Katz: Yeah, we do need to hear from the other 

Work Group Members.
137

Member Ziemer: Is this an SRS vote or is this --

Co-Chair Clawson: This is an SRS issue, I believe. 

Dr. Taulbee: Well, this is a coworker. 

Co-Chair Clawson: Okay. 

Dr. Taulbee: This is under the OTIB-81.

Member Beach: Well, technically, the issue doesn't 

go away. It's the second suggestion, or SC&A's 

suggestion that goes away. But the 

acknowledgment of the classification is still an issue. 

Correct? 

It's kind of a two-part observation. I mean it's an 

observation, of course. But it's a two-part. So, yeah, 

the second part goes away because you're not going 

to do it. It would be too much. But the issue still 

stays. 

Member Ziemer: We're talking about the SRS 

stratification, though, right?

Mr. Katz: So, just to clarify, when you close an 

observation, what you're saying is we're done 

dealing with this, there's nothing more to do with 

this. The alternative is to have a path forward to do 

some more work. 

Member Beach: Right, but --

Mr. Katz: That's all we're talking about. 

Member Beach: I understand that part, but this is 

kind of a two-part deal where it doesn't go away, 

just any work to do anything about it. That part 

goes away. So what do we do with the beginning of 

the issue is not classifying workers correctly? Or 

what does that mean going forward? Is it still an 

issue that some workers may be classified 

incorrectly, which would therefore create a problem 

for them? 

Dr. Taulbee: Okay, it's not a problem for them 
138

individually because that's handled under dose 

reconstructions. 

Member Beach: Okay. 

Dr. Taulbee: Here the misclassification is within the 

total population. And in this particular case, as I've 

got up here, this is Finding 6. There's Finding 6, 

which to follow the example that my colleague, 

Megan, used a few weeks ago on Lawrence Berkeley 

of related issues. This is related to Finding 5, 

Observations 4, 5, and 6. So, Observation 6 is the 

recommendation to do a further sensitivity analysis.

Member Beach: Right.

Dr. Taulbee: Okay. So that's how these are tied 

together. But under this particular issue of where 

they looked at the targeted group, this is actually a 

finding that was listed. And this is along the lines of 

the 9.14 percent of this targeted group. And what 

we did for our analysis was a probability sampling, 

not a targeted sampling. And we looked at it and we 

came up with less than 5 percent. 

So we don't feel that this is a significant issue with 

regards to these datasets, that our classification 

rate is sufficiently low as to our coworker models 

are valid. 

Mr. Katz: So if you're closing it, what you're saying 

is -- if you're closing it, you're saying we agree 

there may be some differences, there may be some 

misclassification, but it doesn't have enough of a 

bearing on the coworker model to destroy the 

model, in effect, to make that model invalid. 

And so you can just close it. And the observation 

would be, if you didn't want to close it and you 

wanted them to do more work before you close it, 

then you'd go down that route. And six months later 

you'd get that information to consider in whether 

you close it or not. So that's sort of what's on the 

table. 
139

Member Beach: Okay. 

Dr. Taulbee: Thank you. 

Member Ziemer: Well, the specific finding here is 

Savannah River. I know the general model applies 

to everybody, but this observation is very specific. 

Mr. Barton: It's only at Savannah River. 

Co-Chair Clawson: You're using Savannah River 

data, but this is -- this is for you guys on that end 

of the table. 

(Simultaneous speaking.)

Co-Chair Clawson: That's where some of the 

confusion is coming on this. 

Mr. Katz: Okay. So, if you want, I mean, Henry's 

group can make a recommendation to close these 

with respect to the coworker model guidelines, that 

this doesn't show any inherent flaw in the coworker 

model guidelines. Your Work Group can make a vote 

as to whether this is -- again, for the Savannah 

River model that we're discussing -- whether this is 

an inherent problem that needs to be solved for the 

model, in which case you have to go down that sixmonth path. If you want, you can do that 

separately. You can do it together in this case, 

because we all understand what's going on with 

this.

Mr. Calhoun: But classification for individual DRs 

could be a different issue all together. 

Mr. Katz: That's totally separate. That's not the 

issue because, again, they used their actual data. 

Member Lockey: Henry, why don't you make a 

motion about your group? 

Co-Chair Anderson: I'm just thinking about it. And, 

I mean, I think there is systematic misclassification 

for some of the job categories. Would you agree to 

that?
140

Member Ziemer: Systematic?

Co-Chair Anderson: Yeah, I mean, they're all said to 

be non. 

Member Lockey: No, I don't agree with that. There's 

no systematic misclassification, no. 

Co-Chair Anderson: But all of those job 

classifications that were mentioned, if you look at 

that list, they're all given to be non-construction 

workers. 

Mr. Katz: But what Bob's finding is, is that there's a

higher rate of misclassification as a result of that for 

that group. 

Co-Chair Anderson: Yes, exactly.

Dr. Taulbee: It's not all of them. 

Co-Chair Anderson: No, it's not.

Member Lockey: It's ten percent --

Co-Chair Anderson: No, but I mean -- but that's --

it's systematic because you coded them all in. You 

could have taken any of the others and coded them. 

Dr. Taulbee: That's not -- let me show you a 

different example here. And this goes to the 

machinists, but this is a reasonable example of what 

we found in going through it. Well, maybe not 

reasonable. I hate to use general terms here. But, 

you know, there is 39 machinists here. Nineteen 

were private contractor, 12 were subcontractors. 

Okay? When we went through, there were 18 that 

were assigned to CTWs in going through their jobs. 

Two were assigned -- so, in this population of 31 

machinists, they weren't all assigned to CTW. And 

they weren't all assigned to non-construction 

trades. All right? Eighteen of them were previously 

assigned to construction trades due to other job 

titles that they had in there that would say 

something like machinist/millwright or 

machinist/maintenance mechanic. And so they got 
141

properly categorized. 

So it's not all one or another is what I'm trying to 

communicate to you. The table that you're looking 

at just had the assistant on there. But we're using 

additional information to come up with that. That's 

kind of a condensed number. 

Co-Chair Anderson: So you're saying there are 

some assistants in there that are coded correctly?

Dr. Taulbee: Yes. Absolutely, yes. Some of them, 

like if it's an assistant pipefitter, yeah. 

Co-Chair Anderson: I mean those that were just one 

word, assistant. 

Dr. Taulbee: Those are the ones that could be 

different. We'd have to go look at other details. 

What I believe SC&A did was they went and looked 

at those other details. And they found that nine out 

of ten of them are correct and one out of the ten 

was not. 

Dr. Chalmers: And I think the list of occupations we 

were looking at were very generic bins that the 

subject matter experts had me put them in. 

Because that came along with the S-node 

(phonetic) that was from the master occupation 

table. And those bins were more generic. 

Co-Chair Anderson: Okay. 

Member Lockey: Ninety percent were classified 

correctly. 

Dr. Taulbee: Of the hard ones. 

Co-Chair Anderson: Yeah, but it's different than the 

overall group. I mean, the whole thing is, will this 

be used for the individual job classifications at any 

point? 

Mr. Katz: No.

Dr. Taulbee: No. 
142

Ms. Brackett: They don't need to be classified 

individually because they have bioassay results. By 

definition, if they're in the coworker study, they 

have their own bioassay results. That's what would 

get used in their individual dose reconstruction. It 

doesn't matter what category they fall into, they 

have bioassay --

Co-Chair Anderson: But you're not going to ever use 

this to look at if you have a large group with a job 

classification, and now you're going to do a 

coworker model that's specific to a subset of the 

workers. See what I'm saying now? If you'll do that 

and you were going to say, oh, that's something --

Dr. Taulbee: I see where you're going, okay. 

Co-Chair Anderson: -- that it's going to be used for. 

And then you could potentially, if you did the 

assistants or the whatever, they wouldn't meet your 

5 percent criteria. 

Dr. Taulbee: Yes. I see what you're saying, yes. If 

we were to further sub-stratify, which we do not 

have plans to do, then I see what you're saying 

that, yes, they could end up from that standpoint. 

Co-Chair Anderson: Yeah. 

Dr. Taulbee: But, I mean, we don't have plans to go 

now to --

Co-Chair Anderson: As far as the impact on the 

overall --

Dr. Taulbee: Right. 

Co-Chair Anderson: -- N for those. I mean, the 

alternative would be --

Dr. Taulbee: Yes. 

Co-Chair Anderson: -- to just as was mentioned, 

take them out all together. 

Dr. Taulbee: If we were to do a coworker model for 
143

assistants at the Savannah River Site along these 

lines and pull all of those people out and look at 

them, they would fail at misclassification rate. 

Co-Chair Anderson: Okay. That was --

Dr. Taulbee: So your criteria would work if we were 

to do that. 

Member Lockey: Via the 5 percent figure. 

Dr. Taulbee: Yes, they would fail that 5 percent 

figure. 

Co-Chair Anderson: Right. I mean, that was my --

Dr. Taulbee: Okay. 

Co-Chair Anderson: I'm just worried that when you 

start breaking groups up into small numbers and 

then bury them in a large group, the large group 

will always overwhelm.

Member Lockey: It will bury them.

Co-Chair Anderson: Unless it's random error, in 

which case it ought to be the same in all of them. 

And this would suggest it's grayer than a random 

error -- or misclassification, not necessarily an 

error. So does anyone out of the committee want to 

make a motion? The Chair shouldn't make a motion. 

Mr. Katz: That's true. 

Member Beach: Yeah, good idea.

Mr. Katz: Proper Robert's Rules.

Member Ziemer: Can you suggest what motion 

you'd like the members to make as you call for the 

motion? 

(Laughter.)

Mr. Katz: Well, I can't make a motion, but I can tell 

you. 
144

Member Ziemer: Right. 

Mr. Katz: I mean, the motion that you guys are 

talking about making is to close the observation. 

Meaning you're not recommending they do this 

further work. And the associated finding is not what 

we call fatal for your coworker modeling guideline.

Member Ziemer: That was exactly what I was 

thinking. 

Mr. Katz: That would be the motion that you might 

be making. 

Member Ziemer: I move we close this observation 

for the, for our Work Group. Are you okay with 

that? 

Member Lockey: Was that a combined motion? 

Member Ziemer: Why don't we just approve it?

Mr. Katz: Or we can do it combined. 

(Simultaneous speaking.)

Mr. Katz: We can do it either way. So if you want to 

make it one simple for everybody, that's fine. I 

think everybody --

Member Beach: Do you supposed Gen might have 

any comments? She's on the coworker model. 

Mr. Katz: Yeah. Once we get the motion -- yeah, so 

Gen, are you there? 

Member Roessler: I'm here. 

Mr. Katz: Do you have any comments about this 

discussion about --

Member Roessler: I want to -- it's been a little 

difficult hearing. I think somebody's not muted. But 

I want to make sure I know which observation 

you're talking about.

Mr. Katz: So it's Observation 6 --
145

Member Beach: Page 24. 

Member Roessler: Okay. So the idea is that, if this 

is closed, then SC&A's -- our suggestion for a 

scoping analysis would not be done. 

Mr. Katz: Correct. 

Member Schofield: Yes. 

Member Roessler: Okay. 

Mr. Katz: Correct. 

Member Roessler: So I understand. I'm ready to 

vote when you get ready. 

Mr. Katz: And Phil, are you okay too? 

Member Schofield: Yes, I am. 

Mr. Katz: Okay. Yeah, loud and clear. And then --

Co-Chair Anderson: Not doing the scoping, close out 

6.

Mr. Katz: Close out 6, yeah. Yeah. Now, the only 

clarification that I need -- are we closing the 

associated findings too? Because the findings, 

you're not going to do anything with otherwise. 

You're basically inherently saying that the finding is 

not fatal for --

Co-Chair Anderson: Right. Yes, that's correct. 

Mr. Katz: Okay, so then you're closing Finding --

Someone help me with this. 

Member Beach: Five, six, and Observation Six. 

Mr. Katz: Okay. Okay, so that is the motion. 

Someone needs to make it because I can't. 

Member Ziemer: I made the motion that we close 

that. 

Mr. Katz: Okay. Paul put forward the motion to 
146

close Observation 6 and Findings 5 and 6.

Member Lockey: I second. 

Mr. Katz: And Jim seconds. And why don't we --

anyone opposed? Does anyone oppose? 

Co-Chair Anderson: On the coworker model. 

Mr. Katz: Right, right. Well, in either. No, either. Is 

anyone opposed to this motion? 

Member Lockey: I'm not sure they can vote on our 

motion. 

(simultaneous speaking.)

Mr. Katz: It's a combined motion. 

Member Lockey: Okay, that's fine. 

Mr. Katz: No one's opposed? 

Co-Chair Clawson: You could have run this whole 

process and had it done by now. 

Mr. Katz: Okay, I don't hear anything on the phone, 

so it's passed. So those two findings and that 

observation are closed. Thank you. 

Mr. Barton: We just had one last slide here. Harry, 

are you still on the phone with us? 

Dr. Chmelynski: Yes, I'm still here. 

Mr. Barton: Okay, the last slide here is really about 

the quality assurance assessment that you looked 

at. And it was Chapter 6, I believe, in our -- and I'll 

read the Observation and perhaps you can comment 

on it. But it's pretty straightforward.

The results shown in Attachment A of OTIB-81 

demonstrate a high degree of confidence that the 

acceptable error rates are within the goals 

established for each test. However, this conclusion 

is dependent on the assumption that payroll ID 

issues identified would not affect the resulting 
147

coworker distribution. That's Observation 7.

So Harry, if you want to give a brief overview of 

what you saw there, and maybe talk about the 

payroll ID issue that was identified that may or may 

not have an effect? 

Dr. Chmelynski: Attachment A to OTIB-81 includes 

the results of a whole list of QA tests that were 

performed essentially to verify the data that's being 

used for the coworker model. This included the 

NOCTS datasets, both in vivo and in vitro, and the 

lab logbook data for americium, neptunium and 

some others, and also some quality assurance, 

which has already been talked about on the 

construction worker classification problems. So that 

serves as a good introduction to what this whole 

Attachment A is about.

There's 13 sections in it, and each section has 

perhaps sometimes more than one test being done 

because some of them have both the critical fields 

and the all fields being down separately. Also some 

of them were done and then done again for various 

reasons. And what we end up with is a list of about 

25 different QA studies that were done. And these 

are all summarized in our report in one of the 

tables. And right now, I lost the page that tells me 

my table number. But it's in Section, what is it, 

about 6? 

Mr. Barton: Yeah, Section 6. I'm looking.

Member Lockey: Section 6?

Dr. Chmelynski: Yeah. I lost the page that has the 

top of the table, so I don't see the table. 

Mr. Barton: I think it's Table 18. 

Dr. Chmelynski: Is that it? Okay. 

Mr. Barton: Table 18 and 19. 

Member Lockey: Getting there. 
148

Dr. Chmelynski: Right. The first table essentially 

shows what the 13 sections contain. The second 

table is the breakdown of all the different tests that 

were done in this quality assurance program. And to 

make it more complicated, there's various 

methodologies used for the tests. Some of them 

started out before the final methodology was 

obtained. And therefore, there was a Round 1 and a 

Round 2 for some of these tests. 

At any rate, I looked over all the tests that were 

done. And I agree with this idea that the critical 

fields should have a stricter standard of 1 percent 

allowable error rate as opposed to the noncritical 

fields, which was a 5 percent now. 

Up till now, we've been talking about the 5 percent 

kind of study. They also did a 1 percent critical field 

study on several of these analyses. And the 

acceptable error rates in each case, whether it was 

a 5 percent or a 1 percent, they did manage to 

come within those errors in almost every case. But 

that took some time eliminating some errors that 

were found from consideration in order to get the 

final number. And in particular, the largest type 

there. And this was very prevalent. And in one case, 

it was at least half of the data. The error was 

identified as a payroll ID error. 

And the notes that I read in that attachment 

indicate that most of these payroll ID error matches 

occurred because one of the documents would have 

a prefix on the payroll ID such as a T-dash or a 1-

dash or a 0-dash. And on the other document, they 

would not have that prefix. So the computer would 

flag them as errors. But yet it was a clear that these 

two were matched; it's just that they were entered 

differently in terms of the payroll ID number. 

So once they eliminated all of those payroll ID 

errors and a few other types of errors of similar ilk, 

they managed to get within the allowable error rate 

bounds. And in some cases, this involved, you know 

thousands of lines of data being checked. And I 
149

looked at one here, thousands of lines of tritium 

data, 14 errors. Result of somewhere around a third 

of a percent, which is really good. 

I tried to summarize all this in a graph at the end of 

the section. And what I did was I took all of the 25 

cases -- I'm sorry, the 13 cases when the final 

analysis was done. And I used those error rates and

I tried to combine all the different datasets into one 

picture. And what I found out was that we have a 

99 percent confidence that the error rates are less 

than 1 percent. Which is pretty amazing because 

that includes some of the 5 percent target goal 

studies too in that simulation. 

So I'm pretty happy with all these results in that QA 

section. And I'm pretty confident that the data we're 

using is what we want to be using. I guess that's it.

Dr. Taulbee: If I could explain a little bit of the 

payroll issue. As Harry mentioned, some people 

would have a -- well, all of the DuPont people had a 

four-digit payroll ID number. But sometimes 

whenever they would enter it into the bioassay 

logbooks or something along that lines, there is a 

row. And Row 1 would be your salaried DuPont 

people. Row 2 is your technical people. Row 4 was 

your construction trades people. 

In Row 1, sometimes they would put 1-dash and the 

four-digit number. Sometimes they would put Tdash for the technical area and that four-digit 

number. And other times they would just enter that 

four-digit number. It's all to the same person. But if 

in the analysis, it was different than what was in the 

source document, it got flagged as a potential error 

even though it didn't have any impact carrying 

forward because we were using that four-digit 

number. And so that's, that difference of the payroll 

numbers. One of the main differences, I think. 

Co-Chair Anderson: Good picking. Easy solution. 

Dr. Taulbee: Yes. 
150

Mr. Katz: Next. 

Mr. Barton: That's it. I mean we can go over the --

(Simultaneous speaking.)

Co-Chair Anderson: So do you want to tell us that 

observation analysis? I would think so. 

Mr. Katz: Yeah. Well, it's --

Co-Chair Anderson: Do we need to for an 

observation? 

Mr. Katz: We don't really need to close that out 

given the nature of it. 

Co-Chair Anderson: Yeah. There was an 

explanation? 

Mr. Katz: Yes, an explanation doesn't have an 

action. 

Co-Chair Anderson: No. 

Member Lockey: Moving right along. 

Mr. Barton: I mean I have a conclusion slide just 

reiterating what we already talked about. 

Mr. Katz: Okay, so we probably don't need to do it 

again? 

Mr. Barton: I don't think so. 

Dr. Taulbee: Can we go back to the first findings to 

find out where we have to -- as to whether we have 

an action item or not? 

Mr. Barton: Oh well, okay. 

Mr. Katz: Well, I know you had responses to some 

of them. 

Mr. Barton: Oh sure, okay. Let me see if I can share 

my window. 

Member Lockey: Finding Number 1 is the bioassay 
151

variability. 

Dr. Taulbee: That's correct. I just want to make 

sure who has which. 

Okay. So back on the bioassay variability, I believe 

that's an SC&A action or not. 

Mr. Barton: I think the variability was we were 

going to get that 2014 report that Joyce had written 

up. And then we'll take a look at that in the context 

of the discussion today --

Dr. Taulbee: Okay. 

Mr. Barton: -- of the discussion of the methods. 

Dr. Taulbee: Okay, just so that people are aware, 

that report that Joyce wrote, we did a response to 

it. And that response is in Attachment D of OTIB-81.

Mr. Barton: We responded to the response.

Dr. Taulbee: Attachment D of OTIB-81.

Mr. Barton: I think the question was where the 

genesis of that came from. So what's in Attachment 

D is actually addressed in the OTIB report which 

was released all at the same time. The variation is 

the same whether it's a DTPA sample or not.

Dr. Taulbee: And that's why I wanted to circle back 

to this one because I don't think that Joyce's 

response is answering Dr. Lockey's question. But I 

don't know that for sure. That's up to you all to 

decide. Our response to Joyce's paper is in OTIB-81. 

We copied it in there as an evaluation of variability 

and our conclusions associated with it. 

What I was hearing earlier today was that you were 

feeling that the combination of variability and the 

MDA levels, which we're not using for americium, 

call into question the entire method. And that's 

where --

Member Lockey: That's what I heard. 
152

Dr. Taulbee: And if that's an issue --

Mr. Barton: Was there another slide in that -- that 

sort of conclusion on this? 

Dr. Taulbee: No. 

Mr. Barton: No. 

Dr. Taulbee: I mean --

Mr. Barton: I thought the stance was pretty much 

that, once you start averaging --

Dr. Taulbee: Oh, that is our next slide is that 

because of the averaging that ends up happening 

between the analytical results, the individual 

bioassay being converted into a Time-Weighted One 

Person One Statistic, the fitting of that data, the use 

of the 50th and 84th percentile, overall this 

variability just gets averaged into a reasonable 

model that we use. But further is some of your 

discussion on the actual analytical method not being 

appropriate and not being adequate. And that's 

actually a different issue. 

Mr. Barton: Well, I think when we look at that data, 

we see certainly troubling observations in the 

variability. And when I see that, well, it averages 

out, that doesn't quite ring true because -- I'm 

going to take this right out of the Implementation 

Guide. This is on Page 3. Prior to this as in prior to 

all these steps, it is necessary to establish that the 

available internal or external monitoring 

measurements were technically capable of 

evaluating the monitored workers' exposure impact. 

If the techniques used to monitor exposed workers 

were inadequate, they clearly cannot be used to 

assess exposures for unmonitored workers. 

Now further on, on Page 5, the quality of the 

available data also needs to be considered. This 

would include a review of appropriate collection and 

analysis of blank samples. When paired 

measurements are available, the precision between 
153

measurements should be examined. If widely 

different results from the same aliquot are 

observed, the effect this might have on the 

usefulness of the data should be considered. 

When I read that, I don't think we're just going to 

try to figure out if it changes the end result. And it's 

prior to this -- that's how the first sentence starts. 

Prior to all these averaging -- because I agree. I 

mean through all -- you know, you average all these 

aliquots and you might have two samples in a day. 

You average those. You get the TWOPOS result. You 

put the TWOPOS result in the distribution of all the 

workers in that year. And then you pick an intake 

regime. And then you, you know, draw your intake 

line through that, then I agree. You know, the 

variability at the very beginning, the front-end 

problem is going to get washed out. But the whole 

point was when you start with this, you have to 

make sure that the data is adequate. Our concern is 

that observed variability.

Co-Chair Anderson: Let me kind of cut to the chase 

here. My view on what our coworker model 

committee asked for is an example of, can it be 

applied, and does it cover all the issues? Not, do we 

agree with how it was done on a specific site? I 

would say that has to be a -- I mean your --

Dr. Taulbee: Right. 

Co-Chair Anderson: We can go round and round on 

has this been done appropriately as opposed to is 

the methods that we as a group decided ought to be 

applied, it can be applied. 

Dr. Taulbee: Right. 

Co-Chair Anderson: How you interpret that, you're 

never going to have the methods particularly to find 

the detail of, is it appropriate in this case or that 

case, I think. So my sense is rather than go through 

some of these issues or in our group saying -- I 

mean one, I didn't see anything you did until your 

presentation. So I haven't had a lot of time to think 
154

about that. I did have the time to look at, is the 

graph that we have been working with -- is that 

appropriate? Does it include all of the issues that 

we'd like to have addressed when you say is this a 

coworker model that can be used? 

So I guess what I would look for our group is to say 

my sense is that it can be utilized. We don't need to 

go back and redraft it to include all sorts of little 

caveat things that may come up under every 

possible circumstance.

It's been an interesting discussion on the modeling 

and all of that, but it can be done. Which model is 

really dependent upon the site-specific issues that I 

don't think our group has delved into over the years 

like Savannah River has done. So that would be my 

recommendation so we can move on to let 

Savannah River deal with it. 

Member Lockey: Do you want to make that a 

motion? 

Mr. Katz: I mean he's the Chair. He's the Chair, so 

one of you guys put the motion. 

Member Ziemer: Well, this is a site-specific issue 

here. That's what you're saying.

Co-Chair Anderson: Yeah, yeah. 

Member Ziemer: The analysis of those samples. And 

I think the concern on the variability has to do with 

the higher points toward the end, toward the tail. Is 

that not the case? 

Dr. Taulbee: That was the ones that they primarily -

-

Member Ziemer: You're not concerned about the 

real low-dose values.

Mr. Barton: We looked at the high values. 

Member Ziemer: Yeah. 
155

Mr. Barton: We did not look specifically for 

variation. 

Member Ziemer: Right. So it's an SRS specific kind 

of issue.

Mr. Barton: That's correct. 

Member Lockey: But in relationship to the coworker 

modeling --

(Simultaneous speaking.)

Member Ziemer: I agree with that. 

(Simultaneous speaking.)

Co-Chair Anderson: It can be done. There are tools 

to do it. Which tool on a site-specific basis, that's 

really specific to the -- you know, to the --

Dr. Taulbee: Okay. 

Path Forward on Coworker Guidelines

Co-Chair Anderson: We aren't going to reject the 

coworker model --

Dr. Taulbee: Methodology. 

Co-Chair Anderson: -- methodology simply if we 

decide that, well, the data isn't good enough at 

Savannah River. It just means --

(Simultaneous speaking.)

Co-Chair Anderson: -- and we picked the site that 

had a beaucoup amount of data. 

Mr. Katz: So then the issue shifts to -- so there's 

nothing for SEC Issues Work Group to do about this. 

Co-Chair Anderson: Right. 

Mr. Katz: There's nothing to close here for you guys 

because this doesn't have a bearing on you being 

able to endorse --
156

Co-Chair Anderson: Right. 

Mr. Katz: -- or not endorse the draft guidelines. 

Co-Chair Anderson: Yeah. 

Mr. Katz: So it falls to the SRS Work Group as to 

what the action is forward on this matter. Because 

that's where --

(Simultaneous speaking.)

Co-Chair Anderson: -- I want to be able to close out 

that this draft had been there and NIOSH had been 

working with for some time. It is a valid and 

appropriate tool to use. 

Mr. Katz: Let me finish. 

Co-Chair Anderson: But for SRS, you don't have to 

resolve this now. 

Co-Chair Clawson: No, we don't. 

Co-Chair Anderson: Right. 

Male Participant: Because the data --

Member Lockey: But we need a path forward. 

Mr. Katz: We need a path forward. But they may 

not be ready to even speak to a path forward 

immediately, in which case, you know, it will get 

addressed in the next Work Group meeting. It's just 

up to SRS Work Group as to what you think -- if you 

can get your heads around this -- what the path 

forward or what the matter is. How you want to deal 

with this. That's all I'm saying. I'm not saying you 

have to solve it now, but obviously it's on the table 

for you. 

Mr. Barton: If I may, the mystery of this again, has 

gone back many years. And the most recent 

response is from NIOSH -- Harry, if you're still on 

the phone, I don't know how much you've gotten to 

take a look at that. And if you feel there's anything 
157

for us to do to respond to the response, that might 

be a potential path forward. Because again, NIOSH 

responded to the section in our OTIB-81 review 

concerning the variability in the most recent 

response to the response. 

Member Lockey: I'm still confused. I'm not 

confused. I'd just like to --

Co-Chair Clawson: Yeah, you are. 

Member Lockey: Yeah, I am. My wife says I'm 

confused all the time. But in relationship to -- are 

there still just -- is there one issue or are there two 

issues? Because one issue is was the test being 

utilized at Savannah River, a technically reliable 

test? I think that's what -- you inferred that it was 

not. 

Mr. Barton: Well, I think that might have been a 

little strong. 

Member Lockey: Then would you -- then pull that 

back. 

Mr. Barton: Yeah. I think what we're looking for is 

the data that we're going to base the coworker 

model on: is it sufficiently accurate and adequate to 

use -- to enter the coworker process? And at the 

very front end of this process, we look at the data 

that we have and we see concerning variability. 

Member Lockey: Okay. So then we get to the 

variability issue, which NIOSH has already written a 

report on -- or Joyce has written a report on and 

you responded. Is that correct? 

Dr. Taulbee: Yes, that's correct. 

Member Lockey: So I think I would recommend, 

Brad, that both those be sent out to committee 

members. 

Dr. Taulbee: Okay. Our response is to OTIB-81. 

Member Lockey: Right, but --
158

Mr. Barton: I'm talking about what you responded 

to on Monday. 

Co-Chair Clawson: But this is part of the confusion 

we're getting into because we're looking at OTIB81, which is coworker, but we're using Savannah 

River data to be able to do it. And right now, to tell 

you the truth, I don't want to deal with this in the 

confusion. I would rather deal with it as Savannah 

River when we get to that point. And I'm sure we're 

going to review that in the Savannah River 

evaluation. 

Member Lockey: Okay. 

Co-Chair Clawson: Because to tell you the truth --

Member Lockey: So this is an OTIB-81 issue? 

Co-Chair Clawson: Yes, that's what we're discussing 

this morning till now. 

(Simultaneous speaking.)

Co-Chair Clawson: I know. But this is what I'm 

saying. This is part of our confusion. And I want to 

be able to deal with that on the Savannah River 

issue. 

Mr. Barton: The implementation guide itself allows 

for this type of analysis to happen. I think that's the 

point from the coworker methodology point here. 

And that process is going on with Savannah River 

currently. So as far as the implementation guide is 

concerned, there's already the provision in there 

that we need to look for this, you know, type of 

thing -- this type of analysis --

Member Lockey: Right. 

Mr. Barton: -- for each site that we, you know --

Member Lockey: And the variability issue has 

already been addressed. So I need to update myself 

on that. 
159

Dr. Taulbee: We included it in this OTIB-81 -- our 

analysis. 

Member Lockey: Okay. 

Mr. Barton: And SC&A has the response to that in 

their review of OTIB-81. And then there's a second 

response to that. 

Mr. Katz: So this goes on the agenda for the next 

SRS Work Group meeting following up on this. 

Co-Chair Clawson: Right. 

Mr. Katz: Okay. So let's capture it well that way, so 

we can -- So it will be on the agenda for the next 

SRS Work Group meeting, whether it's in January or 

February or whenever, to move forward on this. 

Mr. Fitzgerald: Well, we have -- we have NIOSH's 

response to the 0992 response. And we have our 

response to NIOSH's response on 0081 OTIB. So 

you have this. 

Mr. Katz: Yes. 

Mr. Fitzgerald: It all happened in the last couple of 

weeks. Am I right? 

Mr. Katz: Yes.

Mr. Fitzgerald: So you know, in a sense, yeah, we 

have all that for the next Work Group meeting. 

Mr. Katz: Yeah, it's all -- it's all on the table. 

(Simultaneous speaking.)

Mr. Fitzgerald: And the only thing that really may be 

a pertubation is that today we had some discussion 

which led to closure as we went on a few on those 

items. That's what I think you're trying to 

disposition. What are those few items that we can 

take off the table at the next Work Group meeting? 

Dr. Taulbee: Yes, trying to reduce the scope. Trying 

to get things done. 
160

Member Lockey: I wanted to walk back the invalid 

technique. 

Mr. Katz: Okay. Okay, good, good. So that's Finding 

1. 

Dr. Taulbee: Finding 1. Finding 2 is the multiple 

imputation, and this is related to Finding 3 and 

Observations 1 and 2. Do we have as NIOSH any 

action items -- things that we are to follow up on? 

Mr. Barton: I think the action was actually ours to 

take a closer look at RPRT-96.

Dr. Taulbee: Okay. 

Mr. Barton: And the imputation method that came 

out in January. 

Dr. Taulbee: Okay. So the action then is SC&A and 

RPRT-96. 

Member Lockey: What's the action? I'm sorry. 

Mr. Katz: SC&A is going to look at RPRT-96.

Dr. Taulbee: Which details the method. 

Member Lockey: RPRT-96 detailing method. 

Dr. Taulbee: Yeah. And that actually falls to Finding 

2 and 3 really. 

Dr. Chalmers: Have you looked at 71? 

Dr. Taulbee: Yeah, they haven't looked at 71, but 

we might. It's the external one. It's simpler. And 

they have already approved the Pantex coworker 

model -- external coworker model, which dealt with 

it or used it. But if you want to review 71, it seems 

appropriate. 

Mr. Katz: Okay, so that's --

Dr. Taulbee: It's your call. 

Mr. Katz: So that's associated with Finding 2? 
161

Member Beach: Yes. 

Mr. Katz: So RPRT --

Dr. Taulbee: RPRT-71. 

Mr. Katz: RPRT-71. So I guess what we're saying is, 

SC&A, take that under advisement how you 

reviewed that in reviewing this. Right? 

Dr. Taulbee: Yes. 

Mr. Katz: Does that make sense?

Dr. Taulbee: Yeah. 

Mr. Katz: Okay. 

Dr. Taulbee: Excellent. Okay, I think we only have 

one more to go through here, and that is Finding 4, 

claimant cutoff of adding more data. And this is the 

additional 2000 claimants since we cut off our 

coworker models for data analysis. 

Co-Chair Anderson: Is the 2000 just for Savannah 

River? 

Dr. Taulbee: Yes. 

Co-Chair Anderson: Okay. 

Member Lockey: Because there's going to be a lot 

more total. 

Dr. Taulbee: There will be more tomorrow. 

Co-Chair Anderson: Oh no, I mean not just for 

Savannah, but --

(Simultaneous speaking.)

Co-Chair Anderson: -- other coworker models. Are 

you using the same 2011 cutoff date? 

Dr. Taulbee: No, no, not at all. Not at all. 

Co-Chair Anderson: Okay, good. 
162

Member Beach: Bob brought up a good point on this 

because you had to combine certain data points 

because you didn't have enough. So that would be a 

reason to move forward and add the other 2,000 or 

some variable amount of those. 

Dr. Taulbee: I mean, if that's what the Work Groups 

want, we can do that. But you are looking at 

another year. 

Mr. Fitzgerald: That was essentially the trade-off. It 

was just the level of effort necessary to --

Dr. Taulbee: Absolutely. 

Mr. Fitzgerald: -- give you that additional 

advantage. 

(Simultaneous speaking.)

Mr. Fitzgerald: That's kind of a NIOSH -- I mean it's 

the classic balance that I think the agency has 

always has to come to. Is it worth it? And I'm not 

sure we can answer that. 

Mr. Katz: Well, I mean the Work Group -- if the 

Work Group has a perspective on that, I mean 

honestly if that has a bearing on your position on 

how good the coworker model is, then of course 

you're stuck with --

(Simultaneous speaking.)

Mr. Fitzgerald: Yeah, but the part of the equation I 

think the Work Group would have difficulty with is 

the part that NIOSH has, which is the level of effort 

and the issues surrounding that, that would be 

necessary to --

Mr. Katz: Right. 

Mr. Fitzgerald: -- generate it. And that would then 

go with the advantage of doing so. And I think that 

would be a piece of input that Tim would provide 

that, you know, is it worth it given that effort? And 

you know, both the numerator and the 
163

denominator, is it worth it? And how much time will 

it take? I think intuitively, you're saying it's going to 

take a lot, a year at least. 

Dr. Taulbee: Yeah. 

Member Ziemer: Well Tim already also spoke to the 

outcome as not having a great impact, in your 

opinion, at least.

Dr. Taulbee: That is my opinion, yes. That is 

correct. In my opinion, it's not going to have --

Member Ziemer: If we anticipated a substantial 

change in the impact, then it might be worth 

stretching it out. But if it's just going to delay 

closing some issues and getting the claims 

adjudicated, then it's not worth it. 

Mr. Fitzgerald: Yeah. And I think that trade-off is 

one that happens all the time. And I think NIOSH is 

in the position to advise the Work Group as to 

whether that's the case or not. And I think your 

advice then would be -- wouldn't justify the effort. 

Dr. Taulbee: And that is correct. That is smart 

advice. And I'm trying to pull up the actual uranium 

ones to show this -- it's OTIB-81. 

Co-Chair Clawson: And see, from the Savannah 

River Work Group's side, we can take care of that 

quite rapidly. And I believe it's called an --

Co-Chair Anderson: SEC. 

Co-Chair Clawson: -- SEC, yeah. 

(Simultaneous speaking.)

Mr. Fitzgerald: But you know, this balancing would 

be for every single site. I mean this is not -- this is 

a generic question. And we're going to see the same 

question played out at other sites. And that's the 

perspective, I think the Board will need to hear is 

to, you know, how many of these data points do 

you need? And beyond which, it's not going to give 
164

you any return for the investment. And I think 

that's something that only NIOSH can answer. 

Dr. Taulbee: If you look at the uranium for 

Savannah River here that I've got pulled up -- this 

is M type uranium. And you can see that four 

construction trade workers out 1980 through 1990. 

We combined some years. But you don't see a huge 

movement in that data. 

Dr. Chalmers: They're two-year intervals, I think --

Dr. Taulbee: Yeah. 

Co-Chair Clawson: -- all the way through. 

Member Lockey: It's two-year intervals?

Dr. Taulbee: Yeah. 

Member Lockey: Okay. So that fits within the 

guidelines. Okay. 

Mr. Katz: So, I mean this is a matter you can close 

now because I guess you're not going to get more 

information about it. 

Dr. Taulbee: Yeah. I mean we are recommending 

not to pursue it. If you feel it should be pursued, let 

us know and we will. 

Member Lockey: I move that we close. 

Mr. Katz: Well, this isn't --

(Simultaneous speaking.)

Mr. Katz: You're on the right side of me, it's 

confusing. 

Member Beach: So Ted, it's real easy, the outside is 

not. The inside is. 

Mr. Katz: Oh, I didn't notice that. 

Co-Chair Clawson: This being said, this is why we 

have not proceeded any further with this a long 
165

time ago because we did not see the benefit of it. 

But in their evaluation of the coworker data, this 

came out of it. So I don't see any benefit from it 

because we're not going to get any more. 

Member Lockey: It's just going to delay it. And a 

year from now, it will be delayed again. 

Co-Chair Clawson: We've already delayed a lot. 

Mr. Katz: Phil, are you there? 

Member Schofield: I have to agree that I just can't 

see waiting for more data points. I think we have 

sufficient to go ahead and close it at this time. 

Mr. Katz: Okay, that's three. And Dr. Richardson is 

not on the line. Right? So that three out of four is 

enough, though. That's the quorum of the Work 

Group. So Finding 4, we're closing. Not worth the 

push. 

Co-Chair Anderson: At this time. 

(Simultaneous speaking.)

Dr. Taulbee: And we're closing Finding 5 and 6, 

which were the misclassifications. 

Dr. Taulbee: So we are good then. We have a path 

forward.

Mr. Katz: Good work, everybody. 

Co-Chair Clawson: Tim, I told you years ago how 

you could take care of that. And you just don't want 

to listen. 

Mr. Katz: All right, now where are we? 

Dr. Taulbee: Do you want to take a break while 

trying to figure out how to get that connected? 

Mr. Katz: Well, do people need a break right now? 

Member Beach: Sure. 
166

Dr. Taulbee: Because it's going to take a reboot. 

Mr. Katz: I'm sure everybody's happy for a break. 

So ten minutes, what do we need? 

Dr. Taulbee: That's fine. 

Mr. Katz: You guys have a ten-minute comfort 

break. Folks on the line, ten-minute comfort break. 

And I'm just going to put the phone on mute, but I'll 

take it off. 

(Whereupon, the foregoing matter went off the 

record at 2:51 p.m. and resumed at 3:08 p.m.)

Mr. Katz: Are we ready to move on? Okay. Okay. 

We're back online. Let me just check and see. Sorry 

we're a little bit slow. Gen, are you on there?

Member Roessler: I'm on.

Mr. Katz: Great. And how about you, Phil? Are you 

there?

Member Schofield: I'm on, Ted.

Mr. Katz: Great. Thanks, Phil. And is David 

Richardson on by any chance?

No, I don't think we have him today. Okay.

Dr. Taulbee: Are people able to see the presentation 

right now?

Mr. Katz: Can you, folks on Skype, can you see the 

presentation?

(Simultaneous speaking.)

Mr. Katz: Yeah, okay, Gen just said yes, so --

Dr. Taulbee: Okay, good. All right. This next 

presentation is where we're delving into the details 

of stratification and in this particular case it's an 

evaluation of the bioassay data for subcontracted 

construction trades workers at the Savannah River 

Site.
167

And before I get going here I've really got to 

acknowledge the team that did this, and this would 

be the ORAU team. Mike Mahathy was the lead on 

this particular effort. It took way more effort than 

what we initially thought. And this was from the 

table.

I think initially I had mentioned to Brad and the 

other Work Group members that I probably could 

get this done in nine months. That was not the 

case. This took way longer than that and -- just so 

that people are aware of that. But Mike and his 

team did a fantastic job.

I want to go through some background of how we 

got to here and then go through the work permit 

sampling plan and then get into the evaluation.

And I'm doing this in reverse order for a reason, 

okay? And I'll get to that from the background 

standpoint and then do some conclusions. And then 

what I'm going to talk a little bit about at the end is 

an evaluation timeline.

So the background, first of all, as I mentioned this 

morning, coworker model use. We developed 

coworker models, or co-exposure models I should 

call them now, and we will. By the way, we will start 

changing our documentation.

(Simultaneous speaking.)

Co-Chair Anderson: From a public perception --

Dr. Taulbee: Yes.

Co-Chair Anderson: -- that's one accomplishment 

we can pass.

(Simultaneous speaking.)

Member Ziemer: A major accomplishment.

Mr. Calhoun: It's too easy now to do it.

(Laughter.)
168

(Simultaneous speaking.)

Dr. Taulbee: And so we developed co-exposure 

models because we recognized that some workers 

were not monitored, okay? For a co-exposure model 

to be valid it needs to have a representative 

sample. A representative sample is all that's really 

needed for it to be valid. Okay?

If all exposed workers were monitored for every 

radionuclide, there'd be no need for a co-exposure 

model. So coming into this you've got to recognize 

that 100 percent, if we got to 100 percent, well, 

there's no need for the co-exposure model. All 

right?

So one of the issues that came out of the December 

2017 Advisory Board meeting was one of SC&A's 

conclusions, and it was SC&A concludes that the 

bioassay data for -- or for construction trades 

worker subcontractors specifically, and construction 

trades workers generally is demonstrably 

incomplete for 1989 to 1998 and likely before that 

time period and does not satisfy the criteria set 

forth in NIOSH's draft criteria for the evaluation and 

use of coworker datasets.

Okay, now I added some emphasis here because 

the time period that we started here to address was 

this '89 to '98 time period. Now, through further 

discussions we started going back, so let's try and 

address that initial issue that we handled or that 

was left at that Advisory Board meeting.

At that time we indicated that we disagreed. We 

believed that 90 to 87 percent direct monitoring for 

subcontractors is not demonstrably incomplete and 

does satisfy the criteria.

We went on and did an additional analysis that you 

talked about during the last SRS Work Group 

meeting indicating subcontractors were monitored, 

and that evaluation indicated that 91 percent of 

them who were claimants from '91 to '97 have 

some form of internal monitoring data. Okay?
169

Savannah River used a Defense in Depth approach 

for the radiological control program with the 

intention to prevent non-tritium intakes. And the 

reason I say non-tritium there is they actually did 

allow some small intakes of tritium. They didn't go 

to an extreme level of trying to put everybody in 

bubble suits every time they were around tritium.

And as you saw from the previous coworker models 

those doses are really low, okay? So but for all 

others they had, what they had is a zero policy, 

zero intake policy. They had engineered controls. 

They had glove boxes. They had procedural 

controls. They used PPE.

They did surveillance to verify these were working. 

They did air monitoring. They did facility 

contamination surveys. They did personal 

contamination surveys, and they did routine and 

job-specific bioassay, okay? So this is all part of the 

radiological control program.

Mr. Fitzgerald: Tim, just this is a clarification 

question. All right. We're still talking in the context 

of '89 and beyond, right, or beyond '89?

Dr. Taulbee: Yes.

Mr. Fitzgerald: Okay. So this description of Defense 

in Depth is really the so-called Westinghouse era. I 

just want to make sure I understand the context of 

this.

Dr. Taulbee: This -- yes. This is --

Mr. Fitzgerald: This is the --

Dr. Taulbee: -- is specifically for post '89, yes.

Mr. Fitzgerald: Post '89, okay. Thank you.

Dr. Taulbee: Now, engineered controls have been 

there all along.

Mr. Fitzgerald: Right.
170

Dr. Taulbee: Okay?

Mr. Fitzgerald: Right.

Dr. Taulbee: When they first started doing --

(Simultaneous speaking.)

Mr. Fitzgerald: Yeah, I'm talking about the whole 

package is Defense in Depth and all --

Dr. Taulbee: Yes.

Mr. Fitzgerald: -- that. That describes the 

Westinghouse program as we know it.

Dr. Taulbee: Evidence of this though appear back 

into the DuPont.

Mr. Fitzgerald: I agree.

Dr. Taulbee: Okay.

Mr. Fitzgerald: Okay.

Dr. Taulbee: Because there is air monitoring. 

There's facility contamination --

Mr. Fitzgerald: Right, right.

Dr. Taulbee: -- personnel contamination. It's all --

Mr. Fitzgerald: Right, okay.

Dr. Taulbee: Okay, a good clarification. Routine and 

job-specific bioassay, in this time period of '89, '90 

there's really no practical difference between the 

two, okay? It's used to verify the effectiveness and 

procedures of the procedural and engineering 

controls.

It's also used to trigger for-cause or special 

bioassay, okay? If somebody comes up on a positive 

on a routine sample, they go and do an 

investigation.

And it's requested from workers who have a 
171

reasonable potential for intakes but who SRS was 

confident did not have intakes in excess of two 

percent of the annual limit. So it's not that they 

didn't have any intakes, it's that they would be in 

excess of two percent of the annual limit. Okay?

And these are slides that I presented to the Board 

back in December of 2017, this beginning part, 

these first three or four slides, just to recap 

everybody's memory.

Westinghouse further stated that workers 

themselves were the last line of defense in the 

workplace indicator program, which is the reason 

why a confirmatory program for workers was 

conducted.

Okay. Here is how in the 1990s how Savannah River 

did -- radiation work control and bioassay 

monitoring. A worker had to attend Rad Worker II 

training. After their training they were issued a 

radiation qualification card, and there's an example 

of one that I've got up there.

A worker told then go to sign in on an RWP. The 

worker would check the bioassay codes on his 

radiation qualification badge against the RWP 

requirements or the area for the bioassay. Okay?

And what this is, if you look here, you'll see the 

bioassay codes right here, Pu-02, Eu-02 and Sr-01. 

Pu-02 meant plutonium, twice per year. EU is 

enriched uranium twice per year. Sr-01 meant 

strontium-90 once per year.

If you can go back to, and, Josie, if you remember 

that frequency diagram chart that I had in the 

coworker one where it specified the frequency of 

bioassay by area that people were required to do. 

So DuPont did have procedures back then.

But when you get into the Westinghouse area it was 

done on an individual basis like this. So those went 

away, and this was the controlling method as to 

whether somebody would be monitored or not.
172

And there was a determination as to whether this 

person versus that person should be, and it 

depended upon which area they were going into. 

Okay?

Mr. Fitzgerald: So just one clarification on this one 

here. You say 1990s.

Dr. Taulbee: Yes.

Mr. Fitzgerald: But, you know, I think we all 

understand that Westinghouse put the program in 

place, I mean, in terms of RQB and the whole --

Dr. Taulbee: This actually precedes Westinghouse.

Mr. Fitzgerald: Okay. So you're saying this actually 

goes back before 1990?

Dr. Taulbee: Yes, they started using the radiation 

qualification badges before then.

Member Beach: 2/16/90 is the date on the corner 

there.

Mr. Fitzgerald: But the --

Dr. Taulbee: Yeah.

Mr. Fitzgerald: -- so you're also saying that they'd 

had an active RWP program before '89?

Dr. Taulbee: No. At that time it was depending upon 

which area they were going to be going into as to 

whether they had -- what their bioassay criteria was

and the monitoring frequency.

Mr. Fitzgerald: So in terms of who gets --

Dr. Taulbee: It would be specified by the job 

permits at that time.

Mr. Fitzgerald: The work control and bioassay 

monitoring, the RQB, may have predated 

Westinghouse, but the other elements may or may 

not have?
173

Dr. Taulbee: That is correct.

Mr. Fitzgerald: Okay.

Dr. Taulbee: The job plan components of it.

Mr. Fitzgerald: Okay, thank you.

Dr. Taulbee: Okay? And so a worker goes in -- a 

worker goes to conduct -- goes and conducts their 

work. And then the worker leaves a bioassay based 

on either the routine schedule -- if they're on a 

routine schedule for plutonium they did not have to 

leave a job-specific bioassay.

If a worker, say two workers went in, one was a 

sheet metal worker, one was a laborer going into a 

same area to do a job, and they come out and the 

laborer -- it's a plutonium area, and the laborer's on 

a routine bioassay, he doesn't have to leave a jobspecific.

The sheet metal worker wasn't on routine plutonium

bioassay. They were supposed to leave the specific 

bioassay, job-specific bioassay. That was how this is 

to work, same work being conducted. Okay?

Routine versus job-specific bioassay, most of the 

workers, 95 percent according to the site, were on a 

routine bioassay -- monitoring method.

SC&A has postulated that subcontractors were 

primarily on job-specific bioassay. And this is from 

the November 2017 Work Group meeting. The 

question of how complete is complete enough for 

coworker development can only be answered in the 

context of coworker guidelines and stratification 

assumptions that have been validated.

They guide what datasets can be legitimately 

applied. However, 79 percent incompleteness 

strains credulity. And so what SC&A implied is that 

only 21 percent of the subcontractors were 

monitored.

Mr. Fitzgerald: Okay. Before you leave this one, it's 
174

not so much our implication, as you know. This was 

a Westinghouse self-assessment of its own jobspecific bioassay program in 1997 that found only 

21 percent of the bioassays were submitted.

And of course this was self-reported to DOE, who 

turned around and cited Westinghouse under the 

Price-Anderson Act and fined them. And that was a 

compelling reason for what we have done since 

then.

So we're not implying that only 21 percent of the 

contractors were monitored across all years. We 

said back then that with the self-assessment finding 

of 21 percent completeness it's a compelling reason 

to examine going backwards whether or not 

completeness was an issue for preceding years.

And that then led to looking at a number of different 

sources of documents to see whether or not one can 

establish the level of completeness for job-specific 

bioassays in preceding years, which has proven 

difficult because of lack of records.

But as far as applying 21 percent across-the-board 

we did not. That was a finding by Westinghouse that 

we, in fact, said, you know, had a compelling basis 

for looking at the question of completeness.

And the issue of strains credulity comes in when 

we're talking about completeness because I think 

there was a lot of dialogue about how complete is 

complete.

And I think our point is quite apart from all of the, 

you know, rationales for that question, the fact that 

Westinghouse confirmed they had 21 percent for 

that year, I think, is compelling enough to look at 

completeness.

That's not a borderline issue or a gray area. You're 

talking about a year where, you know, 79 percent of 

the job-specific bioassays were not submitted.

So that, it -- just a little more context on that. You 
175

know, we're not -- we did not invent the number. 

We did not suggest that number applied year by 

year. We were saying the fact that that was a 

finding that led to an enforcement action and led to 

resampling by Westinghouse for every one of those 

that were missing certainly suggests that one needs 

to look at completeness for other years to see 

whether or not it's an issue.

Dr. Taulbee: Okay

Mr. Fitzgerald: That was the context of how that 

came about.

Dr. Taulbee: Well, I understand. Okay, from what 

you're saying is that this should be investigated 

further, which is what we've done. That's what 

generated this report.

But in the context here, this is 

basically presenting that our datasets are 

incomplete and that using them strains credulity. 

These are your words, not ours, not DOE's.

Mr. Fitzgerald: Yeah, yes, but, you know, the 

context, don't miss the context because I think a lot 

of times in the presentations I've seen in the past 

there's a quote pulled out without any explanation 

or qualifiers.

And it suggests that we're doing or saying 

something is prohibited, dose reconstruction is 

prohibited. That was used once. That this applies to 

the regular, routine bioassay program. It does not.

This is explicitly on the question of RWP-directed 

job-specific bioassays and using a finding that the 

contractor generated. And we treat it as a red flag. 

And we're saying in terms of how complete is 

complete, the fact that they came up with 79 

percent incompleteness would certainly compel one 

to look at this further, to look at whether or not it 

has implications for the coworker model. And that's 

the context we've raised this.
176

And we've raised it now for two years.

Dr. Taulbee: Yes, sir?

Member Ziemer: Well, Joe, can you remind us what 

year did the event actually occur? Were they looking 

at a particular year, the 21 percent?

Dr. Taulbee: 1997.

Mr. Fitzgerald: That was 1997.

Member Ziemer: '97, okay.

Mr. Fitzgerald: And, you know, they didn't look at 

the previous years because, again, they didn't think 

it was cost-effective to go back and actually look at 

some of those records.

But nonetheless, that one year -- that one year --

and they had, you know, there was indications there 

was an issue --

Member Ziemer: Yeah.

Mr. Fitzgerald: -- and they had done some initial 

sampling before that and found not quite this 

dramatic an incompleteness but still found some 

incompleteness.

And they, at DOE's prodding, this is the local field 

office, went ahead and did a 100 percent sampling. 

And this is the only 100 percent sampling on this 

issue that's been done, quite frankly, because it was 

a contemporary.

They had all the records, which we don't obviously 

now, and this is what they came up with. And it was 

dramatic enough that DOE took action and even 

went so far as in 1998 did a DOE-wide moratorium 

on enforcement actions on this subject by itself, the 

bioassay programs.

Dr. Taulbee: Okay.

Mr. Fitzgerald: So it was a pretty compelling issue.
177

Member Ziemer: But it wouldn't be surprising if the 

subsequent years were much better then.

Mr. Fitzgerald: Well, it --

(Simultaneous speaking.)

Mr. Fitzgerald: Yeah, it certainly -- it was certainly 

an attention-grabber.

Member Ziemer: Yeah.

Mr. Fitzgerald: And it has -- it goes straight to the 

question of how complete is this segment of 

bioassays? And that's the reason I think we wanted 

to flag it as a significant issue. That's kind of a 

clarification.

Mr. Katz: Okay, before anyone else speaks, 

someone on the line has -- they have their line 

open. I can hear their background, and it's not so 

troubling to folks in the room, but it may be for 

other people on the line trying to listen. So can you 

please mute your phone if you're listening? *6 to 

mute your phone or a mute button. Thanks.

Dr. Taulbee: Okay.

Mr. Katz: Sorry.

Dr. Taulbee: Okay, Mike, did you --

Mr. Mahathy: Yeah, so I have something to add, a 

little bit of clarification.

Dr. Taulbee: Sure.

Mr. Mahathy: So there was an assessment of 3,200 

samples --

Dr. Taulbee: I'm getting ready to go through that, 

Mike.

(Simultaneous speaking.)

Mr. Mahathy: Okay.
178

(Laughter.)

Co-Chair Clawson: Hold on, Mike. We'll get to it.

(Simultaneous speaking.)

Mr. Mahathy: -- only -- okay.

Dr. Taulbee: Yeah, the next slide I have up is 

dealing with those 3,200. Okay.

Mr. Mahathy: All right.

Dr. Taulbee: Okay. During this time period, SRS did 

a limited assessment of the 3,200 bioassay 

requirements of all the workers. And here's -- the 

system that they had set up wasn't working like 

they thought. This is what was working. This is what 

they determined was happening. Okay?

And so just to walk you through here is you've got 

3,200 workers signing in on RWPs. Ninety-five 

percent of them go down this path off to the right 

here of leaving routine samples.

Did all of them leave routine samples? No. Some of 

them went on to a delinquency tracking system. 

Those that did ended up down here.

Now, this is saying 95 percent here of these 3,200 

made it directly over here. These got dispositioned 

somehow from that delinquency tracking system. 

I'm not exactly sure how many of them were 

completely followed up. That we don't know.

We do know that five percent were not left as 

required, okay? Ninety-five percent routine 

bioassay, five percent of these job-specific ones.

Mr. Fitzgerald: What timeframe, Tim?

Dr. Taulbee: This is the 1997.

Mr. Fitzgerald: This is '97, okay.

Dr. Taulbee: This is the fine year. This is the NOV. 

Okay? So you go down here and worker submits the 
179

required sampling. Some of them did. In this 

particular case under this 3,200 bioassay sample, 

33 percent submitted their samples.

And I don't know why that keeps popping up.

And so they come over here in sample received. 

And so but 67 percent of them did not, so 67 

percent of five percent results in 3.35 percent or 

about 107 of the 3,200 samples.

Now, this got worse. This was a limited assessment. 

They did a further assessment later in the year. This 

is where that 79 percent and the 21 percent comes 

into play.

But again, it's 21 percent of five percent, 79 percent 

of five percent. Actually, I shouldn't say that. We 

believe it to be 79 percent of five percent and 21 

percent of five percent.

We don't have these numbers as to what N was 

when they did that full assessment. We don't. We 

believe that these ratios held, but we don't know 

that for a fact.

But the point that I'm trying to bring here is, yes, 

SRS had a problem. They didn't use job-specific 

bioassay much. This impact on the coworker models 

for subcontractors is not what it appears when you 

just mention job-specific bioassay because a lot of 

these workers were monitored.

And that you're going to see through the rest of our 

presentation with the bioassay that we find.

So the unanswered questions from the December 

Board meeting was what fraction of the 

subcontractor construction trades workers were 

monitored? And again, I come up here to his 

previous one because we don't know for sure that 

this was really five percent.

But what we're looking for is how many samples are 

down here?
180

Co-Chair Anderson: Where did you get the five 

percent?

Dr. Taulbee: That was in one of the DOE reports. 

Okay. So --

Mr. Fitzgerald: And this is true for 1997.

(Simultaneous speaking.)

Dr. Taulbee: This is 1997.

Mr. Fitzgerald: -- snapshot. Right, okay.

Dr. Taulbee: -- 1997, so this ratio here could be 

different in other years. We don't know. So one of 

our questions is what fraction of the subcontractor 

construction trades workers were monitored? So 

what -- oops -- what fraction of them signing in on 

RWPs requiring bioassay we've got a sample for.

All right? Oops, there we go.

So that was one of the questions. Were 

subcontractors primarily monitored via job-specific 

bioassay? Because that's another question of were 

these job-specific bioassay dominated, and again, 

this is where, you know, there's some -- there was 

a question.

We had no idea whether they were dominated by 

job-specific bioassay that was incomplete or 

whether they were actually following the same 

operations-type of ratio of 95 and five percent.

Did the subcontractor monitoring change over time, 

by area or craft? Did unmonitored subcontractors 

work side-by-side with monitored subcontractors?

Did people signing in on the RWP, going back up to 

this one right here, was this decision done? Worker 

participates in a routine bioassay sampling 

program? Yes.

If they're both signing in on the RWP, this person's 

covered, this person's not under -- going down 
181

through this path.

So were they working side-by-side doing the same 

work? And were these subcontractors monitored for 

the correct radionuclides? Okay. There's a -- and 

SC&A raised a valid point of the bioassay 

moratorium and one of SRS's responses dealt with 

americium, and they said that, you know, we had 

some people that weren't signing in or weren't on 

the proper bioassay, bioassay schedule.

Now, we did an analysis of that, and when they 

went back and did a full audit of it it turns out that, 

yes, there were some that were not on the proper 

schedule, but it was less than -- I believe it was less 

than five percent, something along those lines, that 

were on the incorrect bioassay schedule.

So but this is one of the things we could look at by 

going to RWP. So here's the subcontractor 

monitoring evaluation for 1990 to 1998. Our three 

goals were to determine the percentage of 

subcontractor construction trades workers 

monitored by year; determine whether the 

unmonitored subcontractor construction trades 

workers were represented by monitored 

subcontractors in the same radiological 

environment, the same RWP at the same time; 

determine whether subcontractor construction 

trades workers were monitored for the radionuclides 

of concern given the radiological environment on 

the RWP. Okay.

So we developed a sampling plan. And what we did 

was randomly select subcontractor radiation 

workers from the various areas of Savannah River 

Site, such that an evaluation of the monitored and 

unmonitored workers could be conducted.

So the first step was to define the sampling frame, 

and I've got to tell you this -- I learned a ton in 

doing this about sampling. And Nancy's over here 

laughing at me because I was very naive in 

understanding what this entails and how much work 

is involved in doing so.
182

But the goal was to focus on the actinide exposure, 

okay? So we focused on plutonium, uranium, 

americium, and neptunium work.

We excluded the reactor areas because of the 

tritium. It wasn't much of a dose. It wasn't really 

significant from that standpoint.

Going back to DOE's notice or finding in violation 

back up here to this chart right here, we don't know 

what fraction here is actually tritium and which is 

the actinides.

We do know that the tritium doses are so low that it 

really doesn't play a role, so those were excluded.

We also excluded the standing radiation work 

permits, the SRWPs. So the routine work was taken 

out. Okay.

Now, in a box of records you're going to have 

regular RWPs and standing radiation work permits 

intermixed, okay? They're not one or the other. 

They're all together. Okay, typically a folder here 

and a folder there.

So we went through in developing our inventory, 

developing our sampling frame. We had estimated 

pages based upon our inventory, and this is what 

you get.

Now, when you take out the reactor areas, and we 

have a table for the reactor areas here, but taking 

that out and just focusing on the actinide areas, the 

plutonium areas are F and H.

M area is primarily uranium. A area has uranium, 

plutonium, americium, neptunium. E and Z area 

really there is some plutonium but not much from 

that standpoint. Okay?

So this was the estimated number of pages before 

we did our sampling. These are the folders 

containing RWPs with subcontractor construction 

trades.
183

When we did our inventory we specifically would 

pull folders within the areas within these boxes that 

we pulled. And we went through them to identify 

was there any subcontractor construction trades on 

these RWPs. Okay.

And so here are the folders that we would sample 

from, draw -- and sample from. And you can see 

there's 245 total, and this was spread across -- I 

don't remember off the top of my head how many 

boxes. I don't know, 60 or 70 boxes, something like 

that?

Dr. Chalmers: It might have been more than that.

Dr. Taulbee: It might have been more than that. 

Okay. So pardon me here while I take a sip.

So this was our sampling scheme. If you did it 

proportionally based upon the 245, it would be two 

RWP months from A area, two from E area, 42 from 

F area, 30 from H, one from M, one from Z.

Well, we wanted to try and get across all the sites 

or across all the areas and as best we could. And so 

we came up with a semi-proportional scheme, and 

that's what you see there of the 15 to 10, 30, 30, 

10, 10.

And this comes from this particular area here where 

you look at the pages. We had a lot from F and H 

area, and this is part of how we excluded samples, 

okay?

And what I mean by that is we excluded all of the 

tritium bioassay or tritium work, as well as limited it 

to actinide-type of work. That's going to be the F 

and H areas primarily and subcontractors.

So what we ended up with is this one and a half 

times the semi-proportional to try and get to 158 

RWP months that I'm going to call it here. And that 

means -- and let me try and further explain this. 

The RWPs would be in a folder. The folder could be 

a half inch thick or four inches thick of pages, okay? 
184

A single RWP might have been written for one day. 

Could have been written for a six-month job.

And so what we did from the sampling standpoint is 

we would -- our statistician, Nancy, came through 

the folder sampling here, told us which folder to go 

to. And at that point the HPs went through and 

identified all of the RWPs that had a subcontractor 

on them. Okay?

And in this particular example, this would be sample 

32 within our report, there were six RWPs that had 

subcontractor construction trades workers on them 

in that folder that we grabbed.

We would tell her there were six in this particular 

one. She used a random number generator and told 

us to go to whichever RWP. In this case it was RWP 

number six.

We then went to RWP number six, and we looked at 

how many months does this cover? Like I said, 

some of them were one month, some of them were 

six months, and we would feed that back to here.

She was sitting there right across the table like we 

are right now. And in this case we'd tell her March 

and April, and she'd tell us okay, go and do the April 

RWP.

At that point, we'd go and pull all the subcontractor 

construction trades workers off of that RWP and 

write their information down to later go and capture 

their bioassay. So that's how we came up with this 

random pull that we did.

So we sampled folders and boxes until a minimum 

number of 158 months was reached and a minimum 

number of subcontractor construction trades with 

766 were satisfied.

Member Lockey: How did you reach those limits --

Dr. Taulbee: Those limits were part of the sampling 

plan, and that was the --
185

Member Lockey: Based on?

Dr. Taulbee: There's here in report 92 there were 

some confidence intervals --

Dr. Chalmers: There was a whole simulation we ran.

Dr. Taulbee: -- the simulation.

Dr. Chalmers: And we had some, you know, some I 

guess subject matter experts or estimates of how 

this whole thing would lay out. And so we did a 

whole simulation and calculated confidence intervals 

until we got it as narrow as kind of Tim said he 

wanted it.

And that -- that gave us the 158 months and 766 

subcontractor CTW ---

(Simultaneous speaking.)

Member Lockey: Okay, so a statistical model that --

Dr. Chalmers: Right.

Member Lockey: -- to tell you how far you needed 

to go to make it --

Dr. Chalmers: Basically a sample size calculation --

Member Lockey: Right, got you.

Dr. Chalmers: Nothing as simple as you find in a 

textbook. This was --

Member Lockey: Sort of like a power calculation.

Dr. Chalmers: Yeah. Little bit. This was ridiculous.

(Laughter.)

Dr. Chalmers: So we had to simulate, you know, 

simulate things to get it --

Member Lockey: I just was curious. Okay, thank 

you.

Dr. Chalmers: Yeah.
186

Dr. Taulbee: Okay. And that's all there in the report 

as to how that came about.

In total, we got 662 subcontractor construction 

trade worker RWP month evaluations. Why did we 

miss 766? Well, because in the capture we did not 

pay real close attention to the bioassay monitoring. 

And what I mean by that is the RWPs you can go 

through and you can see that there are 

subcontractors on here. After we captured it if you 

went through and looked at the tasks, in latter 

years they would break it out as to whether 

monitoring was required by task. And so if the first 

task was for operations to go in and, you know, 

shut things down along those lines, then that would 

be like task one. Task two would be building a hut. 

Task three would be these other things.

Only whenever they went in to actually do some 

breaches would they be requiring bioassay for 

certain tasks -- or not bioassay, I'm sorry, wearing 

a respirator. And that was our trigger.

So we captured some RWPs and some people that 

didn't wear respirators because they didn't need to, 

inadvertently while we were doing the sampling.

So this is hard. This was hard to do in live time 

while you're going through here. And hindsight 

being 20/20, any time we do this in the future you 

guarantee we're going to be paying way more close 

attention to this. What this would impact, though, is 

really our power or our confidence of what these 

final numbers mean.

If you go back to the coworker match, the coworker 

use and dataset to criteria for the evaluation, what 

we call the implementation guide. This is one of the 

quotes that Jim had put in there, and it was the 

minimum number of samples should of course be 

considered when considering the number of workers 

potentially exposed to airborne source term.

For example, the number of samples that are 

necessary to be representative of exposures at a 
187

uranium foundry where airborne activity is generally 

widespread will be greater than the number 

required for a small glove box operation where six 

workers were involved in manipulation of plutonium 

parts.

The latter situation it may be that samples of three 

out of the six could be used to bound exposures for 

three who were not monitored. Okay? And that's my 

emphasis added there. Okay?

If you consider this RWP work of interest to small 

activity, in many cases this was -- well, most cases 

this was by far the activity. There would be a few 

people signing on this RWP.

The evaluation criteria that we used was the same 

RWP on the same day at the same time. However, 

time's not exact, and we went by kind of morning 

and afternoon because some people might sign in 

15 minutes later than the other person.

So it's not an exact science here as far as matching. 

It takes people time to dress up -- or to dress, get 

in and out. We did not match on craft. Why?

Well, in our opinion, the exposure environment was 

what the critical component was. The exposure 

environment can vary depending upon the RWP 

work being conducted.

As Joe pointed out earlier, context really matters 

here. Crafts may have similar or different exposure 

potentials.

So let's look at one of our matches here. This is 

example one in our Table 4.7. This would be a RWP 

for 241H. This would be the tank farms. And the 

work description was deconning the V2 riser for hut 

tear down.

So earlier, this particular, in this particular month in 

1992 they did some work on the V2 riser. And then 

once that work was done, this RWP was to go in, 

decon that area and so that the hut could be torn 
188

down.

So they're going to decon it. HP's going to come in 

then and see how well they did. Here are four 

construction trades workers that went in to do this 

work, a laborer, a carpenter went in at around 8:15, 

came out at 11:00.

A sheet metal worker and a laborer went in at 8:30 

and came out at 11:00. The two laborers and the 

carpenter were monitored for plutonium. The sheet 

metal worker was not. All of them were monitored 

for strontium and fission products. Okay?

We believe that this is similar work and meets the 

criteria. Matching on craft here wasn't necessary. 

Okay?

We do not believe it's correct to say that the worker 

was not monitored because they were not 

monitored for all the radionuclides on the RWP. 

Because in this case the worker was not monitored 

for plutonium, one worker was, the sheet metal 

worker, but dose reconstruction can be conducted 

for strontium using his personal bioassay.

The coworker model can be used to estimate his 

plutonium exposure. Okay? Similar work, same 

radiological environment, three of them monitored, 

one not. Okay?

So making a criteria to got to have all the 

radionuclides we think is too stringent. Let's use 

that.

So we went through and did an evaluation. We 

looked at the monitoring percentages for 

radionuclides of concern, plutonium, strontium, 

uranium, americium, neptunium --

Co-Chair Anderson: So again, why if they were 

there at the same time, the same environment, 

well, you could say it was appropriate, the sheet 

metal worker would not be monitored for plutonium 

because he --
189

Dr. Taulbee: No. This is a case where he should 

have been monitored for plutonium. Okay? But he 

didn't. He either didn't leave a sample. This is part 

of that job bioassay sample not being taken, not 

being left by the worker or the worker not being 

told to leave one --

Co-Chair Anderson: Yeah.

Dr. Taulbee: -- by HP. But for whatever reason the 

sheet metal worker was not monitored for 

plutonium. These other three people were. We 

would apply coworker model to this sheet metal 

worker based upon he was monitored construction 

trades worker with these other three.

They were doing deconning of the V2 riser --

Co-Chair Anderson: Right, right.

Dr. Taulbee: -- for a hut teardown.

Co-Chair Anderson: I understand but I thought you 

were using this to validate or invalidate the initial 

table that you had there on the 21 percent or not. 

That isn't -- they weren't using coworker at that 

time.

Dr. Taulbee: No.

Co-Chair Anderson: Okay.

Dr. Taulbee: What I'm --

Co-Chair Anderson: That's okay. I just --

Dr. Taulbee: Okay, yeah. No. We're trying to 

determine what fraction of these subcontractors 

were monitored at this point.

Co-Chair Anderson: But --

Mr. Fitzgerald: I think there's some --

Co-Chair Anderson: -- you're defining monitored 

broadly now to include coworkers being monitored, 

right?
190

Mr. Fitzgerald: Yeah, I think there's some conflating 

is what you're saying because I think the exercise 

itself is to substantiate that there's a sufficient 

representation of the subcontractor data --

Dr. Taulbee: Yes.

Co-Chair Anderson: Yeah.

Mr. Fitzgerald: -- in the coworker model. So you 

wouldn't have the coworker model be applied as 

part of this process now necessarily. It would come 

later if you were to do dose reconstruction.

Dr. Taulbee: Yeah, if we were to do dose 

reconstruction.

Co-Chair Anderson: Okay.

Dr. Taulbee: The initial question was are these 

subcontractor construction trades workers, is their 

data appearing in the coworker model or are they 

being left out of it due to this job-specific bioassay 

issue of them not being monitored? Okay?

And so in this particular case for plutonium we've 

got 75 percent of this work crew being monitored, 

25 percent not.

Co-Chair Anderson: Yeah, okay.

Mr. Fitzgerald: But your match just is -- that's a 

good point. You're matching percentages when we 

get to that point and you're looking at an RWP to 

determine whether or not a worker would have, you 

know, to identify a worker that would have been on 

that RWP.

You're just looking for one of the, I believe, one of 

what's on the RWP, right?

Dr. Taulbee: That's correct. In this particular case --

Mr. Fitzgerald: That's matching the --

(Simultaneous speaking.)
191

Dr. Taulbee: We matched the center, too. That was 

our match. We just looked for one additional worker 

to be on the RWP that was monitored and we called 

it a match. That this worker worked in an area with 

a monitored worker.

So coworker model made up of the monitored 

worker should be able to be applied to the 

unmonitored worker. Okay?

Okay, similar work, okay. Thanks for that.

We evaluated by year, by area and craft. We 

considered what we were going to call the effective 

monitoring based on matched coworkers for specific 

radionuclides. And I'll explain that a little more here 

shortly.

Okay. Six hundred and forty-four, and the example 

I'm going to walk you through, I'm not going to 

walk you through all the radionuclides that are here 

in RPRT-92 because it's, I don't know what, 188 

pages long.

I'm going to walk you through the plutonium 

evaluation that we did. Okay? There were 644 

subcontractor construction trades workers required 

plutonium monitoring from 140 RWPs.

Five hundred and sixty-seven of them were 

monitored for plutonium, or 88 percent of the 

subcontractor construction trades workers. So this 

is the population that we are concerned about 

whether they were monitored and whether they 

have -- they're sufficiently represented in the 

datasets such that the coworker model was valid.

Okay. Five hundred and forty-eight were monitored 

via plutonium urinalysis, 19 via in vivo chest count.

Mean number of days between the RWP and the 

bioassay is 159 days. Five hundred and one were 

within one year, 39 additional within two years. 

That's 540.
192

Some terminated workers were monitored upon 

their return which was greater than two years later, 

so that's what kind of makes up some of this 

difference. Okay? They didn't leave a termination 

sample but they came back a few years later and 

left a sample.

With plutonium, depending upon the solubility type, 

effectively if you have a bioassay at the end you can 

bound their dose, okay?

So here's the evaluation by year. And you can see 

that we did a pretty good job of sampling, I think, 

across the years. The number of RWPs is pretty 

similar and here you can see the number of 

subcontractor construction trades workers that were 

monitored, which was starting in 1991 there.

Eighty-two bioassays required across 17 RWPs and 

there were 78 of them monitored. So 95 percent in 

that particular case had bioassay.

Taking 1994, it's not as good. You have 140 

bioassay required across 32 RWPs and 104 of them 

were monitored. So 74 percent had bioassay.

Now, in this particular case it is if you look at the 

subcontractors without monitoring data, that's this 

column here, okay, that were matched working 

under an RWP with somebody who was monitored. 

That's what we were talking about here.

So the unmonitored we went through and looked at 

other construction trades workers, were the other 

people working with him monitored? And here you 

can see that in '91, three subcontractors were 

matched with the 78 that were monitored, bringing 

this up then to, what, 81 or 99 percent effectively 

monitored.

Go down here to 1994 and you've got 74 percent. 

Twenty or 104 subcontractors were monitored, 20 

additional could be matched to somebody who was 

working on an RWP, bringing this up to 89 percent.
193

So if you look at the direct monitoring over this 

whole time period, '91 to '98, you have 88 percent 

with bioassay, direct bioassay monitoring. Fortyseven were matched to people working on RWPs, 

bringing it to 95 percent. Okay?

This is why we believe, for plutonium, the 

subcontractors in this time period are adequately 

represented in what we will -- for a coworker model.

And we looked at this by craft next, and here you 

have, again, the 644 bioassay spread across 

boilermakers, carpenters, electricians, insulators, 

ironworkers, laborers, millwrights, painters, 

pipefitters. 

And as you see, the other category isn't really that 

big. This is the dominant trades that were doing the 

work that would require the bioassay. And this is 

that breakdown.

We don't see any major under representative group. 

The lowest group is your painters. They might 

actually have the lowest potential here. So all right.

We looked at it by area across the A, F, H, E and Z 

areas. E and Z were lower than F and H, which is to 

be expected. F and H were well-known plutonium 

areas and so we've got lots of plutonium bioassay in 

that area. Okay?

And again, we don't see any major 

underrepresented area, which was one of our goals 

was to look at all the areas, not just one or two 

areas.

So we didn't see any significant difference in 

plutonium monitoring by year, by craft, by area. We 

conducted this evaluation for all radionuclides of 

interest in this time period and that's this particular 

plot.

We've got those year, craft and area breakdowns in 

the report. You can go through and read those, but 

within this is the summation of them all for this time 
194

period of 1990 to 1998.

And what you'll see overall is that about 88 percent 

of the subcontractors have direct bioassay. We 

could match an additional, what was it, about eight 

percent -- no, seven percent to people who were 

monitored. So from an effective monitoring 

standpoint this would come out to 95 percent.

Member Beach: Tim, it looks like you're pretty light 

on 1990.

Dr. Taulbee: Yes.

Member Beach: '91 to '98 is it's pretty clear, but '90 

is pretty light.

Dr. Taulbee: And I'll get to that. It'll be at the very 

end, if that's okay?

Member Beach: Okay. Yeah.

Dr. Taulbee: All right. And then I know Joe and Bob 

are going to go into it in great more detail and I 

think it's going to be an area of further discussion 

absolutely.

Co-Chair Anderson: So is the match with a 

subcontractor coworker?

Dr. Taulbee: Yes.

Co-Chair Anderson: Okay, that's what I thought.

Dr. Taulbee: Yes, it is. Okay?

So most, I mean, subcontractor construction trades 

workers are considered to be more at risk for being 

under-monitored. Okay? That was what prompted 

this particular analysis.

More of your routine construction trades workers 

who were onsite would be under the routine 

bioassay. This is a group that may be more 

transient or for whatever reason might only be 

signing in under job-specific and how does that 
195

initial finding from DOE play out here?

And again, I'm -- this is based upon kind of three 

populations, operations workers, prime construction 

trades workers, subcontractors. And so the fraction 

of subcontractor workers needing a coworker model 

to supplement bioassay is rather small. It's on the 

order of less than 15 percent.

And that is, if you go back up to this one right here 

and you'll see the total here is 88 percent. For 

americium it's more than 15 percent. It's 25 

percent.

But what you'll see here with the plutonium is that 

these are people who have bioassay. These are 

subcontractors, have the bioassay in their file. They 

don't need a coworker model at all. Okay?

Or I shouldn't say at all because they could have 

gaps in the time period where they would need a 

coworker model. But we have bioassay data for 

these people. We get that when we do a dose 

reconstruction. Okay.

So if you take a step back and look at a global view, 

and I'm calling this the 30,000-foot level, of at least 

one bioassay, okay, of the 662 subcontractor 

construction trades workers in this time period, 633 

had one or more of the required bioassay. Okay?

Rated by the area strata and monitoring 

percentage, a point estimate is 95 percent, okay, 

have bioassay.

The bottom line is that in this time period most 

subcontractors were monitored for internal 

exposures and have bioassay data. So the coworker 

model developed from this we feel would be 

representative in this time period. Okay.

The 29 subcontractors, 4.4 percent that did not 

have any monitoring data, 19 of them were directly 

represented by other coworkers. Okay?
196

Only 10 subcontractor construction trades workers 

were not represented by a coworker, so there are 

some. And so DOE going in and fining them for 

violating procedures, people not doing it, sure. 

You've got some that were not monitored, okay?

But this is a small percentage of this total that we 

looked at. We feel the subcontractor data is 

sufficient.

By the way, of those 10, five either waived the 

bioassay required and we have documentation of 

that where they declined to leave a termination 

sample. And there's, Health Physics has no 

authority, no police authority to compel somebody 

so from that standpoint.

So it's really five of 662 were not represented from 

a coworker model that we can find. Okay.

Whoops. So this is that particular summary. So 

considering the majority of the subpopulation, the 

subcontractor construction trades workers, were 

monitored for each of the radionuclides and they 

would normally use the full uncertainty distribution 

of the coworker model for the unmonitored workers.

We feel that, A, the coworker model can be

constructed and subcontractors' doses can be 

estimated.

We also still have that option of the 95th percentile 

if we feel that somebody was going into areas or 

there's documentation. They provide it in their CATI 

and we get it from other ways, their external dose.

There's other pieces that can be evaluated. We can 

assign the 95th percentile and we consider that to 

be bounding.

So our conclusion here for the '90 to '98 time 

period, and that's critical, it's just this time period 

here that we've been focusing on is we continue to 

believe the coworker models developed from the 

workers with monitoring data are sufficient to 
197

estimate the dose to the few workers without 

monitoring data and supplement the monitoring 

data for those with incomplete internal monitoring.

Mr. Fitzgerald: Tim, just to go back to the date, I 

notice you have '90 there.

Dr. Taulbee: Yes.

Mr. Fitzgerald: But the area of your analysis was '91 

through '98? Did that switch? Was that purpose --

do you feel you can apply the coworker to '90 as 

well?

Dr. Taulbee: Yes, I do.

Mr. Fitzgerald: Okay.

Dr. Taulbee: And I will show why I believe that.

Mr. Fitzgerald: Okay.

Dr. Taulbee: Okay?

Co-Chair Anderson: Since this really is results from 

a sample, did you do the confidence intervals?

Dr. Taulbee: On the individual ones? No, we did not.

Co-Chair Anderson: On the percentages?

Dr. Taulbee: No, we did not.

Co-Chair Anderson: Okay.

Dr. Chalmers: Just the final one.

Dr. Taulbee: Just the final.

Dr. Chalmers: Only the final one.

Co-Chair Anderson: I don't --

Dr. Chalmers: The total percentage. You don't --

that works fine.

Dr. Taulbee: This one.
198

Dr. Chalmers: Right there.

Dr. Taulbee: Of whether they had any bioassay, not 

looking at -- because --

Dr. Chalmers: Okay.

Dr. Taulbee: -- in this particular one you could have 

a worker that was required to have plutonium, 

strontium and americium, okay? And so what we did 

here was we said did they have any one of those to 

be monitored? That's what this number here is.

Co-Chair Anderson: You didn't do it by individual.

Dr. Taulbee: No. No. I'm not sure you can --

Co-Chair Anderson: You didn't collect it.

Dr. Taulbee: I'm not sure we can develop the 

confidence interval from that. I didn't know.

Co-Chair Anderson: Well, you have, I mean, your 

sampling design --

Dr. Chalmers: Well, the sampling trials do not --

Dr. Taulbee: For that.

Dr. Chalmers: -- specifically for this --

(Simultaneous speaking.)

Co-Chair Anderson: For any, for any test.

Dr. Chalmers: Yeah.

Co-Chair Anderson: Or for any one of the --

Dr. Taulbee: For any of the bioassays.

Co-Chair Anderson: For any one but not for 

plutonium.

Dr. Taulbee: That's correct.

Co-Chair Anderson: Okay. The highest proportion 

was with the plutonium testing.
199

Dr. Taulbee: It was, yes, but if you go back to the 

very beginning --

Co-Chair Anderson: Yeah.

Dr. Taulbee: -- of this particular task of the question 

of --

Co-Chair Anderson: Generally it would have been --

Dr. Taulbee: Yeah, yeah, determine at what 

percentage of subcontractor construction trades 

workers were monitored in total. Okay. And this is 

not --

Co-Chair Anderson: For any of them.

Dr. Taulbee: For any of them and so --

Co-Chair Anderson: But it would all have been 

probably urinalysis.

Dr. Taulbee: Or in vivo.

Co-Chair Anderson: Yeah.

Dr. Taulbee: Whole body count.

Co-Chair Anderson: Yeah.

Dr. Taulbee: Chest count, sorry. I'm sorry, Liz. I 

learned that internal dosimetrists consider them 

differently.

Co-Chair Anderson: Yeah.

Dr. Taulbee: Whole body count chest.

Dr. Chalmers: But they are different.

(Simultaneous speaking.)

Co-Chair Anderson: You can't call it neptunium --

Dr. Taulbee: Yeah, okay. So that's 1990 to 1998 or 

1991 to 1998, whichever way you want to consider 

it at this point.
200

Okay. Now, let's look at what I'm going to call the 

late DuPont era of 1980 to 1989. Here we only had 

job plans available. Okay? The RWP system hadn't 

been implemented yet so we had job plans and 

special work permits for A area. That was the only 

area that we had.

The job plans were the primary source of 

information. The job plans for other areas we looked 

for and we looked hard for them.

We contacted the site. We worked with the records 

folks to try and locate them. These job plans is 

actually what we believe might have been destroyed 

based upon the interviews with workers.

Not their bioassay data, not their dosimetry data, 

but these job plans, okay? For some reason they 

may not have been considered as radiological 

records or whatever in the 1989 and '90 time period 

as DuPont was leaving. Okay. So we don't have 

them.

We do know that a lot of good, or a majority of the 

work switched to what they call standard operating 

lists, is that the correct term, Mike, the DPSOLs? I 

believe it's standard operating lists where they 

would have these tests --

Mr. Mahathy: Yes. Yes.

Dr. Taulbee: Okay, thank you. Where they would 

have the same tasks listed as the RWPs in the 

breakdown, but instead of sign in and sign out, it 

was initial off. And the initial we couldn't track back 

to a worker, so there wasn't a Social Security 

number like there were on later RWPs or a payroll 

ID or something along those lines.

They still had some work control, which is what I'm 

getting at, here from that standpoint. It was 

different. It wasn't as easy to -- well, and we 

couldn't tag it back to individual workers other than 

these particular job plans in A area where they still 

required them to write down their broker payroll ID 
201

number.

So we could look at A area. The safe work permits is 

SWP, by the way.

Co-Chair Anderson: Yeah.

Dr. Taulbee: Safe work permits were being phased 

out after 1972.

Because we realized pretty early on in our sampling 

frame development that we didn't have these other 

records, instead of doing a random sampling we did 

a census, meaning we captured all of them that we 

could find that had subcontractor construction 

trades workers. So every single one of them that we 

could find. All right?

And so here's the breakdown from 1980 to 1989 

and you'll see that we don't have anything in 1989. 

So as Josie pointed out, 1990, 1989, we've got a 

couple of years here where we don't have any data 

that we can evaluate in this manner.

Okay. We have a number of job plan pages here, 

and then here we did a breakout here of DuPont 

construction trades workers and subcontractor 

construction trades workers. Okay.

So 610 job plans, 200 of them were DuPont 

construction trades workers, 11 were subcontractor 

construction trades workers. Okay, so we have 211 

total or 34 percent are actually construction work. 

The other job plans were operations.

Okay, so these construction jobs were mixed in with 

all of the other job plans that operations did, where 

operations would do something unusual going into 

an area and cleaning it up or something that was 

non, you know, standard type of work. They 

documented that the same way in job plans. Okay.

Overall, we have about three percent of the 

construction work being subcontractors.

Mr. Fitzgerald: Or 773-A, I mean, the A area only.
202

Dr. Taulbee: Yes.

Mr. Fitzgerald: Okay.

Dr. Taulbee: For 773-A. In total, we came up with 

591 subcontractor construction trades worker 

monitoring evaluations with 219 unique 

subcontractors on 145 job plans. So if you compare 

that to our sample down there at the bottom, we 

had 429 unique subcontractors on 146 RWPs.

And as Joe pointed out, this is just A area, okay? 

That '80 to '89 is just A area. All right.

We went through the same evaluation method as 

the '90 to '98, break by craft or by new client, by 

year, by craft. We didn't do by area because we 

only have the one area. Okay. We didn't have any 

data in 1989.

And so here is the breakdown that you get. And in 

this case you'll see that the percent with bioassay is 

less, okay? It's only 80 percent.

1980, it's only 50 percent, but look at the numbers. 

They only had six job plans; three of them were 

monitored. Okay? Or six, from the job plans only six 

bioassays were required. Three of them were 

monitored, three were not, so this is a pretty small 

sample size, especially compared to '81 through '87. 

Okay?

So that's one thing to take into account, that 50 

percent. That's got a high confidence or --

Member Beach: Tim?

Dr. Taulbee: -- a large consortium.

Member Beach: Tim, how many facilities does this 

cover?

Dr. Taulbee: This covers one.

Member Beach: Just one.
203

Dr. Taulbee: Just one a year, okay? We went 

through the crafts, same thing. And again, by the 

way, the lowest percentage here would be the 

electricians at 61 percent. Okay?

But again, when matched to coworkers it actually 

goes up to 98 percent. So they were -- less of the 

subcontractors were being monitored than before, 

sure, but we actually have more that we can match 

to those subcontractors.

Okay. So here's these totals for plutonium, 

strontium and americium, all right? And you can see 

that the total is lower. Americium is quite low, 34 

percent, okay. It has bioassay. Okay?

But a large number of them were matched directly 

to those subcontractors. Okay? Sixty three of them 

are matched to 52 people with bioassay.

So the results of plutonium and strontium are 

slightly a little over, about 10 percent in this time 

period compared to the '90 to '98 time period. 

Americium is significantly lower in this time period.

So we only had the job plans, Josie, to answer your 

question there, for A area, but we had incident 

report data for F and H area. And so we went and 

looked at that. And by the way, that says '80 to '89 

and it actually should be, I believe, '85 to '89. I 

think I corrected another slide later on in here.

So we did have, and this was limited data, but we 

do have indications of reasonable monitoring in 

these other areas during this time period.

The combined evaluations of no significant 

difference by year, craft or area, less than 10 

percent -- less monitoring, about 10 percent than 

the modern era, but significantly lower for 

americium.

This is the incident monitoring data from '85 to '89. 

So if you go back up here to this plot -- no, wait a 

minute -- this one here we have no data for A area 
204

in '89. But in F and H area we have actually do have 

some 1989 data. It's small, but following an incident 

did they do follow up?

So this is if something happened in the workplace 

did these other areas where we have incident data, 

did they do follow up?

Are those workers involved in incidents part of this 

coworker distribution, part of that population that 

we would do the TWOPOS values on, do the further 

analysis of going through the fitting and the intake 

modeling. Are they representative?

And again, this is just the subcontractors. So these 

are subcontractors that are involved here. All right?

You know, to the left we've got plutonium, to the 

right it's the strontium, which we have even less 

data from from those areas. But I mean, a lot of 

these incidents were on the plutonium line so you're 

not going to have much strontium monitoring at 

that time period. So that's why those numbers are 

lower, okay?

So we have significantly lower percentage of 

subcontractors being monitored for americium, 34 

percent here. Okay? What we're interested in in 

Work Group feedback on whether direct monitoring 

of a third of the population with documentation that 

76 percent is effectively monitored is sufficient for a 

coworker model?

Keep in mind that this is subcontractors only. Okay? 

So we've got a third directly monitored, third 

working with the directly monitored, roughly, I'm 

rounding here greatly, and a third that is 

unmonitored with no coworker, of subcontractors.

Considering that less than 15 percent of the 

construction trades work was conducted by 

subcontractors this comes back up to this point, this 

graph here of subcontractor pages, roughly, versus 

DuPont construction trades work.
205

In the coworker model we've combined them both. 

Okay? So to here we've got less than 15 percent, an 

estimate, okay? This is an estimate in this time 

period. Less than 15 percent of the construction 

trades work was conducted by subcontractors.

The majority of the subcontractors were directly 

monitored for plutonium and strontium and a third 

of this subpopulation was monitored for americium. 

Again, the majority, 76 percent of the 

subcontractors are effectively monitored.

Does the coworker model that we developed that's a 

combination of DuPont Construction and 

subcontractors, is that sufficiently representative 

when you consider how we apply it, of assigning the 

full distribution or the option of assigning the 95th 

percentile?

So we continue to believe that the coworker models 

developed from workers with plutonium and 

strontium are sufficient based upon what we found 

here, to estimate the dose to the few workers 

without monitoring data, supplement the monitoring 

data through those with incomplete internal 

monitoring.

And the americium is going to require, I think, some 

further discussion here. Okay? All right.

Well, let's get to the '72 to '79 time period. Now, I 

call it the mid-DuPont. I don't know what else to call 

it. It's post-SEC time period for our evaluation but 

it's kind of in the middle of DuPont era.

Again, we only had the job plans available for A 

area. That was the primary source. Instead of 

sampling we did a census. We looked at all of them.

There was no data at all found from '75 to '79. 

Could not find any job plans with subcontractor 

construction trades on them. There are a few job 

plans but none indicated subcontracted work.

So our evaluation's really limited to those first three 
206

years '72 to '74. Here's the actual job plan pages 

and you can see, I mean, 1,100, 1,000, 146. You 

can see it drops off very quick, and we just do not 

have any of this data in this time period in order to 

do an evaluation.

Interestingly, of the data we do have, again, we see 

this same ratio of about three percent subcontractor 

work from a construction standpoint. So they're 

making up about around 15 percent of the total 

construction work. Okay?

We went through and did the same evaluation as 

before and instead of 219 subcontractor 

construction trades on 145 job plans, we got 31 on 

59. Data is extremely limited.

And again, this is A area only and no data from '75 

to '79.

Co-Chair Anderson: What was the total workforce 

again?

Dr. Taulbee: In that time period I don't know, of 

construction trades workers, oh, that's going to be -

- Liz, could you look that up? That's under the 

americium section here. Yeah.

I want to say construction trades workers --

Co-Chair Anderson: How did they compare to the --

Dr. Taulbee: -- was around about 500 or so. But I 

don't know about, I don't know that that total 

encompasses subcontractor construction trades 

because I'm not sure how they were counted ever 

onsite. But we really don't know if they were ever 

considered in their force plans.

There should be a table in that section of 

subcontractors. Maybe it's -- okay. Liz is going to 

try and see if we can find it.

Co-Chair Anderson: Just curious on at Savannah 

River, I don't know the total workforce over the 

years. Was there a big increase in it over the years 
207

or was this pretty stable from '72 through --

Dr. Taulbee: No, it was not stable. It ramped up 

tremendously in the 1980s and into the early '90s 

and then began to ramp down around '93, '94 time 

period. So there's a big ramp up in the late '80s into 

the '90s.

Okay. So if you look at these results from the years 

you'll see that the plutonium, most of the results we 

have in 1972 and we have 65 bioassay required. 

Only 50 of them were monitored and you're 77 

percent. You can match 11 subcontractors to them.

In '73, 64 required, only 18 were monitored. 

Thirteen we can match to it for 46 percent. Only 28 

percent, though, were directly monitored.

1974 we only had seven, but again, the data is 

extremely limited here. Only one was monitored, 14 

percent. One was matched, 29 percent. This 51 

percent has a huge error bar across it, okay? So 

that just -- keep that in mind here.

We didn't see any significant difference there 

amongst craft. Maybe with pipefitters but, you 

know, that, if you look at the number of bioassay 

that was much larger than any of the other groups. 

Most of these job plans were, I guess, in a sense 

geared toward pipefitters there. They make up a 

larger portion of the population here.

And so here's the total of plutonium, strontium and 

americium. There's only one bioassay required that 

we could find that needed americium and that 

person was not monitored. Okay. So there's only 

one data point under the americium.

The evaluation was limited and dominated by the 

1972 data. So this time period of '72 to '79 is really 

1972 for all intents and purposes, but that's the 

starting point there.

So less than majority of the subcontractor 

construction trades were monitored for plutonium in 
208

'73 and '74.

Strontium monitoring was better, though we only 

have one data point for americium.

Again, we're interested in Work Group discussion on 

the sufficiency of the monitoring data of the 

subpopulation for coworker model. We have less 

than 15 percent of the subcontractor construction 

trades work was conducted by subcontractor 

construction trades. Okay?

Now, we do have coworker models that we've 

developed for construction trades workers. Clearly, 

based upon what we've seen here from this 

sampling, is that is being dominated by the DuPont 

construction trades workers here.

Evaluation indicates some workers were monitored 

for plutonium. The majority were monitored for 

strontium. Again, that's primarily on 1972 though.

We normally use the full uncertainty distribution of 

the coworker model, co-exposure model for 

unmonitored workers wherein have the option of 

the 95th percentile.

So here's our summary and conclusions here. And I 

understand Jerry's talking about the context here so 

I'll go quickly on this.

The initial question was the 79 percent 

incompleteness. Okay. And that's where I'm getting 

at here and they had concluded that the bioassay 

datasets for construction trades workers specifically 

and construction trades workers generally is 

demonstrably incomplete for 1989 to 1998. And it 

didn't satisfy the criteria set forth in the draft 

criteria evaluation use of coworker datasets.

Okay. We, again, respectfully disagree. The 1990 to 

1998 data indicates that in fact, most, 38 percent of 

the subcontractor construction trades workers were 

directly monitored for the radionuclides of concern 

by either routine or job-specific bioassay.
209

A census review of all the job plans in A area 

supplemented by the incident reports from 1980 to 

'89 also indicate that far more than 21 percent, 

most were monitored for plutonium and strontium. 

Americium is 34 percent and may require some 

additional discussion here.

The job plan and RWP data are insufficient to really 

evaluate whether subcontractor construction trades 

workers were sufficiently monitored or represented 

by coworkers from '72 to '79.

There's limited subcontractor construction trades 

work, less than 15 percent during this time interval. 

Some subcontractors were clearly monitored, we 

have their data, therefore they're part of the 

coworker distribution.

But the uncertainty as far as how much, what 

fraction are they represented in the coworker 

model, 15 percent, there just isn't data to try and 

verify that. So we're interested in the Work Group 

discussion on the sufficiency of the monitoring data 

for the subpopulation for coworker model inclusion.

Mr. Fitzgerald: Tim, just to clarify, this sort of goes 

back to one of the original questions. I know this 

bounced back and forth between Work Group and 

NIOSH, which is how complete is complete? I mean, 

I think from what you're demonstrating here it's 

somewhere between 79 and 88 percent were the 

two percentages you thought were most, the most 

monitored, directly monitored?

Dr. Taulbee: Most means 50 percent to me.

(Laughter.)

Mr. Fitzgerald: Well, it's just that we have kind of 

not returned to that question but that sounds like 

what you're broaching to the Work Group is, is that 

the right threshold for completeness or not?

And it's not an objective or quantitative question. 

It's a, I guess, again, a judgement as to how 
210

complete is complete.

Dr. Taulbee: And that's where we're looking for --

Mr. Fitzgerald: And you're saying 50 --

(Simultaneous speaking.)

Mr. Fitzgerald: -- more than 50 percent --

Dr. Taulbee: I feel that that is sufficient --

(Simultaneous speaking.)

Mr. Fitzgerald: That number has shifted over time. 

You're now saying over 50 percent would be, you 

know, complete enough, I guess is what you're 

saying?

Dr. Taulbee: I'm -- yes, with the caveat that it could 

be less than that.

(Laughter.)

Dr. Taulbee: No, no, seriously.

Mr. Fitzgerald: Yeah, I know.

Dr. Taulbee: I mean, I go back here to the 

americium here because you're looking at, let me go 

back up here to this one, this -- and here's the 

reason that I'm saying that. I'm not trying to be 

difficult here.

But you're looking at, for the americium 34 percent 

of the subcontractors, okay? And another third of 

them we know worked directly with those folks. So 

those unmonitored people we would be applying the 

coworker model to, they're -- that direct connection 

is part of that coworker model.

One-third of these subcontractors are part of the 

coworker model now, is what I'm getting at. They 

are represented in there.

Mr. Fitzgerald: Okay, all right.
211

Dr. Taulbee: And so it's not -- we don't, we're not 

parsing out subcontractors, subcontractor 

construction trades and DuPont construction trades. 

We have a combined model and we know that 

subcontractors in this time period conducted less 

than 15 percent of the work.

So a third of them are already in the coworker 

model. They make up less of the construction work 

that was being conducted at the site, 15 percent of 

it. Okay?

So if I was looking at all construction trades workers 

and it was less than 50 percent then yes.

Mr. Fitzgerald: But the 50 percent --

Dr. Taulbee: Taking out this vulnerable population 

I'm not sure that that 34 percent isn't sufficient.

Mr. Fitzgerald: Right, but the --

Dr. Taulbee: And that's what I'm asking for the 

Work Group to --

Mr. Fitzgerald: Right. The caveat though is the 

context that the 15 percent is one facility. We're 

talking still 773-A, right?

Dr. Taulbee: Yes.

Mr. Fitzgerald: Okay. I'm just trying to make sure I 

understand the time.

Dr. Taulbee: But is there any evidence that this is 

different than that for the other areas?

Mr. Fitzgerald: No. I'd turn it around. You have 

evidence for the other 34 major facilities. That's 

kind of what we're going to get into. I don't want to 

get into it here, but --

Dr. Taulbee: Okay. Let me --

Mr. Fitzgerald: -- just to clarify, that's kind of what 

we're talking about, 773, isn't it?
212

Dr. Taulbee: So let me get to the final here. All 

right. So all right. It's limited, okay. This will be --

okay. Our review demonstrated that unmonitored --

I believe that our review demonstrated that 

unmonitored workers worked alongside the 

monitored workers in the same radiological 

environment from the 1980 to 1998 time period.

The bioassay data is present within the individual 

monitoring records and can be used for dose 

reconstruction. These internal monitoring records 

can also be used to develop the coworker models 

and subsequently used in dose reconstruction to 

supplement the gaps in individual monitoring data 

for these subcontractors. Okay.

Again, coworker model use, we developed these coexposure models because we recognize some 

workers were not monitored. For a coworker model 

to be valid it has to be a representative sample. 

That's all that we need to have, okay, is a 

representative sample.

Co-Chair Anderson: Of what though? Of who?

Dr. Taulbee: Of the exposed population.

Co-Chair Anderson: Not just of the construction 

trades workers?

Dr. Taulbee: Well, if you're going to stratify then it's 

got to be --

(Simultaneous speaking.)

Co-Chair Anderson: No, but I mean, the --

Dr. Taulbee: It's got to be both.

Co-Chair Anderson: The point is this morning you 

really showed the whole population.

Dr. Taulbee: I --

Co-Chair Anderson: And now you're showing the --
213

Dr. Taulbee: A sub, part of that.

Co-Chair Anderson: Yeah.

Dr. Taulbee: But this morning I just showed the 

non-construction trades workers. This is a 

subpopulation of the construction trades workers. 

That's what I'm showing.

Again, if all the exposed workers were monitored, I 

mean, if we had 100 percent of the subcontractors 

monitored for americium we don't need a coworker 

model. So that's something to keep in mind.

Actually I'm going to skip this and let SC&A go 

through this because I think this even shows that 

we -- I mean, their conclusion is very similar to 

ours.

So this is kind of our, what we're seeing in the 

evaluation status in a sense, a summary and where

we need further discussion. '72 to '79, to be 

determined. There just isn't data there. We'll have 

to -- what do we want to do here from that 

standpoint?

'80 to '89 we believe plutonium and strontium is 

fine. Americium we need to have some more 

discussion on.

Now, here I want to go through the evaluation of 

time and can we find a more efficient way. And 

maybe this is better for the end of the discussion, 

but this gets to some of what Josie was bringing up 

earlier and Joe was bringing up with regards to we 

only have A area data and so it's not across the site.

But this is the evaluation timeline, okay? So when 

we started in February of 2018, kind of all of the 

major steps, when we did the data capture, and it 

took us 16 months to get this report out, all right.

I thought it was going to take us eight to nine 

months tops. It took double that to get it out.

SC&A has been reviewing it for another four months 
214

for us to get out their comments, okay. So we're at 

21 months right now of getting this out to have this 

discussion. It's very labor-intensive. This is a 

difficult analysis even for a site with very good 

monitoring records.

This is currently being attempted to be repeated, 

RWP analysis from Los Alamos, and I believe, Josie, 

you had an email over there this week from Bomber 

talking about delays already from getting data out 

of Los Alamos.

Member Beach: Got a solution for that.

Dr. Taulbee: SC&A had raised with Bomber, LaVon 

Rutherford, the issue of fairness indicating we did 

this work for Savannah River. We should do the 

work for Los Alamos. If we attempt to do this type 

of evaluation for every site these evaluations won't 

be completed for over a decade, all right.

Can we conduct something simpler and obtain 

similar results or insights? Will it be this detailed? 

Are we going to get down to where we can do that 

analysis, you know, and look at individual workers 

on RWPs?

Again, this is very hard. What we had ORAU look at 

was the subcontractor monitoring using only NOCTS 

data, okay. And we evaluated those subcontractors 

who were externally monitored, so subcontractor 

construction trades workers who were externally 

monitored, and evaluated whether they had any 

internal monitoring results. Yes or no, simple.

We didn't look at tritium, or actually we did but I'm 

not reporting that here. We just looked at the nontritium bioassay, the actinides and whole body 

counts for fission products.

Here's the results. This is our report RPRT-92 and 

RPRT-94, okay. Remember that time period of 1970 

to 1980 where we were talking about having 

difficulty getting records? We're not seeing them in 

the claimant files either, okay.
215

This is monitoring data within the individual files. 

The blue bars are RPRT-92. And in these earlier 

years it's the plutonium bioassay here.

We get to the 1980s and there was an increase in 

subcontractor monitoring that is quite clear when 

you get into that time period.

The reason that I kept using 1990 to 1998 and 1980 

to 1989 is you look in that time period on this plot 

here, we don't have any RWP data, we've got a lot 

of claimant bioassay that's being done in that time 

period. We have a lot of monitoring that's being 

conducted.

We might not yet be able to prove or demonstrate 

the job plans or RWPs, because they don't exist, 

they might have been destroyed for whatever 

reason, but we still have a lot of individual bioassay 

monitoring data in that time period.

So to just discard that -- I don't think it's right to 

just discard this information from NOCTS that's 

showing we have a monitored population in here.

Now, can this particular NOCTS data evaluation be 

done in more detail and instead of any bioassay, 

you look at just americium, look at just plutonium, 

look at -- it can be, but I'm trying to figure out a 

way where we can try to do some analysis without 

going back to the sites and digging through all of 

the RWPs trying to find them, develop a reasonable 

sampling, something that's faster that gets you all 

the answers so that we can move forward with 

other -- some of these sites.

Member Beach: Tim, excuse me, but I thought you 

guys already looked for all the RWPs? Didn't you 

pull those?

Dr. Taulbee: What do you mean?

Member Beach: The RWPs? You said to go back to 

the site and try to find more RWPs.
216

Member Ziemer: The other sites.

(Simultaneous speaking.)

Dr. Taulbee: Other sites.

(Simultaneous speaking.)

Member Beach: Other sites? Okay.

Dr. Taulbee: Yeah, other sites.

Member Beach: I thought we were speaking --

okay.

Dr. Taulbee: Yeah.

Member Beach: Well, I'm --

(Simultaneous speaking.)

Dr. Taulbee: -- potential benefits of doing this, it's a 

simpler analysis. Like I say, when you look at 

individual radionuclides it's more resource efficient.

There's less data capture and coding, more timely 

analysis. There'd be less classification when you 

have the data in-house.

There is a potential detriment. We can't directly 

compare coworkers, therefore the data 

completeness really must be inferred, okay. We can 

look at subcontractors, but we can't look at this 

subcontractor, were they paired with another 

subcontractor on the RWP. We can't do that.

But the other components of this we can with the 

NOCTS data. It's something to think about. And 

with that, I'll turn it over to SC&A for you all's 

presentation.

Mr. Fitzgerald: Just on that last point, I think it's an 

interesting point, but would that not be consistent? 

We're talking guidelines most of the day and just, 

you know, looking at the question of establishing 

completeness, data completeness, this would not --

this would not -- it doesn't look like it would satisfy 
217

this.

I mean, it would definitely be expedient. I don't 

disagree with that, but as far as, you know, 

weighing the completeness of the data as a 

prerequisite it doesn't seem to touch that base as 

far as the guideline. I just want to get your reaction. 

Do you think that's the case?

I mean, this does look more expedient but it doesn't 

seem like it addresses this.

Dr. Taulbee: I honestly think it kind of would 

address that. I'm not, I'm trying to think of how this 

could be, and me thinking live time is dangerous, 

but it would be more in looking at the percentages.

If you saw that there was no monitoring data for a 

particular group, I think that speaks to the 

completeness of what we need to be doing. Keep in 

mind, a lot of these coworker models we are 

developing based upon the NOCTS data that we 

currently have.

And so -- I shouldn't be thinking and talking at the 

same time. But what I'm trying to get at here is 

that we don't see any major temporal gaps. I mean, 

here we do. I mean, clearly there's something going 

on in the late '70s here with subcontractors that 

they were not monitored.

I mean, we can see that this is a low percentage 

here in, what, 1977? Less than 10 percent of our 

NOCTS claimants have bioassay on the --

Co-Chair Anderson: Well, what's the total number I 

guess is the question here?

Dr. Taulbee: That we would have to look at.

Co-Chair Anderson: So if it's 10 percent of 100 --

Dr. Taulbee: Yeah, so maybe that's a way of 

speaking to the completeness issue of, you know, I 

don't know the answer.
218

Mr. Fitzgerald: Well, yeah, like I said --

Dr. Taulbee: But going through it, I mean, you're 

looking at over a decade. I mean it's this is --

Mr. Fitzgerald: I appreciate the dilemma, you know, 

and the question of establishing the completeness of 

the data so you have a representative database, 

you know, if NOCTS is representative then the first 

precursor is to make sure the data is sufficiently 

complete.

And if you can't do that then you don't necessarily 

know -- I mean, intuitively, and actually we did look 

at the NOCTS database, Ron Buchanan did, from a 

standpoint of establishing the scope of facilities 

covered and years.

And I think we were, you know, favorably disposed 

that it seems like it touches the facilities or whatnot. 

But as far as the completeness question, if we, you 

know, go back to the guidelines it would almost 

seem like your --

Dr. Taulbee: Some of the guidelines were written 

from the standpoint of when we get, like, an 

electronic dataset from the site is it complete? 

We've run into issues at Oak Ridge, for example, 

where we got the dataset and we learned that 25 

percent of the results weren't in there.

And so we went back to the site and started working 

with that. So, but from this standpoint using just 

NOCTS I don't believe there's ever been any -- I 

mean, completeness hasn't been evaluated from 

that standpoint, but I'm not -- I can't see how it 

wouldn't show up if you were to look at the 

individual radionuclides.

Co-Chair Anderson: It's only it's all of the people 

that have cancer, right? I mean, so --

Dr. Taulbee: Yeah, right, it's --

Co-Chair Anderson: -- if there's an association 
219

between exposure and cancer this is going to -- I 

mean, I'm not surprised you have the monitoring 

because that's what the whole program's about is 

the cancers --

Dr. Taulbee: Right.

Co-Chair Anderson: -- or some of the cancers.

Dr. Taulbee: Right. I guess the leap of faith -- or 

not leap of faith but the analogy would be these are 

the claimants. These are the people with cancer and 

so they are the ones who might have been harmed 

from that standpoint, therefore did they have any 

exposure and do they have monitoring data? Is that 

from a completeness standpoint? I don't know.

Co-Chair Anderson: But again, it's everybody, you 

know, who had a cancer and filed, so I mean, I'm

just getting at the representative of the population.

Dr. Taulbee: But it's really a random sample, 

NOCTS is really, to a large degree.

Co-Chair Anderson: Well, that's not going to a 

tumor registry and looking at the age distribution 

and say, oh, this is, you know, representative of the 

population. It's --

Dr. Taulbee: Well, no.

Co-Chair Anderson: Well, but it did, I mean, people 

with cancer are not the same as people without 

cancer.

Member Lockey: I think you'd have to look at a 

specific cancer to come to your conclusion.

Co-Chair Anderson: Well, but by and large this is 

going to be radiation where there's a number of 

cancers you could file for. I mean, their -- they've 

all had cancer.

Member Lockey: Yes, but not all of them are -- not 

all of the cancers that are compensated for are 

radiosensitive cancers. So you have to look at --
220

Co-Chair Anderson: Right, but it's --

Member Lockey: You have to look at cancers that, 

as well as documentation there's a dose response 

relationship with radiation. Whether it be thyroid --

Co-Chair Anderson: I've never seen any literature 

say that --

Member Lockey: Something like that, yes.

Co-Chair Anderson: -- you know, that people with 

cancer are representative, you would use that as a 

sample representative of the population.

Member Lockey: Well, that's correct, yeah, that's 

correct.

Co-Chair Anderson: And that's what I'm saying, that 

you have in the criteria that's supposed to be 

representative of the workforce, not a sub-segment 

of the workforce.

Member Lockey: Yeah, that's right. That's right. I 

see what you're saying.

Co-Chair Anderson: That's what I'm saying.

Member Lockey: I see. I understand.

Dr. Taulbee: Okay. This was the food for thought 

here, you know, from that standpoint. And of 

course, yes, I just looked at the time, wow. Right.

(Laughter.)

Member Lockey: Well, we had a good time.

(Laughter.)

Dr. Taulbee: So I don't know if you --

Co-Chair Anderson: We've got 10 minutes.

Dr. Taulbee: I don't know if you want to get SC&A 

started and we pick up tomorrow or what do you, 

what do you want us to do then?
221

Mr. Katz: Well, that's the question is taking 

everybody's temperature in terms of endurance and 

otherwise having to be, for people who are local, 

whether you have to scat for other reasons.

Co-Chair Anderson: It's already too late for rush 

hour.

Mr. Katz: So both of those questions, endurance 

and whether there are people that need to be gone?

First of all, for you folks that are local are there any 

of you that -- is there a certain time when you need 

to be gone?

Dr. Taulbee: I would like to be gone by 5:00 to pick 

up my wife.

Mr. Katz: Okay, so that's what I'm asking.

Dr. Taulbee: Okay.

Mr. Katz: So do we have --

Mr. Fitzgerald: Are we starting at 8:30 tomorrow?

Mr. Katz: Yes.

Mr. Fitzgerald: Okay. I think that would be enough 

time.

Mr. Katz: Do we, yeah, do you have a better sense 

of that? Do you have what's left on --

Mr. Fitzgerald: Well, I would imagine just for our 

presentation --

Dr. Taulbee: Yeah, exactly.

Member Ziemer: -- which I think we can handle in 

the morning.

Mr. Katz: Okay, then it seems like this is probably a 

good breaking point, right?

Dr. Taulbee: They could do their presentation and 

then talk about what it is that we're going to 
222

present next week.

Mr. Katz: Yeah, and folks are going to need to think 

about your big question with respect to putting to 

bed or not putting to bed the draft guidelines then, 

right, because you're sort of raising the question, 

does that have a bearing on --

Dr. Taulbee: No.

Mr. Katz: Oh, it doesn't?

Dr. Taulbee: No, nothing to do with the draft 

guidelines.

Mr. Katz: Okay.

Dr. Taulbee: I think that's settled.

Mr. Katz: Okay. Well, we need to actually do that in 

the meeting at some point, but --

Dr. Taulbee: Well, we could do it now if you want?

Member Lockey: Let's do that now, Henry.

Co-Chair Anderson: I thought we did.

(Laughter.)

Mr. Katz: We didn't because you had that other 

question.

Member Lockey: Are you willing to put to bed the 

complete guidelines and accept them as written?

Co-Chair Anderson: Yes.

Mr. Katz: Okay. We haven't had a motion on that.

Co-Chair Anderson: I can't move that because I'm -

-

Mr. Katz: I know you can't. You can't.

Co-Chair Clawson: I move that we accept --

Mr. Katz: You can't move that either.
223

Co-Chair Clawson: Oh, shoot.

(Simultaneous speaking.)

Co-Chair Clawson: I've got to have something to be 

able to say here and I've been sitting here pouting 

away at it.

(Simultaneous speaking.)

Mr. Katz: -- but this is just the question of the 

coworker guidelines.

Co-Chair Clawson: Get coworker done because I've 

got something I want to --

Mr. Katz: Yeah, of course, of course. No, it's just 

like, it's like, so we don't want to switch horses in 

the middle of the madness here.

Co-Chair Clawson: We've been doing it all day.

(Laughter.)

Mr. Katz: Okay, actually, so we have Paul and Andy 

and --

Co-Chair Anderson: And Josie.

Mr. Katz: -- Josie.

Co-Chair Anderson: Josie.

Member Beach: And Gen.

Mr. Katz: And Gen is on your group, right?

Member Beach: Yes.

Member Ziemer: Do you recall whether the Board 

actually took action on what we're calling the draft?

Mr. Katz: Yeah, I do. The Board tentatively accepted 

the draft with the proviso that this would be done, 

this work would be done and considered, the draft 

would be considered in that light before the Board 

would close on the question of whether that's 
224

complete, the draft guidelines.

And they would --

Member Ziemer: No, they already knew that. They 

already knew Melius' comments at that point, I 

think, right?

Mr. Katz: No, no, that was all in that context.

Member Ziemer: Yeah.

Mr. Katz: Melius said this is tentatively approved. 

The whole Board said it was already -- it was 

tentatively approved but it has to be run through 

this exercise --

Co-Chair Anderson: To be sure nothing is missed.

Mr. Katz: And then we'll bring it back to the Board 

and conclude.

Member Ziemer: Okay.

Mr. Katz: So that's what's left to be done is to bring 

this up to the Board as we think these guidelines 

are good enough as they are at this point --

Member Ziemer: Okay.

Mr. Katz: -- and the Board puts its stamp on it.

Co-Chair Anderson: We're not going to send it back 

to be revised.

Mr. Katz: Or there's nothing to add or subtract.

(Simultaneous speaking.)

Member Ziemer: All right. I move we accept the 

guidelines as they stand.

Mr. Katz: Okay. And then, Jeff --

(Simultaneous speaking.)

Member Ziemer: -- discussion point --
225

Mr. Katz: Oh, sure.

Member Ziemer: -- on the motion.

(Simultaneous speaking.)

Member Ziemer: No I just wanted to point out that -

-

Member Beach: I'll second it. I'll second it.

Member Ziemer: The only difference is they're not 

going to show up as draft.

Mr. Katz: Right.

Member Ziemer: They're going to show up as the 

guidelines.

Mr. Katz: Correct.

Member Ziemer: And if we find that they're 

inadequate at some point they can be changed in 

any event.

Mr. Katz: Or course.

Member Ziemer: So whether we would have Rev 1 

or Rev 2 or whatever it is, so it's not --

(Simultaneous speaking.)

Member Ziemer: I think that one of the concerns is 

they continue to be called drafts so --

Co-Chair Anderson: Yeah.

Member Ziemer: -- no -- let's just call them 

guidelines. Yeah, that's the intent of the motion.

Mr. Katz: Okay. It's been seconded and that's 

discussion and so -- and Gen, do you have anything 

you want to discuss about this?

Member Roessler: Well, I'd like to have you repeat 

what the motion was, it sounded like bedlam to me.

(Laughter.)
226

Mr. Katz: So sorry, it probably did. So the motion is 

to approve the coworker dataset procedures as 

guidelines.

Member Roessler: The guidelines? Okay.

Mr. Katz: Correct.

Member Roessler: Okay.

Mr. Katz: Yeah.

Member Roessler: I got it.

Mr. Katz: Okay. All right, so all in favor?

(Chorus of aye.)

Mr. Katz: Okay, that's everyone plus one.

(Laughter.)

Co-Chair Anderson: One additional comment, I want 

to thank all the Members for their participation but I 

think we can now close down the committee.

Mr. Katz: No you can't.

(Laughter.)

Member Beach: It is, it's called an SEC committee. 

And that's just one small part.

Co-Chair Anderson: Oh, okay. Good point.

(Simultaneous speaking.)

Mr. Calhoun: I second that.

(Laughter.)

Member Beach: Denied.

Mr. Katz: Okay, so then that will get presented at 

the Board meeting then, during this session. And 

now Brad, you --

Co-Chair Clawson: No, it's too late in the day.
227

Mr. Katz: Okay, it's too late in the day. Maybe in the 

morning?

Co-Chair Clawson: We'll have to chance on it.

Adjourn

Mr. Katz: Okay. All right, so then I think we're 

adjourned for the day. Thank you everyone for all 

this amazing work and great discussion. See you 

bright and early at 8:30. 

(Whereupon, the above-entitled matter went off the 

record at 4:45 p.m.)