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U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

CENTERS FOR DISEASE CONTROL

NATIONAL INSTITUTE FOR OCCUPATIONAL

SAFETY AND HEALTH 

+ + + + +

ADVISORY BOARD ON RADIATION AND

WORKER HEALTH 

+ + + + +

SEC ISSUES WORK GROUP

+ + + + +

MONDAY

APRIL 7, 2014

+ + + + +

The Work Group convened via 

teleconference at 1:30 p.m., Eastern 

Daylight Time, James M. Melius, Chairman, 

presiding.

PRESENT:

JAMES M. MELIUS, Chairman

JOSIE BEACH, Member

MARK GRIFFON, Member

GENEVIEVE S. ROESSLER, Member

PAUL L. ZIEMER, Member
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ALSO PRESENT:

TED KATZ, Designated Federal Official

BOB BARTON, SC&A

NANCY CHALMERS, ORAU Team

HARRY CHMELYNSKI, SC&A

JOE FITZGERALD, SC&A

JOHN HANSON, Office of Congressman John 

Shimkus

DeKEELY HARTSFIELD, HHS

TOM LaBONE, ORAU Team

JOYCE LIPSZTEIN, SC&A

ARJUN MAKHIJANI, SC&A

JOHN MAURO, SC&A

JAMES NETON, DCAS

DANIEL STANCESCU, DCAS

JOHN STIVER, SC&A

TIM TAULBEE, DCAS
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T-A-B-L-E O-F C-O-N-T-E-N-T-S

PAGE

Welcome and Introduction

by Mr. Ted Katz...................... 4

NIOSH Evaluation of "Practically

Significant Dose"

by Dr. Jim Neton..................... 6

SC&A OPOS Review

by Dr. Harry Chmelynski............. 54
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P-R-O-C-E-E-D-I-N-G-S

(1:33 p.m.)

MR. KATZ: Welcome, everyone. 

This is the Advisory Board on Radiation and 

Worker Health SEC Issues Work Group. The 

meeting today has a couple of documents. 

And I need to apologize to members of the 

public on the line because those documents 

are not posted yet.

Both of them had issues with 

Privacy Act clearance and clearance for 

making them intelligible to people who can't 

see, which is called 508 clearance, and 

they're not posted yet. But we will get 

them posted just as soon as possible so that 

at least you can go back after this meeting 

and see what those documents include in 

detail.

And those you'll find on the 

NIOSH website, under the Board section of 

the NIOSH website for the OCAS program, the 

DCAS program, under today's meeting date. 
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So now to go on with roll call, 

I'm not going to do conflict of interest per 

se. But keep in mind, everyone that's 

Agency-related on this call, that there may 

be some discussion of Savannah River Site or 

Fernald and keep in mind whatever conflicts 

you might have with those two sites.

(Roll call.)

MR. KATZ: Okay, the agenda for 

this meeting is posted on the NIOSH website 

where I said. Just let me remind everyone 

before I turn it over to mute your phones 

unless you're speaking to the group. And if 

you don't have a mute button, press *6. 

That will mute your phone and then press *6 

again to come off of the mute. 

And, Jim, it's your meeting.

CHAIRMAN MELIUS: Okay. Thank 

you, Ted. And welcome, everybody. This 

meeting is sort of a follow up to our longer 

meeting from a month ago, a few months ago, 

I can't remember exactly when, where we had 
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started discussing coworker model issues and 

discussed a number of points.

And we've had some discussions 

both at the Board level and then we had a 

short Work Group call since this time. But 

we're really looking now at two of the 

things that we had requested at that 

meeting.

One was the NIOSH effort has been 

looking at what's -- I guess what we're 

referring to as a practically significant 

dose. I guess that's the PSD. And the other 

issue is the OPOS, or one person-one sample, 

or one person -- I forget, we had other 

terminology for it -- but which is a review 

of that. We asked for a more comprehensive 

review of that issue by SC&A. 

So we're going to start looking 

at the practically significant dose issue. 

So I'll turn it over to Jim Neton.

DR. NETON: Okay. Thanks, Dr. 

Melius. I'm going to try to use Live 
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Meeting. Can everyone that's on Live 

Meeting see the cover page of the report 

that we put out on February 25th?

MEMBER BEACH: Yeah, I've got it, 

Jim. This is Josie.

DR. NETON: Okay, good.

MEMBER ROESSLER: I can see it. 

This is Gen.

DR. NETON: I didn't prepare 

slides for this. I thought I would just 

sort of skim through this report and hit the 

highlights and then we can discuss from 

there, because it's a fairly short report if 

you don't count the multiple pages of graphs 

that I=ve appended onto the end.

So this is a report of an 

evaluation of what we did call practically 

significant dose. We issued this report in 

February, end of February, after some 

analysis that we did. And as Dr. Melius 

pointed out, that was in direct response to 

a discussion we had at the Work Group 
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meeting on September 26th.

We were hashing about ideas as 

to, you know, how we were comparing these 

different strata of coworker models and the 

idea came out, well, you know, how do we 

know if there's a practical difference? 

There are statistical tests that we can do, 

but let's talk about practical differences.

We hit upon this idea of 

practically significant dose. That is, if 

you change the dose to a case, how much dose 

do you have to add to really have a 

meaningful difference? And we hit on the 

idea of using the NOCTS data set that we 

have.

As you recall, we have over 

40,000 cases in NOCTS. And we decided that 

if we looked at the cases that fell between 

45 and 50 percent of the PoC, that would 

maybe give us an indication of -- and added 

in a little increment of dose to those 

cases, that would give us an indication of 
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how big an effect dose had on the PC 

calculations.

So we did that. We looked at the 

cases between 45 and 49.99 percent. And we 

identified, and this was surprising to me, 

there were only 175 cases that met this 

criterion out of 40,000 cases. That is, 

they were between 45 and 49.99 percent and 

had a single cancer. That's another key 

that we had to think about, only one cancer 

made up that PC value.

So we looked at these cases and 

the idea was to add 100 millirems to each of 

these cases to see what happened to the PC. 

I would point out that a fair number of the 

cancer models were represented in this 

analysis, although not all. I think I have, 

yeah, 25 of the 33 cancer models were 

represented out of the 175.

And about a third of the ones 

that we selected were represented by the 

lung cancer model, which is not surprising, 
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because I think my recollection is somewhere 

in the order 60 percent of lung cancer cases 

have PCs greater than 50 percent. 

By the way, this report was 

distributed to the full Board some time ago, 

so you should all have a copy of it.

We looked a little bit at some of 

the demographics behind the cases. And on 

this page, Figure 1, shows what I think is a 

fairly interesting statistic. It's the 

distribution of ages at first exposure and 

the age at diagnosis.

And you can see from this that 

the age of first exposure, the median value 

is somewhere around 27 years of age, and the 

age of diagnosis is around 68. Well, that 

tells us, and this may be useful in our 

discussion later, that between those two 

there's about 40 years= worth of exposure 

that occurred that would need to be 

reconstructed, between the two median 

values, that is. And of course there's a 
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gradation between that. 

So, nonetheless, many of the 

cases have very long exposure periods that 

need to be reconstructed. And if you 

recall, we decided, as an initial test, that 

we would add 100 millirem dose to each of 

these cases at the point in their exposure 

scenario that we felt would make the biggest 

difference in PC.

And that is, we would add the 

dose for solid cancers at the year of first 

employment, and for leukemias, I think we 

decided that if we added it five years 

before the diagnosis of leukemia that would 

be the most claimant-favorable, or give us 

the biggest change in outcome. 

There were three scenarios 

evaluated. The first scenario was really 

just sort of an artifact of the situation 

where we changed IREP models. We are now 

running IREP Version 5.7. And not all cases 

had been run on 5.7. So we just went back 
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and re-ran, we made sure that all the 175 

cases had their initial starting point as a 

Version 5.7. That's what we would call 

Scenario 1.

Scenario 2 is the situation where 

we added zero millirem as a constant to each 

of the cases at the point in their exposure 

that I just talked about, that is, at the 

beginning of employment for solid cancers, 

or five years prior to cancer diagnosis for 

leukemias. 

You might wonder why we added 

zero dose. Well, it turns out when you add 

a line of input to IREP you change the 

sequencing of the random number seeds. So 

if we didn't have a zero dose addition and a 

concomitant 100 millirem dose comparison, we 

would not be comparing the differences in 

the pure excess relative risk, we would also 

be evaluating differences due to the 

uncertainty associated with the random 

number seed sampling. So that's why we had 
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to have a Scenario 2 which was a zero dose. 

And then in Scenario 3 we added 

the 100 millirem to each of the cases. So 

in reality the real comparison that we need 

to focus on is the difference between 

Scenario 3, which is the addition of 100, 

compared to Scenario 2, which is the 

addition of zero.

Those two scenarios were run 

using the exact same random number 

sequences, so that when you have a 

difference it is in effect just due to the 

effect of the addition of the dose. 

A couple of other things about 

the addition of the dose. We decided to 

make it an acute exposure to photons greater 

than 250 keV. And that just sort of 

simplified the analysis. Photons less than 

250 have an uncertainty associated with them 

and we didn't want that to weigh into the 

analysis. So essentially it's truly a 

constant value that was added. 
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And, let's see, what was the 

other thing I was talking about? The 250 

and, oh, the acute. Acute scenarios don't 

have any adjustments to them, whereas 

chronic might influence the outcome. So it 

was an acute exposure greater than 250 keV 

photons, 100 millirem.

So we ran all three of these, we 

ran these scenarios over all 175 cases, 

doing what we normally would do for cases 

that fell between 45 and 50. That is, we 

would run the IREP analysis with 10,000 

iterations each 30 separate times and take 

the mean of those runs.

That's standard practice for any 

case in our program that falls under those 

parameters. So we did that. And we ended 

up with the statistics that we can see 

summarized here in Table 1, that the maximum 

value under each of the scenarios, and 

particularly I would call your attention to 

Scenario 2 and 3, none of the maximum values 
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exceeded the 50 percent PC.

That is, out of the 175 we ran, 

all either had zero change or increased 

slightly, but did not result in a change in 

compensability decisions. 

If you look at Table 5 on the 

following page where it's highlighted in 

yellow, if you look at the solid cancer and 

CLL line, Scenario 3 compared to Scenario 2, 

there were a 170 solid cancers. The minimum 

difference was zero. The median difference 

was .02. The mean was .06. And the maximum 

change was .34. There were only five 

leukemia cancers included in this analysis. 

And you see on the last line of Table 5 the 

statistics associated with that.

And there were some differences. 

The minimum change was .08. The median was 

.3. The mean was .28. And this is percent. 

And the maximum change is .34 percent. 

There was slightly more increase in the PC 

values in leukemia cases, which we might 
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have expected because leukemia in itself 

tends to be compensated at a much lower 

dose. So a 100 millirem change to a lower 

dose would more than likely have a larger 

effect, at least that's my interpretation of 

that. 

So if you get to the bottom 

line, this is a change, this slide here, 

Figure 5, shows the distribution, the 

changes in PCs between Scenario 3 and 2 for 

all cancers.

And you can see here that the 

median change, the dashed line, is .02. The 

mean change is .06. And the maximum change 

is .34, as we said before. So it's lognormally distributed. It's very heavily 

weighted towards a very small change. There 

are some larger changes out there, not very 

large.

So that's what we ended up with. 

So it's a pretty simple message here that, 

you know, the addition of 100 millirems in 
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these cases didn't do a lot, didn't change 

anything, although, you know, I'm not saying 

it's -- it may be fortuitous that none 

changed because we were on the borderline 

for a couple that could have just as likely 

gone over.

But nonetheless, they didn't 

change much at the maximum point where they 

would make an effect. So the next step 

beyond this is -- I'm not quite sure. I 

thought it would be obvious when we ran 

this. But now I'm very interested in 

hearing some feedback from the Working Group 

as to where we go with this.

Clearly, you know, if you had ten 

years of exposure the change in PC wouldn't 

be .06 times 10. It's not a linear 

function. And in fact when you get closer 

to the cancer diagnosis, the exposure has 

less and less an effect.

As you get within ten years of 

the cancer diagnosis there's sort of this 
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asymptotic, or S-Shaped function that kicks 

in where you get virtually no credit for the 

cancer being caused by a radiation within 

the last two or three years of exposure 

prior to diagnosis. 

So, anyway, that's it in a 

nutshell. I'd be happy to answer any 

questions that people might have.

CHAIRMAN MELIUS: Okay. Thanks, 

Jim. Board Members first, do you have any 

questions?

MEMBER ZIEMER: No questions 

here, this is Ziemer.

CHAIRMAN MELIUS: SC&A, do you 

have any questions?

MR. STIVER: This is Stiver. I 

don't have any questions on this particular 

aspect of it, no. I don't know if Arjun 

might want to say something.

CHAIRMAN MELIUS: Arjun or 

anybody?

DR. MAKHIJANI: Sorry, I was on 
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mute. I didn't realize. I forgot. 

This came up in the last 

conference call. I think they are more or 

less similar findings that were presented 

last time, maybe more complete now.

But my comment was that, you 

know, a lot of what we deal with in SEC type 

of situations is uncertainties in relation 

to internal dose. External dose is 

generally reasonably well-known, and I think 

apart from the neutron case in Rocky Flats, 

I don't know that any SEC is being granted. 

I might be wrong.

So I don't know what significance 

this particular analysis has, you know, that 

you check for adding 100 millirem to the 

external dose. I think trying to identify 

the uncertainties in internal dose, which 

can be quite big -- say, the difference 

between an 84 percentile and a 95 percentile 

dose in various situations can be quite big.

And applying that and going by 
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radionuclide, because it's a radionuclidedependent problem and on organ-dependent 

problem, certain analysis becomes more 

complicated, but also it's more relevant. I 

don't know that this external dose analysis 

is very relevant to SEC investigations. I 

mean, it's theoretically interesting.

CHAIRMAN MELIUS: Are you 

suggesting a specifically different 

analysis?

DR. MAKHIJANI: Yes, yes. I'm 

suggesting that an analysis that adds a 

fixed external dose to see if people are put 

over the top of 50 percent of the 

Probability of Causation criterion is, to my 

memory, not very relevant to SEC cases.

What is generally relevant to SEC 

cases is whether we have the data on how 

accurate the coworker models might be. So 

in relation to the latter question, accuracy 

of coworker models and what we know and what 

we don't know, if we try to gauge the 
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uncertainties in regard to, say, thorium 

dose in a particular situation and examine 

that for its effect on Probability of 

Causation, that might yield a more 

interesting result in terms of practical 

applicability to SEC sufficient accuracy 

questions.

DR. NETON: Arjun, this is Jim. 

I don't disagree with you but I think until 

this analysis, prior to this analysis, we 

had no idea how much a dose would change a 

case, especially looking at the population 

that we have in-house. So, I mean, I think 

it's very informative to know that 100 

millirems is sort of on the cusp of making 

changes.

I mean, I wouldn't know if it 

would have been 5 millirem, 10 millirem, 100 

millirem. So we at least know that now. I 

mean, and the nice thing about external is 

you get rid of the baggage of these other 

uncertainties so at least you're sort of 
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able to focus in on a quantitative change 

based on constants.

Now, I agree, then the next step 

is more than likely to fold in the 

uncertainties maybe. I don't know. It's 

hard to say, because, as I said, internal 

dose tends to be delivered over time, as you 

know. Doses that are delivered within ten 

years of exposure have less and less 

influence on the outcome. It's hard to 

predict.

DR. MAKHIJANI: Yes, well, I 

actually agree with you that, you know, so 

I'm not saying the effort that you've made, 

obviously it's a useful effort because we 

know something quite important today that we 

didn't know then. So I didn't want to imply 

that it was a wasted effort, because it's 

not. I agree with you.

So the question is, you know, 

what's the comment and what the next step 

and where should we go? And my comment was 
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simply directed at the idea that the 

sufficient accuracy question generally comes 

up, as you know, in relation to internal 

exposure.

And if that's the issue to 

resolve, then, you know, maybe in the same 

way that you've done external exposures 

there could be a single sort of incidentrelated exposure at the start of employment, 

you know, in the way that you've done the 

sort of Savannah River, you know, what was 

it, high-five model, a similar approach to, 

you know, applying a dose corresponding to 

the uncertainty at the start of an 

employment, or an intake at the start of an 

employment and letting the doses and 

Probabilities of Causation unfold from that 

might tell us something about how accurate 

we need to be.

I mean, if it's even a very large 

dose doesn't change the outcome, then, you 

know, you're in pretty good shape. But, you 
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know, if a few nanocuries can make a 

significant difference, you know, then there 

will be pretty big issues.

DR. NETON: And maybe the 

situation would be to look at the subset of 

these 175, because we're looking at cases 

between 45 and 50 that had internal 

exposures that contributed to the PC. My 

gut feeling is that many of them did because 

that's when you get the biggest doses.

It's pretty hard to get a PC 

that's in the 45 percent range based on 

external exposure, has been my experience, 

except maybe for those people that worked in 

the very early years, like at Mallinckrodt 

or something. For solid cancer you need 

tens of rem exposure to get into that range, 

if not higher. And that's only usually 

doable with internal exposures to what I 

would consider as target organs or metabolic 

organs.

DR. MAURO: Jim, this is John. I 
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have a question along these lines, because 

Arjun has a great question. If we were to 

assume -- this is a thought problem. Now, 

you delivered 100 millirem to organ of 

concern. Take the time period prior to 

diagnosis that you felt, this is a judgment 

call, would sort of maximize the latency 

consideration.

Now, if that dose, that 100 

millirem were delivered by, let's say, 

tritium to the organ or delivered by an 

internal photon emitter or beta emitter as 

an internal dose in that year, would 

anything change?

DR. NETON: Yeah, I mean -- well, 

I'm not sure about tritium. But I know for 

all internal doses the minimum uncertainty 

is a GSD of three on the internal dose 

estimates.

DR. MAURO: So you would deliver 

it that year, but instead of it being a 

fixed value it would be 100 millirems 
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delivered to that organ. But it would have 

a distribution on it which could give you an 

end tail that could change the picture.

DR. NETON: Yes.

DR. MAURO: Okay.

DR. NETON: But you've got to be 

careful. That's a GSD, that's a log-normal 

distribution, then I'm not sure what you're 

comparing it to. See, now you're talking 

theoretical. You would add -- I don't know 

what you would add even.

DR. MAURO: Thanks. No, no, I 

didn't realize that -- if you did it as an 

internal as opposed to a fixed, 

deterministic number of 100 millirem, you 

would be forced into a situation where you 

would have to factor in this --

DR. NETON: 95th percentile 

values for internal are put in as constants.

DR. MAURO: Okay. So then would 

it be correct to say, if we defined this as 

an internal scenario that we actually did, 
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that was actually done, but you said that, 

no, we're going to assume it's an internal 

scenario where we have a fixed constant 100 

millirem at the 95th percentile, which was 

100 millirem.

DR. NETON: Right.

DR. MAURO: Would you have the 

same result?

DR. NETON: Wait a minute. Say 

that again.

DR. MAURO: In other words, I'm 

trying to find a way to see if there is any 

parity between what you did and how it might 

have relevance to internal exposure.

DR. NETON: Right.

DR. MAURO: And the way I'm 

thinking about is you explained that 

internal does make things more complex 

because when you assign that internal dose 

to an organ, in a given year, you would 

assign a distribution. You would have a 

distribution around that 100 millirem.
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But if we were to define that 100 

millirem, no, no, that wasn't the median of 

the distribution. It was, the 100 millirem 

is your 95th percentile deterministic 

internal dose.

Does that question make sense? 

In other words, define the internal dose in 

terms of the bounding dose, and saying if it 

were 100 millirem for the bounding without 

any uncertainty, but it was delivered 

internally on that year to that organ, would 

the results come out the same?

DR. NETON: I'm still not 

following you.

DR. MAURO: Is the question 

coherent? I'm not sure if I'm even posing a 

question that -- it seems -- I'm just 

thinking about -- see, depositing energy in 

tissue and in organs, in my mind, does it 

really matter if it's depositing that energy 

in that year by an internal emitter or by an 

external source? And so I'm just thinking, 
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in the simplest way, well, why should that 

make a difference?

DR. NETON: Well, you know, it 

also just came to mind that you also have 

the radiation effectiveness factors folded 

in there, too.

DR. MAURO: Yes.

DR. NETON: So those make a 

difference.

DR. MAURO: Right, both beta and 

alpha have that factor.

DR. NETON: Part of this is 

leading me to think that there's so much 

variability in the internal that it may have 

to have a big change in order to see a 

difference, because, you know, you're adding 

a slight -- you know, we're talking about a 

ten percent change.

You know, I think in September we 

were talking about coworker models that 

maybe had a ten percent change in value. 

Okay, let's say the value, the dose value 
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changed by ten percent. Well, you know, 

you've got to raise the effectiveness factor 

uncertainty, you've got all these other 

uncertainties in there that -- although, you 

know, if you do change, as we well know, if 

you change the central estimate you're going 

to change the PC by a proportionate amount 

because, you know, it's not changing --

changing the distribution is a different 

beast then just changing the central 

estimate. I don't know.

CHAIRMAN MELIUS: So, this is Jim 

Melius. Is there an example we could 

develop that we could try to illustrate 

that? I mean, is there a scenario that you 

could -- what's the next step that would be 

useful? Do we want to try look at that in 

some sort of example?

DR. NETON: I think it could be. 

At the top of my head, I can't think this 

through right now. But it's possible that 

we could come up with some other test.
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CHAIRMAN MELIUS: I know they 

want to try to do it sort of comprehensively 

because there's lots and lots of --

DR. NETON: I would like to 

explore the idea that I sort of broached 

last time, though. Maybe it's embedded in 

this analysis. I don't know.

But, you know, we were discussing 

the fact that, you know, we have a coworker 

model that, in my opinion, is -- well, I'll 

call it a general exposure model. And that 

general exposure model admittedly includes a 

series of potential strata, because it's one 

size fits all.

We've acknowledged that. And so 

there may be embedded in that model 

different strata that have different 

distributions, agreed. But what we've done 

is we've developed sort of an approach where 

we have sort of a two cell exposure matrix 

now where we said if we believe that the 

exposure to that unmonitored worker, you 
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know, you have a monitored worker exposure 

coworker.

But now you're going to apply 

that, a monitored exposure distribution of 

workers, you're going to apply that to the 

unmonitored worker. If that worker does not 

appear to have been involved in heavy 

activity and intermittent exposure, that 

sort of thing, we'll use the 50th percentile 

with the full distribution, because clearly 

his exposure would not have been high end.

But then we've got the other 

approach where we say, well, this person 

looks like he could have been exposed 

because he was working in radiological areas 

and doing something that could have 

generated airborne materials. By default, 

then, and this would apply to all 

construction and trades workers, we will 

assign the 95th percentile of the 

distribution, recognizing that we know that 

there's a number of other strata buried in 
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there.

But we're saying we're willing to 

accept the fact that there's at most a five 

percent chance that his exposure was higher 

than that. Now, if we go and pull out the 

strata for these construction workers, now 

we have a better representation of the 

potential exposure of that group, I would 

suggest that we wouldn't give the 95th 

percentile anymore.

We would assign the geometric 

mean and the full distribution to that 

population because it's a better known, 

better characterized population. So then 

the trick is, what is the practical 

significant difference? How high does the 

mean of that strata have to be to exceed the 

95th percentile? The mean and the 

distribution applied to that worker, how 

much higher does that have to be to exceed 

the 95th percentile of what I'll call the 

general exposure model?
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And I think that, to me, is a 

relevant test. I don't know any other way 

to put it. But it just seems that's the way 

one should approach this. I've thought 

about this a ton in the last week. And 

that's what I've come down to, at least in 

my mind, as an appropriate comparison.

DR. MAKHIJANI: This is Arjun. I 

think that sounds like a good idea. I mean, 

I haven't thought about it as much, hearing 

it for the first time. But it sounds like a 

pretty good idea to me.

But if we're going to apply some 

numbers, I would like to see some test where 

we try to gauge -- I guess this is one way 

of gauging the uncertainties, but we will be 

back, if we are to pull out the distribution 

for that stratum that you're going to 

compare it to where you're going to take a 

median or the full distribution and compare 

it in the 95th percentile of the general 

distribution, then you're going to run into 
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this problem of how you extract the stratum 

and how you develop a distribution.

And I think, you know, that's 

been a pretty big hurdle. I think, in 

principle, the kind of test that you're 

talking about would appear to be I think a 

good starting point.

DR. NETON: Well, I think all of 

the other caveats apply in that you have to 

demonstrate that the strata that you pulled 

out is that, you know, the workers were 

monitored, they were being represented as 

the highest exposed or represented the 

workers, you know, all the sort of things 

that apply to coworker models.

But then when you do that 

comparison, I think it might be doable with 

the approach that we were just talking 

about, because if you can add -- it's not 

intuitively obvious how the Probability of 

Causation -- what the difference of the 

Probability of Causation is if you compare 
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the full distribution versus the 95th 

percentile.

We have done this in the past. 

Dave Allen and I talked about this earlier 

in the week, or I guess earlier last week, 

that it seems to us that assigning a full 

distribution is very akin to assigning an 

87th percentile as a constant. I can't 

prove that right now.

But I think that's kind of --

it's going to be variable depending on 

cancer levels. But that's probably a fairly 

good number. So then how much higher does 

that distribution have to be for that 87th 

percentile to exceed the 95th percentile of 

the general model?

And I think we can test that. I 

think that's a testable -- it's sort of 

similar to what we just did, testable over 

claims to come up with some ranges. And 

it's never going to be the same for 

everybody.
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But I think we can show -- in 

general, I think what you'll find out is 

that it's going to take quite a bit of 

difference in -- the stratified model would 

have to have a pretty much higher geometric 

mean to overcome claimant-favorability of 

assigning the 95th percentile of the general 

model.

I'm almost certain that's true. 

But we have to test that.

MEMBER ZIEMER: Dr. Melius?

DR. NETON: Hello?

MEMBER ZIEMER: This is Ziemer. 

Yeah, I think what Jim is suggesting makes 

sense. But, you know, we're going to have 

to try that. We're sort of in the same boat 

as we were when we talked about the external 

adding 100 millirem and seeing what the 

results were. It seems to make sense 

conceptually, but I would certainly like to 

see what it looks like with some actual 

values.
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DR. NETON: I agree, Dr. Ziemer. 

I really can't even begin to articulate how 

this would happen, but I think it is doable. 

We would have to look at the data that we 

have.

MEMBER ZIEMER: Right, right.

DR. NETON: And run through some 

general scenarios. And as we did before, I 

could actually, you know, put a straw man 

out for everyone to look at and make sure 

that we're on the right path and then move 

forward once I get, not agreement, but 

general, you know, feedback from the group.

MEMBER ZIEMER: And I think we'd 

get a better feel for some of Arjun's 

concerns, because those certainly make sense 

as well. And maybe this will give us some 

insight as to what direction to go at that 

point.

DR. NETON: Yeah, and we've kind 

of got the pieces of this for Savannah 

River, I think, in place to some degree. I 
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mean, we've got stratified. 

See, the other situation is it's 

not possible to stratify everywhere. You 

can only stratify where you have, you know, 

the data, like we have in Savannah River. 

It's bothered me that we're saying, well, 

we're going to stratify. If you decide to 

stratify and you can't stratify another 

site, what does that mean?

I think this gives you sort of a 

semi-quantitative way of looking at data 

saying, you know, yeah, but look at how much 

is going to have to change in order for that 

to overcome the 95th percentile. So it may 

inform us at other sites as well. I don't 

know.

DR. MAURO: Jim, this is John. I 

want to understand this a little better. We 

could use the example of comparing the 

totality of the data against, let's say, the 

construction worker.

Let's say you had the data set, 
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you have a distribution which has, on the 

full data set, which has a 95th percentile 

value. Now let's say you were in a 

fortunate position where you have a subset 

of it, which we'll call construction 

workers.

And then you have sufficient data 

to build a distribution. Now, what would be 

the outcome that would be revealing? That 

is, if you found out that the 50th 

percentile of the substrata was below the 

95th percentile, or would you be looking to 

see if the 84th percentile of the substrata 

was below the 95th percentile for the full 

distribution? What would be --

DR. NETON: In effect, that's 

sort of right. I mean, but you'd have to do 

some IREP runs and see how they came out. 

You would take the full distribution and use 

it. And you would take the 95th percentile 

and run it. And whichever one came out more 

claimant- favorable is the way you would go.
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DR. MAURO: Now, if you found 

that the outcome of the full distribution 

for the substrata had a -- and I'm not 

saying the 95th percentile, I understand the 

points you're making. But let's say it 

turns out you decided that certainly if the 

mean came in below the 95th percentile for 

the full set, if the mean of the substrata 

came in below the 95th percentile of the 

full set, that would be informative.

If the 84th percentile of the 

substrata came in below the 95th percentile, 

that would also be informative and 

encouraging. But if the 84th percentile of 

the substrata came in above the 95th 

percentile of the subset, what would that 

mean to you?

DR. NETON: To me, that would 

mean you need to stratify because you're not 

-- you know, you have just demonstrated that 

the less than five percent probability of a 

person exceeding the 95th percentile occurs 
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in that strata.

DR. MAURO: So that's the thought 

process. So really the test that you're 

running is if you find that out that, that 

the 84th percentile of your substrata is 

less than the 95th percentile of the full, 

you have basically convinced yourself, and 

perhaps everyone else, that there's no 

reason to stratify.

DR. NETON: That's correct.

DR. MAURO: Good. That's very 

interesting.

DR. NETON: And that really, to 

me, is the nut of it all. Is the 95th 

percentile disenfranchising anyone because, 

you know, their data are all in this upper 

tail? And all I'm saying is, well, let's 

test that. Let's get the strata and test 

and see if the upper tail is not 

sufficiently, the 95th percentile is not 

sufficiently claimant- favorable. And if 

it's not then, by goodness, yes, sure, we'll 
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go and stratify and use that data set 

because that's been demonstrated.

DR. MAURO: I think you have just 

proposed a strategy for dealing with 

stratification that has an end point that's 

a manageable problem.

DR. NETON: I think so.

DR. MAURO: And I certainly, if 

everyone feels that way, that is, in effect, 

you're saying in dealing with any site where 

you have thousands of workers or hundreds of 

workers and you have reason to believe that 

there are substrata in there that may be 

different than the overall group, have their 

own distribution, and if you are in a 

fortunate enough position to -- now here's 

where the big problem is going to be.

We have enough data from the 

substrata. Propose the question you just 

raised. You could do that first and decide 

whether or not you need a substrata coworker 

model, or you could just use the entirety of 
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the data.

Do you think it's reasonable that 

we would be able to go through such a 

process? That is, are the data such that 

when a question is raised, like the 

subcontractor question, quite frankly, were 

we in the position to do that? I guess 

there are some sites where the subcontractor 

data were there. Is it possible to make 

this test that you just described?

DR. NETON: Not always. But I 

think, where it is, it needs to be done. 

And I think what's also going to be 

informative is, you know, what magnitude of 

difference does there need to be in order to 

say, yes, 95th percentile is not claimantfavorable anymore?

And I think we might be surprised 

at how high that is. I mean, because you 

have GSDs of three, which is the minimum GSD 

that we assign to any dose distribution, 

because that's the -- three is applied by 
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default if it's less than three because of 

the biological variability of the sampling, 

you know, the metabolic models and such.

That's a pretty large GSD. At 

the 95th percentile, the GSD -- I mean, the 

95th percentile value is six times higher 

than the geometric mean. You take 3 to the 

1.645. So the geometric mean would have to 

increase quite a bit in order to be higher 

than the 95th percentile with the 

distribution.

I don't know. Until we test it 

I'm not sure. But the answer is, where we 

can we would do that. Where we can't, I'm 

not sure what we do at this point. But we 

have that same issue whether we do this or 

not.

It's just a way of at least being 

able to test where we have the data, and 

then maybe we can say, well, you know, 

similar sites with similar -- I don't know.

DR. MAURO: You don't want to put 
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yourself in a position where if you do have 

a site, if you have lots of data, you have a 

sense that are some substrata down there, 

maybe a job category or something, the kind 

of work that people did, pipefitters and 

things, where you don't have data but you 

suspect that maybe there's a problem, of 

course, you know, you wouldn't be able to 

test it if you didn't have the data.

So it doesn't really solve the 

problem. But I guess it would only give you 

some -- when you do have the data you could 

start to build a sense of whether or not 

this 95th percentile is fairly robust and 

will deal with most substrata.

DR. NETON: Exactly. I mean, I 

think until we get through a few different 

tests maybe that will be informative. I 

think what it's going to show is that ten 

percent differences aren't sufficiently 

large to move things over.

And by and large, that's kind of 
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what we've been seeing. You know, there are 

some exceptions. But I think it would give 

us something to hang our hat on to say, you 

know, is the 95th percentile really as 

claimant-favorable as we felt it is?

Because that's honestly the 

reason -- I got to thinking about this. 

That's why we picked the 95th percentile in 

the first place, because we don't know where 

that person falls on the curve. So we said, 

well, let's go way at the high end and 

there's a five percent chance at most that 

his value exceeded that. So, anyway.

CHAIRMAN MELIUS: This is Jim 

Melius. I'm just sort of keeping in mind 

we're just trying to get at the issue of 

sufficient accuracy not the issue of 

claimant-friendliness.

DR. NETON: I agree.

CHAIRMAN MELIUS: And so, 

depending on how the data sets are made up 

and, you know, for the sample size and so 
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forth --

DR. NETON: That is true.

CHAIRMAN MELIUS: -- yeah, this 

could lead us astray also. I like the idea 

of doing it. I just think we need to sort 

of keep in mind it's not just proving 

claimant-friendliness that we're trying to 

get at, though it certainly, you know --

DR. NETON: Well, it's a part of 

it. But I do agree with you, Dr. Melius, 

that, for example, if you have a similar 

geometric mean but you end up with a GSD on 

the stratified model of 9, then you end up 

in the situation where, you know, the upper 

bound is some huge number that may or may 

not even be a plausible value. You're 

right.

CHAIRMAN MELIUS: The other thing 

that concerns me is, and it actually comes 

up a lot when we talk about OPOS, is that a 

lot of times our issue is dealing with --

these are not perfect, you know, random 
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samples. And we often don't have complete 

information on how, you know, the monitoring 

was done.

And it certainly often appears to 

be done differently between production and 

subcontractor workers and even among groups 

of subcontractor workers. And so I think 

that we have to keep in mind is how do we 

deal with those sort of issues? Which I 

think comes down often to sort of a judgment 

on how these monitoring programs were done 

and are they really that different and so 

forth, and we'll probably talk a little bit 

about that with the OPOS review next.

But that's all been as important 

a question as sort of, you know, how much of 

a difference is there? So it gets you, 

before you're trying to -- you know, what 

sort of statistical assumptions are we 

violating and how important are they in the 

kinds of comparisons that we're proposing to 

do?
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DR. NETON: Yeah, I one hundred 

percent agree with you. I mean, can you 

really stratify in the first place? Well, 

first of all is the coworker valid, the 

general model valid?

CHAIRMAN MELIUS: And then my 

final point would be that the other sort of 

practical issue that comes up in this is do 

we even have enough information to be able 

to, you know, really identify people within 

stratas? And often it's complicated because 

people may have moved from one strata to 

another as their job changed and so forth. 

And that complicates it also.

But I think that's stuff we can 

talk about. I just think we should be 

careful to keep those sort of practical 

issues in mind as to when these kinds of 

testing and conclusions are appropriate.

And I have a feeling we'll spend 

more time wrestling with the practical 

evaluation of the monitoring programs and 
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the strata than we necessarily will with the 

statistical comparisons, if we can get 

there.

DR. NETON: I agree.

DR. MAKHIJANI: I agree with you, 

Dr. Melius.

CHAIRMAN MELIUS: Other comments 

from Board Members or SC&A?

MEMBER ROESSLER: This is Gen. 

Yes, I have a comment. Beginning with 

Neton's response after Arjun brought up the 

concerns about the internal dose, he said a 

lot of things that really, to me, were very 

convincing.

And then as everyone else is 

talking, including John Mauro, they're all 

convincing. But to me, this is pretty 

complicated stuff. I don't deal with these 

kind of statistics every day.

So I think as Jim goes through 

this process, if we decide he should, I 

would like to see him write down what the 
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hypothesis is and then put some words with 

it so we understand what all this generation 

of numbers means and what the application 

would be, if there is one.

CHAIRMAN MELIUS: One point, Jim 

sort of talked about the process we did for 

sort of developing the current report, and 

he sort of laid out the methodology and we 

commented on that. And I think that sort of 

laid out just what you asked for, Gen. So I 

think he's proposing to do that again this 

time.

MEMBER ROESSLER: Yeah, because 

like John Mauro said, he wants to understand

it better and certainly I need to understand 

it better.

CHAIRMAN MELIUS: Yeah, you're 

not alone.

MEMBER ROESSLER: Good.

CHAIRMAN MELIUS: Other comments 

or questions?

MEMBER BEACH: This is Josie. I 
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don't have anything right now.

CHAIRMAN MELIUS: Arjun, or 

anyone else on SC&A?

DR. MAKHIJANI: Can you hear me?

CHAIRMAN MELIUS: Yes, we can.

DR. MAKHIJANI: Okay. No, I 

agreed with your last comment. I think it's 

a good next step.

CHAIRMAN MELIUS: So at least for 

now, maybe we'll change our mind after 

talking about OPOS. But for now, I think 

the next step would be Jim would sort of 

develop a mini-proposal or plan and 

circulate that to the Board. Is that 

correct, Jim?

DR. NETON: That's my 

understanding.

CHAIRMAN MELIUS: Okay. And we 

will then go on from there. And I do agree, 

even with some caveats, I think it would be 

helpful to do and help us understand this 

issue better.
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And I think it's probably more 

important than trying to figure out all the 

permutations of internal dose issues, 

because we could spend a long time doing 

that and probably get more confused, not 

less confused. Good. Okay.

Why don't we move on to the OPOS 

report. Who is presenting that? We got a 

set of slides this morning.

MR. STIVER: This is John Stiver. 

Harry basically did the heavy lifting on the 

OPOS report and so I'm going to have him 

actually do the presentation. You all 

should have gotten a copy of our report. I 

think it was sent out on the 22nd of 

February.

And the title of course is -- let 

me bring this up -- Staff Review of the 

Proposed One Person-One Sample (OPOS) 

Approach to Coworker Modeling. Barton, 

Chmelynski, Lipsztein and Stiver are the 

authors here.
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And let me go ahead and share the 

slide and then I will turn the reins over to 

Dr. Chmelynksi. Let's see here. Okay, can 

everybody see that?

MEMBER ROESSLER: I can.

DR. NETON: I think you need to 

expand it.

MR. STIVER: Do you need it a 

little bit bigger?

DR. NETON: You can expand it to 

full screen. Upper right hand corner, see 

that little box?

MR. STIVER: Right up here? 

Okay. There we go.

DR. NETON: No, I think it's all 

the way at the top.

MR. STIVER: Okay, does that work 

there? I think it's expanded as far as it 

can go.

MR. KATZ: John, look top right, 

currently sharing. There you go.

MR. STIVER: Okay. Is that 
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better? Can everybody see that?

MEMBER BEACH: That's fine.

MR. STIVER: Okay, Harry, would 

you like to go ahead and get started?

DR. CHMELYNSKI: Sure. There's a 

quick road map here as to what we're going 

to be talking about this afternoon. And 

basically it's the definition of what is 

OPOS and then why is it different from 

earlier procedures that were used to develop 

the coworker models? What are some of the 

reasons for using OPOS? And how extensive 

is the problem that it's going to fix? 

And, finally, we get into more 

technical questions, which is how the mean 

excretion rate, which is what OPOS is trying 

to measure, is related to the intake itself. 

And also how well does OPOS estimate that 

mean excretion rate? And then we'll finish 

up with some problems with implementations 

that we noted. 

So the first, the next slide 
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basically defines what we mean by OPOS. And 

I like to use the term one person-one 

statistic, because OPOS is the arithmetic 

average of some results during the time 

period, which is a statistic. And it 

therefore has uncertainty, which we will get 

to near the end of this discussion. 

NIOSH has introduced OPOS because 

of problems which were noted during the use 

of statistical test procedures to compare 

different strata.

And the two types of problems 

were termed data dominance and correlation. 

And we're going to talk more about these in 

the next couple of slides. We looked at the 

problem both from a theoretical point of 

view and also from a practical point of view 

when you apply it at two different sites, at 

SRS and at Fernald.

So the next slide, let me make 

sure I have the next slide here in front of 

me. Yes, okay. Before OPOS was introduced 
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there were several analyses that explain how 

one should do coworker modeling. And 

basically it started off with the first two 

lines here that say take the results from 

the group of workers -- we assume that all 

the results are log-normally distributed.

And in the old days what we did 

was we took the results from all the workers 

and sorted them from high to low and then 

applied the next three steps, which is then 

termed regression on order statistics.

Basically it's a procedure for 

doing regression on the ranked data in order 

to find the geometric mean and geometric 

standard deviation of the data set, not much 

unlike what people did 50 years ago when you 

could plot on a normal probability paper to 

get a straight line that said this is what 

the normal distribution looks like.

 Now we do it on a computer and we 

do it with the logarithms of the data, and 

it's the same procedure though. And when 
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we're done here what we end up with are two 

data sets. We have the 50th percentile and 

the 84th percentile.

And those numbers have to be run 

then in order to determine what the intakes 

are. And we end up with intakes at the 50th 

percentile and at the 84th percentile. When 

OPOS was introduced, the procedures stayed -

- that's on the next slide -- the procedures 

stayed very much the same.

The only difference in the second 

and third steps, where instead of taking all 

the data over a time period what we are 

suggested to do is to average all of the 

results into one value for each worker. 

There are some complicating issues about 

when you have censored data exactly how you 

compute the average, and we'll get into that 

at the end of the paper.

But in the simplest case it was 

just the average of the values. And then 

instead of using all of the samples for a 
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worker, we used that one value and then sort 

them and proceed as we did before and did a 

distribution for the coworker model.

And, again, that's elaborated in 

the next slide, which shows it pictorially. 

The bottom parts of the slide are pretty 

much the same. The first box is where the 

big difference is. Do you take all the 

results from all the workers in the time 

period of interest or should you average 

them all, and in particular over a one year 

period?

Once you decide to average you 

have to decide what period you want to do it 

over. So NIOSH has suggested we should do 

this averaging over a period of one year and 

then use one value per worker in the 

analyses that follow. Again, that would 

differ from what was done previously for the 

coworker model.

Now, in terms of why these were 

introduced is on the next slide, on Slide 7. 
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There are two problems here. Data 

dominance, which we define as that the large 

fraction of the samples are being submitted

by a small fraction of the individuals.

Now, usually we think that means 

if they had a lot of samples that they 

probably had high numbers. And it was 

repeated sampling for a reason. Although 

that's not always the case. In some cases 

there was just some health physicist who 

decided to test themselves frequently and 

they end up with a lot of samples, not 

necessarily high ones. But, again, it may 

be that a large fraction of the samples in a 

time period were submitted by one or two 

individuals when that happens.

The second issue is the 

correlation issue, which becomes more 

important when you do the hypothesis testing 

as to -- the assumptions of the hypothesis 

test assume that we're going to have 

independent and identically distributed 
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samples from each group. But when we have 

an incident and there are a lot of samples 

from one person they tend to be highly 

correlated, and that introduces a 

complication in interpreting the statistical 

tests.

So we took it on ourselves to 

decide, first off, how much of a problem 

these problems were when they were applied 

at both Savannah River and at Fernald. And 

after doing that, and also some theoretical 

thinking about it, we ended up with the 

following conclusion. It's on Slide 8.

Now, it's always risky to give 

your conclusion before you introduce why you 

got to that point. So I'll go ahead and say 

what our conclusion was, but I'm not sure we 

should start discussing that conclusion 

until we get into some of the more 

substantive issues that led us to that 

conclusion.

Basically what we're saying here 
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is that we think that, yes, there are 

problems with using pooled data. The 

problems are of the type that we just 

described, the data dominance and there is 

some correlation there.

But we don=t see that OPOS is 

going to solve these problems in a good way, 

except in a particular case where we do have 

a lot of samples taken from an individual 

after an exposure and it=s clear that's what 

happened. And then in that case we would 

agree that we should use OPOS to reduce that 

data down to one value for that worker for 

that time period.

On the other hand, in most of the 

cases we find that there's not a whole lot 

of them and there's not clear evidence of 

data dominance. And in those cases we're 

not sure why there's any reason to use OPOS.

The second issue here is that if 

you do use OPOS you have to account for 

uncertainty in the estimate. It is a 
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statistic and it will be influenced by a lot 

of things that should be taken into account 

under the general term of uncertainty.

If we don't do that we're going 

to end up, I think, with a standard 

deviation and a 84th percentile run for the 

intakes that may be too low to really 

reflect the variability and the uncertainty 

in the data. 

There may be many people who want 

to argue with these conclusions now, but 

maybe we should reserve that argument until 

when we're finished here and then we'll come 

back to Slide 8 and see how maybe this 

applies or doesn't.

So the first issue then we're 

going to talk about that led us to that 

conclusion is the data dominance problem. 

Certainly it does occur in the data. There 

are cases where there is a large number of 

incident-related samples taken from a few 

workers.
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And if you throw them in all 

together in a collection, say you have 50 

from a worker of that type and then you have 

another 100 workers and they all have one or 

two samples, then you will have this problem 

of data dominance.

And we do think that is going to 

be a problem. So that's why we say that, 

yes, when you do have that problem then you 

should use OPOS. How frequently would that 

happen? Well, we've looked at, for 

instance, at SRS for plutonium sampling and 

the table on Slide 10 shows the results we 

found.

And basically what we said was 

let's look in every quarter and see how many 

workers have so many samples in that 

quarter. And the number of samples is on 

the left of the table. And the number of 

counts we made are in the next column, and 

then there's a cumulative percentage.

And then we did the same exercise 
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again by year rather than by quarter. We 

said how many samples did they have in that 

year? And we counted up the number of the 

people that had one, the number of people 

that had two and we put them in this table, 

and again showed the cumulative on the 

rightmost column.

And basically what this table 

says is that 99 percent of the time there's 

no more than three samples in a quarter. 

And 95 percent of the time there's no more 

than four samples in a year. So what we're 

dealing with then is relatively rare cases, 

which are down near the bottom of the table.

The ones that really cause data 

dominance, there's a handful of them 

basically. If you think about it, on an 

annual basis there might be 100 out of 

10,000 where they have more than, just say, 

eight. But I think generally when we think 

about data dominance we're talking greater 

than ten and maybe even larger, that there 
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was repeated sampling for an individual.

And that occurs very infrequently 

it seems. So we'll come back to this slide 

again later on in the talk. And, again, I 

have to admit we only looked at this at SRS 

in that particular table. So it's not clear 

that the same results would be found 

elsewhere for plutonium and it's not clear -

- and our report does discuss other isotopes 

at Savannah River.

And we find that there are very 

similar patterns of frequencies. Uranium 

tends to have the highest number of samples. 

And the percentages are a little different 

for uranium. Where you might see 95 percent 

of the samples that workers have them for or 

less, that might be 90 percent instead of 95 

when you do uranium.

We also looked at this question 

of data dominance at Fernald but we did it 

in a different fashion. And I was hoping 

maybe Bob could say some words about what we 
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looked at at Fernald, if he's on the line.

MR. BARTON: Sure, Harry. This 

is Bob. You know, one of the things we 

tried to look at is how many of these type 

samples where it's an incident sample or a 

special sample where you would have sort of 

an off-normal sampling protocol.

For example, if you had a special 

project or if there was an incident. And 

these are characterized at Fernald by their 

particular sample coatings. I don't want to 

get too far down into the weeds here, but 

the special samples were type 50s and 

incident samples were labeled as a type 40.

So one of the things that we took 

a look at was how many of these type samples 

did you have in a given year and what effect 

did they actually have on eventually the 

derived intake rates? So in other words 

what you would kind of expect to see is that 

if you had a year where you had a 

significant portion of special or incident-
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related samples, then you would think that 

the derived intake rates or the excretion 

patterns would go up because you would have 

a data dominance problem.

And when we actually got down to 

it, we really didn't see that sort of 

pattern. We didn't see the causal 

relationship between the addition of these 

incident samples or increased incident 

samples versus what ultimately becomes the 

derived intake rate for the claimant.

So we didn't see the opposite 

either, and there were certain years where, 

yes, that did appear to be the case. But 

there were other years where you would have 

a decrease in these, you know, data 

dominance type samples but you really didn't 

observe any sort of change in the derived 

intake rate.

So that was kind of specific to 

Fernald. Is Joyce on the line? Do you have 

anything to add on to that?
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DR. LIPSZTEIN: Hi, I'm on the 

line. I think everybody -- I don't know if 

it was well-explained. Just one thing. The 

Revision 1 was done with the older 

methodology with OTIB-0019. And the 

Revision 2 was using the OPOS methodology. 

I don't know if people understood that. 

That's why we were comparing the two, 

Revision 1 with Revision 2. That's it.

MR. BARTON: I guess sort of our 

conclusion there was that we didn't see that 

causal relationship between the number or 

percentage of incident or special sampling 

that was in the distribution versus what you 

actually got with the intake rates. And we 

didn't see that relationship.

So I guess, you know, from our 

end we weren't really convinced it was that 

much of a problem, at least in the case of 

Fernald. And, again, that's very specific 

to that site. But we really didn't see data 

dominance as an issue per se in what we 
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looked at there.

DR. LIPSZTEIN: And one question 

that was always stood to us is whether -- or 

we want to know whether the OPOS methodology 

was claimant-favorable. If it was claimantfavorable it would always give us an intake 

rate that is higher than in the other 

method.

So we compared the intake rates 

from the two revisions, and for some years 

one of the revisions had the higher intake 

rate, for other years there was another 

intake rate, the Revision 1 was higher. In 

other years the Revision 2 had higher intake 

rates.

So neither methodology had the 

systemic bias that will always yield a 

higher intake rate. So there is no 

claimant-favorability in either of the two 

methods.

MR. STIVER: Yeah, this is 

Stiver. I want to just add one thing. We 
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looked at Fernald and Savannah River because 

those were the only two sites for which the 

OPOS methodology had actually been 

implemented at the time that we did our 

paper.

And I'll just reiterate what 

Joyce said, that at Fernald the use of OPOS 

versus not using it, or the number of 

incidents or special type 50 code samples 

really did not have any impact on the 

ultimate derived intake in either case. So 

there doesn't appear to be any correlation 

from those variables in either data set.

Anyway, Harry, if you would like 

to go ahead and continue.

DR. CHMELYNSKI: Okay. I guess 

we're on Slide 12 now. And this is on 

correlation. I looked at some of these 

examples of workers with many samples over a 

short period, presumably following 

incidents.

And to tell you the truth, 
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there's not as much correlation in them as 

you might expect to see. They jump around

an awful lot. But there are some examples 

in our report that show that there is a 

pattern there where, of course, as time goes 

on the urine excretion concentrations fall 

off.

But you might see one at zero 

right after the incident and another one a 

week later and then in between there's some 

high ones. So there's very strange patterns 

of results following an incident. It's not 

always as clear cut as one might think to 

even find the incidents.

And certainly just because a 

worker had a lot of samples doesn't 

necessarily mean they're correlated. We 

would like to see some evidence of 

correlation other than the fact that there 

are just a lot of samples there.

The issue of correlation also was 

raised in some other cases where exposures 
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tend to drag out -- if there are exposures, 

the results of those exposures may drag out 

for periods longer than a year even. And 

there is, again, in this sense, a 

correlation that may stretch beyond the one 

year period due to that single incident.

Those issues are more important, 

I think, when you're doing strata comparison 

than when we're just talking about how to 

estimate a coworker model. But they are 

something we should consider when we do any 

strata comparisons, that these workers with 

a high number of correlated samples should 

certainly be -- well, according to our 

recommendation, OPOS should be used in those 

cases before we do these strata comparisons.

Now, when we think about OPOS for 

building a coworker model and for building a 

comparison, a hypothesis test that compares 

strata, we're talking at sort of a high 

level about it. Now, what we're going to do 

now in the rest of the paper here, the rest 
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of these slides, is look at OPOS at a more 

micro level, for a single worker, and how 

well does that value allow us to determine 

his intake?

And clearly we're going to be 

doing this for lots of workers and later on 

we're going to take all those values and 

throw them together on a curve. But right 

now we're just looking at the process of 

getting a single OPOS value for a worker.

And one of the concerns we have 

here is that -- I'm sorry, I'm on Slide 13 

at this point, frequency of monitoring. And 

there's a lot of words on this slide, but 

it's a very simple concept.

If there's an incident in 

November and we have ten samples taken 

following up on that incident on one worker 

and again ten samples taken again on a 

different worker, well, that's good. We've 

measured what happened after the incident.

But now let's say one of the 
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workers had routinely been tested for every 

month before the incident. Well, he'd have 

ten more samples than the other one. And 

let's say the other one didn't have those 

monthly tests.

They both have the same exposure 

in the last two months or sometime in 

November, say. But yet in one case we're 

going to mix together all the 20 for one 

worker and assign him a dose that's only 

about half of what we assigned the other 

worker simply because one of them had more 

sampling done before the incident occurred 

than the other one did.

So one of our problems, then, 

with OPOS is that it is highly dependent on 

how many samples the worker had and the 

relative timing of those samples with 

respect to his actual exposure. 

Now, if the exposures are 

continuous that may not be so much of a 

problem. But when you're talking about 
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exposures after incidents and trying to 

reduce it down to a single number for that 

worker that's reflective of what he got in 

the incident, it's disturbing to see that 

you get two different answers for two 

workers who may have a previous history of 

monitoring that differ.

And if you extrapolate that line 

of thought into the process of comparing two 

groups of workers, we think that's 

sufficient reason that they should have both 

been tested with the same sampling protocol. 

Both groups. If not, then it's 

possible for instance that the nonconstruction workers were routinely tested 

and over a whole long period and they were 

given lots of samples compared to 

construction workers who only had a few 

samples. Even if people have the same 

exposure at some point in an incident then 

we would see different numbers --

(Electronic interference.)
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DR. CHMELYNSKI: Is there a 

problem or --

DR. MAURO: It went away.

DR. CHMELYNSKI: Oh, good. Let's 

not worry about what it was and hope it 

doesn't come back. Okay, so that was the 

frequency of monitoring problem. 

Now, on Slide 13, the title here 

is kind of cryptic. But what we're talking 

about is how well does OPOS characterize the 

data for a worker and how well does OPOS 

characterize the intake that worker had 

given the data that we see?

Those are two separate questions. 

And one of them, which is the relationship 

on the right between OPOS and intake, one of 

them NIOSH has a long discussion about, 

which basically says that if there is a 

series of assumptions made about the use of 

weighted least squares and what the weight 

should be, that if you go through this long 

analysis you will end up with the answer 
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that the mean excretion rate is indeed 

proportional to the intake that the worker 

had. Then this result has been used by many 

people in the field to summarize the 

exposure following incidents. 

A second question that we looked 

at is the arrows between the data and the 

OPOS, which is how well does OPOS estimate 

the mean excretion rate for a worker?

And it may sound like an obvious 

answer, that, well, you're taking the 

average and therefore it's the mean. But 

since it's a statistic, the question is how 

well does it estimate the mean excretion 

rate?

When we looked into the question 

of the least squares regression approach to 

relating the intake to the mean excretion 

rate, we find that both NCRP and the IMBA 

manual itself state -- I'm not going to read 

this whole thing. But if you look at the 

fourth line of the NCRP 164 quote in bold, 
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it says the formulas assume only one intake.

And that's the first problem that 

we run into here. It's not clear that OPOS 

can be used except in those situations. It 

could certainly be used, but this 

proportional relationship that is claimed 

between OPOS and the intake isn't 

necessarily valid if we're talking about 

cases where those assumptions the weighted 

least squares regression analysis are based 

on, when those assumptions don't hold.

And we listed some of these 

assumptions and when they don't hold on the 

next page. The thing in the box is actually 

a quote that's out of the manual that says 

least squares can be used only in cases of a 

single intake with explicit air values on 

each data point and a single bioassay 

quantity.

Well, the real important part is 

the single intake. When they have multiple 

intakes the equations are going to look 
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different and there's not necessarily the 

proportional relationship no matter what 

type of assumptions one makes about the 

weights for the regression.

DR. NETON: This is Jim. I hate 

to butt in. But I just want to make one 

quick comment.

DR. CHMELYNSKI: Yes.

DR. NETON: Single intake can 

also be a single chronic intake.

DR. CHMELYNSKI: Yes, I agree.

DR. NETON: So that's a big 

difference between what we're comparing. 

Just trying to point that out, because we're 

assuming chronic intakes here.

DR. CHMELYNSKI: Well, then we'll 

have to look at these. What we're finding 

is that the intakes -- first off, that the 

excretion results should only be after the 

intake. And, again, that was an incidentrelated intake. And that the urine 

activities shouldn't be lumped together, the 
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ones after the intake and the ones before 

the intake, to do a single computation of 

the OPOS value. 

Finally, in particular, we don't 

want to see cases where there are no intakes 

lumped together with periods where there are 

intakes.

DR. LIPSZTEIN: Harry, this is 

Joyce. Should I answer Jim now or would it 

be better to --

DR. CHMELYNSKI: Yes, I'm sorry. 

Since we started here I should have given 

you an opportunity to respond. Yes, go 

ahead.

DR. LIPSZTEIN: Jim, it doesn't 

matter what you assume, if it's chronic 

intake or a non-chronic intake, after you 

get the 50th percentile of the whole 

distribution. It doesn't matter what the 

assumption was.

The problem is that you have some 

urine excretion samples that come from a 
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single intake and then all those that are 

incident-related, they came from the same 

incident, could be either a chronic or a 

single intake. But it was a unique intake 

and you cannot mix them with -- what I mean 

mix is from these excretion rates from no 

intakes and then try to get the intakes from 

this. It's not proportional anymore.

DR. NETON: We'll talk after on 

that.

DR. LIPSZTEIN: Okay, okay. 

We'll talk about that at the end. Okay.

DR. CHMELYNSKI: The Slide 17, 

and we have several points where we might 

come back here, I think. But to finish the 

discussion, we're going to look at next, 

starting on Slide 17, how well OPOS performs 

in terms of estimating the mean excretion 

rate.

And here we're not talking about 

how the OPOS relates to the intake, but 

simply a matter of, well, we samples at a 
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couple points in time for a quantity which 

varies over time, which is the excretion 

rate. And the time ordering is an essential 

part of the calculation when you look at 

what the mean value is over the period.

And we'll see why that happens 

pretty soon. But in particular, we're 

looking at what we call the time-weighted 

average urine concentration over a time 

period.

And one way of thinking about 

this is if all the urine were excreted 

during the year were collected in one 

container and then we analyzed what the 

concentration in that container was, 

assuming this is a long-lived nuclide. That 

would be what we call the mean, the timeweighted average urine concentration.

And I think it's the one we do 

want to use when we do the intake 

calculation. Now, weighted least squares 

ignores the ordering of these observations 
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in time, and so does OPOS. So what we did 

was we did an analysis of what happens when 

you do look at how it varies over the year, 

how the excretion rate varies over the year.

And on Slide 18 we have just a 

hypothetical case where there was some 

exposure near the beginning of a year and 

the excretion rates fall off as the year 

goes on. And in this particular case we're 

going to assume that, well, we did a good 

sampling of eight samples regularly spaced 

along that curve.

And when you do that, we see that 

we get a good approximation to what the 

integral under the curve is. Now, why do we 

want the integral under the curve? Well, 

the mean value theorem says that if I take 

that integral and divide it by the time 

period found at the bottom of the page, the 

end of the graph here, then that will give 

me the mean value of the function.

And since the time period we're 
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covering here is one year and we've decided 

to use that units of one year, then it's 

just divide the integral by one and you get 

the mean value. So the integral is equal to 

the mean value in these examples.

And in this case, it's a cubic 

that I picked. So I can calculate the true 

mean. And we see that if you go back to 

calculus you can approximate that true 

integral fairly easily by doing equally 

spaced sections. And that=s what=s called 

the Riemann sum, which also turned out to be 

the same answer you get when you do OPOS.

That example is based on equally 

spaced observations in time. But usually 

that's not what happens. And we've looked 

at that also at SRS for the plutonium data 

that showed up on Slide 10. And we looked 

at the number of days between successive 

bioassays for a worker and we ended up with 

this interesting plot on Slide 19.

And what this shows is that, yes, 
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there are a lot of workers who were tested 

every 90 days or quarterly. And there was a 

little less, but a lot who were tested semiannually. And it looks like there's a lot 

who were only tested once a year.

And if you look out there at 720 

you see even there's some that were tested 

every two years. But in general it's hard 

to pin down exactly what frequency the 

testing was done at any particular time 

during this period at SRS.

So what we started thinking about 

was, well, when you look at the data and you 

look at the times of the year when the data 

were done, a lot of them tend to look pretty 

random. Maybe a sampling program came on 

and they decided they had to start doing 

some testing or maybe they were selecting 

people at random for testing.

But I'm not quite sure. I didn't 

see any of the kind of evidence of 

systematic, quarterly testing that one might 
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expect. So what we looked at then was how 

would OPOS work if you did have random times 

during the year and take samples at those 

times?

And here we have an example where 

I took eight random times, two of them ended 

up being pretty close together, actually at 

both ends of the spectrum here. And in 

between they are roughly equally spaced, but 

not really so. And the point here is we 

really don't know what the curve is doing in 

between these sampling points.

So there is some uncertainty as 

to what the area under the curve really is. 

In this case I worked it out and OPOS came 

out to be lower than the true mean, partly 

because we had too many samples in the low 

ends, on those two ends, than in the middle, 

in this example. But more importantly, 

the confidence bounds are quite broad. They 

stretch pretty much from the lowest 

observation, not quite up to the highest. 
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But again I had eight samples here and it's 

very rare that we get that many.

It's very easy to make this 

example jump up over the highest sample when 

you look at the confidence interval on the 

mean. If you do the specific calculation, 

it's informative because, as we know that 

the sampling distribution of the mean 

follows the Student t-distribution, and the 

confidence intervals are calculated from the 

variance of that distribution. And the 

variance of that distribution, I have a 

formula for it here, the degrees of freedom 

over the degrees of freedom minus two.

And then you say, well, if it's 

two then you get DOF over zero and what does 

that mean? Well, that's why we say the 

degrees of freedom have to be greater than 

two before we can actually build a 

confidence interval on the mean.

But since the degrees of freedom 

is n-1, what we're really asking then is 
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that we have at least four samples before we 

can compute the variance of the estimate. 

And going back to Slide 10, we find that in 

95 percent of the cases, we don't have four 

-- I'm sorry, in 90 percent of the cases we 

don't have four samples.

If you look on Slide 10 for the 

annual data, three or less, 90 percent of 

the workers. So what that says is that most 

of the time we won't even know what the 

sampling variance of OPOS is, or how 

accurate it is or how uncertain it is. It's 

unbounded.

And that, to me, raises some big 

questions. Why would we use OPOS if indeed 

it could give us a number that's smaller 

than the smallest number, higher than the 

highest number in terms of a confidence 

interval? So our answer was how well does 

it estimate, OPOS? It doesn't seem to 

estimate it very well.

I'm sorry. How well does it 
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estimate the mean excretion rate, and OPOS, 

as an arithmetic average, doesn't do it very 

well when you're only talking samples of 

size three or less. Now, maybe when you get 

up to four or five, ten, you at least have a 

confidence bound where you can start 

thinking of OPOS as a meaningful number.

But, again, that relates back to 

why we said it's okay to do it when you 

actually have a lot of samples following an 

incident, because then it seems to make 

sense and you can actually get an estimate 

that means something. 

So, finally, while we were 

looking at these issues we discovered there 

were some problems in the way OPOS was 

implemented in the past. I'm not sure if 

this is currently being done in the newer 

reports, but what we said at the beginning 

of the discussion was that when you do a 

calculation of the arithmetic average to 

compute OPOS you run into a problem when you 
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have censored data, or non-detects is a 

common word for that situation.

You can't compute the average. 

So NIOSH has proposed to use a slightly 

different calculation called the maximum 

possible mean. And in this, the idea is to 

use the censoring level, or the MDA, 

whichever one you call it, for data that are 

reported as less than MDA.

Now, this is something that has 

often been done in the past where you just 

use the detection limit as the value rather 

than trying to say it's half of the 

detection limit or zero or whatever. It 

seems safe and that's why it's called the 

maximum possible mean.

It seems safe to use the 

censoring level as the value that that 

measurement represents because we know it's 

below that, and therefore it seems claimantfavorable if we use the censoring level for 

that data point. Now, if we do that for all 
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the censored data it might turn out that all 

of them now are censored data, and in that 

case you still -- in that case you would be 

computing the mean of all the censored, of 

all the MDAs.

And that's probably the best you 

can do. But we admit, in that case, the 

answer is that it's a censored value, it's a 

non-detect. Even OPOS is a non-detect in 

that case.

And then, finally, if there is at 

least one that was not a non-detect, if 

there was one detect, then we would go ahead 

and compute MPM the way that the algorithm 

says, which is to take it as a simple 

average. Most of them might be the 

detection limits that are thrown in that 

average.

But there will be at least one 

uncensored value, and therefore we will 

treat that answer as an uncensored result 

for that person. This is the way that the 
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procedure for maximum possible mean was 

described, but when we looked at it in 

practice it turns out it's not that simple, 

it becomes a question of what is the 

censored value?

Is it a value that's not written 

down? Or is it a value when it's written 

down, does that count as a value even if 

it's less than the detection limit or 

doesn't it? And that's what this question 

boiled down to. What we found was in a lot 

of cases as long as there was an explicit 

value written there, it was used in the 

calculation of the maximum possible mean, 

even though it might have been below the 

censored value and even though it might even 

have been negative.

If it was reported then it was 

included in the calculation. We found that 

this was not following the instructions for 

how the maximum possible mean should be 

computed and suggested that that procedure 
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should be made clear to workers, to the 

workers at NIOSH, that when you look at 

these you should not use any entry below the 

censoring level to compute the maximum 

possible mean.

And I guess that was the last 

slide. Yes, okay. Now, like I said, it's 

on the last slide, questions?

CHAIRMAN MELIUS: Why don't we 

start, Jim, Jim Neton, do you or NIOSH have 

questions?

DR. NETON: Where do I begin? I 

don't have so many questions as I have some 

comments.

CHAIRMAN MELIUS: Okay. That's 

fine.

DR. NETON: Okay. I think 

probably about the only place that I could 

say we 100 percent agree with SC&A on their 

analysis is that individual worker intakes 

should be used to reconstruct doses, if 

possible. And we've discussed that once 
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before, why we don't do that.

What I would say is the coworker 

model is a distribution of worker exposures, 

not bioassay results, which is sort of where 

SC&A is suggesting that we go back to. And 

in my mind, a simple example, maybe not an 

exact example, would be is if I was trying 

estimate the average height of the Advisory 

Board and I had one measurement for 

everybody but had ten measurements for Bill 

Field, SC&A would appear to suggest that I 

should use ten Bill Field measurements into 

the 25 data set to calculate the average 

height of the Advisory Board.

And that just seems intuitively 

wrong to me. Again, it's not exact, but 

that's really what I'm hearing them saying, 

because really the best representation of 

the worker's intake is some statistical 

evaluation of his bioassay data during a 

given time period.

I'm very willing to discuss 
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tweaks to our approach regarding 

representativeness of the mean or 

incorporation of variance in the model. 

Those are all on the table. But I really 

fundamentally object to the idea that I 

would have to ignore the fact that an 

individual coworker model should be based on 

a distribution of samples as a pooled set of 

data.

I mean, that's the first point, I 

guess. You know, I don't know if we stop 

there or I can keep going, but --

DR. CHMELYNSKI: Well, I would 

like to answer one question, which is what 

if every worker had four samples, one a 

quarter?

DR. NETON: That would be fine. 

You would take the average of the four --

DR. CHMELYNSKI: Yeah, but why 

would you have to?

DR. NETON: Because they may 

vary. They're going to go up and down.
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DR. CHMELYNSKI: I understand 

they may vary. But why would you have to 

take -- you know, they're all equally 

weighted, they all have four samples. Why 

not just calculate the distribution?

DR. NETON: In that unique 

situation it probably would end up at the 

same place. But what I'm saying is the 

estimate of his total excretion is the mean 

value of those samples times 365 days. That 

would be the true estimate of his excretion, 

not the four samples put into a data set. 

In some cases you have four, some cases you

have more. 

I'd like to address this idea of 

incident samples as well. We have, since 

this program began, gone under the 

fundamental premise that a chronic coworker 

exposure model, a chronic exposure model in 

general, is bounding for workers who have 

incident-related samples.

We went through this with 
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Mallinckrodt many times. We vetted IG-002, 

which is the basis for this. And it's no 

different for coworker models. So if there 

is an incident sample embedded, or a series 

of incident samples embedded in a routine 

coworker model, we're going to assume that 

those represent a chronic exposure scenario.

And if there was one value in 

that monitored period, a year for example, 

we would assume his excretion during the 

year was that incident sample times 365 

days. This is a bounding technique that 

we've applied across the board to all dose 

reconstructions and also our coworker 

models.

So the fact that there were 

incident samples in there with changing 

retention fractions, such as indicated in

Slide 18, really don't matter. We are 

assuming a chronic exposure scenario for 

this coworker model.

DR. LIPSZTEIN: May I try to 
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answer you, Jim?

DR. NETON: Yes.

DR. LIPSZTEIN: Is it possible 

for someone to put, you know, from our draft 

paper, on Page 41 there is a table.

DR. CHMELYNSKI: In our paper, 

Joyce?

DR. LIPSZTEIN: On Page 41 of our 

draft. Jim, we are not talking about not 

using an average for -- using all points 

from an incident as different points in the 

table. What we are talking about is that 

once you have an incident and then you have 

related excretion rate that is related to 

that incident.

So if you take the average of the 

excretion rates that were taken just for 

that period of time after the incident, and 

you take the average as one of the points of 

the worker, then you can use the old 

approach as you always have used before, 

because what was happening is some types of 
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incidents or special work were not taken 

into account on the old methodology.

But what you cannot do is when 

you take the average you are mixing periods 

of no intake with periods of intake. So 

let's see. If you look at this first table 

here, I measured ten workers -- that my 

calculation was just of ten workers, okay.

And suppose there was an accident 

in November and December and the workers 

were monitored regularly, but there was no 

exposure from January to October. So you 

measure the excretion rate and it was always 

equal to the minimum detection activity, 

which was one, for example.

And then suddenly in November you 

had a high excretion rate and in December 

you had a high excretion rate. When you do 

the OPOS for the year you take into account 

the period of non-exposure mixed with the 

period of exposure. So you get OPOS that 

are 10, 10, 11, 11, 10, 11, 9, 9, 8, 8 for 
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this population of ten workers, right?

So the geometric means for those 

OPOS would be 9.6. Then you are going to 

apply a chronic intake to this geometric 

mean of 9.6. Now, what happens if the 

workers were not regularly monitored? Then 

you have the next table.

Suppose the workers were only 

monitored in November and December, only 

when the incident happened. Then you would 

have the same exposure but the OPOS would be 

much higher and the geometric mean of the 

OPOS would be 53 instead of 9.6.

So when you calculate the intake 

rate, the coworker intake rate, for this 

second population of workers, you are going 

to calculate the chronic intake rate for an 

excretion rate of 53 units of activity, 

while on the other sample of workers, which 

had the same activity, you are going to 

calculate a chronic intake rate based on an 

excretion rate of 9.6.
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So that's what I'm saying. You 

cannot mix periods of no exposure with 

periods of exposure because they are going 

to influence the intake rate even if you do 

a chronic intake rate. So what I'm saying 

is that what you should do is that the 

November and December when there was the 

period of excretion rate related to that 

incident, then those two should be averaged.

And it would be another point, 

then that's different. And then when you 

did the other coworker model, instead of 

doing it through the whole year you did it 

by periods of time, so when you look at the 

periods of time you can see that there was a 

time period when there was an incident at 

that installation.

And then the intake rate for that 

period of time can be higher than when you 

look at the whole year. So what I'm 

questioning is the application of this 

methodology on a year basis instead of using 
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it on a period basis or a smaller period 

basis as you did on the other coworker 

model, on the previous coworker model.

DR. NETON: We rarely do anything 

less than a year when you have type S 

material or even type M. It just doesn't 

change that much.

DR. LIPSZTEIN: Well, I don't 

know, because you are going to base it --

like in this example, if you have a whole 

year you would base the chronic intake rate 

on 9.7 -- 9.6, I'm sorry, on 9.6. But if 

you did the other samples where workers were 

not monitored before the incident you are 

going to base the intake rate, the chronic 

intake rate for type S -- it doesn't matter 

-- 153. So you'll get a much higher intake 

rate.

DR. NETON: But let me finish. 

I've got a couple of things to say here. 

One is I think this example is obviously 

skewed towards the extreme end of the 
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possibilities. I'm not sure this is going 

to happen very often, and I can also posit 

that it would happen where the exposures 

were in January in February which will 

counterbalance each other over a large data 

set.

The second thing is, look at the 

data set that you generated. If you did not 

use OPOS, what would be the 95th percentile 

of the distribution? It would be around 50 

dpm per day or 50 units per day, because 

you've got these high values. And that is 

wrong.

The average value of excretion 

during this time period is 9.7 dpm per day 

times the year, will give you 2,500 dpm 

exposure per year, which is correct. That's 

their chronic excretion during that year, 

not 50 times 2,500. I think you've actually 

proved our point that OPOS is a better 

statistic here.

DR. LIPSZTEIN: No, no, no, 
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because when you have -- you introduced the 

OPOS to compare regular workers with 

construction workers. And then you have the 

people that are not regular employees of 

institution, and we saw this at Fernald very 

well, they were measured only when there was 

a special work or a special incident.

And then they have exactly this, 

they have like a period of time where they 

were monitored and they had very high 

results. And then other periods of time 

they didn't have any results. So you cannot 

compare this with the other type of workers.

So what happens, let me put it, 

what happens, you have one year-round -- I 

put November and December was on purpose 

because I did not want to do the correlated 

samples before. But you have a non-exposure 

until October and then November and December 

you have an exposure and it probably would 

continue on the other year.

So why stop in one year? So the 
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correlation, it goes from one year to the 

other. I think the problem with it is that 

you are taking always a year. If you take 

by quarter of years like you did before, you 

would see that in January, February and 

March there was no exposure in that 

installation. April, May and June there was 

no exposure, and then in the last quarter of 

the year there was an exposure that probably 

reflects on the next year.

DR. NETON: I would argue, 

exposures if they go into the next year will 

bias the model high because we're going to 

take --

DR. LIPSZTEIN: Yes, yes, of 

course, yes.

DR. NETON: -- bias it high 

because we're going to assume that continued 

excretion in the beginning of the next year, 

all those samples are going to relate it to 

chronic exposure during that year.

DR. LIPSZTEIN: Exactly. So what 
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happens you say that you would assume a 

higher Probability of Causation and you are 

not because the next year will correlate 

with it. So what you have is that -- the 

problem is the bounding per year because 

before you didn't have bounding per year.

And now what you do, you not only 

bound per year but then you have years put 

together to find the same intake rate for 

several years in a row. So when you had it 

before you could see which time periods have 

the same intake rate and that was correct.

And then if you have an 

unmonitored worker that worked at an 

installation in November and December, he 

had a much higher probability of having --

DR. NETON: I one hundred percent 

disagree with you. Maybe I need to put this 

in writing because I'm not getting anywhere.

DR. LIPSZTEIN: Okay.

DR. NETON: This example proves 

OPOS in my opinion. I would be happy to 
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discuss in writing.

DR. LIPSZTEIN: Okay, please do.

DR. TAULBEE: This is Tim 

Taulbee. Can I make a comment please?

CHAIRMAN MELIUS: Yes, go ahead, 

Tim.

DR. TAULBEE: Okay. In this 

particular example I agree with Gen here in 

that I mean effectively you've kind of 

proved our point. And I know Jim is going 

to write this up. But just think about this 

from the construction trades workers in the 

example that you just gave.

By averaging the regular workers 

who were monitored throughout the year who 

are not monitored based upon an incident 

type of basis, they have a lower overall 

value from the OPOS standpoint, whereas the 

subcontractors or construction trades, if 

you will, end up with a higher bioassay 

result.

When we did the comparisons from 
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Savannah River, this is exactly what we were 

looking at, was people who did not have this 

large I guess dilution is what you're trying 

to refer to it as of this monitoring time 

period versus construction trades that were 

just monitored after the end of the job or 

an incident type of scenario.

We didn't see any difference 

between the two. Based upon your example 

here we should have we should have seen a 

large difference where construction trades 

would have been higher using the OPOS 

result.

DR. LIPSZTEIN: Yes, but when you 

go to sample you see it. At Fernald you see 

this. And --

DR. NETON: Fernald was made an 

SEC, in my understanding, because of that 

exact issue. It's incumbent upon NIOSH to 

demonstrate that the coworker model, the 

stratified model that is used, is 

appropriate and for various reasons and we 
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would do that.

I mean there's caveats associated 

with that. You're pointing out an example 

at Fernald that was made an SEC for exactly, 

valid reason that the construction workers 

or trades workers were not monitored 

adequately. It's true. We 100 percent 

agree with that and we would add a Class if 

that were true anywhere else.

DR. LIPSZTEIN: Actually, we 

agreed to discuss next week the rest of it.

DR. NETON: But you understand 

the Class was added.

DR. LIPSZTEIN: I see, I 

understand. And I was going to argument a 

little bit more. But I think as you've told 

me that you are going to write it, maybe 

it's better not to bore everybody with the 

discussion. And when I have the written 

discussion, then I'll write again. Maybe 

it's better that way.

DR. NETON: We could talk about 
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this page for hours.

DR. LIPSZTEIN: Yes, yes, right. 

I'm okay with that.

DR. MAKHIJANI: This is Arjun. 

Could I comment on Tim's statement?

CHAIRMAN MELIUS: Yes, please do 

because if you don't, I will.

DR. MAKHIJANI: The idea that the 

analysis of NIOSH showed that construction 

workers and non-construction workers were in 

the same distribution actually was quite 

controversial. And when we did the 

analysis, if you remember our report, and 

Harry went into it at some length, so Harry 

please comment if I'm off base or want to 

augment.

What I think NIOSH showed is that 

if you use the test that they used, which 

was basically biased, I'm not using that in 

a pejorative sense, in the direction of 

concluding they were the same when the area 

of ignorance was actually quite big in the 
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sense that they could have been quite 

different but the test would still conclude 

they were the same.

Remember we had all that argument 

about Type 1 and Type 2 tests. And in the 

examples that we provided, we showed that 

the tests would conclude they were the same 

even when they were very different.

And this was a conclusion that 

was very dependent on the total number of 

OPOS samples. And even when there were more 

than 30 samples, there were sometimes 

problems. So I wouldn't -- I don't actually 

agree that NIOSH has shown that the 

distributions are the same. They have not.

DR. NETON: And I understand what 

you're saying, Arjun. I think the relevant 

point is that we need to show that the 

coworker model that we're using is a valid -

- not a coworker model but the stratified 

model that we're looking at, we can make 

some reasonable statistical assumptions 
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from.

And the example that Joyce has 

put forth, it may be that's not a valid 

comparison. I'm not arguing that point.

DR. MAKHIJANI: Right. I'm just 

responding to what Tim said just to set the 

record straight.

DR. NETON: If you only have two 

months= worth of, two incident samples on an 

entire construction workforce that was 

working the entire year, it may be hard to 

justify that that model is valid, and we can 

reconstruct those doses. So I don't 

disagree with that point.

CHAIRMAN MELIUS: I would just 

add it's not only it would hard to show that 

you couldn't reconstruct those doses but 

you'd really be hard put to justify 

comparing these, you know, a model based on 

the construction workers with a model, you 

know, coworker model based on the strata 

that's the production workers.
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DR. NETON: Right. You know, it 

came up the last time we discussed. And 

SC&A actually made some mention to it in 

their report and I'd like to clarify that a 

little bit.

You know, the use of incident 

samples in coworker modeling is tough. And 

I think I said if I had a situation where it 

was a hundred percent incident-based 

sampling and there was quite a variable 

exposure in the work place and not good work 

place monitoring controls, then I would hard 

pressed to say that we could do a coworker 

model.

You have to be careful. I'm not 

saying you can't. But the burden, it 

becomes a lot harder burden of proof to 

demonstrate that model is bounding and is 

not just bounding-plus. So I think we're on 

the same page there. I just wanted to make 

clear on that.

DR. MAURO: Jim, this is John. 
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During this conversation I heard something 

very interesting. I think Harry said it. 

He said that the problem is the averaging 

time for OPOS that's being employed or 

considered -- I think it's on the order of 

one year for each worker, but if it was on 

the order of a quarter, it would work.

I guess I'm not quite sure why 

that would be the case. Did I hear that? 

Is that the essence of really where the 

dilemma is with OPOS is that you're 

averaging over too large a time period?

DR. NETON: Well I think Harry's 

point was that if you take averages over a 

longer time period it's likely to have some 

issues with the mean values because, you 

know, where the samples were collected and 

that sort of thing.

I mean, I don't think it's 

necessarily related to the -- it certainly 

would be better to have quarterly samples. 

We of course don't have that very often. I 
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would say that it seems to me that SC&A's 

concern, at least in my opinion, boiled down 

to the representativeness of the mean of the 

OPOS value and the incorporation of variance 

in the model that's generated from that.

And I think those two things can 

be dealt with. For example, I'm not 100 

percent married to the mean value. Maybe 

the -- it may make sense that the timeweighted average is a better approach 

because that gets away from this inadequate 

or this, you know, unrandom distribution of 

samples, or random distribution of samples.

And in the variance itself SC&A 

did an -- I notice you didn't talk about it 

today in your analysis, but you had this 

page where you weighted the OPOS statistics 

based on I believe the number of samples. 

It's hard to tell what you did there, and 

came out, and essentially regenerated the 

general distribution of samples.

DR. CHMELYNSKI: Yes, what I did 
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in that example was I went, I carried 

through with the idea that each OPOS 

statistic had an uncertainty and I used 

exactly the same logic that was used to 

justify the proportional relationship of the 

OPOS value to the intake.

In other words I used weighted 

least squares and knowing the relative 

variances I assigned those weights, and lo 

and behold, when you come up with the 

answer, it says what the ratio estimator 

always says. It says you add them all up 

and divide.

DR. NETON: You assume that the 

variance is proportional to the number of 

samples, which I don't think is true. 

That's something that needs to be verified. 

We've looked at that and I don't think it's 

true at all. But that's something we need 

to look at.

In fact it may be true that the 

variance is inverse to the proportion of 
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number of samples because you have people in 

higher exposure scenarios with variable 

exposures.

DR. CHMELYNSKI: Yes, but you're 

using a log-model and that's a variance 

stabilization which already takes into 

account what you just said.

DR. NETON: Well, I'm not sure.

DR. CHMELYNSKI: Larger values 

certainly do have larger variances. But we 

do the logarithms.

DR. NETON: I don't think that 

the assumption that you made that the 

variance is proportional to the number of 

samples is really valid. You didn't, and 

you picked a three-year time period which is 

kind of interesting.

I mean there's a lot of issues I 

had with that analysis. We would never, you 

know, you tell us not to use more than one 

and now you're using three. That tended to 

incorporate a lot of values where you had 
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multiple, many multiple samples per person, 

which kind of skewed the distribution.

If you look at annual doses, that 

curve would be to the left, to the right or 

right on the OPOS value. You know, I don't 

think that's a representative analysis that 

was done. That's a different issue.

But I do believe -- I do 

understand that the variance needs to be at 

least considered somehow in the model. 

Again, I would say that the abandonment of 

one person, one value just does not make 

sense to me.

If it's okay in the extreme case 

because it really weights it, then it's not 

necessarily an indication of why it should 

be ignored in the normal case just because 

it doesn't make a big difference. That's 

not clear to me why that follows.

You know, it's either right, the 

technique is either correct or incorrect. 

It's not only applicable when you have large 
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data dominance that makes a huge effect and 

then, by the way, if you have four samples, 

you ignore it because it doesn't make a big 

difference. I just don't follow that logic 

at all.

DR. CHMELYNSKI: Well, I guess 

one of the things is, like for instance, if 

you had -- if we did have analysis on a 

quarterly basis, if you look at Slide 10, 

first off almost all the big problems go 

away because almost everybody only has one 

or two. And I don't really mind averaging 

those.

And the ones that have a lot 

we're going to use OPOS on anyway. So we 

get them down to one. And we would have one 

value per quarter for each worker who had 

any samples in that quarter. And in that 

case I don't see too much of a problem with 

OPOS.

But when you keep expanding it 

over a longer and longer period, it starts 
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diluting it too much.

DR. NETON: But I'm wondering if 

the time-weighted average wouldn't be a 

better estimator.

DR. CHMELYNSKI: Yes, I was 

thinking of that. But then you have this 

problem of, you have an additional point and 

you're not quite sure how much time to 

assign that one and then you have one at the 

end, near the end of the period and you're 

not quite sure what to do with that time.

So you're right, though. I agree 

that the time-weighted average is the right 

answer. I wasn't sure how to do it.

DR. NETON: Well, I think, you 

know, rules can be developed for that. But 

to me the time-weighted average is an 

estimator of the total picocuries excreted 

in the year. That value times the annual, 

the 365 days gives you the total amount of 

chronic intake that has been inhaled and 

excreted during that year.
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And it's an overestimate because 

we agree that carryover happens with longlived radionuclides. So I think it's a 

perfect solution. I don't see anything 

wrong with it. It takes care of a lot of 

the issues.

And I think we have the data to 

do it. I think the original count was, 

well, maybe we're gilding the lily too much 

because, you know, I can get into it later 

and we don't have time today, but if you 

start looking at the coworker models that 

are fixed, the uncertainties really become 

larger year to year than within the year 

itself.

When you start developing chronic 

exposure models over a period of 20 years 

and look at the variability in the 

individual OPOS data points that make up 

that fit, you have factors of 5 to 7 to 10 

differences in the intakes. But I would not 

argue against the use of a time-weighted 
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average because to me that's a tried and 

true -- that's been used for years as a 

rough indicator of intake for workers, and 

the average value during that year 

represents it. I don't know. That's my 

opinion.

CHAIRMAN MELIUS: Do other, any 

of the other Board Members have comments?

MEMBER ZIEMER: Well, this is 

Ziemer. I'm trying to evaluate what the 

next step is here. Do we need something 

more formal, a response from NIOSH? I think 

I'm understanding what Jim is suggesting 

here and it makes sense to me.

CHAIRMAN MELIUS: I should have 

pointed out earlier I discussed this with, I 

guess Jim and I can't remember if Stu was 

involved in the, Jim and I did is to, in 

order to sort of expedite this meeting, we 

sort of skipped the need for a formal 

response and figured it was easier and in 

some ways more productive just to do it by 
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discussion.

My suggestion actually is, where 

I think part of our problems in discussing 

this is sort of understanding the procedure 

that's involved and how the decision is made 

as to what are, you know, when is it 

legitimate to look at strata and when is it 

not, you know, in terms of what kind of 

monitoring was done.

The issue we just talked about in 

terms of Savannah River and so forth. And 

also when you would, when you get concerned 

about OPOS, use of OPOS or some other, you 

know, variation of OPOS in a model if it's 

only a few incidents that are in a very 

large, you know, sort of chronic intake 

database, monitoring database I don't think 

it makes much difference or, like as Harry 

just said, if you're just having, you know, 

a couple of samples per person, it probably 

doesn't make much difference.

And I think we still have some 
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decision criteria as to when we would be 

applying this and when is it appropriate and 

when is it inappropriate and how do we go 

about determining that? You know, so we're 

backing up a little bit away from OPOS, but 

eventually getting to OPOS as one of the 

ultimate tools that might be used in certain 

circumstances.

I think it might be more 

productive, because I think some of the 

problem we get into, we're having is that 

we're sort of using extreme examples. And 

I'm not sure we'd ever get to some of those 

examples in a practical way or at least I'd 

like to think that we would, you know, be 

able to avoid them in terms of our initial 

evaluation of data sets.

And I think there are also some 

much more fundamental problems in looking at 

different strata in terms of, you know, 

developing coworker models both in terms of 

how many people were monitored in a given 
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year. Is there data from preceding or 

subsequent years?

Information we have or don't have 

on changes in production or process or 

something that would give us more or less 

confidence in the development of the model. 

And I think those are much more likely to 

come up and I think, I'm not sure those have 

ever been written down.

In fact, I'm pretty sure they 

haven't been at least for internal models.

MEMBER ZIEMER: Yes, of course I 

think I heard some agreement between SC&A 

and NIOSH on the time-weighted average 

issue. It's not clear to me that the 

questions that Joyce raised were, if she 

fully understood what Jim was saying or is 

there some additional information that needs 

to be generated on that matter.

CHAIRMAN MELIUS: I think Jim 

offered a written response there. I tended 

to agree with Joyce, but I didn't quite 
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understand what Jim's response was. So that 

may just be me.

MEMBER ZIEMER: I understood 

Jim's response. But I think we need to make 

sure that SC&A is sort of aboard or if the 

objection still holds, to make sure we 

understand clearly what the difference 

remains to be.

CHAIRMAN MELIUS: I think we 

could all benefit from that. I agree.

DR. LIPSZTEIN: I just want to 

make one point that when we compared the 

uranium intake type S for Fernald from the 

OTIB that you used the coworker model that 

was done before, that was Revision 1 of 

OTIB-78 and then we compared the intake 

rates for 78, Revision 2.

And there are years where the 

95th percentile and the 5th percentiles were 

double or more than double ones from the 

other. So really, it makes a difference 

which way you do the coworker model. It's 
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not something that you'll get similar 

results.

So this issue has to be resolved. 

One of those has to be the correct one. 

Like for example in the 59 and 60, the 95th 

percentiles for the Revision 2 was 28 

micrograms per day intake while the 95th 

percentile intakes from Revision 1 was 47.

So you have values that you have 

a large difference not only here but also on 

the 5th percentile intake rates. So this is 

a very, it's not something that, you know, 

just a few results and doesn't make a 

difference. It's a completely different 

approach of doing coworker model that has to 

be solved because big difference probably 

important when you do the PoC.

DR. MAURO: Jim, I have a 

question for you. It just hit me. When you 

do this experiment that we talked about 

earlier where you do these two 

distributions, one for the strata and one 
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for the full set, you're going to process 

some bioassay data to get these two 

distributions. When you process that --

DR. NETON: Not necessarily, 

John. I mean I think we'll use already in 

place values.

DR. MAURO: Okay. So you're 

going to use the -- that was my question. 

So you're going to --

DR. NETON: It really doesn't 

matter. I don't think we have to go from de 

novo bioassay data to come up with the

doses. I think we need to be mindful of 

what the distributions are.

DR. MAURO: Okay. I guess my 

question is, you're going to get these two 

distributions. Are you going to get them 

working from OPOS, working from real 

reconstructed intakes using the classic 

method with IMBA or are you going to use it 

using pooled data to get the distributions?

You can do it from three 
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different approaches.

DR. NETON: Well, no, but I think 

you're connecting the analysis of the 

coworker individual annual doses versus how

we compare distributions.

DR. MAURO: Yes. I am, maybe --

DR. NETON: I think you can 

compare the distributions without going back 

to the raw data. I think you --

DR. MAURO: So what distributions 

will you be comparing?

DR. NETON: I think what I'm 

comparing is if you took a 95th percentile 

value and then you can calculate what the 

distribution, the full distribution would 

have to be. Well you'd have to start with a 

distribution. I don't know yet, John. I 

guess I'll have to comment on that.

DR. MAURO: You see why I asked 

the question?

DR. NETON: I wasn't thinking 

about going back to raw data.
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DR. MAURO: I didn't say you 

should, I just wanted to know, right now it 

sounds like you're still thinking about how 

you'd come at the problem.

DR. NETON: It may have ended 

being OPOS data. I don't know. But I would 

like to go back a step and see if I can get 

some kind of agreement that, since this is a 

coworker model, a worker model not a sample 

model, that somehow under some format we 

would use one exposure value per person per 

evaluated time period.

To me I just don't understand why 

that isn't true. And like I said before, 

SC&A has agreed in the extreme case that 

it's valid. I don't know why it's not valid 

in the normal case.

And their argument seemed to be 

that you don't have to do it in a normal 

case because it doesn't make any difference. 

Well, I would say if it's valid in the 

extreme it's valid in normal and then just 
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use that as the default.

They've already agreed that if 

you had equally spaced bioassay samples, 

it's a perfectly valid approach. If we end 

up using a time-weighted average for not 

equally spaced bioassay samples, you'd end 

up with a much better estimate of the annual 

intake and excretion.

I just think, I just don't 

understand why one can argue that it's a 

coworker model, not a bioassay sample model 

and you shouldn't use individual values. 

Agreed that the variance somehow needs to be 

dealt with. I mean, I'll agree to that.

CHAIRMAN MELIUS: Jim, this is 

Jim. Yes, but I think that is the 

fundamental concern about it. And I think 

that's -- it doesn't mean that it's not in 

certain situations a concern that can be 

ignored or doesn't make, you know, any 

practical difference.

But I think we have to sort of 
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understand sort of the situations where it 

does or doesn't make a difference. And I 

think that's sort of what we're struggling 

with. And I think that's your, so your 

discussion with, interchange with Joyce was 

about that. Where does it make a 

difference, where doesn't it?

DR. NETON: No, well, maybe. I 

think --

DR. LIPSZTEIN: Jim, can we go to 

page 88 of our draft?

DR. NETON: Sure.

DR. LIPSZTEIN: There's a table, 

C-3.

DR. NETON: Okay.

DR. LIPSZTEIN: Okay. Can you 

see it? It's Page 88.

DR. NETON: Yes, I got it.

DR. LIPSZTEIN: Got it, okay. 

You have, for example, for 54 you have the 

95th percentile is 45,000 micrograms per 

day. And the 95th percentile for 54 on 
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Revision 1 is 91,658, while the 5th 

percentile is the same rate. One is double 

the other.

And you have no differences, the 

same number of samples, the same samples 

that were used. So it's just the same, the 

difference is on the statistics or how you 

use it. It's just the methodology.

So this has to be taken into 

account. It's not only the number of 

samples that are repeated or that are 

correlated because you have this big 

difference when you have a lot of samples 

like, for example, the samples related to 50 

are the ones that you had the same worker 

repeated many times.

So in 59, 60 period of time, you 

have a difference, but you had 4,573 samples 

by 50 that were used in Revision 2 but were 

totally ignored on Revision 1. But in 54 

were the same samples.

So, you know, the methodology 
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has, which one is the correct one? And it's 

very important to have this.

DR. NETON: This gets to a 

critical evaluation of the samples that are 

used in the calculation.

DR. LIPSZTEIN: Yes.

DR. NETON: I don't want to get 

too much talking about --

DR. LIPSZTEIN: Yes, yes, you're 

right.

DR. NETON: -- but I think --

DR. LIPSZTEIN: I agree.

DR. NETON: -- if you know, for 

example, that you have a series of incident 

samples and a person was chelated, I would 

be the first person to suggest that those 

samples be removed from the analysis.

DR. LIPSZTEIN: But those, I 

think those samples that were labeled 50 

they were not chelated. But I agree. If 

they were chelated, no way. And if they had 

a wound instead of inhalation also you 
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cannot count that.

DR. NETON: I can tell you from 

my knowledge that those samples were not 

routine samples, okay.

DR. LIPSZTEIN: No, no, they were 

not. That's why they were labeled 50, yes.

DR. NETON: Probably more than 

likely. So once you have that knowledge 

then you need to make a conscious decision. 

Are those valid to put into the coworker 

model or not?

So what you're getting to is sort 

of the ground rules of how you analyze the 

data. That really has nothing to do with 

OPOS. It has to do with what type of 

samples are included in the general coworker 

model itself.

DR. LIPSZTEIN: Yes, but the OPOS 

was introduced to deal with this: people 

that had many samples, right?

DR. NETON: Right. But if you 

have a number, and I don't want to talk 
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about Fernald specifically, but if you had a 

number of samples that you knew were totally 

incident driven in the middle of a routine 

monitoring program, you might want to, if 

you could definitely determine that they 

were incident samples, I would suggest that 

they don't belong in the coworker model.

I mean, now there are many cases 

where you just can't tell. And that's the 

problem. So this gets down into the 

professional judgment and vetting of the 

samples, but not so much one person, one 

statistic or one measurement or one value 

because to me it's a model for a person not 

the distribution of bioassay samples.

And again, in the extreme case, 

it's obvious even to SC&A. But in the 

general case, I think it equally is 

applicable. If I have five samples, four 

samples equally spaced the mean value is a 

better representation of that guy's total 

excretion during that evaluation period. It 
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just is.

To me it's a very simple concept. 

You know, again the mean of the Advisory 

Board is not the mean including ten 

measurements, repeated measurements of Bill 

Field.

DR. LIPSZTEIN: No, but that's 

not the same because when you have a --

DR. NETON: Repeated data on an 

individual measurement. Yes, it is.

DR. LIPSZTEIN: No, no, no. But 

when you have an excretion rate, even if you 

had the mean for them, if you had 30 

measurements of that incident or if you just 

had two measurements of that incident spaced 

between them, then the OPOS would be 

different.

But that's why I think that the 

incident case if they are to be handled by 

the OPOS they should be handled as one 

number for that incident because of the 

number of samples.
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DR. NETON: If we don't know it 

was repeated measures and we don't know it 

was an incident, we will treat it as a 

chronic exposure value.

DR. LIPSZTEIN: I'm not 

discussing this; what I'm discussing is if 

you have samples that were labeled 50 for 

example.

DR. NETON: I don't want to talk 

about the Fernald cases.

DR. LIPSZTEIN: That's the only 

one we have to make reasons. But anyway, 

okay, but suppose you have someone that was 

exposed and he had 50 samples taken in a 

period of two months. And another person 

was exposed in the same incident but he had 

one sample taken just after the accident and 

at the end of the, after the accident.

So because he had less samples 

taken, his OPOS would be much less. So what 

I mean is that for the whole workers of that 

population of workers would depend on the 
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frequency of monitoring that the Health 

Physics has imposed at that installation.

So that's what, what is allowed 

with this methodology because it depends on 

how the Health Physics has determined that 

the number of samples were to be taken.

DR. NETON: The program there 

that can be used in the incident samples 

that are unknown to be an incident and be 

dealt with and treated as if chronic 

exposure occurred and they will bias the 

value towards claimant-favorable.

DR. LIPSZTEIN: I don't think so. 

Do an example. I'll wait for your written 

response.

DR. NETON: We discussed this 

five, seven years ago, maybe ten years ago 

at Mallinckrodt. We went through numerous 

examples of why a chronic exposure model 

will bound infinite level samples.

We do this with routine dose 

reconstructions. If a person has one 
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bioassay sample two thirds of the way into 

the year, we will fit a chronic exposure all 

the way through that so that it goes through 

his value that was excreted two thirds of 

the way through the year.

And that's how we assign the 

dose. That's exactly what we do. That has 

been vetted through this Board years ago. 

You can always postulate and speculate on 

extreme situations where that might not be 

the case.

But it was decided that in 

general in an overwhelming majority of 

cases, it's claimant-favorable and that's 

what we use. Otherwise you can't do 

anything. You're hand-tied.

DR. LIPSZTEIN: No, you have been 

doing it for the whole period until now. 

You did dozens of coworker models without 

using it.

DR. NETON: And they're chronic 

exposure models. That's what I'm saying.
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DR. LIPSZTEIN: Yes.

DR. NETON: We're saying that the 

chronic exposure model now should include 

some better estimate of the person's annual 

excretion during that year. And our opinion 

was that the mean value is better. If the

time-weighted value is better, so be it.

But that's what we're trying to 

say is if you have repeated measures on a 

person, you're going to say the best 

estimate of that guy's exposure was the 

first sample that was taken at the beginning 

of the year. Now that's not true. It's 

some average of the samples that were taken 

throughout the year is a better estimate of 

his intake.

It's just, what you excrete is 

what you inhaled. It's directly 

proportional assuming, as long as you can 

agree that carryover excretion is going to 

bias the model high. We're going to assume 

that he inhaled the chronic exposure during 
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that year. I don't know any other simpler 

way to put.

It's, to me, it's technically 

correct. It's not scientifically 

undefensible or whatever was said in the 

first paragraph of SC&A=s slide. I think 

it's the best way, best we can do given the 

data that we have.

MEMBER ZIEMER: Mr. Chairman, 

this is Ziemer. I think we've reached the 

limit of what we're going to accomplish 

today. I wonder what our next steps are.

DR. NETON: I agree.

CHAIRMAN MELIUS: Thank you, 

Paul. Yes, I think we have at least one 

next step which was Jim was going to write 

up a proposal and circulate it, on that.

The second next step was that I 

was asking, and I think we had talked about 

this at the last sort of in-person Work 

Group meeting, but maybe my memory is 

faulty, is that we should, if I'm trying to 
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still understand sort of the whole criteria 

that go into the development of these 

coworker models and the application of one 

person-one sample and the comparisons -- and 

I thought you were going to develop a 

procedure for that. I thought we had talked 

about that at the first meeting. I think it 

would be very helpful to have an outline of 

that at least so we, for discussion because 

I think that would help to maybe narrow the 

differences and avoid some of the issues 

about some of the examples we're dealing 

with.

DR. NETON: Dr. Melius, I agree 

with you. And what I really had suggested 

was I would come up with an implementation 

guide for coworker models. And I've drafted 

pieces and parts of that. But 

unfortunately, you know, my thoughts change 

as we have our discussions.

CHAIRMAN MELIUS: Okay, I'm 

sorry.
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DR. NETON: That's okay. But I 

still, my basic tenets haven't changed, but 

I do agree that certain things maybe need to 

be shored up and such. But I could probably 

have, it would be a rough, you know, sort of 

the premises behind coworker models and 

maybe not the full details because obviously 

we're still debating how that goes.

But I can do that. I don't know 

if it will be before the Board meeting or 

not, to be honest.

CHAIRMAN MELIUS: Yes, it doesn't 

have to be before the Board meeting.

DR. NETON: I do agree that is a 

deliverable that I had offered up.

CHAIRMAN MELIUS: Okay. I 

thought maybe I had --

DR. NETON: No, no, no. I read 

all 300 pages of the transcript last week.

CHAIRMAN MELIUS: Wow. I'm 

impressed. But I think that really would be 

useful at this point. The more I look at 
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this issue, the more I get --

DR. NETON: And I think maybe as 

rough as I have it, I can polish it up a 

little bit and just put it out for, you 

know, discussion. It may have some question 

marks in it and it may have some incomplete, 

you know, concepts.

But I think the beginning of it, 

in my mind, is pretty solid. This whole 

idea of chronic exposure models and, you 

know, that kind of thing. I think if we 

could agree to the basic tenets that I put 

in there or to the extent we can, then we 

can maybe move forward. I get a sense that 

maybe some of the basic concepts that I've 

assumed have been agreed upon maybe are not 

necessarily so based on some of the 

feedback.

CHAIRMAN MELIUS: I think the 

other important point is that, if we're 

going to be coming up, we're going to look 

at strata within potential coworker models 
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is, how are we going to look at incident 

versus, you know, routine monitoring and how 

do we take that into account? I think 

there's also some issues about how, you 

know, how many people have been monitored, I 

think, makes a difference in -- say within a 

given year or something in terms of how 

these models apply and some of the 

statistical concerns we may have.

If it's, you know, only one 

percent of the group that's monitored I 

think we obviously have a lot more concerns 

than if it's, you know, 90 percent or 

something. So if you can, you know, 

whatever you've done and polish up and give 

us a basis for discussion of that because I 

think that would be helpful.

Are there any other sort of 

deliverables that would be useful for people 

out of this conversation? Paul, did you 

have any?

MEMBER ZIEMER: No, I think what 
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you just described would be satisfactory.

DR. NETON: So just to make sure 

I have three things on my plate for NIOSH. 

Sort of the hypothesis, the experiment into 

how we're going to go about evaluating 

stratification, differences in strata versus 

the general model.

CHAIRMAN MELIUS: Right.

DR. NETON: And then a discussion 

of SC&A's write-up on Page 41 which has to 

do with this hypothetical coworker or 

hypothetical exposure cohort. And then the 

third one is some description of the 

criteria that go into coworker models, 

whether it's an annotated outline or what. 

But I'll just put it out there for people to 

chew on.

CHAIRMAN MELIUS: Okay, great.

DR. NETON: I'll do that in my 

spare time.

CHAIRMAN MELIUS: Good, I'm glad 

you have some.
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DR. NETON: Yes, that sounds 

good.

CHAIRMAN MELIUS: Okay, any other 

Board Members with comments or questions?

MEMBER BEACH: None here, Jim. 

This is Josie.

CHAIRMAN MELIUS: Okay.

MEMBER ROESSLER: And I agree 

getting this in writing will help get us all 

understanding just what we're doing.

CHAIRMAN MELIUS: Okay. Good. 

Okay. With that, Ted, do we have anything?

MR. KATZ: No, just thank you. 

That was a great meeting.

CHAIRMAN MELIUS: Okay.

DR. NETON: All right. Bye.

CHAIRMAN MELIUS: Okay, thanks 

everybody. Have a good day.

(Whereupon, the above-entitled 

matter went off the record at 4:02 p.m.)

 
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