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U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES1

CENTERS FOR DISEASE CONTROL

NATIONAL INSTITUTE FOR OCCUPATIONAL

SAFETY AND HEALTH 

+ + + + +

ADVISORY BOARD ON RADIATION AND

WORKER HEALTH 

+ + + + +

SEC ISSUES WORK GROUP

+ + + + +

THURSDAY

SEPTEMBER 26, 2013

+ + + + +

 The Work Group convened in 

Conference Room A-11, National Institute for 

Occupational Safety and Health, 4676 Columbia 

Parkway, Cincinnati, Ohio, at 9:00 a.m., 

James M. Melius, Chairman, presiding.

PRESENT:

JAMES M. MELIUS, Chairman

JOSIE BEACH, Member

R. WILLIAM FIELD, Member*

GENEVIEVE S. ROESSLER, Member
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ALSO PRESENT: 2

TED KATZ, Designated Federal Official

NANCY CHALMERS, ORAU Team

HARRY CHMELYNSKI, SC&A*

DeKEELY HARTSFIELD, HHS

STU HINNEFELD, DCAS

JOSH KINMAN, DCAS

JENNY LIN, HHS

ARJUN MAKHIJANI, SC&A

TOM LaBONE, ORAU Team

JOHN MAURO, SC&A*

JAMES NETON, DCAS

DANIEL STANCESCU, DCAS

JOHN STIVER, SC&A*

TIM TAULBEE, DCAS

*Participating via telephone

 
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C-O-N-T-E-N-T-S 3

Call to Order and Opening Remarks 4 

 Ted Katz

Roll Call 5 

Opening Remarks 8 

 James Melius

 Chairman

SC&A Review of Coworker Dose Modeling 11

 Jim Neton 11

 NIOSH

 Questions and Comments 114

 Harry Chmelynski 167

 SC&A

Work Group Discussion on Related 261

Matters
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4

1 P-R-O-C-E-E-D-I-N-G-S 

2 9:17 a.m.

3 MR. KATZ: Good morning, everyone 

4 in the room and on the lines.

5 This is the Advisory Board on 

6 Radiation and Worker Health, SEC Issues Work 

7 Group.

8 I apologize for the late start 

9 but we had security matters for getting into 

10 a federal facility, and we're done with all 

11 of that.

12 So, for everyone's information, 

13 there is an agenda and several presentations, 

14 two presentations and two papers, all posted 

15 on the NIOSH website, on the Board site under 

16 meetings, under today's date. So, you can 

17 follow along with the presentations as they 

18 are given and you can see the background 

19 materials that are being discussed. We are 

20 not focusing on a specific site, so we don't 

21 have any conflict-of-interest matters to 

22 cover here.
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5

1 So, let's just run through 

2 attendance, beginning with the Board.

3 (Roll call.)

4 MR. KATZ: Welcome. No members 

5 of the public right now. Okay. So, that's 

6 it for matters.

7 Folks on the phone, please mute

8 your phone except when you're addressing the 

9 group, just so we don't have any audio 

10 problems: *6, if you don't have a mute, to 

11 mute your phone, and *6 again to take 

12 yourself off mute.

13 And, Jim, it's your meeting.

14 CHAIRMAN MELIUS: Okay. Welcome, 

15 everybody, now that we can get started.

16 I just want to introduce a little 

17 bit. This meeting, while in some sense it is 

18 responding to an ORAU Technical Report and 

19 the review of that, which is a little bit 

20 somewhat narrow in terms of its focus.

21 We are also at the same time 

22 dealing with sort of bigger issues related to 
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6

1 how do we deal with -- what's sufficient 

2 accuracy. And, also, there are lots of other 

3 coworker issues other than some of the ones 

4 we have focused on in these reports. 

5 So, I would like to spend a fair 

6 amount of time today talking about that and 

7 putting those other two issues and sort of 

8 the general coworker issue as well as the 

9 general sufficient accuracy issue, because I 

10 don't think we can address the more narrow 

11 focus without dealing with those other two 

12 issues. I think they provide both context 

13 and in some ways really the way to resolve 

14 some of the differences we may have or 

15 differences in interpretation we may have 

16 over this more narrow issue.

17 So, I just want to say that 

18 upfront. And so, some of what we may say, it 

19 is not really a criticism of, for example, 

20 what Tom's done and other people at ORAU have 

21 worked on. It is more of let's sort of step 

22 back and sort of how do we use this and what 
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7

1 are some of the limitations, and what are 

2 some of the strengths of it, and where can 

3 these kinds of approaches be appropriately 

4 applied?

5 I think we all have somewhat 

6 different perspectives on it. I am an 

7 epidemiologist by background. So, I tend to 

8 think of exposure modeling and so forth from 

9 an epidemiological perspective, where that is 

10 different, I think, for health physics or 

11 sampling sort of perspective, or how a 

12 toxicologist or a laboratory scientist might 

13 think of some of these statistical 

14 approaches.

15 So, we need to sort of then take 

16 all of our backgrounds and sort of what 

17 information we have, and then put it in the 

18 context of a compensation program, which is 

19 really very different, and really very 

20 different from in some ways what this 

21 environmental sampling or another sampling 

22 that has been done at these facilities has 
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8

1 been intended for. It was intended to 

2 protect people, and now we are trying to use 

3 it for something else. And I think not a use 

4 that is very common necessarily, not a use 

5 that there are a lot of publications or rules 

6 on, or whatever, as we have discovered.

7 And I think we are sort of making 

8 this up as we go along, so to speak. I think 

9 we just have to recognize that and do the 

10 best we can.

11 But I just wanted to put that 

12 out. We will talk more later I think more 

13 specifically about this. But one reason I 

14 asked for an in-person meeting was so we 

15 could do this in a less formal way and maybe 

16 a little less rushed than we are with 

17 conference calls and other things. And so I 

18 do appreciate people that took the time to 

19 come here today. We beat the government 

20 shutdown or whatever may happen next week.

21 (Laughter.)

22 MEMBER BEACH: Barely.
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9

1 CHAIRMAN MELIUS: Barely, yes. 

2 Yes, if your plane is delayed, you may be in 

3 trouble.

4 (Laughter.)

5 We'll see if government employees 

6 and contractors are stranded at airports for 

7 weeks. And I'm a former federal government 

8 worker, and I have lived through that also.

9 Anyway, I think we will start 

10 with Jim and his presentation, and then let's 

11 sort of go from there. But I don't know if 

12 anybody else has any comments at this point. 

13 If not, then go ahead, Jim.

14 DR. NETON: Thank you, Dr. 

15 Melius.

16 I would like to say I do 

17 appreciate the Working Group convening. I 

18 think this is one of the last major issues 

19 that we need to come to grips with. We have 

20 dealt with a lot of other issues, such as 

21 surrogate data and all those other things. 

22 And I think this is a key issue. Believe it 
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10

1 or not, I have been looking forward to this 

2 meeting because I think there are a lot of 

3 open issues that we can collectively maybe 

4 get our heads together and come to some 

5 resolution on.

6 I would just like to take the 

7 beginning of the meeting and present a 

8 truncated version, a shortened version, of 

9 what I put forth at the Board meeting, which 

10 is what we are doing with coworker models and 

11 what sort of drove that thinking. And then 

12 maybe like a 10,000-foot level, nothing 

13 really deep, into the statistics.

14 This, to me, is the biggest 

15 vexing issue in coworker modeling, is 

16 bioassay samples, how you take a bioassay 

17 sample and convert it into something that is 

18 meaningful for someone who doesn't bioassay 

19 sample. Obviously, we have a lot of 

20 measurements on people. And you have to 

21 figure out, well, if the person wasn't 

22 monitored, what potential do they have for 
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11

1 internal exposure, if any?

2 MR. KATZ: Before Jim goes on --

3 DR. NETON: Yes.

4 MR. KATZ: -- let me just check. 

5 Harry and Bill, can you hear well?

6 MEMBER FIELD: I don't have any 

7 problem hearing.

8 MR. KATZ: Okay.

9 DR. CHMELYNSKI: Yes, it's okay.

10 MR. KATZ: Okay. Very good. 

11 Thanks.

12 DR. NETON: So, the second slide 

13 is the summary of how we go about doing 

14 internal dosimetry coworker calculations, a 

15 little box model here. Obviously, we start 

16 with the urine data. And the second box is, 

17 we'll call them the OPOS Urine Data box.

18 And that is probably one of the 

19 areas where we have some significant 

20 disagreement at this point with SC&A, is what 

21 do you do with the urine data that you have? 

22 We have a lot of monitoring data. Not 
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12

1 everybody was monitored at the same rate. 

2 People who had a higher potential of exposure 

3 have more samples in a given time period than 

4 those that weren't. People that had 

5 incidents were sampled at a higher rate.

6 So, the concept was developed by 

7 the ORAU team, that NIOSH subscribes to, 

8 which is this OPOS statistic: one person, one 

9 sample. If you have, for instance, 100 

10 bioassay samples and 30 of them are from one 

11 person, it makes no sense to include those 30 

12 samples individually in the distribution. We 

13 are recommending that we take the average of 

14 those samples and use them as sort of -- it's 

15 sort of a bad word -- but a surrogate for 

16 their intake, because that is more 

17 representative of what their intake was, not 

18 the individual samples.

19 So, you have the OPOS urine data, 

20 and then we convert that to a distribution of 

21 some type. It has been our experience, and 

22 it's well-known by the Board, that worker 
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13

1 monitoring data typically fits a log-normal 

2 distribution. And if you do a cumulative 

3 probability plot, you get a nice function 

4 that one can fit the 50th and 84th percentile 

5 of the data. And I have got an example of a 

6 plot here that we use.

7 This would represent the intake 

8 for a specific year or a specific time 

9 period. Most often it's a year. If you have 

10 enough bioassay data on a year-by-year basis, 

11 we will generate a log-normal distribution 

12 for each particular year and, as indicated, 

13 calculate the geometric mean in the 84th 

14 percentile, which is one geometric standard 

15 deviation.

16 And most of the time they fit a 

17 fairly nice straight line, as you can see 

18 here. And that is used in the intake 

19 calculation.

20 This is where we have a 

21 fairly -- well, there's a disagreement on 

22 whether or not this particular function in a 
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14

1 given year, since it's all workers, 

2 represents all workers or are there 

3 stratifications in there of workers? And 

4 that is probably one of the key issues we 

5 want to talk about today: how do we determine 

6 if that data set is representative of all 

7 workers? Are they or are they not?

8 And that is almost a step 

9 backwards from a lot of discussion in the 

10 RPRT-0053, which is the sort of nuts-and11 bolts statistics of how you go about 

12 determining if there is stratification. In 

13 my opinion, one first needs to decide whether 

14 that needs to be stratified in the first 

15 place. That's my opinion.

16 Okay. So, you take an individual 

17 year's worth of plot, for example, bioassay 

18 data, and then you have to convert that to 

19 some sort of an inhalation intake. You can't 

20 just say, well, the 50th percentile excretion 

21 is .5 picocuries per liter and do anything 

22 with it. One has to figure out what that 
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15

1 means in terms of how much radioactive 

2 material the person breathed in.

3 And so the next step in the 

4 process is to use the ICRP models and fit 

5 intake curves through the data points. So, 

6 for example, each one of these blue data 

7 points is one of those graphs. So, the 50th 

8 percentile in this graph, the geometric mean 

9 in this graph, would be here. And then you 

10 take the next year, plot it here or here or 

11 here, and then one fits a chronic intake 

12 function through the data points. And it's 

13 just a piece. We do this on a piece-by-piece 

14 basis because the data tend to be variable. 

15 And so there is some judgment involved here.

16 This fits a fairly nice curve. 

17 But you notice that there's a lot of 

18 distribution about these points. So, for 

19 example, here's one point and another point. 

20 This point is way down here. One fits a 

21 weighted least squares regression analysis 

22 essentially through these points.
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16

1 So, remember, if the data were 

2 stratified, like on the previous slide, and 

3 there was some difference, one could 

4 calculate, say, a 10-percent difference in 

5 the geometric mean of the distribution. One 

6 would wonder how big an effect that would 

7 have on the fitting of this curve, which is 

8 where the rubber really meets the road.

9 So, we take, here I think it's 

10 like 14 data points. You have a few of those 

11 data points. One could show that, for 

12 instance, construction trade workers are 

13 slightly different. I'm not convinced that 

14 it makes a big difference in the overall fit 

15 here.

16 Another thing to remember is that 

17 the data are fit. This is just the 50th 

18 percentile. We also fit another curve, which 

19 is the 84th percentile of the bioassay data, 

20 which would generate another graph way up 

21 here. That would be the geometric standard 

22 deviation of the distribution. That 
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17

1 typically is a minimum in our program of a 

2 geometric standard deviation of 3. We use 

3 that as a default minimum, no matter what the 

4 data say. But, typically, it can be a GSD of 

5 4 or 5.

6 So, the input in the IREP, you 

7 convert this intake to dose. The intake is 

8 not the geometric mean of the distribution. 

9 It's the geometric mean with the entire GSD 

10 around it, and that's what is sampled in the 

11 IREP program. The intake is converted to 

12 dose, of course, through that particular 

13 order.

14 So, we are saying our best 

15 estimate of the intake for this particular 

16 person is this fitted line, but we don't know 

17 it with a large degree of certainty. So, 

18 we're going to allow for it to be up to, you 

19 know, with a certain geometric standard 

20 deviation, that would be sampled. So, it's 

21 not an individual point that's put into the 

22 IREP. It's the distribution of all those 
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18

1 points. I think that is a very important 

2 thing to remember.

3 And, again, a 5- or 10-percent 

4 difference in one of these points, where you 

5 throw a GSD of 5 on top of it, it gets into 

6 what we have been calling, is there really a 

7 practical difference here in the calculation?

8 DR. MAKHIJANI: And the red dots 

9 at the left?

10 DR. NETON: That would be a 

11 different fitting regime. For instance, you 

12 have years and years. You would fit this to 

13 a different function than this because it 

14 obviously has some different exposure 

15 potential. So, you would fit a chronic 

16 exposure for these years and say that's my 

17 intake during these years. Then you fit a 

18 chronic exposure to the next regime that 

19 seems to fit a reasonable function.

20 So, there is subjectivity 

21 involved here. We'll have, for over a 30-

22 year plant operating period sometimes -- Tom, 
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19

1 help me out -- three, four different regimes, 

2 maybe five or six chronic models.

3 MEMBER ROESSLER: It's just 

4 orange or red points are very distracting 

5 because they weren't labeled.

6 DR. NETON: Yes.

7 MEMBER ROESSLER: But I thought 

8 maybe that was back-calculating for this 

9 individual.

10 DR. NETON: No.

11 MEMBER ROESSLER: But that's just 

12 a different --

13 DR. NETON: That is a different 

14 exposure regime, I'll call it.

15 MEMBER ROESSLER: Okay.

16 DR. NETON: See, so, when we fit 

17 these chronic models, you pick the place on 

18 the curve that looks like it could reasonably 

19 be represented by this chronic model here, 

20 but you would go here and fit another chronic 

21 model here. It would be way up here.

22 So, if a person worked during 
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20

1 this period, he would get this intake. If a 

2 person worked during this period, he would

3 get a different intake.

4 An interesting outcome of this 

5 is, if a person worked during both of these 

6 periods, you would give him this intake. At 

7 this intake, his predicted urinary excretion 

8 would be way up here. It's a way 

9 overestimate of what the person really 

10 inhaled because it's an artifact of the way 

11 we fit these little chronic intake pieces.

12 Tim?

13 DR. TAULBEE: In the earlier 

14 years, those red dots tend to be higher 

15 because you're looking at the 1950s and 1960s 

16 data.

17 DR. NETON: Right.

18 DR. TAULBEE: And then, as 

19 radiation protection programs progressed, 

20 they all decreased. This is why we do some 

21 of this piecemeal fitting, is because of 

22 changes within the program.
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1 DR. NETON: Yes. If you look at 

2 any of our coworker models in the back, 

3 you'll see there's always at the end a series 

4 of curves, using Type S, Type M, fitting them 

5 to show what the intake patterns are during 

6 those years. And that's what we assign.

7 And so we are assuming that the 

8 person is chronically exposed during this 

9 entire time period.

10 DR. MAKHIJANI: If I 

11 remember -- and I don't have all the curves 

12 from RPRT-0053 in my head -- but this seemed 

13 to be fairly typical of what the curves look 

14 like.

15 DR. NETON: Yes.

16 DR. MAKHIJANI: And so this 

17 really sharp discontinuity, that's kind of 

18 strange.

19 DR. NETON: It is. It is.

20 DR. MAKHIJANI: So, one can 

21 understand that programs improved, but then 

22 to have a kind of a cliff where suddenly the 
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1 bioassay measurements become much lower than 

2 they were the year before or six months 

3 before is a little mysterious as a 

4 characteristic.

5 DR. NETON: Well, yes.

6 DR. TAULBEE: In some cases, the 

7 process or the program ended. And so they 

8 stopped producing, say, thorium or americium, 

9 curium, californium. And so you do see a 

10 sharp decrease of the exposure potential.

11 DR. NETON: Yes, and it's even 

12 more complicated than that because, remember, 

13 these people didn't necessarily quit at this 

14 time period, and they were exposed. So, 

15 they're still excreting some residual amounts 

16 into here, which is contributing to this as 

17 well. So, I don't know exactly how high this 

18 was. All we know is this is what we have 

19 experienced.

20 The alternate way would be to 

21 fit -- there's a number of different ways to 

22 do it, but this is the way we decided on 
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1 doing it, which is an extremely claimant2 favorable approach. Again, if I worked 

3 during this entire time period, I would 

4 receive an intake up here for this period; I 

5 would receive an intake based on this fit for 

6 this period.

7 And you know that if I had this 

8 intake, I would still be excreting over in 

9 here, but it's not even considered. It is 

10 just like a separate intake, like step 

11 functions almost.

12 DR. MAKHIJANI: It seems like 

13 that.

14 DR. NETON: Yes, and that's the 

15 way we have been doing this from the very 

16 beginning. This is nothing unique to 0053 or 

17 anything else. This is the way coworker 

18 models work.

19 But I just want to point out how 

20 claimant-favorable they are and how -- and 

21 this is what I was trying to get at at the 

22 Board meeting; I did a lousy job -- how a 
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1 minor perturbation in this, because of some 

2 10-percent, 15-percent difference in the 

3 geometric mean, is kind of lost in the way 

4 the models are built. These models are 

5 very -- there is a professional judgment 

6 involved here, and there is also uncertainty 

7 in the fits themselves.

8 I mean, we put a GSD of 5, or 

9 whatever, on these points, each of these 

10 points. So, you know, you will give a person 

11 an intake and, say, it's the midpoint with a 

12 whole geometric standard deviation of 5 as 

13 his dose. But the fit itself also has its 

14 uncertainties, about a 10-percent uncertainty 

15 in just fit to those data points.

16 So, it makes me wonder about 

17 these stratification adjustments that we 

18 could talk about later, how really meaningful 

19 they are or how practically significant they 

20 are, given what we are really doing to 

21 implement these internal coworker models.

22 DR. MAKHIJANI: Could I ask a 
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1 question about your box chart?

2 DR. NETON: Sure.

3 DR. MAKHIJANI: All the prior 

4 coworker models were not based on OPOS, 

5 right?

6 DR. NETON: That's correct.

7 DR. MAKHIJANI: So, this is new.

8 DR. NETON: OPOS is new.

9 DR. MAKHIJANI: So, you're 

10 essentially saying that the prior coworker 

11 models will be revised according to this?

12 DR. NETON: Yes, we would have to 

13 do that. Yes, the OPOS, it would actually 

14 tend to reduce the exposures, in my opinion.

15 MEMBER BEACH: That was one of 

16 the answers that was given in the report, 

17 that they would have revise.

18 DR. NETON: Yes, we would have to 

19 revise. The OPOS, it makes sense in light of 

20 our current thinking. I mean, you know, you 

21 don't think about this five or ten years ago.

22 DR. MAKHIJANI: Oh, yes.
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1 DR. NETON: But, in my opinion, 

2 it makes the most technical sense of 

3 anything. And I know SC&A has their opinions 

4 on the statistical issues with that. But if 

5 you think about, again, 100 workers 

6 monitored, 100 bioassay points, and one 

7 worker has 30 of them in one year, those 30 

8 samples, the average of those 30 samples more 

9 accurately represents his intake than putting 

10 all 30 into a cumulative probability 

11 distribution. And that's all we have been 

12 saying, and it makes perfect sense to me. 

13 And we can talk about that more.

14 DR. MAKHIJANI: Sure.

15 DR. NETON: I don't want to get 

16 too far --

17 DR. MAKHIJANI: Right, right.

18 MEMBER ROESSLER: Has this sort 

19 of an approach been used in any other fields?

20 DR. NETON: What, the one person, 

21 the one sample?

22 MEMBER ROESSLER: Yes, I think I 
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27

1 read in the report it hasn't really.

2 DR. NETON: Well, ideally, 

3 though, if you think about it, we would take 

4 and just calculate intakes for each person, 

5 right? And do a cumulative probability plot 

6 of the intake in a given year.

7 So, I have 100 workers who were 

8 monitored in a year. I would calculate the 

9 intake for every single worker and generate a 

10 cumulative probability plot of their intakes. 

11 But we can't do that. We don't have enough 

12 granularity to do that.

13 So, what we are saying is an 

14 average of an individual worker's bioassay 

15 sample is sort of a surrogate for intake. It 

16 is directly proportional to their intake. 

17 The amount, the average mount of uranium you 

18 excreted during that year, is more 

19 representative of your intake than putting 20 

20 data points on a cumulative probability plot 

21 and saying that's the population 

22 distribution. It's not. You have to think 
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1 about worker distributions.

2 And so what we're saying is we're 

3 plotting a cumulative probability 

4 distribution of the workers' exposures, where 

5 one worker happens to have 20 bioassay 

6 samples. Well, our surrogate -- I hate to 

7 use the word surrogate -- our approach to 

8 defining that worker's exposure is to use the 

9 average value, not the 20 data points, which 

10 would make up 20 percent of 100 bioassay 

11 points.

12 DR. MAURO: Jim, this is John 

13 Mauro.

14 DR. NETON: Yes.

15 DR. MAURO: I'm sorry I didn't 

16 introduce myself in the beginning.

17 I have a quick question. You 

18 said something very important just now that 

19 was always at the heart when I was thinking 

20 about it. I always thought, in a perfect 

21 world. you would try to build a coworker 

22 model, and you had data for, let's say, the 
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1 100 workers, let's say, in a given year. And 

2 you would look at each worker by himself and 

3 say, okay, let's try to estimate the intake 

4 for Worker No. 1 for that year, and we would 

5 come up with his intake. And then, we would 

6 do Worker No. 2, Worker No. 3.

7 In my mind, in a perfect world, 

8 that would be your best data set upon which 

9 to build a coworker model. But you're saying 

10 that is not the case?

11 DR. NETON: I'm saying that would 

12 be the perfect --

13 DR. MAURO: I didn't quite follow 

14 that.

15 DR. NETON: I'm saying that would 

16 be the perfect world, but we can't 

17 necessarily do that.

18 DR. MAKHIJANI: Why not? I don't 

19 understand that.

20 DR. NETON: Tom, maybe you can --

21 MR. LaBONE: Consider the time it 

22 would take, if you had 100 people, how long 
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30

1 would it take to reconstruct their doses for 

2 each year for 50 years, for example? It's 

3 just the time it would take to do that is 

4 prohibitive if you consider how many dose 

5 reconstructions have we done, as far as best 

6 estimates, and how long has it taken to do 

7 them. So, we are talking about every one of 

8 these would have to be a best estimate.

9 DR. MAURO: I think that's why I 

10 asked the question. So, I do hear agreement 

11 that that would be an ideal circumstance, but 

12 it is an enormous burden to try to do that.

13 DR. NETON: Right.

14 DR. MAURO: Okay. Because I 

15 misunderstood --

16 DR. NETON: Right. I'm sorry. 

17 Maybe I wasn't clear. But, if you think 

18 about it, John, the average value of a guy's 

19 urine data ends up being sort of an 

20 indication of picocurie per liter days during 

21 that monitoring period of excretion. And, in 

22 my opinion, picocurie per liter days of 
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1 excretion is a very good indicator of intake. 

2 It is directly proportional to your intake, 

3 right?

4 DR. MAKHIJANI: But, you know, it 

5 is very radionuclide- and solubility6 dependent. I know you're excluding --

7 DR. NETON: Well, that's not 

8 relevant. I mean, no, it doesn't make any 

9 difference. What you say is true, but the 

10 models are for each independent solubility 

11 class and nuclide. We have a model for every 

12 single solubility class and every single 

13 nuclide that we're trying to reconstruct. 

14 They're all different. That's why we have so 

15 many.

16 But you're right, I mean, the 

17 uranium, we'll do solubility Type M and Type 

18 S. You will see at the back of every one of 

19 our coworker models curves that fit both. 

20 And so we covered the waterfront of the 

21 possible exposures. And then, on top of 

22 that, we'll take the highest one, the highest 
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32

1 exposure potential, for the organ that is 

2 being reconstructed.

3 DR. TAULBEE: And one of the 

4 things to keep in mind with these models, 

5 this is for a coworker. So, we are taking 

6 these data from monitored workers and 

7 applying it to an unmonitored worker in this 

8 particular scenario.

9 So, if you go back to Jim's 

10 example of if you have 100 data points and 30 

11 are from one individual worker, by using 

12 OPOS, now each worker is counted individually 

13 into this general model that we are applying 

14 to unmonitored workers, instead of one worker 

15 dominating the entire scenario. So, that's 

16 where the power of the OPOS statistic comes 

17 in.

18 And, as he is pointing out, the 

19 average of that is a pretty good surrogate 

20 for what their intake was, without going 

21 through the onerous calculations that Tom was 

22 talking about.
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33

1 DR. NETON: And there's 

2 conservatism built in it because, remember, 

3 you have the complication of the censored 

4 data sets as well, and there is a slide that 

5 kind of talks about that a little bit, how we 

6 have been conservative in that respect as 

7 well. We don't take censored data as zero. 

8 We'll assume that it is equal to the 

9 detection limit. So, that's even another 

10 level of conservatism that is built into the 

11 calculation.

12 MEMBER FIELD: Jim, this is Bill. 

13 I had a quick question.

14 Is the assumption that the 

15 monitored workers are the ones with the 

16 highest potential for exposure?

17 DR. NETON: Well, we would 

18 maintain that it's either the monitored 

19 workers had the highest potential for 

20 exposure or at least were representative of 

21 the exposure potential of the workers.

22 And I think the key, then, 
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1 becomes in defining what we mean by 

2 representative.

3 MEMBER FIELD: Right.

4 DR. NETON: Because people can 

5 have different opinions on what that means. 

6 But if it is representative, I mean, if all 

7 strata were monitored representatively, and 

8 then you get this 95th percentile, and we 

9 have a pipefitter who wasn't monitored, I 

10 believe that the 95th percentile is an 

11 adequate bounding value for his exposure.

12 It could be higher. I mean, you 

13 have to pick some number. We sort of define 

14 the 95th percentile as a reasonable bound, 

15 but there is always a 5 percent chance it 

16 could be more than that.

17 MEMBER FIELD: Right.

18 DR. NETON: But, you know, you 

19 can't build a program around that. You have 

20 to pick some --

21 MEMBER FIELD: Right. I 

22 understand. Thanks.
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1 DR. NETON: Yes.

2 CHAIRMAN MELIUS: But you are 

3 also saying that you would use the same 

4 coworker model even if everybody was 

5 monitored for each individual --

6 DR. NETON: If everybody was 

7 monitored, we wouldn't have any coworker 

8 model.

9 CHAIRMAN MELIUS: Yes.

10 DR. NETON: The coworker model is 

11 only for people --

12 CHAIRMAN MELIUS: Yes. Okay. 

13 That is sort of what you said before. I'm 

14 sorry.

15 DR. NETON: Yes, maybe I'm 

16 talking in circles.

17 CHAIRMAN MELIUS: No, no, no. It 

18 was John's fault.

19 (Laughter.)

20 DR. NETON: Yes, and the real 

21 trick is to look at the workers that weren't 

22 monitored and figure out what their potential 
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36

1 exposure was. And that has a lot to do with 

2 looking at the radiological protection 

3 program that was in place in that time 

4 period, and not only looking at the program, 

5 but then looking to see did they really 

6 follow up on what they said they were going 

7 to do.

8 And that is what I think we mean 

9 by representative, is they had a program in 

10 place to do that. In my opinion, most of the 

11 time the highest-exposed workers were 

12 monitored just because that makes sense to 

13 me. Why would you not monitor the highest 

14 exposed?

15 Bioassay samples are expensive. 

16 If you are trying to set your program up so 

17 that you make sure that your workers don't

18 exceed this regulatory limit, the way they 

19 did that -- and Dr. Melius pointed out 

20 earlier -- is these programs were not 

21 designed to really estimate dose. They were 

22 designed to protect workers. The best way to 
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37

1 protect your workers is to monitor the 

2 highest-exposed workers to make sure that 

3 they are not exceeding the regulatory 

4 threshold. It just makes sense to me.

5 They weren't trying to 

6 reconstruct the dose of all the workers. 

7 They were trying to say, are my highest8 exposed workers close to being over the 

9 threshold? That's what they were doing.

10 DR. MAKHIJANI: Well, I think, 

11 you know, we have gone over this in various 

12 contexts.

13 DR. NETON: Sure. Yes.

14 DR. MAKHIJANI: And I think it's 

15 not always true, it's not always the correct 

16 assumption You know, the neutron exposures 

17 in Rocky Flats, for example, come to mind. 

18 They didn't know -- they made a certain 

19 assumption about who was the highest exposed, 

20 but it turned out that some other group was 

21 at some potential for higher exposure.

22 DR. NETON: No argument.
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1 DR. MAKHIJANI: So, there is a 

2 judgment about that.

3 In the case of construction 

4 workers versus non-construction workers, 

5 which is a lot of what we have been talking 

6 about, there seemed to be some kind of 

7 decision that construction workers were not 

8 as much exposed. So, they weren't as much 

9 monitored.

10 But the evidence we have from 

11 construction workers is that that wasn't 

12 necessarily the case. At least at Savannah 

13 River, for instance, they have said very 

14 clearly, with many examples -- and there is 

15 other documentary evidence to that effect, 

16 too -- that they were doing work that had as 

17 much exposure potential, at least very often, 

18 not always, as production workers.

19 But the monitoring data is very 

20 thin. And when you consolidate it into a one 

21 person, one sample per year, then you wind up 

22 with this problem very often. With certain 
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39

1 radionuclides, you have very few data points. 

2 But, leaving that aside, I think the idea 

3 that a certain -- so, it's not intentional, 

4 but there was an assumption around who was 

5 monitored.

6 At Nevada Test Site, it turned 

7 out the health physics people were more 

8 monitored than anybody else, and not 

9 necessarily because they had the highest 

10 exposure potential. It was because they were 

11 the closest to the program, and there was a 

12 certain assumption behind it.

13 DR. NETON: Here we have to 

14 differentiate between an incident-driven 

15 bioassay program and a routine monitoring 

16 program.

17 DR. MAKHIJANI: Sure.

18 DR. NETON: At the Nevada Test 

19 Site, the exposure potential is considered to 

20 be almost -- not non-existent -- but it's so 

21 low that the monitoring was not required. 

22 They didn't expect people to get anywhere 
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1 near the regulatory limit. 

2 And so, the only time often that 

3 they sampled was when there was an upset 

4 condition. There was a known air sample was 

5 high. That is a different issue, I think, 

6 than when you have a routine bioassay program 

7 for uranium or plutonium where workers are 

8 routinely selected to be monitored on a 

9 periodic basis, which is what you have at 

10 Savannah River.

11 My question to you with the 

12 construction workers, is were or were not the 

13 highest-exposed construction workers 

14 monitored? See, that is the issue that one 

15 has to deal with. It is not that weren't 

16 they monitored. Were the highest-exposed 

17 ones monitored or not? And it is quite 

18 likely that a lot of construction workers 

19 weren't monitored. Either they were more 

20 lower exposures or they worked in different 

21 areas that weren't required, didn't require 

22 monitoring.
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1 DR. MAKHIJANI: Actually, the 

2 monitoring data are so thin, for some 

3 radionuclides at least -- I haven't looked at 

4 uranium and plutonium. So, it may be 

5 different for the major radionuclides, and 

6 usually is.

7 DR. NETON: Yes.

8 DR. MAKHIJANI: But, for many 

9 radionuclides, there just is insufficient 

10 information to know, because there was some 

11 kind of policy assumption that you are not 

12 monitoring these people, because they are 

13 incident-driven and you only monitor them 

14 when they are incident-driven, even at 

15 Savannah River Site, it seems. And this has 

16 been NIOSH's opinion also.

17 So, they had routine exposure 

18 potential. Then you have a problem that, 

19 because they are not monitored for routine 

20 exposure, you don't know what the exposure 

21 potential was.

22 DR. TAULBEE: You know, you 
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42

1 indicate this at Savannah River. You are 

2 saying that you don't feel the construction 

3 trades workers were -- that they were 

4 undermonitored. But if you look at some of 

5 the data that we are looking at, take 

6 americium, curium, californium, for example, 

7 1973. We've got 115 construction trade 

8 workers monitored in that year. The 

9 following year there's 86. The year before 

10 that there's 109.

11 If you look at the actual non12 construction trades workers, yes, we're 

13 looking at about a factor of 10 higher where 

14 we are looking at a thousand workers. But 

15 this is for americium, curium, and 

16 californium. It is confined to two areas.

17 And so if you look at the 

18 procedures as to who was monitored onsite and 

19 their reasoning, they go through and they 

20 identify maintenance workers and building 

21 services. They were monitored at the same 

22 frequency as the chemical operators and so 
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43

1 forth, by procedure. And we see that in the 

2 data when we look at relative proportions of 

3 population.

4 So, there is a disconnect here as 

5 to, at least with that particular site, as to 

6 what we are hearing from some of the 

7 interviews and what we are seeing in some of 

8 the data.

9 DR. MAKHIJANI: Insofar as I 

10 remember the analysis of the data that we 

11 have looked at, there hasn't been a 

12 demonstration of what you have just said: 

13 that here were the construction workers -- I 

14 mean, apart from the question of whether we 

15 have a representative sample of construction 

16 workers or not, which remains to be settled. 

17 But we haven't seen, at least I haven't seen, 

18 an analysis that the construction workers who 

19 were monitored worked in these locations.

20 And for thorium, for example, we 

21 actually don't have a notation in the records 

22 as to who was working with thorium. And they 
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44

1 weren't the same places where americium, 

2 californium, curium, or the same times 

3 necessarily.

4 So, you've got this disconnect. 

5 You are trying to dose reconstruct for one 

6 thing, and you've got another set of data. 

7 But the processing was happening at different 

8 times and places. So, how do you know 

9 whether the most exposed people with thorium 

10 were monitored or whether that data set is 

11 representative for this other radionuclide? 

12 So, it is a pretty big puzzle.

13 DR. TAULBEE: Let's get into a 

14 site-specific-type issue.

15 DR. MAKHIJANI: Yes.

16 DR. TAULBEE: What I am trying to 

17 bring it back to is from a construction 

18 trades in general across all sites --

19 DR. MAKHIJANI: Right.

20 DR. TAULBEE: -- and I was using 

21 this as an example here.

22 But, I mean, jumping back to that 
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45

1 initial point of representativeness, there is 

2 lots of weight-of-evidence type of 

3 information that should play into that 

4 particular role. And maybe we haven't done a 

5 good job of explaining all of that details in 

6 the report, and perhaps that is something 

7 that we should do in future coworker-type 

8 models, in explaining that, why we feel this 

9 is representative.

10 DR. NETON: Yes, I think we have 

11 this little section we call pedigree of the 

12 data, and the pedigree of the data usually 

13 talks about number of bioassay samples and 

14 quality of the data. Does it have a 

15 sufficient detection limit, censoring, that 

16 sort of stuff. But we never really get into 

17 the next level, which is are the data 

18 representative? If we are going to build a 

19 coworker model, are those data sufficiently 

20 representative that we can use it to do that?

21 In some cases, I don't know how 

22 you would even define that, though. Savannah 
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46

1 River happens to be a site where we have a 

2 lot of data to look at.

3 CHAIRMAN MELIUS: But I think if 

4 we are going to -- I mean, I think everyone 

5 agrees that all this is very site-specific 

6 when it goes to application. There's lot of 

7 different scenarios we can come up with and 

8 we have already experienced.

9 But I think you're correct, Jim. 

10 I think if we are going to be using these 

11 coworker models, we need to sort of have a 

12 checklist of what kind of pedigree issues do 

13 we look at, and probably more level of detail 

14 on the administrative aspects of the 

15 monitoring program, for example.

16 I think there are also issues, 

17 just, you know how many people do we have 

18 that were monitored? How are we, then, 

19 applying their monitoring data to how many 

20 people? What's the proportion between the 

21 two?

22 I mean, I think one of the things 
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47

1 that Arjun was sort of referring to was that 

2 if we only have five people monitored and 

3 there's 6,000 people that were exposed, then, 

4 you know, that is a different scenario, and 

5 that is also an exaggerated scenario, because 

6 I don't think you would be doing a coworker 

7 model in that case. But it's that thing. 

8 It's a lot different than if you have 90 

9 percent of the people monitored.

10 And then you have to go year-by11 year, what do you have in terms of production 

12 data, source-term data that would tell you 

13 should exposures be going up or down? What 

14 was happening with the radiation protection 

15 program, and so forth?

16 DR. NETON: I think the 

17 percentage of workers that were monitored is 

18 kind of fraught with some air of uncertainty 

19 because you have to look at were the workers 

20 exposed? My classic example is, you know, in 

21 a hospital, maybe 2 percent of the workers in 

22 a hospital are monitored because only 2 
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48

1 percent of the people work with radioactive 

2 sources in hospitals. So, if you have a very 

3 small percentage of workers that are 

4 monitored, it may be because those would be 

5 only ones that had high potential for 

6 exposure.

7 That would have to be 

8 demonstrated or discussed, but I think that 

9 is true in many cases, especially for these 

10 exotics. Maybe two dozen people work with 

11 these exotic radionuclides. And so it's not 

12 surprising that you will have 20 samples or 

13 30 samples, even though the site population 

14 is 6,000.

15 CHAIRMAN MELIUS: But if we are 

16 applying the results from the 20 to the 

17 6,000 --

18 DR. NETON: Yes, that's a 

19 problem.

20 CHAIRMAN MELIUS: -- that's a 

21 problem on that.

22 DR. NETON: Yes.
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49

1 DR. NETON: Often, the difficulty 

2 has been to show that the population of 

3 workers who had the exposure potential, that 

4 that was the universe of workers who had the 

5 exposure potential.

6 DR. NETON: Well, but, again --

7 DR. MAKHIJANI: I think it's 

8 tough.

9 DR. NETON: Yes, it is. But, 

10 again, I think if you look at what they are 

11 doing, these are compliance-driven programs. 

12 If I had a compliance-driven program, I would 

13 make sure that the workers I thought had the 

14 highest potential to be exposed were 

15 monitored to demonstrate that they didn't 

16 exceed the regulatory limits. I wouldn't 

17 start monitoring the lowest exposed workers. 

18 In fact, I wouldn't even do representative 

19 workers because that is a lot of money spent 

20 without much -- unless maybe to demonstrate 

21 that your controls were adequate.

22 But, in general, though, I think 
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50

1 it can be -- well, you have to demonstrate 

2 it. But I think the way the regulations were 

3 in place at the time, the highest-exposed 

4 workers were monitored, by and large.

5 And one can't, then, pull out a 

6 subset of workers, for example, and say, "Oh, 

7 this set of workers has a higher mean value, 

8 geometric mean, than the coworker model," and 

9 say that's proof that the model is 

10 inadequate, because they were the highest11 exposed workers. And you have got to look at 

12 why these other workers weren't monitored. 

13 It's as important, I think, to talk about why 

14 the other workers weren't monitored, as to 

15 why the other ones were.

16 I mean, because if you look at 

17 the job categories of workers that were 

18 monitored, and then oftentimes these 50th 

19 percentile values are applied to almost 

20 administrative-type or people that had job 

21 assignments that appeared to not involve very 

22 high exposures. The 50th percentile with a 
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51

1 GSD of 5 is applied to people such as clerks 

2 that may have rotated around the plant, 

3 security folks, firefighters, inventory 

4 control people. Those are the type of people 

5 that get the 50th percentile.

6 And then the 95th percentile is 

7 reserved for the Class where maybe the guy 

8 was monitored, but we can't find his bioassay 

9 data. And he was a chemical operator. Well, 

10 then they would receive the 95th percentile, 

11 or the pipefitters. And I think the 95th 

12 percentile is bounding.

13 To start making these strata up 

14 at the 95th percentile, I don't know. Given 

15 what we are doing with all this, to me, it 

16 seems to be giving credibility to a level of 

17 precision and the available data that isn't 

18 there. That's my opinion.

19 CHAIRMAN MELIUS: Yes, but I 

20 think that -- without beating this example to 

21 death, I think there needs to be sort of a 

22 demonstration of that at some point. You are 
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52

1 already claimant-friendly. Any change in 

2 procedure is going to have a minimal effect.

3 DR. NETON: Yes.

4 CHAIRMAN MELIUS: As much as we 

5 want to avoid, you know -- and we have talked 

6 about it in terms of sufficient accuracy 

7 dealing with the residual period, a period 

8 when we know exposures were low. We're not 

9 going to spend a lot of time worrying about 

10 that or developing complicated coworker 

11 models, or whatever, for those time periods 

12 because it just doesn't make sense in terms 

13 of any outcomes that we might have.

14 DR. NETON: We could do that --

15 and we have thought about this quite a bit. 

16 It is hard, though, to come up with a good 

17 example. I mean, any example you come up 

18 with is just that. It is an example of one 

19 case. And one can always speculate some 

20 other scenario that would end up with a much 

21 higher --

22 DR. MAKHIJANI: Really, Jim, what 
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53

1 you have raised is a very important thing in 

2 the whole sufficient accuracy argument.

3 DR. NETON: Yes. Right.

4 DR. MAKHIJANI: It's that you 

5 have, within construction workers, you know, 

6 when we did the analysis for the tritium, 

7 most of the construction workers did jobs 

8 that appeared to have lower exposure 

9 potential in most periods than the all 

10 workers, at least if I am remembering our 

11 charts correctly.

12 But that wasn't always the case. 

13 Sometimes there were big differences, and 

14 pipefitters and laborers I think were the two 

15 that stood out. And you can imagine, 

16 physically, from the nature of their work, 

17 that you expect they're working with the 

18 valves and pipes that carry high-level waste, 

19 and so on and so on and so on, or in the 

20 reactors. So, you expect that result from 

21 the nature of their work.

22 And so I think for those kinds of 
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1 workers, based on the nature of the work they 

2 did, some kind of demonstration is needed 

3 that, well, if you are doing an all worker 

4 model in which that particular group of 

5 workers is a small minority, that what you 

6 are doing is adequate.

7 DR. NETON: But what you are 

8 saying is these were the monitored workers 

9 that are contributing to the upper tail of 

10 the distribution to begin with.

11 DR. MAKHIJANI: But there are 

12 very small number of construction monitors 

13 who were monitored. One of the points that 

14 we made is that, especially when you do all 

15 this aggregation, the construction worker 

16 data is lost.

17 And maybe, Harry, you can pitch 

18 in because this is a point that you made. 

19 It's lost in the all worker data.

20 DR. NETON: But they are in this 

21 distribution, Arjun. And if they are up 

22 here, they are covered. If they are down 
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55

1 here, they are covered. Because we would 

2 take a pipefitter and give them the 95th 

3 percentile, this entire distribution.

4 DR. MAKHIJANI: You are giving 

5 them the 95th percentile of the production 

6 work. So, you're giving them the 95th 

7 percentile basically of the production worker 

8 distribution. Because there are very, very 

9 few construction workers in there.

10 DR. NETON: Right, but they're in 

11 there, and if they are in the upper tails --

12 unless they are above the 95th percentile, 

13 unless all tritium-exposed workers are above 

14 the 95th percentile, which I doubt, then I 

15 think the 95th percentile is bounding.

16 We tend to confuse high 

17 monitoring results with a certain worker 

18 population and saying they were highly 

19 exposed, but then now we have to look at the 

20 unmonitored worker. What does it mean for 

21 them? And those high-exposed workers are 

22 built into the distribution.
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56

1 And, yes, if they are in here, 

2 unless you can demonstrate that they are well 

3 above the 95th percentile somehow, I don't 

4 know --

5 CHAIRMAN MELIUS: I mean, I can 

6 think of scenarios where they wouldn't be 

7 above the 95th -- or they would be above the 

8 95th, and well above it, if all your 

9 production workers were quite low. It 

10 depends on the situation. I think that needs 

11 to be -- at least it has to be evaluated in 

12 some way.

13 DR. NETON: Right.

14 CHAIRMAN MELIUS: And the problem 

15 with that, I mean, I think we all know, is 

16 that often we don't have very many of a 

17 particular group of workers monitored. So, 

18 it may be difficult.

19 But what data we have, I think we 

20 have to look at it and take account of it. 

21 Is that a fair assessment? It may very well 

22 be in this particular example, but it may not 
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57

1 be in other situations.

2 DR. NETON: But, again, if you go 

3 back to the premise that the highest-exposed 

4 workers were monitored, the unmonitored 

5 workers were not exposed as highly as the 

6 monitored workers. I mean, if you can 

7 demonstrate that, that the highest-exposed 

8 workers were monitored, then you're trying to 

9 reconstruct a dose for someone that has no 

10 monitoring data. And there may be valid 

11 reasons why they weren't monitored, because 

12 their exposure potential is low or much 

13 lower; they were down in here. You can't 

14 assume because a few data points show a high 

15 exposure that all coworkers should receive 

16 that exposure.

17 CHAIRMAN MELIUS: Yes, but I 

18 don't think you can assume the other way, 

19 either. I think you have to base it on some 

20 level of information and facts.

21 DR. NETON: Right. You have to 

22 look at the radiation protection program that 
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58

1 is in place at the time.

2 CHAIRMAN MELIUS: Yes. And, 

3 again, if it is one worker and there was an 

4 incident or something, that is very different 

5 than if it were 30 people that were monitored 

6 out of 100, or whatever, that would fit into 

7 that group.

8 And a lot depends on how could 

9 their exposures have differed from those of 

10 the average production worker or the 

11 distribution of production workers, as an 

12 example.

13 MEMBER FIELD: Jim, this is Bill. 

14 I had a question.

15 DR. NETON: Sure.

16 MEMBER FIELD: That question 

17 about the assumption that the highest-exposed 

18 workers were monitored, and I think the 95 

19 percent percentile would probably be 

20 bounding. But, just for the record -- I am 

21 not advocating this -- but why wouldn't the 

22 99 percent percentile be used?
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1 DR. NETON: Why wouldn't it be? 

2 It's convention. That is what we've adopted 

3 in this program in the very beginning. There 

4 is no real reason why it couldn't be used, 

5 but this is what we have chosen as sort of a 

6 default value. And that was actually early 

7 on in dealing with SC&A and these models. 

8 That's what we both sort of agreed upon.

9 DR. MAURO: Bill, this is John 

10 Mauro.

11 MEMBER FIELD: Yes, John.

12 DR. MAURO: One of the reasons I 

13 became comfortable with the concept of the 99 

14 percentile value, whether we are dealing with 

15 external or internal, is the way in which 

16 it's being implemented is by year. So, if 

17 you have a worker that is there for many 

18 years --

19 MEMBER FIELD: Right.

20 DR. MAURO: And I would agree 

21 with you. If you were looking at a worker 

22 that was there just for one year, and you 
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1 wanted to assign some number to him, one 

2 could say, using the 95th percentile, well, 

3 there is a 5-percent chance that his exposure 

4 might have been higher.

5 But that is not the case, though, 

6 if a worker is there for many years. To say 

7 that this same worker happened to fall above 

8 the 95th percentile year after year after 

9 year, now you are getting into the realm of 

10 infinitely-small probabilities.

11 So, I have become comfortable 

12 with the concept of the upper 95th percentile 

13 as being the basis for constructing your 

14 coworker model because it's almost intuitive 

15 that, do you really believe it is likely that 

16 the same worker is going to be in the upper 5 

17 percentile year after year after year? So, 

18 that's how I became comfortable with that 

19 position.

20 MEMBER FIELD: Yes, I understand 

21 that. But if you have workers with a short 

22 duration of exposure, it sounds like, if you 
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61

1 want something to be surely bounding, the 99 

2 percent percentile might be worthwhile 

3 considering.

4 DR. MAURO: I understand that, 

5 and I am inclined to agree. Most of the 

6 time, when we were doing our work, we noticed 

7 that the workers were there for many years. 

8 But you're right, if it is a single year, 

9 that is a reasonable question.

10 But while I still have the time, 

11 we jumped over this OPOS -- bear with me. I 

12 know we're into the stratification part of 

13 the conversation, and that is by far the 

14 single most important question. But I do 

15 want to put the OPOS question to bed because 

16 I think it's something clearly separable from 

17 the stratification question, unless I am 

18 wrong.

19 I think it is important that we 

20 say, listen, if we have a population of 

21 workers and we all know that they come from 

22 the same distribution -- okay, we know that 
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1 there is no strata, okay, there is only one 

2 strata, okay? But we also know that only 10 

3 percent of the people, whatever the percent, 

4 of those workers have bioassay data, and some 

5 workers have maybe two samples, one sample, 

6 some have 20 samples. But only a percentage 

7 of this single strata. I'm sort of 

8 stipulating that. 

9 I just want to make sure that 

10 everyone is comfortable and agrees that the 

11 OPOS approach to simplifying the construction 

12 of a coworker model is valid. Namely, not 

13 doing -- remember, the question I originally 

14 asked was, why wouldn't you do the complete 

15 workup of each of those individuals? Let's 

16 say it is 100 individuals for that year. And 

17 that would be a burdensome effort. That is, 

18 you would have to model the intakes using all 

19 of the bioassay data for each person, as 

20 opposed to the averaging approach, the OPOS 

21 approach.

22 So, that question, that issue of 
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1 OPOS as a strategy, in my mind is a separate 

2 issue from the stratification problem. Is 

3 everyone comfortable that, if we know we are 

4 dealing with a single strata, and we want to 

5 build a coworker model for that single 

6 strata, the OPOS approach is okay? And that 

7 is, we are comfortable reducing each person 

8 to a single average concentration in the 

9 urine as being a metric for the purpose of 

10 building a coworker model. 

11 I think it's important that we 

12 get that behind us, so that then we could say 

13 that, okay, we're okay with OPOS as a method 

14 for building a coworker model for a single 

15 strata. Now the question becomes, you know, 

16 how do you deal with the possibility that 

17 there may be multiple strata that we have to 

18 deal with? Or are the two confounded in some 

19 way? Right now, in my mind, they are 

20 separable, but maybe I'm wrong.

21 DR. MAKHIJANI: Well, you know, 

22 John, I don't know, there have been a number 
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64

1 of reports in which we have dealt with this 

2 question. You know, we haven't said we 

3 accept or reject it. As you noted in your 

4 report, we haven't kind of given you a 

5 finding on that because we see that there is 

6 some basis for your argument that when you 

7 have 20 samples from a single worker, that at 

8 the same time we have had other problems with 

9 it.

10 You know, when we get into the 

11 OPOS, we can discuss them. But we haven't 

12 been comfortable with the OPOS approach. And 

13 so we've raised concerns about it both in our 

14 review of RPRT-0053, and then, as we got 

15 deeper into it, when the model was actually 

16 applied in neptunium and thorium and 

17 americium, we actually developed more 

18 concerns with how it was being applied. 

19 So, we have a significant number 

20 of concerns with OPOS as it stands today in 

21 the reports that we have sent to the Board.

22 DR. NETON: Well, I guess my 
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65

1 question is, if it's not OPOS, then what is 

2 it? You know, if you are advocating for 

3 using the individual data, then we just can't 

4 accept that. And I don't know any other 

5 better way than to use the OPOS method. So, 

6 that's kind of where we are.

7 DR. MAKHIJANI: One reason we 

8 haven't -- and, Harry, you know, please say 

9 something. And I'm sorry, actually, I should 

10 have asked Joyce to be in on this discussion. 

11 I didn't think of it.

12 But many of our concerns are 

13 expressed in the most recent report we've 

14 sent you. So, one concern is the way the 

15 OPOS data are compiled, you've gone into the 

16 logbooks and used the raw data rather than 

17 when the logbooks say report less than .3 or 

18 some censored level, and you use all the 

19 negative numbers and the numbers that are 

20 zero or very close to zero, much less than 

21 the detection limit, and then average them 

22 all. Very often, you come out not only with 
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66

1 a number that is much less than the MDA, but 

2 with a negative result for the OPOS value.

3 And that's clearly physically 

4 unacceptable to have a negative number for an 

5 average exposure of a worker for a year, 

6 because, if you apply that in a dose 

7 reconstruction, you get a negative radiation 

8 dose.

9 DR. TAULBEE: I don't recall 

10 that.

11 DR. MAKHIJANI: Sorry?

12 DR. TAULBEE: I don't recall that 

13 happening a lot.

14 DR. MAKHIJANI: It does happen a 

15 lot, in some cases. If you look at the late 

16 `80s, if you look at the late `80s for 

17 americium, californium and curium data, you 

18 will find that it happens a lot.

19 DR. NETON: I would suggest that 

20 is an implementation issue.

21 DR. MAKHIJANI: Well --

22 DR. NETON: Now, are you saying 
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67

1 that OPOS is okay except for how we implement 

2 it?

3 DR. MAKHIJANI: No. I'm just 

4 raising that as an example of a problem.

5 Then, there is the issue of 

6 losing some of the variability.

7 A third issue that I have, for 

8 instance, is if, as appears to be the case at 

9 the Savannah River Site, one group of workers 

10 has an incident-driven monitoring and the 

11 other group has both incident and routine 

12 monitoring, dominated by routine monitoring. 

13 If you are compressing -- so, there is a use 

14 of OPOS for comparing. And when you compress 

15 the data into a single sample, and you 

16 already have very few samples to start with, 

17 now you have got far fewer samples which are 

18 non-comparable. And you can say you're going 

19 to compare incidents with incidents, as you 

20 said in your report, but that's not what 

21 actually happens in practice.

22 You are comparing an incident-
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68

1 driven monitoring set, which assumes that 

2 certain exposures are only incident-driven, 

3 which assumption may not be correct, and I 

4 would argue for some construction workers, at 

5 least what they have said, it isn't correct. 

6 And you are comparing it with a much larger 

7 data set that was collected based on a 

8 different idea of exposure potential. So, I 

9 think --

10 DR. NETON: Well, that would only 

11 tend to drive the data high. I mean, it 

12 would bias the models high.

13 DR. MAKHIJANI: Not necessarily. 

14 We recognize, of course, that it would, but 

15 if you missed all the routine exposures of 

16 one group of workers, then you have missed a 

17 lot of exposures for many workers because you 

18 are not monitoring them.

19 DR. NETON: Oh, well, I'm 

20 confused then. Because we would have a 

21 routine program intermixed with some incident 

22 results. I mean, there is no doubt in my 
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69

1 mind that routine programs are going to show 

2 up positives and they are going to do more 

3 follow-ups because there was an incident that 

4 the routine program detected. That is what 

5 we are talking about here.

6 I don't think that you are going 

7 to mix a routine monitoring program for 

8 uranium with an incident-driven program for 

9 uranium. They are sort of part and parcel of 

10 the same monitoring program. It's just you 

11 do more follow-ups when you have a positive 

12 routine. Or there was evidence of an upset 

13 condition where you had a high airborne and 

14 you said, "my goodness, these people are in 

15 trouble, let me take some urine samples." 

16 Well, those are going to drive the 

17 distribution to the high end. It's 

18 conservative.

19 DR. MAKHIJANI: What we've said 

20 in the specific instances in which we studied 

21 -- because these are all new, so we have to 

22 take the examples as we have looked at the 
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70

1 actual data and its application.

2 In the particular applications, 

3 what we've said is that there are many 

4 workers who may have had routine exposure 

5 potential and who may have had incidents. In 

6 fact, construction workers have said, you 

7 know, incidents weren't followed up for them.

8 And so if there wasn't the 

9 routine monitoring program for this one group 

10 of workers, we have an insufficient data set 

11 where all the --

12 DR. NETON: That is different 

13 than OPOS, though. OPOS is used when we 

14 have routine monitoring data, a routine 

15 monitoring program in place.

16 DR. MAKHIJANI: Well, you have 

17 said for construction workers at Savannah 

18 River you didn't have a routine monitoring, 

19 and you are still using OPOS for it. That's 

20 part of our problem.

21 DR. TAULBEE: That's not true. 

22 We have not said that it was not routine. 
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1 There were some that were monitored 

2 routinely. The maintenance folks that were 

3 inside the facility were monitored routinely, 

4 and those were construction trades. There 

5 were pipefitters within that group, and they 

6 are included as part of that routine. And 

7 then there were others who are incident8 driven. So, you've got both.

9 Now, the relative population of 

10 operators to building maintenance is 

11 different, yes, but there was both routine 

12 and incident for both populations.

13 DR. MAKHIJANI: Unfortunately, I 

14 don't have a searchable report.

15 MEMBER ROESSLER: Arjun, what I 

16 am trying to get as I weigh this is, if you 

17 don't use OPOS, then what is your 

18 alternative? And why would that be better? 

19 That is, I think, what we are really talking 

20 about. We can't just toss something out 

21 unless we have another route to follow.

22 DR. MAKHIJANI: Well, normally, 
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1 we haven't -- you know, we weren't tasked to 

2 come up with an alternative. We were tasked 

3 to review what was on the table. And I would 

4 agree that we haven't, so far as I know, we 

5 haven't put an alternative on the table.

6 But, if the objections to the 

7 OPOS are valid, then it's a very important 

8 question as to what you would use. I'm not 

9 saying it is not a legitimate question. It 

10 is important and it needs to be considered.

11 We haven't put an alternative on 

12 the table. We haven't said that OPOS doesn't 

13 have merit. We have said that it has certain 

14 problems that need to be addressed. And 

15 maybe we should look at the question of what 

16 the alternative would be, quite apart from 

17 how the OPOS data was in practice compiled, 

18 which is a big problem.

19 CHAIRMAN MELIUS: I have read 

20 some of the SC&A reports, recent reports on 

21 SRS. I think the answer to John's question 

22 is that we need to look at OPOS, we need to 
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1 see -- it has benefits, potential benefits. 

2 It has potential limitations. And those are 

3 probably going to be site- and situation4 specific. I think we can look at those in 

5 that context.

6 Certainly, the issues that SC&A 

7 has raised about OPOS and stratification, the 

8 evaluation of stratification, I think are 

9 significant. Can they be overcome? Do they 

10 mean we don't use this technique? I don't 

11 know. You know, Gen's right, what are the 

12 alternatives?

13 I actually was thinking, as we 

14 were talking, this may be the first 

15 time -- if we decide that you can't use OPOS 

16 and that your whole coworker approach is 

17 negative, it will be the first time we have 

18 written a report to the Secretary saying 

19 NIOSH has sufficient data, but doesn't want 

20 to use it, the dose reconstruction.

21 (Laughter.)

22 Or refuses to make the time and 
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1 effort. We might get a letter back in that 

2 case, I think.

3 DR. NETON: One other on this 

4 point on the OPOS that we hadn't mentioned, 

5 is that there is a correlation of data, which 

6 to me is a statistical issue that can't be 

7 ignored. I mean, if you have 20 samples on 

8 one person and incorporate them individually 

9 in the distribution, recognizing that they 

10 are fully correlated because it is the same 

11 guy being sampled repeatedly, it just doesn't 

12 make any statistical sense.

13 MEMBER ROESSLER: So, what we 

14 should be weighing is what you just pointed 

15 out, the really big issues that are of 

16 benefit, against maybe some of the small 

17 concerns.

18 CHAIRMAN MELIUS: Exactly. I 

19 agree, Gen. And I'm sorry to interrupt. But 

20 I think we need to evaluate how big, how much 

21 difference does it make or doesn't make? My 

22 statistical training, you know, if you had 
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1 multiple samples from a person, that was a 

2 no-no to combine those. You would never do 

3 that. But, you know, that was theoretical 

4 statistics, not necessarily practical 

5 statistics.

6 And I think we have to see what 

7 level of difference it makes and what the 

8 situations, and try to understand what 

9 variability there is and what accounts for 

10 that variability within an individual with 

11 multiple samples.

12 DR. NETON: I think I would 

13 appreciate it if SC&A would review this from 

14 the implementation perspective, which is the 

15 intake calculation perspective. I get the 

16 sense from looking at the SC&A report that it 

17 was a purely statistical review. It didn't 

18 incorporate the practical significance of 

19 what a coworker model really is, which is an 

20 intake model.

21 And if you are trying to generate 

22 an intake model, you need to start with 
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1 intakes, as John Mauro talked about, 

2 recognize we can't do that. This is the most 

3 reasonable alternative, in our opinion. And 

4 if anybody can come up with a better 

5 approach, we are all for listening for it.

6 But we can't just isolate your 

7 review in a statistical vacuum and say, you 

8 know, there's heteroscedasticity and all this 

9 kind of stuff. I mean, this is the practical 

10 significance of the correlation of data with 

11 people, and you're trying to get an intake 

12 for everybody. If you have one sample, there 

13 is no question. Picocuries per liter days 

14 for the whole year, that's his intake. But 

15 if you have five samples, you have to 

16 estimate their intake, and it's not each of 

17 those samples in the distribution. So, 

18 that's the nuts and bolts of our opinion.

19 DR. MAKHIJANI: Yes, I agree that 

20 our review of RPRT-0053 was essentially 

21 statistical, but our subsequent reports in 

22 which a review of the method is automatically 
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77

1 a part of it -- and Joyce was a big part of 

2 both reviews -- we actually have some health 

3 physics implementation, dose calculation, 

4 intake calculations type of concerns that 

5 were laid out both generally as with regard 

6 to the sufficiency of the data, and also in 

7 regard to the use of OPOS.

8 I mean, new concerns came out 

9 when we actually tried to take this set of 

10 concerns and look at how the method was 

11 actually applied in the two cases that we 

12 have reviewed. And so, actually, in a way, 

13 it might be useful to look at all those 

14 findings together. I know NIOSH hasn't had 

15 time, perhaps, to look at especially the most 

16 recent report that just went out a couple of 

17 weeks ago. And it's a pretty long, 

18 complicated report. But that might be a 

19 useful thing to do.

20 CHAIRMAN MELIUS: Before we drink 

21 the OPOS Kool-Aid --

22 (Laughter.)
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78

1 DR. NETON: Well, I certainly 

2 haven't looked at those reports in any 

3 detail.

4 DR. CHMELYNSKI: If I could 

5 interject here. This is Harry Chmelynski.

6 MR. KATZ: Yes, go ahead, Harry.

7 DR. CHMELYNSKI: Okay. I would 

8 like to go back to the two plots that were 

9 shown in the PowerPoint presentation and just 

10 make a couple of comments.

11 First, on the slide that says the 

12 regression -- using the regression on order 

13 statistics procedure. One of the things I 

14 think that is hidden in this plot is a big 

15 assumption that up there on the far right

16 there is the worker who is 20 times the 

17 geometric mean. And what ROS does is assume 

18 that, out of those 140, or whatever it is, 

19 140 non-detects, there must be one of them 

20 that is down there 20 times lower than the 

21 GM.

22 In other words, the ROS method 
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79

1 assumes a symmetry around a geometric mean. 

2 So that for every worker who has high 

3 exposure, we are assuming there is somewhere 

4 in those non-detects another worker that has 

5 just as low of an exposure compared to the 

6 geometric mean.

7 In this graph, we are talking 

8 about almost half of the data points that we 

9 are making an assumption for, that they are 

10 all symmetric to what we see here. Now, 

11 nobody can decide whether that is true or 

12 not. But sometimes, when you start getting 

13 down to a factor of 20 or 50 or 100 below the 

14 GM, it stretches the imagination that, 

15 indeed, there are workers down there.

16 DR. NETON: I would disagree, 

17 Harry. There are many people that have zero 

18 exposures or very close to zero exposures. I 

19 mean, that's the --

20 DR. CHMELYNSKI: But you can't 

21 measure this, though. Twenty times below the 

22 GM, are you sure you can say that?
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80

1 DR. NETON: No, you can't measure 

2 it, but I am saying all we're saying is it's 

3 below that. I mean, there have been 

4 studies --

5 DR. CHMELYNSKI: Whether or not 

6 you're not just saying it's below that, by 

7 assuming a log-normal distribution, you 

8 actually are assuming they are on the line 

9 all the way down there.

10 DR. NETON: Actually, we have 

11 another TIB on this that deals with the 

12 distribution of detectability. If a person 

13 had zero samples, you have a normal 

14 distribution of detectability around the 

15 detection limit. But I don't see your point, 

16 really, because all --

17 DR. CHMELYNSKI: I'm just saying 

18 that we are making an awful big assumption 

19 here that, out of the 100-and-some non20 detects here, that we know how they are 

21 arranged on that line.

22 DR. NETON: Yes.
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81

1 DR. CHMELYNSKI: And that is a 

2 big assumption, is all I'm pointing out. I 

3 am not saying that it's necessarily wrong.

4 DR. NETON: What significance 

5 does that have, though, in terms of 

6 reconstructing doses?

7 DR. CHMELYNSKI: Well, there is 

8 the whole question of sufficient accuracy. 

9 Exactly what does this log-normal plot mean? 

10 How well did we estimate the log-normal 

11 distribution that we say we are going to be 

12 using on the next page?

13 Okay. Now, we get to the second 

14 page. Several times an issue was raised 

15 saying, well, if these points were 5 or 10 

16 percent higher or lower, what difference 

17 would it make? Well, we're not talking 5 or 

18 10 percent here; we are talking factors of 5 

19 and 10. That is a big difference between 5 

20 and 10 percent.

21 DR. NETON: Where is the 5 --

22 DR. CHMELYNSKI: I just don't see 
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82

1 this -- that these are plus or minus 10 

2 percent, what difference would it make? That 

3 presumes that there are no differences in 

4 the --

5 DR. NETON: Wait, wait. Factors 

6 of 5 and 10 --

7 DR. CHMELYNSKI: That, again, is 

8 by assumption because there is not enough 

9 power to determine if there are.

10 DR. NETON: Wait, wait, Harry. 

11 Five and 10 on what, on each of the points?

12 DR. CHMELYNSKI: A factor of 5 

13 and 10.

14 DR. NETON: On what?

15 DR. CHMELYNSKI: On the 

16 individual points for an exposure. I mean, 

17 you don't know that the two groups that are 

18 the same. So, you are assuming that the 

19 guy -- that they all fit on this curve. Now, 

20 in fact, if there was a difference of 5 in 

21 the two populations, you are going to use the 

22 same curve for both of them. That's my 
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83

1 problem with it.

2 DR. NETON: I'm missing it. I 

3 think what you are saying is there is so much 

4 variability, it's very hard to detect small 

5 differences in values. Yes, I'll agree with 

6 that.

7 DR. CHMELYNSKI: Well, your term 

8 of small, which, again, goes back to 5 and 10 

9 percent, and my idea of small when you're 

10 talking factors of 5 and 10 --

11 DR. NETON: Are you saying that 

12 there are individual coworker models that are 

13 stratified that have a factor of 5 or 10 

14 difference in the geometric mean?

15 DR. CHMELYNSKI: I'm saying that 

16 you couldn't see that when you did your test 

17 if the sample sizes are too small.

18 DR. NETON: Right.

19 DR. CHMELYNSKI: That's all I'm 

20 trying to say here. You know, we are making 

21 a lot of things here by assumption, 

22 basically. There is not enough data to 
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84

1 support either the first plot or the second 

2 plot.

3 DR. NETON: Right, but my point 

4 was that, you know, one can stratify and pull 

5 out some construction workers and show that, 

6 "oh, my goodness, there's a 10-15 percent 

7 difference in this particular year," and use 

8 that as an argument that the data need to be 

9 stratified. And I'm saying that's not going 

10 to make a difference in the overall 

11 practical -- it is not going to make a 

12 practical difference in the dose 

13 reconstruction. That is what I was trying to 

14 argue. Just because you could come up --

15 DR. CHMELYNSKI: I agree, if you 

16 are talking 5-10 percent, then I agree there 

17 is not a difference. But I just don't see 

18 that we are only talking those small 

19 differences.

20 DR. NETON: Well, have we seen 

21 those kinds of differences in the stratified 

22 data? That's what I'm trying to say. Have 
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85

1 we seen a factor of 5 or 10 difference? I 

2 would agree, if there is a factor of 5 or 10 

3 difference in a data set that we had compared 

4 to the coworker model, there's an issue 

5 there. I would agree that's true.

6 DR. TAULBEE: If you look at the 

7 americium, curium, californium, the exotics 

8 at SRS, there is one year where there is a 

9 factor of 4, and the other ones it's less 

10 than a factor of 1. There is one year, 1985, 

11 where construction trades are a factor of 4 

12 higher. One year.

13 DR. CHMELYNSKI: And that is if 

14 we just rely on arithmetic calculations on 

15 the actual data, which is a small data set. 

16 But, in terms of the hypothesis17 testing, again, this is going to get back to 

18 the power question, which hasn't been brought 

19 up yet, but maybe we should defer until 

20 later, as to whether the sample sizes here 

21 are sufficient to make these kinds of 

22 statements.
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86

1 DR. MAKHIJANI: Could you go back 

2 to the previous chart? 

3 When you were explaining the 

4 below MDA measurements -- and that slide when 

5 Harry made his point -- you said that the 

6 usual assumption is that below MDA 

7 measurements are assumed to be normally 

8 distributed.

9 DR. NETON: Well, they can be, 

10 yes. There is a component of that --

11 DR. MAKHIJANI: That is what you 

12 often assume in your dose reconstructions, 

13 right? Individual dose reconstructions are 

14 often done, maybe not always, but generally 

15 done that way. The below MDA measurements 

16 are assumed to have a certain distribution 

17 around --

18 DR. NETON: No, they're not 

19 normally distributed. What is it? For an 

20 internal dose reconstruction, when you have 

21 below the MDA, we assign the MDA as the 

22 midpoint of the distribution. The 95th 
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87

1 percentile is the -- I've forgotten this. 

2 It's not a normal distribution.

3 DR. MAKHIJANI: Unfortunately, I 

4 don't have that thorium report in front of 

5 me, because we listed all the ways in which 

6 you do that. You sometimes use MDA over 2 

7 for every point.

8 DR. NETON: Right.

9 DR. MAKHIJANI: And sometimes 

10 there is a distribution around the MDA with 

11 zero as the minimum and MDA as the cut-off.

12 DR. NETON: Right.

13 DR. MAKHIJANI: And one of the 

14 problems we had -- and this relates to how 

15 the OPOS data were actually compiled -- is 

16 that you didn't do that when you compiled the 

17 OPOS data. Although you say that censored 

18 data are going to be treated in a 

19 certain -- yes, you say that in the report, 

20 but if you look at what is considered as 

21 censored data, in the actual data 

22 compilation, we were surprised that this 
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88

1 procedure wasn't actually followed, because 

2 not all of the points that are treated that 

3 are noted in the logbooks as report less than 

4 a certain value, whatever the MDA is, were 

5 not treated.

6 That is part of the objection we 

7 have been raising. The actual compilation is 

8 -- very often you get numbers that are zero, 

9 less than zero, for the OPOS values because 

10 you didn't adopt the same procedure as you do 

11 in your dose reconstructions for compiling 

12 less than MDA data.

13 DR. NETON: Wait, wait. Dose 

14 reconstructions where we have data are 

15 different than assembling coworker models.

16 DR. MAKHIJANI: I understand 

17 that, but --

18 DR. NETON: When you have real 

19 people data, we are not going to use a 

20 coworker model, remember.

21 DR. MAKHIJANI: But you are 

22 compiling real people data here. You are not 
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89

1 compiling -- to prepare a coworker model, you 

2 are compiling real people data into some kind 

3 of a distribution.

4 DR. NETON: That's what we're --

5 DR. MAKHIJANI: And when you come 

6 out with data points that are below zero, 

7 below actual arithmetic zero, sometimes with 

8 great frequency, because you are not actually 

9 using the censored value that is written in 

10 the logbooks.

11 DR. NETON: Those are going to 

12 appear down in -- they are not even going to 

13 be reported on this curve. They are censored 

14 data at that point. If it was below zero --

15 DR. MAKHIJANI: But they are not 

16 being treated as censored data.

17 DR. NETON: But it doesn't matter 

18 because it is part of the cumulative 

19 distribution. I mean, they are down here, 

20 Arjun. I mean, when you do a cumulative 

21 probability plot, they all fall down in here, 

22 not up in here, which is what we are trying 
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90

1 to estimate.

2 DR. MAKHIJANI: If you take a 

3 look at the thorium report that we just sent 

4 you and look at the years in the `80s that 

5 are called out in there, and look at how many 

6 negative numbers you actually have, 

7 arithmetically-negative numbers, as numbers 

8 to be used in a coworker model, I think you 

9 would be surprised.

10 DR. NETON: I think we are 

11 confusing two different things here.

12 DR. TAULBEE: We will look at it.

13 DR. NETON: There's the thorium 

14 report --

15 (Simultaneous speaking.)

16 DR. TAULBEE: The maximum mean 

17 methodology, we will look at as to how that 

18 occurred, because I don't think that --

19 DR. NETON: Yes, I can't speak to 

20 that. It sounds odd to me, what you are 

21 saying. And if we did, maybe we didn't 

22 follow our own method.
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91

1 DR. MAKHIJANI: Yes, it certainly 

2 surprised us.

3 CHAIRMAN MELIUS: Can I suggest 

4 that, since Jim has already made it through 

5 the first four slides in about an hour and a 

6 half, that why don't we take a short break? 

7 We will see if we can speed him up. He needs 

8 a little more coffee.

9 (Laughter.)

10 Okay. Why don't we reconvene in 

11 15 minutes, at quarter of?

12 MR. KATZ: Okay. I'm just 

13 putting the phone on mute.

14 (Whereupon, the foregoing matter 

15 went off the record at 10:34 a.m. and went 

16 back on the record at 10:49 a.m.)

17 MR. KATZ: Okay. We're back.

18 I'll just check. Bill, do we 

19 have you on the line still?

20 MEMBER FIELD: Yes.

21 MR. KATZ: Great.

22 CHAIRMAN MELIUS: Jim, do you 
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92

1 want to try another slide?

2 DR. NETON: I might try to move 

3 on. All I have to say is that it has been a 

4 very interesting, I think somewhat -- maybe 

5 not productive, but evolving conversation.

6 (Laughter.)

7 Okay. So, here's how we do the 

8 coworker model. And I just wanted to talk 

9 about the application, you know, how these 

10 coworker models are really used.

11 I alluded to this when I talked 

12 about the intake slide. Based on the 

13 potential for exposure, you take the 

14 unmonitored workers, and they are not all the 

15 same flavor. You have workers could have 

16 frequented the area, been exposed to airborne 

17 particulate, weren't working directly with 

18 materials. Then there's the workers who had 

19 their nose to the grindstone, so to speak, 

20 chemical operators, that sort of thing.

21 And so I like to say that we 

22 essentially have a two-component job exposure 
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93

1 matrix: the 50th percentile with the full 

2 distribution and the 95th percentile. That 

3 is our job exposure matrix, and it's very 

4 simple.

5 So, the full distribution would 

6 be applied to these sort of -- how would you 

7 want to call it? -- intermittently-exposed or 

8 not-heavily-exposed workers, with the full 

9 GSD. So, again, the 50th percentile with a 

10 GSD, a minimum of 3. Sometimes the 

11 distributions are tighter than that, but we 

12 have recognized the biological variability of 

13 the urinary excretion. It's a limiting 

14 factor of 3, just because of the way the 

15 models are and differences, various 

16 differences, in the way excretion patterns 

17 work. I won't go into the details of that, 

18 but we have adopted a GSD of 3.

19 So, again, that intake is 

20 converted into a dose. You know, if you so 

21 many picocurie-per-day intake over this time 

22 period, chronically, what's your dose to the 
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94

1 liver, if that is the cancer of interest?

2 And so the liver would be 

3 assigned a dose that would be proportional to 

4 the GSD with the full distribution. The dose 

5 is directly proportional to the intake.

6 DR. MAKHIJANI: Proportional to 

7 the GM.

8 DR. NETON: Yes, the central 

9 estimate of the dose is proportional to the 

10 GM, and then the GSD is added on top of that.

11 DR. MAKHIJANI: I see.

12 DR. NETON: And that is sampled 

13 repeatedly in IREP. So, it'll sample the GM, 

14 it'll sample the 95th, the 99th. It will go 

15 through just like Monte Carlo is supposed to 

16 work, recognizing that the program pays at 

17 the 99 percentile. And so you can't exactly 

18 figure out how that skews the sampling of 

19 that distribution, but, clearly, adding that 

20 uncertainty does skew the PC value in the 

21 positive direction because you are allowing 

22 for this uncertainty.
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95

1 I thought at one point we could 

2 actually figure this out, but you can't 

3 because the cancer models themselves have 

4 uncertainties. And if you have a very 

5 uncertain cancer model, even with a GSD of 3, 

6 it might not contribute much to the 99th 

7 percentile. 

8 So, it's not obvious, but it does 

9 at least -- it has to skew. The larger the 

10 uncertainty, the more it skews and biases the 

11 result and keeps the value high.

12 DR. MAKHIJANI: Is that true 

13 based on what you said when Harry was talking 

14 about, you know, for each point, let's say, a 

15 factor of 20 above the GM, you have a factor 

16 of 20 below the GM. So, you are sampling the 

17 whole space that is below the GM. And in 

18 many cases you've got these artificially 

19 reconstructed points that are below the MDA 

20 that may be a factor of 100, a factor of 50 

21 below the MDA. So, you are also sampling 

22 them as frequently because they are half the 
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96

1 points that are below.

2 DR. NETON: You are, but the 

3 program selects the upper 99th percentile of 

4 the PC value.

5 DR. MAKHIJANI: Oh, that's a 

6 different --

7 DR. NETON: You generate a 

8 distribution of PC values that are 

9 proportionate to that envelope.

10 DR. MAKHIJANI: But the question 

11 is whether you're generating a distribution, 

12 a dose value that is necessarily claimant13 favorable when you sample the whole 

14 distribution based on a GSD.

15 DR. NETON: You do. You do. 

16 Trust me.

17 DR. MAKHIJANI: You do?

18 DR. NETON: Yes. Definitely. We 

19 went through this before. In fact, for the 

20 most part, it is almost as if you would pick 

21 the 84th or 80-something percentile as 

22 central. We have done this before.
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97

1 If you take out the GSD and make 

2 it a constant, and you keep moving your value 

3 higher and higher as a constant, you will get 

4 about the same PC as if you put in a constant 

5 around the 80-something percentile of the 

6 distribution. That is not a hard-and-fast 

7 rule because, again, it varies a lot, but we 

8 have done this. In fact, that is going to be 

9 a discussion, a topic of conversation 

10 tomorrow on the DuPont Deepwater Works, where 

11 we have demonstrated that, that putting the 

12 GSD about it is as claimant-favorable as 

13 having a higher centralized --

14 DR. MAKHIJANI: Okay.

15 DR. NETON: It's true.

16 DR. MAKHIJANI: I just haven't 

17 seen that.

18 DR. NETON: Yes. It's true.

19 Okay. So, there's that, but, 

20 then, you know, if the person appears to have 

21 been a pretty-heavily-exposed worker, based 

22 on job category and such, we give the 95th 
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98

1 percentile. Again, our two-part job exposure 

2 matrix. So, it is possible that either the 

3 worker wasn't monitored or they lost his 

4 monitoring data, or whatever. We would 

5 default and we would tend to be somewhat 

6 claimant-favorable in this respect, like we 

7 do in most things. So, that is the way we 

8 apply the coworker model.

9 And it says here each situation 

10 is evaluated on a site- and case-specific 

11 basis. I think some of the dose 

12 reconstruction, remember, we went through 

13 this process.

14 However, you know, this is all 

15 assuming that the one-size-fits model and the 

16 stratification has become -- it has been 

17 talked about for years, actually, but it is 

18 sort of coming to the head now, in 

19 particular, I think in relation to the 

20 Savannah River, which is where we happen to 

21 have data that allows us to evaluate 

22 stratification. I think most other sites 
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99

1 wouldn't have the data to allow you to do 

2 this.

3 And so, to handle stratification, 

4 the ORAU team was tasked with looking at how 

5 we are going to do this. And that ended up 

6 resulting in the RPRT-0053, which is subject 

7 of an SC&A review. It introduced the concept 

8 of the one person, one sample. And that was 

9 a direct result of trying to compare 

10 distributions of populations, and you really 

11 can't do that very easily unless, you know, 

12 OPOS works.

13 Well, the reason we did 

14 that -- we talked about it -- minimizes the 

15 issues with the correlation of data. You've 

16 got 20 samples from one person. They are all 

17 correlated.

18 In doing so, we tried to be 

19 conservative and use a maximum possible mean 

20 approach. I have examples of what that 

21 means. If you have all positive values, you 

22 are just going to take the average positives. 
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100

1 If you have one positive and, say, 

2 three -- or two positives and two less-than 

3 values, you are going to assume that they 

4 were all positive and take the mean just like 

5 you did in the first example, reported as 6. 

6 If they are all below the detection level, 

7 you are going to take the mean of the values 

8 and calculate it and report it as less than 

9 that mean.

10 Arjun has raised some issues 

11 about negative values. We need to look into 

12 that. I am not familiar with that problem 

13 right now.

14 DR. TAULBEE: I can see how it 

15 happened, but I can see where we have 

16 potentially misapplied this in that, when you 

17 have a raw result of, say, two counts in 24 

18 hours and the background was four counts. 

19 And so, you could end up with a negative 

20 result, but I believe we should have been 

21 truncating it at detection level at all 

22 times.
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101

1 DR. MAKHIJANI: And that's what 

2 we thought. So, it is something that crept 

3 in in the process, and I don't know that it 

4 applies to everything. We only came across 

5 it when we tried to -- I don't know what we 

6 were investigating, and we thought let's look

7 at the raw data. And when we went to the 

8 logbooks, we found these problems.

9 And so, I think definitely, I 

10 don't know if it applies to all the 

11 compilations or only to that americium one. 

12 I think it applies to all of them, but I'm 

13 not sure.

14 DR. NETON: That's a valid point.

15 DR. TAULBEE: Because this is how 

16 it should have been.

17 DR. NETON: Yes.

18 DR. MAKHIJANI: But wasn't.

19 DR. NETON: And that, to me, is 

20 an implementation issue --

21 DR. MAKHIJANI: Right.

22 DR. NETON: -- not an OPOS issue.
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102

1 DR. MAKHIJANI: Right. I agree.

2 DR. NETON: Okay. So, if OPOS 

3 does work, then how could one use the 

4 OPOS-derived cumulative probability 

5 distributions to look at stratification? You 

6 know, it's possible that there were subgroups 

7 in there, but it is our opinion that you have 

8 to have some basis for stratification to have 

9 occurred or to be valid. It doesn't seem 

10 reasonable to go and start parsing the data 

11 in the various different permutations looking 

12 for differences unless you have some valid 

13 reason for doing so. There has to be some 

14 underlying rationale as to why people that 

15 worked in a certain area who had a lot of 

16 activity going on are going to be different 

17 than someone else who didn't, that sort of 

18 thing to stratify the data.

19 And so, we came up with two types 

20 of tests, depending upon sort of the quality 

21 of the data that you have. There is the 

22 Monte Carlo Permutation Test, which is used 
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103

1 if the data are not heavily censored. If you 

2 have the majority of the data, the 

3 overwhelming majority of the data are

4 censored, so you have a lot of data where you 

5 can generate things like a log-normal 

6 distribution and start doing comparisons of 

7 the different log-normal distributions.

8 In some cases the data are so 

9 heavily censored that you can't do that. You 

10 can't presume any distribution function, and 

11 that is where the Peto-Prentice Test was 

12 implemented.

13 I do say -- and this is sort of 

14 not a minor point, but it is a point -- you 

15 have to evaluate the effect of multiple 

16 comparisons. Once you start doing dozens of 

17 comparisons and you have a 5-percent chance 

18 of detecting something, you're going to, by 

19 sort of random chance, have positives because 

20 you did so many comparisons.

21 DR. MAKHIJANI: Before you go on, 

22 I just want to put something on the record. 
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104

1 Because in the report that you issued, you 

2 kind of raised this question of data 

3 dredging, as if we had gone looking for some 

4 collection of data points that would be 

5 bigger than some others. We didn't do that.

6 This whole process started with 

7 your RPRT-0052 in which you look at 

8 construction workers and non-construction 

9 workers. And you had actually stratified 

10 construction workers according to the jobs 

11 that they actually do. So, it wasn't your 

12 stratification or our stratification. It was 

13 the stratification that was present at the 

14 sites and how they classified workers 

15 according to their jobs that they did.

16 And in that evaluation, you will 

17 remember that the pipefitters kind of stood 

18 out.

19 DR. NETON: Right.

20 DR. MAKHIJANI: And so, when we 

21 did the internal, we used the same process. 

22 So, it wasn't a data-dredging thing, and that 
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105

1 is very important to put it on the table 

2 because the way it was presented in your 

3 report is as if we were sort of arbitrary 

4 looking for problems, and we weren't. We did 

5 the same stratification as you did in 

6 RPRT-0052, and that stratification was made 

7 by the sites, not by you or us.

8 So, that is what these 

9 comparisons have come out of. And I just 

10 want to be clear on the record that we did 

11 not engage in any data-dredging operation.

12 DR. NETON: Okay. Fair enough.

13 So, the Monte Carlo Permutation 

14 Test -- and these are outlined in 53. I got 

15 the sense that the SC&A comments on these 

16 tests were not necessarily that they're 

17 invalid tests; it is really more of the 

18 implementation of the test, you know, what 

19 confidence levels might be used and that sort 

20 of thing, and how valid they might be in 

21 teasing out these distributions.

22 But, like I said, you have to 
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106

1 have the data that are log-normally 

2 distributed to some degree and not heavily 

3 censored. And then, you take your 

4 stratification, based on some a priori 

5 characterization, like construction workers 

6 versus non-construction workers, and you take 

7 these two populations. You have already 

8 identified, you are able to identify them 

9 within your single function as independent. 

10 And you calculate a geometric mean and a 

11 geometric standard deviation for each of 

12 those two strata.

13 Okay. So, now you have got two 

14 geometric means and two geometric standard 

15 deviations. You calculate the difference 

16 between those two and you plot this on a 

17 graph, the Y coordinate being the geometric 

18 mean and the X coordinate being the geometric 

19 standard deviation.

20 So, you have one data point 

21 there. What is the plot of the geometric 

22 mean and the geometric standard deviation? 
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107

1 And then, you do a Monte Carlo simulation and 

2 you pull out -- let's say I had 150 

3 construction workers and 250 non-construction 

4 workers. And then, you randomly sample 150 

5 times, 250 times, 150 times, 250 times, and 

6 you calculate all the possible combinations 

7 of geometric means and standard deviations 

8 that come out of that analysis and you get 

9 something that is kind of pretty to look at, 

10 but you get this sort of envelope of possible 

11 differences in geometric standard deviation 

12 and geometric means, and you plot them.

13 This would be, typically, 10,000 

14 iterations. And then you compare the 

15 difference in the data points of the strata 

16 that you are evaluating, this black dot here, 

17 and determine whether it falls in, this would 

18 be like the 95th percentile envelope of those 

19 differences.

20 If the data point falls within 

21 that envelope, you can say that I can't 

22 conclusively say they are different, 
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108

1 statistically different. Or, if the data 

2 point falls outside, like in that graph, then 

3 you have concluded they are. So, it is kind 

4 of an interesting way of comparing 

5 permutations within the data set to see if 

6 you can tease out that difference that you 

7 have identified, you know, that isolated 

8 strata that you identified. Can you find 

9 that somewhere within this data set? And on 

10 the left example, clearly, it is not 

11 statistically different and on the right it 

12 is.

13 So, that is what we have proposed 

14 in 53 to be able to review strata. And I am 

15 sure there's a lot of SC&A comment on power 

16 of this and statistically appropriateness and 

17 that sort of thing. But just to remind 

18 people of what that is.

19 The second test, the Peto20 Prentice Test, is a much simpler test, and 

21 when it is very heavily censored, you really 

22 can't generate or assume any distribution. 
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109

1 You end up with essentially a rank, a 

2 Wilcoxon ranked-order test. You have ranked 

3 the data from A to B, a modification of that, 

4 a fancy version. I don't know, maybe I am 

5 simplifying it too much.

6 But you end up ranking the data 

7 and identifying which data points belong to 

8 Strata A and which data points belong to 

9 Strata B. And you essentially compare the 

10 differences between where those data points 

11 fall on the strata. And if you had, for 

12 example, the data points for one strata fall 

13 pretty high up, you're going to end up with a 

14 much larger test statistic than if they fall 

15 lower on the curve. Or, alternatively, if 

16 they are randomly distributed throughout this 

17 curve, the differences will come out to be 

18 insignificant, and that is the value test.

19 I will let the statisticians deal 

20 more with how this is exactly implemented. 

21 It is a pretty simple test. And they have 

22 done a lot of reviews of this test and feel 
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110

1 that it is a pretty robust test for looking 

2 at the differences in the strata when you 

3 have all this censored data.

4 MEMBER ROESSLER: So, how do you 

5 make the decision between the one that says 

6 not significant and not significantly 

7 different?

8 DR. NETON: Okay. There's a test 

9 statistic.

10 MR. STANCESCU: The Peto-Prentice 

11 Test is a P-value that is computed, and you 

12 compare that with the significance level 

13 0.05.

14 MEMBER ROESSLER: Okay.

15 MS. CHALMERS: The P-value is 

16 actually on the plot? It is real tiny on the 

17 plot --

18 MR. STANCESCU: Yes.

19 MS. CHALMERS: -- but it is on 

20 there?

21 MR. STANCESCU: Yes. It is the 

22 P-value. For the first one, the P-value is 
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111

1 0.17, where there is no significance. For 

2 the second one, where there is a significant 

3 difference, the P-value is 2.51 to the minus 

4 11.

5 MEMBER ROESSLER: Oh, okay. My 

6 glasses aren't quite strong enough.

7 MR. STANCESCU: Yes.

8 MEMBER ROESSLER: Okay.

9 DR. NETON: Yes, I don't want to 

10 get into the details of the test statistics, 

11 but --

12 MEMBER ROESSLER: Yes, that's 

13 better. Okay. Oh, okay, I see it. Okay. 

14 Thanks.

15 DR. NETON: Anyway, those are the 

16 two tests that we would use to look at 

17 stratification, if we had some a priori 

18 reason to suspect that the data could be 

19 stratified.

20 And my summary really just sort

21 of rehashes what we have been talking about 

22 for the last hour and a half or so. You 
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112

1 know, we believe that a single coworker model 

2 is appropriate unless there is some reason to 

3 suspect. If there is a reason to suspect, we 

4 are proposing the one person, one sample be 

5 used. Actually, we are proposing the one 

6 person, one sample be used for all coworker 

7 models.

8 Given that, then, if there is 

9 reason to suspect stratification, we propose 

10 that we use this Monte Carlo Permutation Test 

11 and the Peto-Prentice Test to evaluate the 

12 significance of that difference.

13 DR. MAURO: Jim, this is John.

14 DR. NETON: Yes?

15 DR. MAURO: On those examples, 

16 are those real cases, where you found the one 

17 place you did have the stratification and the 

18 one you didn't? Did I miss that?

19 DR. NETON: Tom or Daniel would 

20 have to answer that. I don't know.

21 MR. LaBONE: Those are real 

22 cases.
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113

1 DR. NETON: Those are real cases.

2 DR. MAURO: They are or are not?

3 MR. LaBONE: They are.

4 DR. NETON: Yes.

5 DR. MAURO: They are? Oh, okay. 

6 Good. Thank you.

7 DR. NETON: So, that's my 15-

8 minute slide presentation.

9 (Laughter.)

10 It took a little over two hours, 

11 but that's okay.

12 CHAIRMAN MELIUS: Should we take 

13 another break? No.

14 (Laughter.)

15 You did the second half, 

16 actually, the second two-thirds or three17 quarters quite quickly, and so forth.

18 Arjun?

19 DR. MAKHIJANI: Can we go to the 

20 previous slide? Yes, the Monte Carlo slide.

21 Harry?

22 DR. CHMELYNSKI: Yes, I did want 
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114

1 to make some comments about the Monte Carlo 

2 Permutation Test.

3 And I agree, I think it is a neat 

4 concept to do it this way. I just have some 

5 problems. I have some problems that I am 

6 concerned about.

7 First off, it only is based on an 

8 assumed distribution. The geometric mean and 

9 the geometric standard deviation are the 

10 parameters of the log-normal distribution. 

11 So, willy-nilly, we assume that is the right 

12 distribution regardless of how well it fits.

13 Now, when we then apply the test, 

14 we look for differences on this two15 dimensional plot between the sigmas and the 

16 GSDs and the GMs, however you want to phrase 

17 them or parameterize them.

18 And yet, it is very difficult to 

19 see on these plots how far apart two 

20 distributions actually might be. Even on 

21 this graph that I am looking at here in the 

22 upper lefthand corner for the Monte Carlo 
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1 Permutation Test I see a black dot and a red 

2 dot at zero, but, for the life of me, I can't 

3 figure out how far apart those two points 

4 are. And I'm not even sure you can put a 

5 metric on this graph, given that one is a 

6 standard deviation, a GSD, and the other is a 

7 geometric mean that has units and the other 

8 one doesn't. It sounds great, but I just 

9 have problems of trying to interpret what 

10 this means.

11 The second test, however, the 

12 Peto-Prentice Test, I, too, like. It is a 

13 non-parametric test. Therefore, we don't 

14 have to assume what kind of distribution.

15 However, there are two things to 

16 keep in mind with any hypothesis test, and 

17 Bayesian statisticians commonly point out 

18 these problems. The classical statisticians 

19 refuse to recognize them.

20 (Laughter.)

21 The first one is that, if you 

22 have enough data points, you will always 
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1 reject the null hypothesis for any finite 

2 difference that you can think you are trying 

3 to look for. If you don't have enough data 

4 points, the test will have a very difficult 

5 time trying to reject the null hypothesis, 

6 and especially if you make a stringent alpha 

7 or a stringent probability requirement for 

8 the test.

9 So, when you are done here, this 

10 hypothesis-testing scheme seems to work 

11 pretty well when you are in the middle range 

12 of data, somewhere around 30 to a couple of 

13 hundred maybe. And that tends to where we 

14 like to use it.

15 Unfortunately, it is being 

16 applied here in places where it probably 

17 shouldn't be. And again, this gets back to 

18 the power calculation questions.

19 Those are my general comments on 

20 these two slides. We have a whole set of 25 

21 slides. I'm not sure we are going to go 

22 through them, but each of these, a lot of 
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117

1 them deal with this issue of what is the 

2 power of these tests.

3 MR. LaBONE: I had a couple of 

4 responses. This is Tom.

5 First of all, we wanted to have 

6 two tests because in the one chart that Jim 

7 Neton had, the little flowchart -- can we put 

8 that back up? -- we considered the further 

9 along that flowchart you were towards 

10 Probability of Causation, the more relevant 

11 your decision would be.

12 So, for example, a decision made 

13 at step two with the OPOS data would be less 

14 compelling than a decision made at step four 

15 with GM and GSD. So, we wanted a way to 

16 check simultaneously the GM and GSD. You had 

17 two parameters you were looking at.

18 I can send you references for 

19 this test. I think it is a fairly standard 

20 representation of looking at the slope and 

21 intercept of a line, if it concerns you. But 

22 we also needed to go backwards again to step 
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118

1 three because we had this issue with the 

2 censored data.

3 So, again, if you would like 

4 additional references on that type of 

5 presentation, I think it is fairly obvious 

6 for the non-statistician looking at that plot 

7 to say, hey, what we observed is not within 

8 the 95th percentile ellipse of this data that 

9 you would expect to be generated randomly if 

10 there was no difference. And so, it is 

11 fairly obvious, looking at the plot, that 

12 there is a difference; there is not a 

13 difference. So, it was just for ease of 

14 interpretation. That was pretty much the 

15 comment.

16 Again, there was a reason behind 

17 having two tests. And again, we could choose 

18 from them. And I think, in general, they 

19 tend to come up with very similar results 

20 when they are both applicable.

21 Daniel, do you agree with that?

22 MR. STANCESCU: Yes.
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119

1 MR. LaBONE: Yes? Okay. So, 

2 that was the comment I had.

3 DR. CHMELYNSKI: Just one more 

4 question. Can you tell me how far apart 

5 those two points are?

6 MR. LaBONE: Does it matter?

7 CHAIRMAN MELIUS: Yes, I think it 

8 does. Actually, something Jim brought up 

9 earlier, if they are not very far apart, do 

10 we really care?

11 MR. LaBONE: You're asking what 

12 is the practical significance?

13 CHAIRMAN MELIUS: Yes, yes.

14 MR. LaBONE: Okay. This is 

15 statistical significance.

16 CHAIRMAN MELIUS: Yes.

17 MR. LaBONE: Okay. RPRT-0053 is 

18 based on statistical significance.

19 CHAIRMAN MELIUS: I know, but we 

20 need to look beyond that.

21 MR. LaBONE: Okay. But, in order 

22 for me to tell how far apart that is, you 
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120

1 have to tell me what is important to you.

2 CHAIRMAN MELIUS: Right.

3 MR. LaBONE: And without that, 

4 there is no used talking about how far apart 

5 they are.

6 CHAIRMAN MELIUS: No, it is the 

7 conundrum we have with what is sufficient 

8 accuracy.

9 MR. LaBONE: In statistical 

10 tests --

11 CHAIRMAN MELIUS: Yes.

12 MR. LaBONE: -- versus practical 

13 significance?

14 CHAIRMAN MELIUS: Right.

15 MR. LaBONE: Yes.

16 CHAIRMAN MELIUS: Yes.

17 MR. LaBONE: Again, there is no 

18 used talking about that unless you can tell 

19 me what's important to you. And I don't know 

20 that.

21 DR. CHMELYNSKI: I firmly -- oh, 

22 I'm sorry.
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121

1 MR. KATZ: Go ahead, Harry.

2 DR. CHMELYNSKI: Okay. I firmly 

3 agree that this is a question of how big of a 

4 delta are we willing to accept.

5 MR. LaBONE: Yes, I agree.

6 DR. CHMELYNSKI: I think the 

7 whole idea of power is all based on that one 

8 statement: how large of a delta are we 

9 willing to accept?

10 And here, you don't even know 

11 what it is that we are trying to accept. 

12 But, at least when you do the Peto-Prentice, 

13 you are actually looking at the delta. And 

14 even then, it is hard to make a decision how 

15 big of a delta you are willing to accept.

16 So, this is where the real 

17 problem with power of these tests comes in, I 

18 think, is that no one is willing to make the 

19 decision. What we are saying is, hey, look, 

20 I don't see any significant difference, but 

21 nobody is willing to say what a significant 

22 difference is.
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122

1 MR. LaBONE: It is statistically 

2 significant.

3 DR. CHMELYNSKI: I understand 

4 that, but I am just saying, if it is more 

5 than 20 apart, is that going to bother you? 

6 If it is more than 200 apart, 500 apart? I 

7 don't see anybody willing to put their heels 

8 on the ground and say, "Ah, this is what I'm 

9 trying to test for." I would like to know 

10 what we are trying to test for before we say, 

11 "Ah, we didn't see it."

12 MR. LaBONE: What you have to do 

13 is define for me, in order to do that, what 

14 is the difference that is of practical 

15 significance to you in a Probability of 

16 Causation decision if you have two neptunium 

17 results.

18 DR. CHMELYNSKI: I agree, that's 

19 the question.

20 MR. LaBONE: Okay. I don't know 

21 how to do that. I have asked, and it is not 

22 clear to me for every type of cancer, for 
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123

1 every sort of intake regime, how you come up 

2 with a single estimate of practical 

3 significance. If you have some suggestions 

4 on that --

5 DR. NETON: Yes, where you end up 

6 is, is there such a thing as de minimis dose 

7 differences in this program?

8 MR. LaBONE: Yes, yes.

9 DR. NETON: Because dose drives 

10 PC. And de minimis dose, I don't know that 

11 anybody is willing to sign up and say that a 

12 100-millirem dose is insignificant or 1 

13 millirem, well, maybe 1 millirem. But where 

14 do you draw that line? And then, that dose 

15 difference, again, it is built into this 

16 intake model, but, then, it is converted to 

17 an individual organ dose on a case-by-case 

18 basis.

19 So, you know, you can take this 

20 model and calculate a liver dose, a lung 

21 dose, a kidney dose. So, it is a very 

22 complicated scenario.
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124

1 We have talked about this a lot. 

2 Can we identify that significant difference? 

3 And it always comes back to a de minimis dose 

4 difference. And I'm not sure that it can be 

5 defined.

6 CHAIRMAN MELIUS: Or we're not 

7 willing.

8 DR. NETON: Or we're not willing 

9 to.

10 MR. LaBONE: Make it even easier. 

11 What external dose is basically of no 

12 interest to you? Is it 100 millirem, 500? I 

13 don't know.

14 DR. NETON: Because that is what 

15 it comes down to.

16 MR. LaBONE: Yes.

17 DR. NETON: We would be 

18 stratifying models and fitting these curves 

19 and coming up with very different scenarios 

20 for no real benefit possibly. But, again, we 

21 would have to figure out what the dose 

22 difference is, and I'm not sure --
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125

1 CHAIRMAN MELIUS: And it may be 

2 different for different workers. And how long 

3 you worked there or what kind of work you did 

4 and what your exposures were, and so forth.

5 And I think when we were first 

6 talking about this, we said, well, you know, 

7 any exposure could be critical because it 

8 might get you from 49.9, you know, whatever, 

9 get you over the top, so to speak, in terms

10 of doing dose reconstruction.

11 MR. LaBONE: You are talking 

12 significance testing.

13 CHAIRMAN MELIUS: Yes.

14 MR. LaBONE: You're talking what 

15 we're doing here.

16 CHAIRMAN MELIUS: Yes. Yes. And 

17 I think, on the one hand, we need to wrestle 

18 with that issue.

19 I think when we were looking at 

20 using statistical testing, I think we have to 

21 sort of think of how are we going to utilize 

22 those; what assumptions do we make going in, 
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126

1 and so forth. And I think some of the 

2 differences between what Tom and ORAU wrote 

3 up and what SC&A is this sort of, well, which 

4 assumption applies in which situation? Do we 

5 assume that, should we come in and assume 

6 that there is stratification? Or do we 

7 assume that there is no stratification and 

8 say that only if it is statistically 

9 significantly different do we then apply 

10 stratification. And that is going to vary by 

11 sample size and depend on a whole bunch of 

12 other things. And as we said, we can have a 

13 huge amount of data and find something 

14 statistically significant that's of maybe 

15 very little practical significance.

16 DR. NETON: Part of the problem 

17 of being very generous in assuming 

18 stratification, in other words, very 

19 claimant-favorable to stratify for one set, 

20 is you are robbing from Peter to pay Paul.

21 If you assume a priori that I am 

22 going to say this data set is stratified and 
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127

1 you have a very lax statistical acceptance 

2 criteria, you are taking dose away from the 

3 other strata by definition.

4 DR. CHMELYNSKI: I agree with 

5 that, but it glosses over the reality. Let's 

6 say you have 1,000 and a couple from the 

7 construction workers. And now, what you are 

8 selling is that, if I leave those 

9 construction workers out, I am robbing the 

10 non-construction workers of that little 

11 contribution.

12 However, if you turn it around 

13 and say I have a handful of my construction 

14 workers, and now I am going to, instead, mix 

15 in 3,000 data points from the non16 construction workers, you are actually 

17 hurting them more in the terms of trade and 

18 trade facility.

19 And so, I think the general 

20 statement is, yes, that you will always 

21 be -- you can't be claimant-favorable to both 

22 sides. But I think what we are interested 
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128

1 here is being claimant-favorable to the 

2 highly-exposed workers. And what we are 

3 doing is not --

4 DR. NETON: Well, we don't know 

5 if they are the highly-exposed workers. That 

6 is what we are trying to find out.

7 But the other issue is, if you do 

8 stratify on a year-by-year basis, one has to 

9 accept the fact that in some cases it is 

10 going to be the dose is less. You can't 

11 always just cherry pick the high ones and 

12 say, well, it's higher in 1956. And if it is 

13 lower in '55, that's the way the chips fall. 

14 So, I don't know.

15 DR. MAKHIJANI: Obviously, a 

16 stratification decision has to be made on 

17 some objective criteria, not whether somebody 

18 is going to get a higher and lower dose in 

19 any particular year.

20 DR. TAULBEE: If I could use an 

21 example of tritium, let's say, at Savannah 

22 River, and if you look at the people in the 
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129

1 tritium facility versus the 100 areas, the 

2 reactors, the reactors, believe it or not, 

3 have a significantly higher exposure to 

4 tritium than the people in the tritium 

5 facility. It is was working in a disassembly 

6 basin. They got larger intakes doing 

7 maintenance activities out there.

8 But what we are doing is we are 

9 applying this to unmonitored workers. And 

10 so, if you look at the population of the 

11 reactor workers that had this higher exposure 

12 and compared to the tritium facilities, you 

13 will see statistical differences. But both 

14 sets, I mean, if you talk to the workers, 

15 they talk about leaving urine samples out 

16 there, whether they are construction trades 

17 or not. And so, we end up with about 80 

18 percent of the people working in those areas 

19 have tritium-monitoring data.

20 So, now we are applying this 

21 model to the 20 percent that were not 

22 monitored in this particular case. So, 
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130

1 stratification, you are making kind of a 

2 decision of this person should be in the 

3 reactors versus this other area, and we can 

4 do that. But the whole coworker model, 

5 especially if you apply like the 95th 

6 percentile, as Jim was talking about, I think 

7 is appropriate. It is easier for us. We 

8 don't have to go through and try to evaluate 

9 more of where this person worked, at which 

10 time period, which year he was here at the 

11 tritium facilities. This year he was over at 

12 the reactor facilities. The general coworker 

13 model seems to work.

14 So, there is a case where we see 

15 a statistically-significant difference, and 

16 it is a big one. Well, I shouldn't say "big 

17 one" because it is actually more like 10 

18 millirem to 30 millirem. So, it is not huge 

19 from a dose standpoint, but it is 

20 statistically significant.

21 So, this is a case where one 

22 general coworker model I think is 
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131

1 appropriate.

2 DR. MAKHIJANI: I think this goes 

3 back to a question that came up earlier. 

4 When you can actually demonstrate, rather 

5 than assume, that people with the highest 

6 exposure potentials were systematically among 

7 those who were monitored, and most of them 

8 were monitored, then you have a very good 

9 taste.

10 But in many of the cases that we 

11 are talking about, the monitoring data for 

12 these neptuniums, the thoriums, and so on, 

13 are pretty thin in some cases. And americium 

14 data are plentiful in some years and not so 

15 plentiful in other years. And in some cases 

16 for neptunium the data on construction 

17 workers are pretty thin in almost years, if I 

18 remember correctly.

19 So, in those cases you actually 

20 have a much bigger problem because you have 

21 to go and demonstrate that the construction 

22 workers who were monitored, were actually 
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132

1 monitored, were in the areas where they had 

2 exposure potential relative to other 

3 construction workers. And that needs to be 

4 demonstrated. And I think, so far, it has 

5 just been assumed.

6 DR. TAULBEE: I would agree and 

7 disagree.

8 (Laughter.)

9 Where I agree is that we 

10 certainly need to do the evaluation, and we 

11 have, where I disagree with you saying we 

12 assumed it. We didn't assume it. We did 

13 evaluate it, but we have not documented it 

14 well.

15 DR. MAKHIJANI: Okay.

16 DR. TAULBEE: And that is 

17 something that we can do.

18 DR. MAKHIJANI: I think your 

19 report said you assumed it. So, that is 

20 according to your report.

21 (Laughter.)

22 CHAIRMAN MELIUS: The footnote 
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133

1 got left out.

2 (Laughter.)

3 DR. TAULBEE: There were other 

4 things that we evaluated. The words 

5 "Technical Report," identifying incidents,

6 the bioassay control procedures, who was 

7 monitored and when and why, and then, the 

8 followup of the number of samples that we 

9 have relative to the general population 

10 working in those buildings. So, those are all 

11 things that we qualitatively analyzed before

12 that assumption.

13 DR. MAKHIJANI: If I could circle 

14 back to the prior discussion that Jim raised 

15 and Tom was saying about what delta is 

16 significant, what dose level is significant, 

17 you know, we had this discussion in a very 

18 different context of the 250-day discussion. 

19 And I remember Jim Neton saying that, you 

20 know, 1-rem dose could make a difference in 

21 some cancers, leukemias I think, if I 

22 remember correctly.
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1 And so, I think we have some 

2 notion of a level of dose that does make a 

3 difference for at least one cancer, 500 

4 millirem or 1 rem in some circumstances. So, 

5 the kind of differences that are factors of 2 

6 and 3 in that chart that is up being 

7 displayed, your slide No. -- for people on 

8 the phone -- I don't --

9 DR. NETON: Four.

10 DR. MAKHIJANI: -- slide No. 4, 

11 we could potentially evaluate what's 

12 important and whether it meets the practical 

13 significance criteria, which hasn't been done 

14 so far, as you said. So, potentially, it can 

15 be done with some objective practical 

16 criteria.

17 DR. NETON: Yes. Yes. It would 

18 be somewhat cumbersome, though, because, 

19 remember, these models, these fits go in 

20 piecemeal order through the 30 years. So, 

21 the new fits to these individual data points 

22 that make up the 50th percentile would have 
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135

1 to be rerun and come up with a new intake 

2 regime, so to speak, you know, Intake Regime 

3 1, 2, 3, 4, and then, compare that to some 

4 dose of consequence based on a presumed 

5 hypothetical case. I mean, I don't know how 

6 else you would do it. You would say, okay, 

7 if I had liver cancer, I was exposed during 

8 these years, what dose difference will that 

9 make?

10 DR. MAKHIJANI: Well, you could 

11 come up with a general number of dose of 

12 consequence that is conservative, which is 

13 what you were doing when we discussed the 

14 250-day question.

15 DR. NETON: Yes, yes.

16 DR. MAKHIJANI: The 500 millirem 

17 or 1 rem; I can't remember the exact number.

18 DR. NETON: I like the line of 

19 thought here because it kind of ties in with 

20 the residual period and small doses 

21 versus -- you know, how meaningful are these 

22 small doses in the residual period, which is 
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136

1 kind of a similar issue, not similar issue, 

2 but similar problem.

3 MR. LaBONE: Think about it for 

4 how would you do this for external dose. 

5 Take the easy case.

6 DR. NETON: Yes.

7 MR. LaBONE: Okay? Are we going

8 to stratify on external dose?

9 DR. NETON: Yes.

10 MR. LaBONE: Okay. And so, how 

11 would you --

12 DR. NETON: But that's another 

13 issue, though.

14 MR. LaBONE: How would you come 

15 up with the de minimis for external for all 

16 cancers? I mean, so do the easy one first, 

17 and then, move on to the tougher one.

18 DR. NETON: Yes, that is a very 

19 good point.

20 MR. LaBONE: Yes.

21 DR. NETON: So, you take 

22 internal, and we haven't talked about it, 
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137

1 but, I mean, clearly, if you are stratifying 

2 for internal, you are going to stratify for 

3 external, right?

4 CHAIRMAN MELIUS: Which may have 

5 been a mistake.

6 (Laughter.)

7 There's too many complications, 

8 but, conceptually, I think you've got to 

9 remember, if we go back when we were 

10 initially struggling with SEC decisions, and 

11 so forth, it was, well, show us how you would 

12 do the dose reconstruction. It is a little 

13 different issue. And then, as a result of 

14 that, I think people then could come to an 

15 agreement, well, you know, we haven't worked 

16 this out yet, but it is not going to make 

17 that much difference or it is straightforward 

18 or this would be a good procedure, and so 

19 forth.

20 And I think the same approach 

21 might be useful here without getting tied to 

22 trying to come up with what the value should 
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138

1 be. Let's just do the calculation or do the 

2 calculation around some arbitrary --

3 DR. NETON: Well, you can come up 

4 with the intake difference. It is pretty 

5 readily -- I mean, that's not hard. It would 

6 be cumbersome, but it is doable, right? I 

7 mean, you fit your new -- you stratify the 

8 data and you come up with your different 

9 geometric means, for example, for 

10 construction workers and you fit them into 

11 the model, as if you are going to have a 

12 separate model. And then, you compare the 

13 intakes that come out of that analysis.

14 In my opinion, see, that's where 

15 the difference is. If the intakes fall 

16 within the uncertainty here, you are not 

17 really changing --

18 MR. LaBONE: But you can't work 

19 off the intakes because it is the time 

20 period. It is the dose up to the date of 

21 diagnosis. And so, even for a particular 

22 intake rate --
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139

1 DR. NETON: Well, no, you would 

2 have to compare it for the intake regimes 

3 that we have.

4 MR. LaBONE: Yes. Okay.

5 DR. NETON: I mean, because, in 

6 reality -- and I talked about this 

7 earlier -- what we do when a person straddles 

8 the intake regimes is you give them both.

9 MR. LaBONE: Yes.

10 DR. NETON: And then, what 

11 happens is you end up with an overestimate of 

12 the intake by a factor of 3, 4, 5; I don't 

13 know. Clearly, this intake contributed a lot

14 more dose than this one, and this continued 

15 on, you know, this person still continues to 

16 get dose from this way out into here, you 

17 reset --

18 (Laughter.)

19 CHAIRMAN MELIUS: I am very 

20 impressed with that one.

21 (Laughter.)

22 DR. NETON: Anyway, I think I 
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140

1 made my point with this.

2 CHAIRMAN MELIUS: Very well, very 

3 dramatically.

4 (Laughter.)

5 DR. MAKHIJANI: You know, we 

6 actually, for external dose at Savannah River 

7 Site, we actually did stratify for the 

8 specific construction worker category of 

9 pipefitters. If you remember the TIB-52 

10 discussion where the construction worker dose 

11 reconstruction method is laid out, mainly for 

12 external dose, we called out pipefitters from 

13 among the construction workers.

14 And part of the thing that is 

15 underlying some of our thinking is we showed 

16 that pipefitters were more exposed, even 

17 among construction workers. So, there is a 

18 different adjustment factor for them that we 

19 all agreed would be appropriate. So, in that 

20 case we agreed there was kind of a coworker 

21 model --

22 DR. NETON: Well, but we have got 
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141

1 to go back and recognize that TIB-52 was a 

2 pretty rudimentary look at the data set. I 

3 mean, it was a long time ago.

4 And when we all did that, I 

5 recognized that that was probably not the 

6 most robust scientific analysis. It was the 

7 best we could do, given the data we had at 

8 the time. I am not saying it was wrong. It 

9 is just there are much better statistical 

10 approaches to be employed now, and that is 

11 where we are at.

12 DR. TAULBEE: I mean, we could go 

13 back and redo TIB-52 using the --

14 DR. NETON: Well, exactly.

15 DR. TAULBEE: -- Monte Carlo 

16 Permutation as well as the --

17 DR. NETON: Yes.

18 DR. TAULBEE: -- Peto-Prentice, 

19 and see, does that still hold? Is it 

20 greater? I don't know.

21 DR. MAKHIJANI: That would be 

22 interesting. I mean, would that adjustment 
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142

1 factor go away? Would we say pipefitters are 

2 no different using this test and sort of 

3 moosh away the differences?

4 DR. NETON: I don't know, but I 

5 wouldn't be surprised if it didn't.

6 DR. MAKHIJANI: You know, one of 

7 the things that, in my understanding -- and I 

8 am not into all the modern statistical, but I 

9 have some understanding of these 

10 things -- one of the things that stood out 

11 for me, when we were reviewing your RPRT-0053 

12 was, and which you have made very explicit in 

13 your response, is that accepting the null 

14 hypothesis doesn't mean you're saying the 

15 null hypothesis is true. You are just saying 

16 that you are accepting it because you can't 

17 reject it.

18 DR. NETON: Right.

19 DR. MAKHIJANI: And what we were 

20 saying is that that is not good enough. And 

21 then, in some circumstances it could be very 

22 bad. And there was some discussion of how 
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143

1 bad it could be when you have very few data 

2 points, that it could be bad by a factor of 

3 2, 4, 5, 6, 10. So, we are not talking about 

4 5 and 10 percent.

5 And I know you have this whole 

6 argument among the statisticians about 

7 prospective and retrospective data, and I 

8 understand that to some extent. But the 

9 objective fact is that, if you don't know 

10 whether these distributions are the 

11 same -- and Harry said this in a different 

12 way just a few moments ago -- and you put a 

13 few construction workers who were highly 

14 exposed in a sea of large numbers of 

15 construction workers who have data, you are 

16 going to lose that. You're going to lose the 

17 claimant favorability for those workers, if, 

18 in fact, their distributions are actually 

19 different and your test isn't good enough to 

20 tell you.

21 DR. NETON: Well, wait a minute. 

22 We need to differentiate between the people 
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144

1 who were monitored and not monitored. You're 

2 saying you're going to put the highly-exposed 

3 construction workers into the data set 

4 because they were monitored. We need to 

5 figure out what was the exposure potential 

6 for those that weren't monitored. That's my 

7 point.

8 I mean, you get very confused 

9 with it. Because construction workers have 

10 high data points doesn't mean that the 

11 unmonitored workers were in that same 

12 category. Do you know what I'm saying?

13 DR. MAKHIJANI: Yes, I do get 

14 that point, and we have kind of done this a 

15 couple of times. Because most construction 

16 workers, at least for certain radionuclides 

17 that are important in the kind of decisions 

18 that we are talking about were not 

19 monitored -- well, you need to demonstrate 

20 that the construction workers who were 

21 monitored were --

22 DR. NETON: That's right. It 
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145

1 comes down to that --

2 DR. MAKHIJANI: You really need 

3 to do that.

4 CHAIRMAN MELIUS: Yes. Yes, we 

5 need to know what these populations are, how 

6 they were monitored, how they were exposed, 

7 and there's all sorts of different -- and, 

8 you know, we are also limited by the data 

9 information available to us. I mean, we see 

10 this all the time when we do these SECs. We 

11 have what appears to be a very narrow Class 

12 and end up with the whole site because of 

13 lack of information on where people actually 

14 worked. And that applies to whether they are 

15 monitored or not monitored often.

16 DR. NETON: I think that is an 

17 interesting precedent, and I wasn't part of 

18 that because I am conflicted at Fernald. 

19 But, recently, a Fernald Class was added for 

20 construction workers. I don't know what 

21 drove that decision, but somehow at some 

22 point in the deliberation process it was 
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146

1 decided that the construction workers were 

2 either completely not monitored or the data 

3 that they had were not representative of the

4 overall -- I don't know what happened there.

5 MR. HINNEFELD: Well, I can go 

6 through that if you want.

7 DR. NETON: It might be 

8 interesting to understand the logic behind 

9 that.

10 MR. HINNEFELD: This is the first 

11 thing I know enough to say anything about in 

12 this whole meeting.

13 (Laughter.)

14 The question was, the 

15 construction contractors or subcontractors at 

16 Fernald, were they evaluated appropriately 

17 for monitoring in the way that the 

18 NLO -- that is the main contractor -- in the 

19 way the NLO workers were monitored? Because 

20 NLO monitored almost all of the NLO workers, 

21 and we have a really big data set from the 

22 NLO workers, and it is all computerized.
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147

1 Now in that computer program, 

2 before 1986, there are essentially no 

3 contractor data, even though there were 

4 contractors working there. And so, the 

5 question became, well, why is that? Did they 

6 not monitor construction workers? Did they 

7 not really look at the work and determine 

8 whether they should have been monitored? Or 

9 did they monitor them and not save the data? 

10 Or did they save the data and we haven't 

11 found it in our capture?

12 And from my own experience, when 

13 I started even in the eighties, there was 

14 still an attitude that a construction worker 

15 isn't, you know, they're not really a rad 

16 worker because, theoretically, they are here 

17 for a short period of time for a particular 

18 job and, then, they are gone. And they are 

19 not going to be here the whole year. And so, 

20 they are not going to hit the annual limits, 

21 so to speak.

22 So, there was a little bit of 
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148

1 that attitude even when I started in the 

2 eighties. And so, it is hard to argue that 

3 the NLO was evaluating construction 

4 contractors thoroughly in terms of should 

5 they be bioassay monitored.

6 There were instances when 

7 contractors were monitored, and there have 

8 been some data sets captured, either on 

9 correspondence or, much later, on what we 

10 were called the urine sample request cards 

11 for construction workers. And you can pick 

12 them out because it will even have the 

13 construction contractor's name written on 

14 that card or it will have a badge number, the 

15 badge number series or sequence that was 

16 specific for subcontractors. So, you could 

17 find them in that data set later on.

18 And so, in the instances where we 

19 did have bioassay data, starting in about 

20 1984 through -- '83, '84, '85, there was some 

21 bioassay data, and then, very sporadic before 

22 then. 1983 was the first year when I think 
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149

1 we had more than 30 contractors sampled in a 

2 given year.

3 So, for some of those early 

4 sampling episodes, the contractors were quite

5 heavily exposed in the work they did. There 

6 was one circumstance, well, at least here is 

7 this one construction job or one job done by 

8 contractors where NLO did analyze, saying 

9 these people should be monitored, whether 

10 they were monitored from the start or whether 

11 once they started to observe what they were 

12 doing they started to be monitored.

13 So, there was a group of about a 

14 dozen or 14 contractors. You had several 

15 bioassay samples over several months' time in 

16 a single year, which seems like that would 

17 have been the duration of the work they did. 

18 They were taking the processing equipment out 

19 of Plant 7.

20 And those people's exposures, had 

21 you calculated their exposures based on their 

22 bioassay data, those were higher than what 
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150

1 the coworker model would have predicted for 

2 them. Even using 95th percentile values, 

3 they were still higher.

4 So, it appears, then, from that 

5 sampling of that group, which, of course, 

6 were bioassay sampled, there is potential for 

7 contractors to be exposed more heavily than 

8 the coworker model, which is built on the NLO 

9 workers, than that would indicate. So, you 

10 have that piece of data.

11 There are large absences. There 

12 is very few contractor bioassay data until 

13 you get to really 1984. There were a few in 

14 1983.

15 And there wasn't really any 

16 evidence to make us conclude that NLO was 

17 carefully evaluating contractors and doing a 

18 consistent job of evaluating and collecting 

19 or recording in a fashion that was 

20 retrievable. So, we didn't really know, of 

21 the contractor bioassay data we have, we 

22 didn't know if we had just a smidgeon of a 
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151

1 whole lot of what was done or if we had 

2 everything that was done, and whether it was 

3 analyzed correctly in terms of how much 

4 should be done.

5 So, there's too many questions to 

6 say that we should be able translate this 

7 coworker data set from the NLO workers and 

8 say that really represents the work of the 

9 construction workers. And, in fact, there 

10 are construction workers who are claimants, 

11 or not claimants but advocates, who worked 

12 there in the eighties, the early eighties, 

13 and said, you know, there was nobody around. 

14 "We couldn't get them to frisk the equipment 

15 when we were remodeling the pilot plant" or 

16 the conversion facility and the pilot plant. 

17 "We didn't have a rad tag. You know, we 

18 didn't have anything."

19 One guy said, "Heck, I went and 

20 got a survey meter and surveyed this stuff 

21 that we were tearing out and found out it was 

22 contaminated, and almost got fired for 
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152

1 stealing the surveys."

2 (Laughter.)

3 So, those are the stories you 

4 hear about.

5 So, from that standpoint, then, 

6 starting in '83, there were than 30 people 

7 sampled, but they were all sampled late in 

8 the year. It didn't seem to be very 

9 representative of a year's worth of work. 

10 1984 and 1985 have pretty nice populations of 

11 contractor data that were captured on these 

12 urine data cards. We seem to have captured 

13 essentially all of the urine data cards for 

14 those years because the majority of them are 

15 NLO people, and you can find those data in 

16 the database. But there were some 

17 contractors that you can clearly identify. 

18 And so, those were all compiled.

19 And so, we built models. What 

20 would the coworker model be for just 

21 contractors for '84 and '85? And we used the 

22 Peto-Prentice Test to show that '84 that is 
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153

1 different. You know, that will give you a 

2 different value than the NLO workers would 

3 have. In '85, I think it was still 

4 significantly different, but you could argue 

5 that there is no practical difference in '85 

6 because it is statistically different, but 

7 the dose reconstruction doesn't come out very 

8 close.

9 And then, starting in '86, then, 

10 they were -- I think I have got these years 

11 right -- starting in '86, then, they are in 

12 the HIS-20 database. So, the construction 

13 workers are there and are a part of the total 

14 population then also.

15 And again, most people, at that 

16 time almost everybody was monitored, 

17 including construction workers, because that 

18 presented a contractor change from NLO to 

19 West. So, essentially everybody was 

20 monitored going forward from then.

21 So, based on our conclusion or

22 the Advisory Board's conclusion, ORAU 
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154

1 maintained its position that the coworker 

2 approach was adequate. The Advisory Board 

3 concluded that the data to show that the NLO 

4 workers' exposures were representative of 

5 construction workers just wasn't there, that 

6 you couldn't really draw that conclusion. 

7 And so, that is why the Class was there.

8 I hope that was halfway clear. I 

9 didn't expect to have to speak today.

10 (Laughter.)

11 DR. MAKHIJANI: I have a question 

12 about '84. In '84, when you did have data 

13 and did this test, did the construction 

14 workers come out above the NLO workers or 

15 below them? 

16 They came out above, even in '84?

17 MR. HINNEFELD: Yes. It was just 

18 higher. But the Board concluded that there 

19 is sufficient data in '84 --

20 DR. MAKHIJANI: Right, right.

21 MR. HINNEFELD: -- in a 

22 construction-worker-specific coworker model.
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155

1 DR. MAKHIJANI: Right.

2 MR. HINNEFELD: And it is the 

3 same for '85. There is sufficient data. And 

4 then, like in '86, I think almost everybody 

5 is monitored.

6 DR. MAKHIJANI: Right.

7 MR. HINNEFELD: I don't know if 

8 they even need a coworker approach after 

9 1986.

10 DR. MAKHIJANI: So, the Board 

11 kind of made a stratification decision for 

12 '84 and '85 that it was justified, but there 

13 were enough data to do it?

14 MR. HINNEFELD: Yes.

15 DR. NETON: I am not sure that 

16 was helpful, but it was a good thing to hear.

17 (Laughter.)

18 MR. HINNEFELD: No, I didn't 

19 suggest that it was helpful.

20 DR. NETON: Well, I didn't know. 

21 I didn't know, but I think what it points to 

22 is that each site is a little different. I 
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156

1 mean, you know, the Fernald site has its --

2 CHAIRMAN MELIUS: It is good to 

3 know what information there is --

4 DR. NETON: Right, exactly.

5 CHAIRMAN MELIUS: -- and knowing 

6 about the site.

7 MR. HINNEFELD: And I have 

8 personal experience at the site, and that did 

9 influence my behavior.

10 CHAIRMAN MELIUS: And even at 

11 Fernald, just going back to when they were 

12 first building the site, did you find --

13 MR. HINNEFELD: Well, yes, when 

14 they were first building the site --

15 CHAIRMAN MELIUS: Saying that 

16 construction contractors and workers were 

17 being exposed.

18 MR. HINNEFELD: We felt like the 

19 people who were building the plant wouldn't 

20 be exposed. But there are memos out there 

21 between a couple of HASL folks saying, you 

22 know, "Poor Joe Quigley," who was their 
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157

1 former colleague, who is now the Health and 

2 Safety Tracker at Fernald, "he's really got 

3 his hands full with this work starting up in 

4 these plants, and construction workers and 

5 everybody running all over the place, 

6 essentially."

7 So, there was essentially some 

8 evidence that parts of the plant would be 

9 built and they would start shakedowns or 

10 running radiological materials while the 

11 construction workers were in the same 

12 building, building other things. And so, 

13 there wasn't this exclusion. There wasn't 

14 this clean turnover from construction to 

15 operations. And so, that is why it goes all 

16 the way back.

17 DR. TAULBEE: I think that is 

18 typical at all sites.

19 MR. HINNEFELD: Well, at Fernald, 

20 it was fairly -- you know, we were able to 

21 do --

22 DR. TAULBEE: You see startup 
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158

1 dates at Savannah River, but the building 

2 hadn't been turned over by construction to 

3 operations yet.

4 MR. HINNEFELD: Yes.

5 DR. TAULBEE: But, yet, they had 

6 already started.

7 MR. HINNEFELD: Yes.

8 DR. TAULBEE: And so, both of 

9 them were there for a period of a year or 

10 so --

11 MR. HINNEFELD: Yes.

12 DR. TAULBEE: -- for each 

13 building.

14 CHAIRMAN MELIUS: I think that 

15 wasn't the assumption going into the meeting.

16 MR. HINNEFELD: No. We felt like 

17 where they are building a new facility they 

18 won't be exposed.

19 CHAIRMAN MELIUS: Yes, yes. 

20 Right.

21 MR. HINNEFELD: So, why worry 

22 about the early --
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159

1 CHAIRMAN MELIUS: Yes, there is 

2 that overlap. And if we hadn't of found 

3 those memos, I don't think -- we would have 

4 left them out.

5 I don't know when lunch is 

6 coming.

7 MR. HINNEFELD: Lunch is coming 

8 anytime.

9 CHAIRMAN MELIUS: Okay.

10 MR. HINNEFELD: It was being 

11 picked up at about 11:35, as I understand, or 

12 we were leaving to pick it up at 11:35, and 

13 it's just a few minutes. So, it will be here 

14 pretty soon.

15 CHAIRMAN MELIUS: Well, let me 

16 tell you what I was thinking of, and these 

17 two issues are related. There may be other 

18 discussions we want to have also.

19 But one is to spend some time 

20 going through what are some of the factors we 

21 should be taking into account or looking at 

22 in terms of developing coworker data sets, 
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160

1 and sort of a checklist of things. I think 

2 we have talked about many of them. But sort 

3 of thinking what would be helpful to think 

4 about.

5 Some of them deal with the 

6 statistical testing. Some of them deal with 

7 more sort of general issues that come up.

8 The second, which may come out of 

9 that or may precede that, is what we have 

10 already started a little bit, but sort of 

11 what could we do that would help us 

12 understand what factors and to what extent we 

13 need to focus on certain factors. How do we 

14 evaluate? Maybe it is better to say, how do 

15 we evaluate certain issues? And what would 

16 be helpful for doing that?

17 We already talked about should we

18 look at an external coworker model and see if 

19 that would -- it should be much simpler and 

20 maybe that lends itself a little bit more to 

21 more straightforward evaluation and sort of 

22 helping us look at this issue, and so forth.
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161

1 And then, secondly, should we do 

2 the same or some calculations, or whatever we 

3 want to call them, on some of these coworker 

4 models for internal exposures that might also 

5 help us to decide how we approach this? 

6 Because we want to be evaluating the right 

7 things and looking at the right issues, and 

8 so forth. Every site is different.

9 But, also, we don't want to waste 

10 a lot of time or have time wasted by ORAU or 

11 SC&A or NIOSH and everybody involved on 

12 factors that aren't going to be important or 

13 aren't really going to affect that, or may 

14 only affect the models in certain relatively15 rare situations, or whatever. And somehow, 

16 how do we tie this back into sort of the 

17 sufficient accuracy question that we have 

18 been trying to wrestle with at the same time?

19 So, it is more like, I guess, 

20 sort of brainstorming and thinking. Some of 

21 the sites we are evaluating now, like 

22 Savannah River, may be helpful, but also some 
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162

1 of the sites we haven't talked about.

2 And that would include this issue 

3 of the multiple sampling on individuals, and 

4 so forth, which I think is something else 

5 that we need to sort of think how do we 

6 evaluate that or decide whether it is 

7 appropriate or not appropriate to use, or 

8 does it make a difference? Maybe that is the 

9 bigger thing, is to what extent does it make 

10 a difference.

11 Does that make sense to 

12 everybody?

13 DR. MAKHIJANI: Could I ask if 

14 Harry has any more comments on the technical 

15 things?

16 CHAIRMAN MELIUS: Well, then, 

17 that was the other thing I was going to 

18 mention. I am not sure just before lunch is 

19 fair to Harry, but --

20 DR. MAKHIJANI: No, no.

21 CHAIRMAN MELIUS: -- I'm not sure 

22 right after lunch is, either.
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163

1 (Laughter.)

2 But at some point I think we 

3 should come back, and probably right after 

4 lunch, I will say. I will drink caffeinated 

5 beverages or something, and we will come back 

6 and go through some of -- if he has some 

7 issues -- but I think we need to go through 

8 the entire presentation. There may be 

9 selective things that would be helpful and we 

10 should weigh-in.

11 DR. MAKHIJANI: I think there are 

12 three or four slides in there. I can talk to 

13 Harry over lunch --

14 CHAIRMAN MELIUS: Oh, okay.

15 DR. MAKHIJANI: -- and work it 

16 out.

17 CHAIRMAN MELIUS: And I'm sure 

18 John Mauro will have wise words for us also 

19 at some point.

20 (Laughter.)

21 Silence.

22 (Laughter.)
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164

1 DR. MAURO: You baited me because 

2 I do have one, but I am not going to bring it 

3 up.

4 (Laughter.)

5 CHAIRMAN MELIUS: I was just 

6 whispering to Ted to call the operator and 

7 have them disconnect John.

8 (Laughter.)

9 We wouldn't do that to you, John.

10 DR. MAURO: I am going to save 

11 this for later. I have got a nice one for 

12 you.

13 (Laughter.)

14 CHAIRMAN MELIUS: Yes, usually, 

15 your ideas are spontaneous. So, write this 

16 one down and remember it.

17 (Laughter.)

18 DR. MAURO: You're right.

19 CHAIRMAN MELIUS: We always used 

20 to have fun. You know, when you came to all 

21 our meetings, John, we would try to predict 

22 what you were actually going to say at the 
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165

1 microphone.

2 (Laughter.)

3 DR. MAURO: Did you have a pool?

4 CHAIRMAN MELIUS: We never knew 

5 whether you were for or against.

6 (Laughter.)

7 MR. KATZ: Nobody made any money.

8 (Laughter.)

9 CHAIRMAN MELIUS: See what 

10 happens when you're at a distance, John? Now 

11 we can say what we --

12 DR. MAURO: I miss the action.

13 (Laughter.)

14 CHAIRMAN MELIUS: Is lunch here?

15 MR. HINNEFELD: I'll check.

16 CHAIRMAN MELIUS: Oh, you'll 

17 check?

18 MR. HINNEFELD: I think it should 

19 be here anytime.

20 CHAIRMAN MELIUS: Why don't we 

21 break then?

22 MR. KATZ: So, I think we will 
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166

1 break for lunch. And how long do you want to 

2 take for lunch?

3 CHAIRMAN MELIUS: Forty-five?

4 MR. KATZ: Forty-five minutes? 

5 So, about quarter to 1:00?

6 CHAIRMAN MELIUS: Quarter to 1:00 

7 we will be back.

8 (Whereupon, the foregoing matter 

9 went off the record for lunch at 11:52 a.m. 

10 and went back on the record at 12:48 p.m.)

11 

12 

13 

14 

15 

16 

17 A-F-T-E-R-N-O-O-N S-E-S-S-I-O-N 

18 12:48 p.m.

19 MR. KATZ: Good afternoon. We're 

20 back online.

21 Let me just check and see that we 

22 have -- Harry, do we have you on the line?
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167

1 DR. CHMELYNSKI: Yes, I'm here.

2 MR. KATZ: And John Mauro?

3 DR. MAURO: I'm here.

4 MR. KATZ: Great.

5 Bill can't make it this 

6 afternoon.

7 CHAIRMAN MELIUS: Stiver?

8 MR. KATZ: How about John Stiver? 

9 John, are you on, too, Stiver?

10 (No response.)

11 Okay. Well, let's carry on.

12 CHAIRMAN MELIUS: Yes.

13 DR. MAKHIJANI: So, Harry, I will 

14 go through your slides.

15 Slide 2, review. It is up here.

16 DR. CHMELYNSKI: Okay. Slide is 

17 on, you say?

18 DR. MAKHIJANI: Yes.

19 DR. CHMELYNSKI: Okay. This 

20 slide simply points out how we conducted our 

21 review of RPRT-0053. We not only reviewed 

22 the report itself, but also three documents 
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168

1 that employed the techniques in 0053 to use 

2 them to compare construction workers with 

3 other workers at the Savannah River Site for 

4 neptunium, mixed fission products, and the 

5 exotic trivalents.

6 DR. MAKHIJANI: And I might add, 

7 only to the extent that it applied to the 

8 statistics method, not in terms of the actual 

9 data sets in detail.

10 DR. CHMELYNSKI: Right. It was 

11 only a very narrow issue as to how the 

12 comparison tests were applied with these 

13 three data sets.

14 The next slide, which is on page 

15 3, reviews a discussion we had earlier on the 

16 use of r-squared for ROS regression. 

17 Personally, I think this does relate to the 

18 question of sufficient accuracy because the 

19 r-squared is not the appropriate measure of 

20 goodness of fit here. And NIOSH in their 

21 response, as you can see below in bold, also 

22 indicated that r-squared was not used to 
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169

1 evaluate the fit of the plots.

2 But this does raise a pretty 

3 serious question. What was used? And when 

4 you think about, you know, we are talking in 

5 that Monte Carlo simulation plot that you 

6 showed two graphs there were 40,002 log7 normal distribution fitted using ROS. I 

8 wonder how well they did fit. And certainly, 

9 the answer that statisticians can see whether 

10 they fit wasn't used because there's 40,000 

11 of them. So, I am not sure anything is being 

12 used to measure goodness of fit.

13 Is there any response on it?

14 MR. LaBONE: I can respond to it, 

15 but I would need to go back to Jim Neton's 

16 slides.

17 DR. MAKHIJANI: You have the hard 

18 copy.

19 MR. LaBONE: It's the third and 

20 fourth slide where he showed -- in general, 

21 the fourth slide shows where the internal 

22 dosimetrist would go through and fit to come 
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170

1 up with a chronic intake. And the internal 

2 dosimetrist judges the fit, the quality of 

3 that fit, as far as does basically that line 

4 capture the central tendency of those data 

5 points. He does not use r-squared. He does 

6 not use any other statistic associated with 

7 that fit. It is just basically in his 

8 professional judgment does that fit.

9 And so, going back to the third 

10 slide, the third slide is fit by the 

11 statistician. And so, the statistician would 

12 go through and apply the same process. They 

13 don't look at r-squared. They say, she would 

14 say, does that line capture the central 

15 tendency of data adequately for what we are 

16 going to use it for?

17 And so, it is basically 

18 professional judgment that is used in both 

19 cases to decide is the fit adequate. Now 

20 that is not exercised in 10,000 iterations in 

21 the Monte Carlo calculation. But that is 

22 when you actually do this, implement 
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171

1 Procedure 53, that is how it would be done, 

2 RPRT-0053.

3 DR. CHMELYNSKI: So, this would 

4 apply to the black dot on the Monte Carlo 

5 simulation, you're saying, basically?

6 MR. LaBONE: Yes. This is what 

7 we actually saw. And then, the cloud with 

8 the confidence, the 95-percent confidence 

9 ellipse would be from the simulation. So, 

10 yes, this is the black point, except for the 

11 one at the middle, which is just the center 

12 of the cloud.

13 DR. CHMELYNSKI: Okay. So, 

14 essentially, the answer is that it is the 

15 statistician's judgment when he actually does 

16 look at it, but in Monte Carlo, then, it is 

17 not actually -- there is no measure of 

18 fitness of things?

19 MR. LaBONE: No.

20 DR. CHMELYNSKI: Okay. So, let's 

21 go on, then, to slide No. 4, which is one 

22 that we -- I don't think I am going to read 
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1 that. This is something we have already 

2 talked about quite a bit this morning, 

3 representativeness of the data that is 

4 available and completeness, 

5 representativeness in the sense does it cover 

6 all the groups of the unmonitored persons, 

7 and completeness in that did we actually get 

8 the workers that should have been monitored.

9 MR. LaBONE: Yes, I agree.

10 DR. CHMELYNSKI: Those two 

11 questions I can't answer, but they are here 

12 as findings and we have some responses.

13 So, go ahead. Sorry to interrupt 

14 you.

15 DR. MAKHIJANI: No, no, I think 

16 we did settle this morning that NIOSH is 

17 going to do some demonstration about who the 

18 monitored construction workers were.

19 DR. TAULBEE: Well, I think this 

20 is part of that checklist --

21 DR. MAKHIJANI: Right.

22 DR. TAULBEE: -- that Dr. Melius 
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1 was wanting to try to develop within this 

2 group.

3 DR. NETON: This is not germane 

4 really to 53. This one precedes 53. And 53 

5 starts with the fact that you have got a 

6 monitored population.

7 DR. TAULBEE: Right.

8 DR. NETON: I mean, all that 

9 other stuff would need to precede 53 before 

10 we go into a 53 analysis.

11 DR. MAKHIJANI: Should I turn the 

12 slide?

13 CHAIRMAN MELIUS: But I think we 

14 need to make that sort of explicit.

15 DR. NETON: Oh, I agree. Yes.

16 DR. MAKHIJANI: So, should I turn 

17 the slide?

18 DR. CHMELYNSKI: Yes. We can go 

19 to page 5. One other point in the bold here. 

20 We do still feel it is necessary to examine 

21 subgroups of the construction workers, and 

22 not just all construction workers as a single 
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174

1 group.

2 DR. TAULBEE: Which construction 

3 workers? I mean, laborers, pipefitters? I 

4 mean, a priori is where you've got to try to 

5 come up with this grouping that you want to 

6 evaluate. So, all of them? Do we go down to 

7 junior or to journeymen within each trade? 

8 How far do you go?

9 CHAIRMAN MELIUS: We will come 

10 back to that because it is all part of this 

11 other issue, but I don't think it necessarily 

12 has to be a priori, either, because I think 

13 just for the reason you said. We can end up 

14 doing lots of comparisons that aren't going 

15 to be very helpful and meaningful, and so 

16 forth. So, it has got to be sort of a 

17 process of deciding. But some of it is going 

18 to be driven by the data itself, the nature 

19 of these data, because they aren't random 

20 samples from a population, and so forth.

21 MR. LaBONE: But you can't use 

22 the data set to come up with your hypothesis 
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175

1 and then test it with the same data set is 

2 the problem. So, you are supposed to go 

3 through and identify what you want to test 

4 ahead of time or use a different training 

5 data set and then come and test it.

6 CHAIRMAN MELIUS: I don't 

7 necessarily agree with that, but let's come 

8 back to it.

9 MR. LaBONE: Okay.

10 DR. MAKHIJANI: It is also the 

11 question of professional judgment in this 

12 particular area as to what you are going to 

13 use --

14 CHAIRMAN MELIUS: Yes.

15 DR. MAKHIJANI: -- based on what 

16 work was being done.

17 Okay. Next, I'm changing the 

18 slide, 6.

19 DR. CHMELYNSKI: Well, I guess we 

20 have already started the OPOS discussion, 

21 too, and we have had some statements about 

22 the variability, and NIOSH does accept that 
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176

1 this procedure would result in smaller 

2 geometric standard deviations. And it does 

3 raise the question as to what should be done 

4 for all the claimants whose cases have been 

5 processed so far using any other methodology 

6 that didn't include OPOS.

7 For many years, the idea was to 

8 collect all the data and use them as one 

9 pool. Now we are saying that that wasn't the 

10 right way of doing it. So, again, I think a 

11 lot of this boils down to how the data -- to 

12 what happens to the data as you go through 

13 the process of first modeling the urine 

14 concentrations and, then, trying to go on and 

15 figure out what the intakes were. And I 

16 think those are really the important 

17 questions on OPOS, is how the modeling works.

18 So, I will leave one that one as 

19 already being discussed.

20 The next slide, which is page 7, 

21 we also discuss this. It is exactly what the 

22 term sampling protocol means. I keep using 
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177

1 that word, but NIOSH suggests that the right 

2 terminology is in the approach there in that 

3 first box, internal dosimetry monitoring 

4 program.

5 And as long as the workers were 

6 in that program, that seems to be enough for 

7 NIOSH to make the protocol similar. We are 

8 not so sure that you can combine the special 

9 sampling that is done due incidents along 

10 with the routine sampling. That is comparing 

11 apples and oranges to some degree.

12 Finally, I want to raise a point 

13 on page 8, though, about the sampling 

14 protocol issue. I think this is in NIOSH's 

15 own words, that the CTWs were potentially 

16 subject to different bioassay practices than 

17 other workers. The CTWs, many of whom are 

18 subcontractors, I guess is the right word, 

19 commonly submit bioassay samples after 

20 suspected intakes and at the completion of 

21 jobs. So, there does not seem to be any 

22 evidence for a routine monitoring program for 
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178

1 those workers.

2 DR. MAKHIJANI: And let me just 

3 say that we use the words "sampling 

4 protocol," and it is confusing. We 

5 understand why NIOSH took in the way that 

6 they did. But what we mean is the monitoring 

7 protocol for construction workers, as is 

8 clear from the way we interpreted the NIOSH 

9 report.

10 Should I move on to the next one?

11 DR. CHMELYNSKI: Okay, page 9. 

12 This is identified as Finding 5 in our 

13 report. And this has to do with the idea 

14 that we only have a fairly-small number of 

15 samples in a lot of the comparisons that we 

16 are trying to make.

17 My own feeling is that trying to 

18 push out to the 95-percent confidence level 

19 when you know you are faced with small sample 

20 problems is not claimant-favorable because it 

21 tends to diminish the chance we will detect 

22 any differences.
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179

1 NIOSH takes a slightly different 

2 point of view, saying that if you carried 

3 that to an extreme, in other words, moved to 

4 the 50-percent confidence level, would that 

5 be better? I am not saying it is worse, 

6 but --

7 (Laughter.)

8 On the other hand, you know, when 

9 this program was set up 50/50 was where the 

10 boundary is. So, there's some justification 

11 for using alternative significance levels in 

12 order to be claimant-favorable.

13 But the point here is that, if 

14 you do make 90 percent for your alpha, you 

15 are going to end up with a large beta, which 

16 means a Type 2 error, not being able to 

17 reject when perhaps you should be.

18 So, the next slide is the 

19 beginning of a fairly-long discussion, and 

20 that is on page 10. It has to do, again, 

21 with the small sample sizes.

22 There is a theoretical issue here 
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180

1 about, once you have seen the data, should 

2 you use anything that you have learned when 

3 you do the hypothesis test. Well, some 

4 people are very purist on conducting 

5 hypothesis tests and say you can't do any 

6 analysis of power after the data has been 

7 collected and analyzed. There is sort of a 

8 nebulous area where the data has already been 

9 collected, but we really haven't looked at it 

10 that much.

11 (Laughter.)

12 I'm not quite sure the same 

13 arguments apply there.

14 But, on the other hand, I don't 

15 know of anybody who is willing to say that 

16 you can try a hypothesis test, first off, not 

17 knowing what difference you're looking for 

18 and, secondly, not caring how much power you 

19 have to detect that difference. That is 

20 disturbing to me for the reason we will see 

21 on the next page.

22 But, basically, the argument 
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181

1 presented here is that a retrospective power 

2 analysis doesn't give you any new 

3 information. And I agree. If you 

4 specifically use the sentence that is in this 

5 box here that includes the words "confidence 

6 intervals of the estimated parameters." 

7 However, we don't have confidence intervals 

8 of the two-sided type. We only have one9 sided confidence intervals that you can imply 

10 from the hypothesis tests that are being 

11 done.

12 On the next page, we will see an 

13 example, on page 11, of let's say we did a 

14 hypothesis test on data that had the same 

15 variability, and here is one case where we 

16 had a large sample size -- that is Case 1 on 

17 the bottom -- and another case where we had a 

18 small sample size, and that is Case 2 on the 

19 top.

20 Both of these, the 95-percent 

21 confidence interval for delta includes the

22 value of zero, which means that no 
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182

1 significant difference could be found. 

2 However, the upper case with the small sample 

3 sizes shows that the confidence interval for 

4 delta extends all the way up to perhaps 300. 

5 Again, we don't know what we are measuring,

6 so the units aren't on this graph.

7 But the point is that, even if we 

8 don't do power analysis, at least if we saw 

9 the confidence intervals, we would have some 

10 feel for how well we were able to estimate 

11 delta. And if we had that feel, then the 

12 next question we would come back to is the 

13 same one we had earlier: how large of a 

14 delta are we willing to accept? Is the graph 

15 on the bottom what we want or is the graph on 

16 the top what we want? And that depends on 

17 whether 300 is the biggest difference we are 

18 willing to accept or maybe 50.

19 So, the confidence interval is 

20 just another way of expressing the hypothesis 

21 test and they have the same questions that 

22 are raised. I don't think you can do either 
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183

1 of these. You can't interpret the confidence 

2 interval. You can't interpret hypothesis 

3 test unless you have some feel for how big of 

4 a delta that test could detect and how big of 

5 a delta you are willing to accept.

6 Following on page 12, there are 

7 some other statisticians who do recommend 

8 carrying out power calculations based on if 

9 there are statistics. One is Gelman, who is 

10 a Bayesian, and Bayesians tend to have 

11 heretical views toward hypothesis testing in 

12 general.

13 But even EPA takes this same 

14 approach on page 13, where their guidance for 

15 data quality assessment, which is a little 

16 different process than data quality 

17 objectives -- data quality assessment is what 

18 happens at EPA when the QA people go in and 

19 look at what was done.

20 And what they are saying here is 

21 that, yes, you have to look at the 

22 variability that you actually observed in 
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184

1 order to confirm you had a large enough 

2 sample size. And this was instructions for 

3 the WRS test, which is a little different 

4 than Peto-Prentice. But at least it is an 

5 indication that a lot of people think it is 

6 not so bad using the analysis.

7 NIOSH's point of view on this is 

8 a very purist and theoretical view of 

9 hypothesis testing, which is that you can't 

10 do power analysis if you have already done 

11 the data collection. Or, rather, it is not 

12 important to do. Well, we still feel it is 

13 important.

14 And I guess we should stop there 

15 because I would like to hear some feedback on 

16 what these power issues boil down to. Should 

17 we do them or shouldn't we do them? Are we 

18 going to figure out how big a delta we are 

19 willing to accept or not?

20 MR. LaBONE: This is Tom.

21 Let me start with basically a 

22 description of what we are trying to do. And 
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185

1 that is, we are doing this test to make a 

2 decision: should we use a stratified model 

3 or unstratified? So, basically, our two 

4 paths forward are fixed. And so, we are just 

5 doing this test to decide that.

6 And so, my argument about the 

7 small sample sizes is that that argues to use 

8 the unstratified model. Basically, you are 

9 saying we don't have enough data. If I can't 

10 see a difference when I do the tests, what 

11 makes me think that I can develop a 

12 reasonable model with that same set of data?

13 And so, we are not trying to 

14 prove a drug works or doesn't work here. 

15 What we are trying to do is which path do we 

16 take, stratified or unstratified. So, that 

17 is the first thing that it comes down to.

18 All of the EPA guidance and 

19 everything that you have in here about power 

20 presupposes, and it is implied in it, that 

21 you can go get more data. And so, EPA says, 

22 test this, and if it is not powerful enough, 
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186

1 go back and get more samples. We can't go 

2 back and get more samples.

3 And so, that is why I think we 

4 feel that it is kind of meaningless. We are 

5 given a data set. We can't improve it. It 

6 is what it is. We do the test and then we 

7 make a decision depending upon what we get.

8 So, in that process it is just, 

9 you know, if you go back and say this is not 

10 powerful enough, all that means is that we 

11 are just going to use the stratified model.

12 Tim?

13 DR. TAULBEE: Let me interject 

14 there because you just said something that I 

15 am not sure we have actually investigated 

16 from one standpoint. In some cases you're 

17 right, the data we have is the data we have. 

18 We can't go get the code back and get more 

19 data. But there are other cases where we 

20 can. We are using the NOCTS data set because 

21 it is more readily available, but there is 

22 more data available at the sites to where we 
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187

1 could go back and get them.

2 In the case of the americium, 

3 curium, californium, there isn't any more. 

4 We use the logbook data. In the case of 

5 plutonium, uranium, strontium, mixed fission 

6 products, there's a lot more data that we 

7 could go back and get. So, I think it 

8 depends upon the particular standpoint.

9 From that, what are your thoughts 

10 on, if we are dealing with a limited data set 

11 to start with that we know there is more 

12 data, is there any benefit of doing a power 

13 calculation then?

14 MR. LaBONE: Just like Harry 

15 said, you do what is practically significant, 

16 what effect is of interest to us. Take a 

17 look at those confidence intervals, and if 

18 that value falls inside that confidence 

19 interval with zero and you can get any more 

20 data, then, yes, we should go get more data. 

21 I mean, again, you would have to give me that 

22 number that is of significance first.
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188

1 DR. NETON: That brings up a 

2 whole other issue about --

3 MR. LaBONE: Yes.

4 DR. NETON: -- the cost and the 

5 time.

6 MR. LaBONE: Yes.

7 DR. NETON: I mean, that's --

8 DR. TAULBEE: But that kind of 

9 plays into the role of just taking the 

10 external dose example of -- you know, that 

11 data is readily available. And if we could 

12 decide on a value that is of significance to 

13 help us with the internal --

14 DR. NETON: Yes, yes.

15 DR. TAULBEE: -- then we could 

16 apply this.

17 DR. NETON: We could flesh that 

18 out a little bit, but, yes, I tend to agree 

19 with you.

20 DR. TAULBEE: Yes.

21 DR. NETON: I mean, there is a 

22 reason we used the NOCTS --
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189

1 CHAIRMAN MELIUS: What would be 

2 the gain from adding more, getting more 

3 databases? Yes, there is a cost to it, but 

4 is that cost worth what you will get out of 

5 it, which is sort of what you were talking 

6 about earlier, Jim, in terms of how much of a 

7 difference would we see, and so forth. Maybe 

8 we can predict that with some capability or 

9 something. Again, it comes back to what 

10 level are we interested in.

11 DR. NETON: Well, but you should 

12 be able to predict how much more data you 

13 need, right?

14 MR. LaBONE: Yes, yes. 

15 Absolutely. Yes.

16 CHAIRMAN MELIUS: That is usually 

17 the purpose of the power --

18 MR. LaBONE: Yes, but that is the 

19 a priori. You design it, yes.

20 CHAIRMAN MELIUS: If we are going 

21 to get -- I think there's more samples. 

22 There's all kinds of --
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190

1 DR. NETON: There is a 

2 significant difference value that we are, 

3 hopefully, going to talk a little bit about 

4 later.

5 MR. LaBONE: Yes. Anyway, if we 

6 can get more data, then what Harry is saying 

7 is correct. It is just usually when we get 

8 this, we assume that we can't get any more 

9 data; this is it.

10 DR. NETON: That is often the 

11 case, more often than not, I would say. Only 

12 in cases where we are going to do the NOCTS 

13 data, and we use NOCTS data for a reason, 

14 because the data were there, but they are not 

15 coded. It is not readily available. It 

16 would take a monumental effort, if not years 

17 and hundreds of thousands of dollars.

18 Anyway, that is probably the 

19 subject of a different discussion.

20 DR. MAKHIJANI: But in the case 

21 where you cannot get more data, which is the 

22 case that you have already gone to the 
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191

1 logbooks, and so on, as Tim was talking 

2 about, and you still have a small number of 

3 samples, which is the case, say, with the 

4 neptunium data, there are two alternatives. 

5 There are three alternatives.

6 You can always decide we don't 

7 have enough data. The amount of data is 

8 inadequate, and then that is a question for 

9 the Board to decide. And that is an example 

10 that Stu was talking about earlier. They had 

11 some data and it was kind of evident that the 

12 data is inadequate.

13 MR. LaBONE: They had a 

14 systematic inadequacy there.

15 DR. MAKHIJANI: Yes, right.

16 MR. LaBONE: Yes, yes. I mean, 

17 it was --

18 DR. MAKHIJANI: Basically, one of 

19 the issues that has concerned us -- and I'm 

20 sorry Joyce isn't on the phone, but I will 

21 try to represent the situation as best I can 

22 for the team -- is that construction workers 
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192

1 were thought to be not at routine exposure 

2 potential. So, they were only monitored when 

3 incidents came to light. But that may not 

4 actually be correct.

5 So, it may be a parallel 

6 situation or it may not be. We don't have a 

7 definitive conclusion about that. But, 

8 certainly, we have put this issue on the 

9 table in both the reports, the analysis of 

10 actual data that we have put on the table for 

11 you, more so with the neptunium than with the 

12 thorium.

13 DR. NETON: I would agree with 

14 you that, if it could be demonstrated the 

15 construction workers were on an incident, a 

16 certain fraction or a fraction of the 

17 construction workers were on an incident18 driven bioassay, not part of a regular 

19 monitoring program, then that would be not 

20 appropriate to incorporate that data into the 

21 overall routine monitoring data. I think 

22 that is true.
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193

1 DR. MAKHIJANI: But that is what 

2 NIOSH has said itself.

3 DR. NETON: Well, I saw that.

4 DR. MAKHIJANI: Right.

5 DR. NETON: And it kind of made 

6 me take some pause on that comment --

7 DR. MAKHIJANI: Yes.

8 DR. NETON: -- because, you 

9 know --

10 DR. TAULBEE: That's not the 

11 case, though.

12 DR. NETON: Okay. If you really 

13 have an incident-driven program, there is a 

14 separate -- well, okay, I just would agree 

15 with Arjun's point that, if there is this 

16 sort of dichotomy in monitoring, you know, 

17 incident-driven versus routine, I am willing 

18 to accept the routine with incident inside of 

19 it, sort of a different situation.

20 DR. TAULBEE: Right. I agree 

21 with that.

22 DR. NETON: That would only tend 
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194

1 to bias the results high, but they are still 

2 on a routine program. But if you only have 

3 incident-driven, then I have got some concern 

4 there.

5 MR. LaBONE: I can't comment on 

6 Savannah River, but, in general, the question 

7 is, did you adequately characterize the 

8 intakes? The actual monitoring program is 

9 really not significant.

10 DR. NETON: Yes, yes.

11 MR. LaBONE: It is, did you 

12 accurately characterize the intakes that the 

13 people had?

14 DR. NETON: And demonstrate that 

15 the only time there were exposures was when 

16 there was no incidents.

17 MR. LaBONE: Yes. For example, 

18 you could have a job-specific-driven 

19 monitoring program that only when they went 

20 in and did work were they monitored when they 

21 came out.

22 DR. NETON: Well, that's not 
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195

1 incident.

2 MR. LaBONE: No, but if there are 

3 different types of programs, and you can't 

4 look at these names, it is, again, did they 

5 adequately monitor them? Did they capture 

6 the intakes if they occurred?

7 DR. NETON: Agree, agree.

8 MR. LaBONE: And independent of 

9 site, that is the thing that is important. 

10 And if you did that, then you can combine all 

11 that data.

12 DR. NETON: Right.

13 MR. LaBONE: And that was the 

14 comment that we made. But you have to judge 

15 did you capture all the intakes.

16 DR. NETON: Okay.

17 DR. MAKHIJANI: Are we done with 

18 13, Harry? Did you get the feedback that you 

19 were looking for?

20 DR. CHMELYNSKI: I think it is 

21 also going to be hard to resolve that kind of 

22 issue as to exactly who was monitored for 
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196

1 what reason.

2 If we go on with the slides, I 

3 think -- what are we on now?

4 DR. MAKHIJANI: Fourteen. We are 

5 on 14.

6 DR. CHMELYNSKI: Fourteen, right. 

7 This is the discussion we just had, I think, 

8 that if the data are already there, why are 

9 you doing the power analysis. I think we 

10 have already discussed that.

11 DR. MAKHIJANI: Fifteen.

12 DR. CHMELYNSKI: Okay, 15. Yes, 

13 again, Arjun mentioned that we have done 

14 these studies, and we looked at the data for 

15 a set like neptunium and we do see the number 

16 of samples that are there. And we did some 

17 simulations to look at how well one would be 

18 able to discriminate between the two groups 

19 of workers.

20 And it seemed to us that, even 

21 under ideal conditions, using pure log-normal 

22 distributions, even if you don't have any 
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197

1 non-detects, you still wouldn't be able to 

2 see reliably a difference of factors of 4 to 

3 10.

4 And this particularly happens 

5 when you get up to the GSDs at around, of 

6 over 3, 4 and 5 or so, which are very common 

7 in this data set. Once you get up that high, 

8 it is very hard to find evidence that the 

9 tests will be able to detect anything that is 

10 in this range of factors of 4 to 10.

11 Now there are some other 

12 simulations reported in NIOSH's response in 

13 the Appendix A. And as far as I could tell, 

14 none of those had any high GSD values. So, I 

15 think 3 was the highest.

16 So, what those graphs tend to 

17 show is that the Peto-Prentice Test and Gehan 

18 Test, which is pretty much an WRS test unless 

19 you are dealing with a lot of ties -- I'm 

20 sorry, but when you have non-detects, you do 

21 have a lot of ties. So, that is probably why 

22 Gehan is used as a basis here.
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198

1 Even though both tests do about 

2 the same, the Peto-Prentice Test is a little 

3 better in those simulations, probably not 

4 enough to write home about, but a little bit 

5 better.

6 On the other hand, again, we 

7 don't know that we can detect any of these 

8 differences that are pretty large, in my 

9 mind, for a factor of 4.

10 So, we leave that again to this 

11 issue of how large of a sample you are going 

12 to need is going to be dependent on how big 

13 of a delta you want to detect.

14 DR. NETON: This is Jim. I just 

15 had a thought, and it could be totally wrong, 

16 but I am going to throw it out there anyway.

17 (Laughter.)

18 It seemed to me, if you have a 

19 GSD that is in the 4 and 5 range, that is 

20 going to drive a very high 95th percentile. 

21 I mean, clear. And we are assigning the 95th 

22 percentile based on a GSD of 4 or 5.
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199

1 So, wouldn't the stratified data 

2 set, to make a difference in the 95th 

3 percentile, have to be a difference of a GSD 

4 of 4 or 5 higher than the mean to make a 

5 practical difference in the distribution that 

6 we are assigning at the 95th percentile? Do 

7 you know what I'm saying?

8 You know, we are way out here, a 

9 GSD of 5. We are saying we are giving this 

10 guy a GSD of 5 difference from the mean. And 

11 so, for a stratified coworker data set to 

12 make a practical difference up here, to 

13 change that number, it would have to have a 

14 very large difference in the geometric mean. 

15 It is almost like it would be impossible to 

16 change that number substantially unless there 

17 is a huge difference in the data sets.

18 Am I wrong on that?

19 CHAIRMAN MELIUS: No, I don't 

20 think you are wrong. I was going to say I 

21 think there is a relationship there.

22 DR. NETON: But we can't detect 
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200

1 very well.

2 CHAIRMAN MELIUS: Yes.

3 DR. NETON: But, at the same 

4 time, you don't need to because you are 

5 already way out here on the distribution. To 

6 make a change there, you have to have a huge 

7 difference.

8 CHAIRMAN MELIUS: Yes, but 

9 anytime we are way out there and applying it 

10 to a larger population, you start to worry is 

11 that plausible. You know, you would just 

12 take care of the tail.

13 DR. NETON: Well, no, but that is 

14 what we do with the 95th percentile, what we 

15 assign for people who could have been heavily 

16 exposed. And that is what we are saying.

17 CHAIRMAN MELIUS: Well, see, that 

18 is a key difference. You are saying you are 

19 applying it to everybody, is what you 

20 actually --

21 DR. NETON: Well, not everybody.

22 CHAIRMAN MELIUS: No, I know. 
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201

1 And, see, I think that is another thing we 

2 need to think about and take into account. 

3 Because if we are segmenting that, or 

4 whatever you want to call that, you know, 

5 some people get the 95th, some people get 50, 

6 that makes some difference in terms of how we 

7 are approaching this, yes.

8 DR. NETON: But the end result 

9 would be, if we stratified it and it was 

10 lower, they would receive a lower, 

11 construction workers would receive a lower 

12 dose than they are already getting. I mean, 

13 that would be the end result. I am not sure 

14 we are going to spend a lot of energy to do 

15 that.

16 CHAIRMAN MELIUS: No, no.

17 DR. NETON: But I need to explore 

18 that concept because I really do think that, 

19 with large GSDs, you would have to have huge 

20 differences to drive the change in the 95th 

21 percentile.

22 CHAIRMAN MELIUS: But that also 
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202

1 comes back to how do we distinguish, 

2 then, who gets the 50th and who gets the 

3 95th.

4 DR. NETON: Well, yes. Well, 

5 that is not a coworker, I mean, that is not a 

6 stratification issue. That is a sort of way 

7 we do business, dose reconstruction.

8 CHAIRMAN MELIUS: Well, but it 

9 has the same impact. I mean --

10 DR. NETON: Well, yes. Yes, but 

11 it is a different issue, though, I think. I 

12 didn't think that the issue on the table was 

13 getting rid of the 50th and the 95th. It was 

14 deciding what the appropriate distribution 

15 was to be used to assign the 50th and 95th 

16 percentiles. That is what I thought we were 

17 talking about.

18 DR. MAKHIJANI: No, I don't think 

19 it is a distinct issue. I agree with Jim on 

20 this.

21 Because you may argue that the 

22 95th percentile and the GSD is high, so big 
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203

1 that it will cover the most exposed workers, 

2 a large fraction of them.

3 DR. NETON: Yes.

4 DR. MAKHIJANI: But you can't 

5 argue the same for the 50 percentile. To 

6 figure the people in that box, you have to 

7 know is the 50 percentile the construction 

8 workers. You know, how do you know -- how 

9 are you going to decide which construction 

10 workers are comparable at the clerical 

11 workers?

12 DR. NETON: All construction 

13 workers are going to fall into the 95th 

14 percentile. I don't see how they wouldn't. 

15 That has been our way of doing business for a 

16 long time. These guys are workers that are 

17 in the radiation-exposed areas working. And 

18 the 50th percentile, remember, is not a fixed 

19 point. It is a full distribution. We are 

20 acknowledging there is uncertainty.

21 DR. MAKHIJANI: Yes, I understand 

22 that.
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204

1 DR. NETON: So, I think between 

2 those two you have sort of bounded the 

3 exposures.

4 CHAIRMAN MELIUS: But I think it 

5 is more than bounding. Especially as we are 

6 trying to do these kinds of comparisons, the 

7 applications, the coworker applications, I 

8 think we need to sort of be careful about it.

9 DR. NETON: I am trying to figure 

10 out, if we teased out a construction worker 

11 coworker model, strata, then would we use the 

12 50th percentile, the full distribution? 

13 Would that be more appropriate because that 

14 is the representative distribution of that --

15 MR. LaBONE: You would not use 

16 the 95th.

17 DR. NETON: I wouldn't use the 

18 95th because now I would have a distribution, 

19 and we can do that, but I don't know. I 

20 could see only numbers going down, doing this 

21 type of analysis.

22 CHAIRMAN MELIUS: Yes, but saying 
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205

1 what distribution is appropriate, I mean that 

2 should be the goal, not does it go up or 

3 down.

4 DR. NETON: Well, but, at the 

5 same time, we have already demonstrated that 

6 it is very hard to tease these out because 

7 there are small numbers, and it is hard to 

8 show the difference, the significance. I 

9 mean, so in a way it is what it is. These 

10 are the data sets we have, and then we are 

11 bounding based on a plausible upper bound.

12 I mean, I don't know. We can 

13 talk more about that.

14 DR. MAKHIJANI: Should I move to 

15 the next one, Harry? Or did you have more 

16 comments?

17 DR. CHMELYNSKI: No, I don't. 

18 But I see on the next page, on page 16 -- let 

19 me just back up a second. Page 16 is titled, 

20 "Finding About Worker Changed Jobs". And 

21 indeed, that was a concern of ours, and 

22 especially one of Joyce's, I think.
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206

1 Since we are already saying we 

2 don't know much about who did what, it is 

3 hard to tell whether you are doing this right 

4 or not in terms of throwing people into one 

5 group and the other group, since we know that 

6 some construction workers start becoming 

7 regular workers. That fouls up the 

8 comparison once you start having people cross 

9 the line between the two groups in a given 

10 time period.

11 However, I thought it was 

12 interesting to see down in NIOSH's response 

13 that they point out, again, that to stratify 

14 these models, you have to be able to assign 

15 people to a meaningful job title. Well, I 

16 don't know how exactly specific those job 

17 titles have to be.

18 But the point is that here we are 

19 pointing out that it is a hard task to do 

20 that. And yet, on the other hand, just 

21 moments ago, we hear that, "Oh, we are going 

22 to give those guys the 95th percentile." Now 
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207

1 if that is going to be applied to all 

2 construction workers, that is one thing. But 

3 when you start trying to think about the 

4 subgroups, we are not even sure which ones we 

5 could put in there.

6 So, I guess what we are trying to 

7 say here is both. If we don't know what they 

8 are doing, what jobs they are doing, but, 

9 yet, when we get around to dealing with this 

10 issue, we will know what kind of jobs they 

11 are doing, I guess that is reasonably 

12 uncomfortable.

13 DR. TAULBEE: I think this really 

14 depends upon the site. You know, RPRT-0053 

15 was designed to be generic, and there are 

16 some sites where we can get down to 

17 meaningful job titles on virtually everybody, 

18 and there are other sites where we cannot, 

19 where we can just basically categorize them 

20 as the construction trades or non21 construction trades. So, it really varies 

22 between the different sites as to what level 
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208

1 of data we have in order to categorize 

2 people.

3 DR. MAKHIJANI: This is just a 

4 question/observation. At Savannah River Site 

5 we have job titles on everyone.

6 DR. TAULBEE: Yes.

7 DR. MAKHIJANI: So, they are part 

8 of the worker records. But we don't 

9 necessarily have a meaningful amount of data 

10 corresponding to every job title. So, we 

11 can't necessarily develop.

12 So, if you have, you know, 12 job 

13 titles for construction workers, we have 

14 those job titles. They belong to the site.

15 DR. TAULBEE: Yes.

16 DR. MAKHIJANI: And there were 

17 specific types of work they were generally 

18 doing, you know, carpenters, electricians, 

19 whatever. But we don't necessarily have 

20 enough data to put them in an exposure 

21 matrix.

22 DR. TAULBEE: We don't have 
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209

1 enough monitoring, internal monitoring 

2 data --

3 DR. MAKHIJANI: Right. That's 

4 what I mean.

5 DR. TAULBEE: -- for some 

6 radionuclides.

7 DR. MAKHIJANI: Right.

8 DR. TAULBEE: Other radionuclides 

9 we do.

10 DR. MAKHIJANI: Right, right.

11 DR. TAULBEE: So, some of them, 

12 that is why you end up with the small 

13 numbers. But take plutonium, for example; 

14 there is thousands of results. You won't run 

15 into any of these small numbers of workers 

16 issues.

17 DR. MAKHIJANI: Right, and we 

18 haven't argued about plutonium or uranium, 

19 precisely because of that, I think, because 

20 we recognize that there are large numbers of 

21 data.

22 DR. TAULBEE: But the same 
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1 general type of work of a construction trades 

2 worker going into a plutonium facility or a 

3 californium/curium facility, which were 

4 smaller than the plutonium facilities, their 

5 same type of work is actually similar, the 

6 type of work that they would be doing and the 

7 monitoring associated with it, compared to 

8 the operators.

9 So, you could do a comparison of 

10 the plutonium from that standpoint of do 

11 pipefitters come up higher or just 

12 construction trades in general come up higher 

13 from that standpoint. That could be 

14 informative from that standpoint.

15 DR. MAKHIJANI: It could be, yes, 

16 I agree.

17 CHAIRMAN MELIUS: So, you haven't 

18 done that?

19 DR. TAULBEE: No, we haven't gone 

20 and collected all of that data.

21 CHAIRMAN MELIUS: Okay.

22 DR. TAULBEE: We used NOCTS 
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211

1 because we had 4 to 5 hundred per year. So, 

2 we didn't bother to go get the thousands --

3 CHAIRMAN MELIUS: Okay.

4 DR. TAULBEE: -- for that 

5 comparison.

6 DR. MAKHIJANI: Should I move to 

7 the next one, Harry?

8 DR. CHMELYNSKI: Yes, please.

9 All right. We are on page 17. 

10 Now we get into the statistical discussion, I 

11 guess, although I am not sure how long we 

12 want to drag this out.

13 (Laughter.)

14 But I still feel that we have to 

15 know what the power of the test is. I don't 

16 care if we are doing it on retrospective data 

17 or not. I think that, if you deal with this 

18 small of sample sizes, it is hard to trust 

19 any hypothesis test result.

20 And I think that in the response 

21 here that NIOSH made, I think they also 

22 recognize that you have to be able to define 
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212

1 the size of the effect, as we have said 

2 several times now, in order to figure out 

3 whether there is a difference and whether the 

4 test has any power to detect that difference.

5 Lacking a measure of what we 

6 think is sufficient accuracy, we are left 

7 doing hypothesis tests that sort of tell us 

8 some random numbers sometimes when we get 

9 very small samples. And we are trying to 

10 base important decisions on those random 

11 numbers here, it seems to me.

12 So, if we go on to the next page, 

13 continuing that same line of thought, NIOSH 

14 has done a lot of research here in figuring 

15 out what is the right test to do when you 

16 have less-censored log-normal data. Now, of 

17 course, we don't know it is log-normal, but 

18 we do know we have non-detects. So, it 

19 pretty much fits into that.

20 Now just knowing that the Peto21 Prentice test is the most powerful test 

22 available for these kind of data doesn't tell 
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213

1 us what the power is. And I am still maybe 

2 the old school. I want to know what the 

3 power is before you tell me what the test 

4 result is, because test result doesn't mean 

5 much without that information.

6 Now, getting down to the 

7 specifics, so what we are talking about is, 

8 is 30 samples going to be enough? That is 

9 what NIOSH stated. I am not quite sure how 

10 they came up with that number, although I 

11 have seen it quoted in some other places, 

12 too.

13 When you think about all the 

14 different kinds of distributions with all the 

15 different GSDs, it is hard to believe there 

16 is any single sample size that would be 

17 appropriate across all these different 

18 comparisons we are trying to make.

19 And I think one has to sort 

20 through them and start thinking how big a 

21 sample we are going to need to detect how big 

22 of a difference. The simulation results that 
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214

1 we report on page 19 we reported last time. 

2 Some I kind of elaborate on them.

3 But, basically, the gray area on 

4 that table shows where the Type 2 error rates 

5 are low, at least low enough for my mind. 

6 Maybe some people go on down to .05, but I am 

7 willing to get them down to around 10 percent 

8 or so.

9 And if I am using an alpha of .05 

10 and I happen to apply it to some data where 

11 both of them have a GSD of 4, I am already up 

12 to a 15-percent error rate. And then, 5 and 

13 6, we start getting even much higher error 

14 rates. And again, we have the graph that 

15 shows the steepness at the .05 level in this 

16 curve, rising almost up to 35 percent. 

17 Thirty-five percent of the cases we were not 

18 able to reject the difference that we know 

19 was there.

20 Well, in this case, again, no 

21 matter how many simulations you do, you can't 

22 cover all the cases. So, maybe this isn't 
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215

1 sufficient to say that this is always bad. 

2 But we certainly haven't found any simulated 

3 results that show us that it is a good one.

4 DR. MAKHIJANI: Well, could I add 

5 to that, Harry, we actually gave examples 

6 from actual data in the thorium report, and 

7 Harry did an analysis for four years. In all 

8 cases, there were more than 30 data points. 

9 And we showed that, depending on the ratio of 

10 GMs and GSDs, that sometimes you could have 

11 fewer data points, more than 30, like I think 

12 38, in which it looks like the analysis was 

13 good, that you could actually make a good 

14 comparison, keeping both effects of error 

15 down. Sometimes you could have far more data 

16 points, but because of the way the GMs and 

17 GSDs are related, 60 or 70 data points may 

18 not be enough to give you a result with some 

19 confidence.

20 And we don't have the details 

21 here, but I think this little strip chart is 

22 illustrative of the actual cases that we 
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216

1 analyzed with more than 30 data points. This 

2 uses exactly 30.

3 Sorry, Harry.

4 DR. CHMELYNSKI: Oh, no problem.

5 Are there any other questions on 

6 that? We are pretty much wrapping it up 

7 here.

8 CHAIRMAN MELIUS: Where does the 

9 30 come from?

10 MR. LaBONE: The 30, we were 

11 always taught 30.

12 (Laughter.)

13 No. The question is sometimes 

14 you will have 30 data points and the entire 

15 population was 100 people. So, you are 

16 sampling a good portion of the population.

17 CHAIRMAN MELIUS: Okay.

18 MR. LaBONE: Other times you 

19 don't know. Sometimes it is all uncensored, 

20 which is good, solid data.

21 CHAIRMAN MELIUS: Right.

22 MR. LaBONE: Sometimes it is 
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217

1 censored. And so, actually, that 30 is 

2 attempt to make sure somebody doesn't try to 

3 go through and do a model with two points.

4 CHAIRMAN MELIUS: Yes. Okay.

5 MR. LaBONE: Okay? So, it is 

6 more of a thing, and again, all these 

7 analyses are done by statisticians. That is 

8 written into the report. And they are 

9 supposed to look at this and make a 

10 professional judgment, is what I am turning 

11 out nonsense?

12 CHAIRMAN MELIUS: Okay.

13 MR. LaBONE: Because the data are 

14 just -- there is no data here. There is only 

15 one uncensored data point, for example.

16 And so, it was just a general 

17 guideline to give the statisticians someplace 

18 to start. And so, that is kind of like where 

19 it came from.

20 CHAIRMAN MELIUS: Okay. That is 

21 sort of what I assumed.

22 MEMBER BEACH: What I am 
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218

1 wondering, does the percentage ever play in 

2 that? If you have 100 people and you have 

3 got 30 samples, that is a pretty good 

4 percentage. But if you have 1,000 people or 

5 3,000 people and only 30 samples, then your 

6 percentage goes way down.

7 MR. LaBONE: What they taught us 

8 in sampling class is that one sip of a well9 stirred pot of soup is sufficient.

10 MEMBER BEACH: Is sufficient?

11 MR. LaBONE: So, it depends 

12 upon --

13 MEMBER BEACH: It depends on what 

14 is in the soup?

15 MR. LaBONE: No, is it a random 

16 sample? Did you collect from -- yes, is this 

17 a random sample? Is it a good sample 

18 versus -- once you get to, I guess, a certain 

19 size of sample, the population size really 

20 doesn't matter that much.

21 MS. CHALMERS: As long as it is 

22 representative.
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219

1 MR. LaBONE: As long as it is 

2 representative, yes.

3 MEMBER BEACH: Okay.

4 MR. LaBONE: So, it is not 

5 exactly proportional, like you might think.

6 CHAIRMAN MELIUS: You can 

7 characterize the mean income of the United

8 States by interviewing 10 people, or 

9 whatever.

10 MR. LaBONE: Yes, yes.

11 CHAIRMAN MELIUS: Yes, yes.

12 MR. LaBONE: Political polls.

13 CHAIRMAN MELIUS: Yes, yes, 

14 right. Yes.

15 MR. STANCESCU: Actually, you can 

16 do this test. I mean, EPA is doing the Gehan 

17 test, which is like a slightly different 

18 version of Peto-Prentice, with 10 samples in 

19 each group.

20 But, you know, depending on how 

21 much censoring you are -- we wanted to be 

22 confident that we have enough power to detect 
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220

1 the differences. So, 30, we thought maybe a 

2 30 or 4-percent censory, we think is good 

3 enough to detect the difference. We put 

4 these power curves at the end just to show 

5 that the power of the Peto-Prentice Test is 

6 enough to detect these differences.

7 I mean, it is very hard probably 

8 to agree what is enough power. I mean, most 

9 of that, sufficient, I want to say 80 percent 

10 is enough. I mean, we are not doing a 

11 clinical study to get 99 percentile. So, it 

12 is probably very hard to agree what is 

13 appropriate power here.

14 MR. LaBONE: I think I'm sensing 

15 the primary disagreement is based on whether 

16 you can or cannot go back and get additional 

17 data. I don't know what Harry thinks about 

18 that. But, again, if you cannot go get more 

19 data, to me, this doesn't get us anywhere. 

20 Whereas, if you can go get more data, then, 

21 yes.

22 CHAIRMAN MELIUS: Well, I also 
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221

1 think it is a question of how it is applied. 

2 So, it is what use is being made of this and 

3 what are the implications of that for dose 

4 reconstruction, which, again, isn't a fault 

5 of the statistics, or whatever, but that is 

6 what helps us to understand it, and so forth.

7 At least now I know 30 isn't a 

8 Holy Grail that I had missed 

9 someplace because my education is so --

10 (Laughter.)

11 MR. LaBONE: When normality kicks 

12 in, yes.

13 (Laughter.)

14 DR. MAURO: While 

15 listening -- this is John -- while listening 

16 to this conversation on the reason for 30, 

17 and I went online.

18 (Laughter.)

19 And it is really funny to see 

20 what this says. That the only reason 30 was 

21 regarded as a good boundary was because it 

22 made pretty students' T tables in the back of 
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222

1 textbooks that fit nicely on one page.

2 (Laughter.)

3 I just found that on the web.

4 DR. NETON: I wouldn't believe 

5 everything I read on the web.

6 DR. CHMELYNSKI: It must be true 

7 if you saw it on the web.

8 DR. NETON: That's right.

9 (Laughter.)

10 DR. MAURO: You know, I had to do 

11 it.

12 (Laughter.)

13 MR. LaBONE: No, a lot of thought 

14 went into the numbers because every one we 

15 came up with Tim said, "Can't you go lower?"

16 (Laughter.)

17 You know, "What about 29?"

18 (Laughter.)

19 DR. MAKHIJANI: Harry, do you 

20 want to comment on that?

21 For my part, I would agree with 

22 what Tom said. If you have very small 
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223

1 numbers, you are in trouble, and that so long 

2 as 30 is a guideline, rather than some hard3 and-fast number that fell from the sky, 

4 acknowledging that sometimes more than 30 may 

5 not be enough --

6 MR. LaBONE: Especially if you go 

7 back and do the analysis that he is talking 

8 about, you may demonstrate that it is not 

9 enough, yes.

10 DR. MAKHIJANI: Harry, did I 

11 misstate anything?

12 DR. CHMELYNSKI: No, no.

13 DR. MAKHIJANI: Okay.

14 DR. CHMELYNSKI: I think that 

15 this issue does get down to the very core of 

16 what is going on in terms of -- I guess the 

17 way you said it earlier was the way I think, 

18 too.

19 There are really three outcomes 

20 here. One is the test can tell you that they 

21 are different. The test can tell you they 

22 are the same. But then there is the case 
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224

1 where you don't have enough data to answer 

2 the question. And I just keep feeling that 

3 we keep beating our head against the wall 

4 trying to say, "Oh, we can answer this 

5 question," when, in fact, the statistics 

6 doesn't give you the answer if the data set 

7 isn't good enough.

8 DR. MAURO: This is John again.

9 I am listening, and please shut 

10 me down if I am going someplace where I 

11 shouldn't go.

12 But I think the dilemma is this, 

13 and it comes from my experience in doing 

14 blind dose reconstructions: we are trying to 

15 standardize the process, streamline the 

16 process that will help dose reconstructors 

17 deal with the limited data that might be out 

18 there.

19 And just let me say that, when I 

20 am doing a blind dose reconstruction, and I 

21 am just confronted with the person and a 

22 whole bunch of data and a lot of history of 
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225

1 the site, and that sort of thing, and I am 

2 doing my dose reconstruction, a difference 

3 that makes a difference is if I think it is 

4 possible that a person could have gotten an 

5 intake or an external exposure that is of 

6 such a magnitude that can make it a 50-

7 percent Probability of Causation.

8 So, it becomes a case-by-case 

9 problem. And so, in a way, the answer to the 

10 question, you know, statistical power and 

11 level of uncertainty and confidence levels, 

12 and you are trying to decide that upfront, I 

13 don't know if it is possible to do that 

14 because it only has, the question only has 

15 meaning when it is applied to a real case 

16 where 100 millirem may make a difference.

17 So, I guess all I am saying is to 

18 bring it back down to earth in my world, what 

19 I call the "common-sense world" of doing dose 

20 calculations, what I do is I actually look at 

21 a person. Then, I look at all the data at 

22 that site that is available to me. And I 
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226

1 say, is it possible that this guy could have 

2 gotten a lot higher exposure because of his 

3 work and because of data we have regarding 

4 him, the time period, what he did, and the 

5 other records? And it almost becomes one 

6 where you are doing the diagnostic, you know, 

7 where you have to use a certain degree of 

8 judgment and ask yourself the question, is it 

9 possible that this guy could have had this 

10 much intake? Because that is what you are 

11 going to need to get him over 50 percent.

12 In a way, I am making an argument 

13 that, to a large extent, this is a dose 

14 reconstruction program, but to a certain 

15 extent it is really a compensation program. 

16 And the two sometimes are problematic. 

17 Sometimes you really can't reconstruct the 

18 dose, but you probably can make a statement 

19 that it looks like it is virtually impossible 

20 that this guy could have gotten more than 50 

21 percent.

22 And then, right now, 
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227

1 unfortunately, the conversation we are having 

2 is we are trying to come up with all of the 

3 statistical tools that will allow us to go 

4 through a process that will get us where we 

5 want to go to make a good decision. And I 

6 don't think in the end you can accomplish 

7 that. I think in the end the question 

8 becomes, on a case-by-case basis, have you 

9 done the right thing by way of this guy in 

10 terms of trying to assign the highest11 plausible exposure? And I don't know if you 

12 could standardize that.

13 And I know it is a little 

14 blasphemous to raise it this way, but I think 

15 we are in a place that maybe we can't solve 

16 this problem.

17 CHAIRMAN MELIUS: There goes the 

18 SC&A contract.

19 (Laughter.)

20 MR. KATZ: It's up in December 

21 anyway.

22 (Laughter.)
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228

1 DR. MAURO: I was afraid of that. 

2 I wasn't going to call in. 

3 (Laughter.)

4 CHAIRMAN MELIUS: I'm sorry, 

5 John, I couldn't resist it.

6 DR. MAURO: I know.

7 CHAIRMAN MELIUS: You went from 

8 common sense to a Ouija board I thought there 

9 for a while.

10 (Laughter.)

11 DR. MAKHIJANI: Do we have any 

12 more? I think we are done. Yes, I think we 

13 are pretty much done.

14 CHAIRMAN MELIUS: John, you 

15 finished us off.

16 (Laughter.)

17 DR. MAKHIJANI: Harry, did you 

18 want to go further? I think we are done with 

19 the analytical comments, right? Did you want 

20 to go through the rest of the slides?

21 DR. MAKHIJANI: There are several 

22 recommendations concerning one-sided versus 
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229

1 two-sided tests.

2 DR. MAKHIJANI: Yes.

3 DR. CHMELYNSKI: And, in fact, I 

4 do like the idea that NIOSH throws up here 

5 about testing for a difference which has a

6 practical significance rather than one that 

7 has a statistical significance.

8 DR. MAKHIJANI: That is slide 22, 

9 right?

10 DR. CHMELYNSKI: Slide 22, yes. 

11 There is a formalism here for doing a test 

12 where it has the null hypothesis that, 

13 indeed, there is a difference. And then, the 

14 alternative is that, no, there is not a 

15 difference. I am not sure I would require 

16 that, for all X, then, at least one X should 

17 necessarily be in there, but I will have to 

18 think about that, the way this is phrased.

19 But this is pretty much what we 

20 were asking for, which is, could you turn it 

21 around? Rather than making the assumption 

22 they are the same, can we assume they are 
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230

1 different and, then, look for evidence in the 

2 data that causes us to abandon that position?

3 So, I do think this is a positive 

4 step, trying to look for the significant 

5 difference. But I will point out that they 

6 have a "D" in there. So, it has the same 

7 problem of the other three discussions we 

8 have had. Someone has to figure out how big 

9 a difference is important to find.

10 And not being able to do that 

11 leaves me wondering why we are doing 

12 hypothesis tests if we don't know what it is 

13 we are looking for.

14 MR. LaBONE: We are doing the 

15 hypothesis --

16 DR. CHMELYNSKI: That's the end 

17 of my discussion.

18 (Laughter.)

19 MR. LaBONE: We are doing the 

20 hypothesis test because, again, the whole 

21 purpose of RPRT-0053 was to say, should we 

22 stratify or not? So, again, we have this 
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231

1 binary decision to make. And so, it was, 

2 what is your technical basis for making 

3 decisions to stratify or not stratify?

4 And so, again, we looked at this 

5 equivalence test early on, but, again, after 

6 talking to a number of people and we could 

7 not come up with practical significance, we 

8 just had to move away from it and just go to 

9 statistical significance. That is why we put 

10 it in there.

11 I think we understand what you 

12 are asking for. It is just we couldn't do 

13 it. We didn't know how to do it.

14 DR. CHMELYNSKI: Well, I don't, 

15 either, I have to admit.

16 (Laughter.)

17 MR. LaBONE: Yes. We agree.

18 (Laughter.)

19 Yes, it is a subject matter 

20 decision. It is not a statistical decision. 

21 Yes, yes.

22 CHAIRMAN MELIUS: But I think if 
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232

1 we were able to take on that issue in some 

2 way, that the statistics could be much more 

3 helpful.

4 MR. LaBONE: It will fall way out 

5 of the --

6 CHAIRMAN MELIUS: Yes, yes. I 

7 think that is sort of the bottom line, not 

8 that we have to give up, but the fact that we 

9 would get more information and be able 

10 to -- maybe another way to look at it is we 

11 would have more agreement and better ability 

12 to look at different situations and agree on 

13 how to approach that, and so forth.

14 Tim, you had a --

15 DR. TAULBEE: Couldn't we kind of 

16 take a step back and get away from the 

17 internal for a minute and just look at the 

18 external? Is there any way we could come up 

19 with a practical difference that everybody 

20 could agree with on the external? Then, we 

21 could apply these methods and see how they 

22 come out.
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233

1 CHAIRMAN MELIUS: Yes. No, I 

2 think that should be our next discussion. 

3 And I confess it has been a long while since 

4 I have even looked at an external coworker 

5 model. I don't know --

6 DR. TAULBEE: If you go back 

7 through Tom's breakdown of how we get to 

8 dose --

9 CHAIRMAN MELIUS: Yes.

10 DR. TAULBEE: -- we are already 

11 at the end at that point --

12 CHAIRMAN MELIUS: Yes.

13 DR. TAULBEE: -- with the 

14 external. So, we have a badge --

15 CHAIRMAN MELIUS: Right.

16 DR. TAULBEE: -- associated with 

17 the people. So, we get rid of a lot of these 

18 other censored data type of issues associated 

19 with that. And if we can come up with a 

20 difference that everybody is comfortable 

21 with, then maybe that would help inform this.

22 CHAIRMAN MELIUS: And we have to 
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234

1 come up with a name for that difference, so 

2 we don't cause shockwaves, or whatever.

3 (Laughter.)

4 MR. LaBONE: De minimis is 

5 probably not a good word.

6 CHAIRMAN MELIUS: Yes, de minimis 

7 is not a good word, you know.

8 Let's figure out what number we 

9 want to -- what would be helpful, and then we 

10 will appoint another Work Group to name it, 

11 though, something. I don't know.

12 (Laughter.)

13 People have been clamoring for 

14 more Work Groups, and Wanda will volunteer; I 

15 guarantee it.

16 (Laughter.)

17 DR. TAULBEE: And DOE and NRC 

18 have effectively defined 100 millirem as 

19 their basis for monitoring people.

20 MR. LaBONE: That's true, yes. 

21 But that's not a good reason to choose it for 

22 this.
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235

1 DR. MAKHIJANI: You have to throw 

2 some numbers out there.

3 MR. LaBONE: That is true.

4 DR. MAKHIJANI: I think it is 

5 good starting point in thinking about it.

6 CHAIRMAN MELIUS: And the basis 

7 for that is?

8 DR. NETON: I'm not aware of a 

9 basis for why it's 100.

10 DR. MAKHIJANI: It is 100 

11 millirem is background? Is that probably the 

12 basis for it?

13 DR. NETON: No, no. What's 

14 external background, about 100, right?

15 DR. MAKHIJANI: External, natural 

16 background without radon --

17 DR. NETON: It's about 100.

18 MEMBER ROESSLER: What are these 

19 millirem units you keep using?

20 DR. NETON: I refuse to move 

21 over. Sorry.

22 I think it could be sort of an 
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236

1 increment of a natural background. Because 

2 if you have -- I haven't looked at the tables 

3 in a long time; they have changed, but it is 

4 about 100 millirem internal, 100 millirem 

5 external, and throw radon in there, which is 

6 another 100 or so. Three sixty comes to mind 

7 in total.

8 CHAIRMAN MELIUS: What about for 

9 what we talked about earlier in terms of -- I 

10 think we tied Probability of Causation. So, 

11 the model we are using, I think it may be 

12 more useful, maybe not.

13 And so, we talked before of 

14 taking sort of -- you know, what would make 

15 this substantial or some difference in the 

16 reconstruction for a radiosensitive cancer, 

17 leukemia? We talked about 500 or a rem.

18 DR. NETON: Oh, for a PoC of 50 

19 percent?

20 CHAIRMAN MELIUS: Yes, yes.

21 DR. NETON: About a rem, I think. 

22 You could get the 500 under some very 
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237

1 extreme --

2 DR. TAULBEE: Oh, I wouldn't say 

3 you could get to the 99 percentile out of 500 

4 millirem, but I am saying that where it could 

5 really begin to make a difference is if 

6 somebody already has a few rem type of 

7 scenario. Then, 500 millirem would kick them 

8 over. If you were to see it at the 45th 

9 percentile for leukemia, it would take about 

10 500 millirem to get them over the 50th 

11 percentile.

12 DR. NETON: I don't know. I 

13 mean, there's all kinds of different 

14 permutations that you have to look at. 

15 That's the problem. But I think 100 millirem 

16 would not move things because it is not a 

17 linear scale, right?

18 DR. TAULBEE: No, it is not a 

19 linear relationship.

20 DR. NETON: Right.

21 DR. TAULBEE: And 100 millirem 

22 wouldn't move it very much. We haven't done 
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238

1 that calculation, but, I mean, that's 

2 something we could look at and see.

3 DR. NETON: What if we look at a 

4 few different ones with the external, 100, 

5 500, 1 rem?

6 DR. TAULBEE: We would have to 

7 come up with some various combinations of 

8 scenarios that we think are sort of 

9 maximizing that difference somehow, although 

10 one could always -- I don't know. It would 

11 be hard to -- I wonder if there is a way one 

12 could computerize this and come up with a 

13 maximum, you know, a sensitivity analysis 

14 almost of some sort.

15 MR. KATZ: Well, you don't have 

16 to use the very worst case. You don't have 

17 to base this on that. You just need to find 

18 something that is reasonable as a case of 

19 concern.

20 DR. NETON: Yes.

21 MR. KATZ: I mean, it doesn't 

22 have to represent the very worst case.
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239

1 CHAIRMAN MELIUS: Not the worst 

2 case, but it needs to be --

3 DR. NETON: Pretty close.

4 CHAIRMAN MELIUS: -- close to a 

5 worst case because you don't want to dismiss 

6 it if it is --

7 MR. KATZ: No, no. I am just 

8 saying Jim is saying, you know, you can never 

9 think of all the permutations that could make 

10 for a worst case.

11 CHAIRMAN MELIUS: Yes.

12 MR. KATZ: And you don't have to 

13 get that far, I don't think.

14 CHAIRMAN MELIUS: No, no.

15 MR. KATZ: Just sort of a 

16 reasonably-bad case, whatever, it seems like 

17 would be more than adequate because you are 

18 talking about developing a coarse tool in the 

19 first place.

20 DR. NETON: One thing we do have 

21 is a 40,000 completed dose reconstructions, 

22 so we could add 100 millirem. How many are 
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240

1 under 50 percent? I have forgotten. But the 

2 ones over 50 obviously wouldn't come into 

3 play, but the 30 percent or 60 percent that 

4 are under 50 percent, you could almost look 

5 at those.

6 CHAIRMAN MELIUS: Or look at the 

7 ones 45 to 50.

8 MR. KATZ: Take 45 to 50.

9 DR. NETON: Oh, yes, yes, that's 

10 true, yes. Yes, take the ones that are 

11 closest, so you get the 100 millirem. And 

12 that is about as representative of a sample 

13 as we are going to get of what we have dealt 

14 with. I am not sure if there are issues 

15 doing that or not.

16 DR. TAULBEE: If you had one line 

17 of 100 millirem --

18 DR. NETON: No, no, no. I'm 

19 talking about using real data to -- I don't 

20 know why; I worry about a lot of things.

21 (Laughter.)

22 That's my life.
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241

1 CHAIRMAN MELIUS: Just don't send 

2 out letters.

3 (Laughter.)

4 DR. NETON: Well, I mean, I am 

5 just thinking if we have the definitive list, 

6 or not definitive, but a very good 

7 representative listing for distribution of 

8 potential effects.

9 MR. KATZ: That seems like fairly 

10 compelling.

11 DR. MAURO: Jim, I think that is 

12 a great idea. I mean, what is the smallest 

13 dose that could be added to the 40,000 that 

14 would bring someone that was under over?

15 DR. NETON: It wouldn't be 

16 40,000. It would be the ones that are under 

17 50.

18 DR. MAURO: Right. Exactly.

19 DR. NETON: It would be 60 

20 percent of our cases.

21 DR. MAURO: The 30 percent or so, 

22 right. We are talking only 10,000. I think 
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242

1 about 30 percent out of the 40,000 have been 

2 compensated, on that order.

3 CHAIRMAN MELIUS: But I am not 

4 sure we want to go through and try to find 

5 the smallest dose.

6 DR. NETON: Yes, that's true.

7 CHAIRMAN MELIUS: There is sort 

8 of a practical --

9 DR. MAURO: Yes. But the idea I 

10 like.

11 CHAIRMAN MELIUS: Yes.

12 DR. MAURO: I mean, at what point 

13 does it make some sense to make some changes 

14 to the compensation decisions? We have such 

15 a history of data. That is a practical way 

16 to do it, yes.

17 CHAIRMAN MELIUS: And I think if 

18 you limited yourself, I mean, if you limit 

19 yourself to more radiosensitive --

20 DR. NETON: Yes, we would pick 

21 some cases, the ones that were 40 to 45.

22 CHAIRMAN MELIUS: Yes. Yes.
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243

1 DR. NETON: Interestingly enough, 

2 see, that doesn't factor in the -- what am I 

3 trying to say here? When you get to things 

4 like internal dose, there is not a one-to-one 

5 incremental increase in the organ dose based 

6 on increase in the inhalation rate because 

7 the organs have different simulations.

8 DR. TAULBEE: But if we can't do 

9 this for the external, there is no way we can 

10 do it for the internal.

11 DR. NETON: That's true. Yes, 

12 yes. No, I will grant you, yes. And what I 

13 am saying is it would be less of an effect 

14 from an internal exposure because it would 

15 only affect those organs that assimilate the 

16 material. And you could limit the test cases 

17 to those situations like lungs and liver, and 

18 whatever.

19 I think it is worth pursuing.

20 CHAIRMAN MELIUS: Yes.

21 DR. NETON: And we never 

22 thought -- I mean, we talked about doing 
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244

1 something like this, but this just sort of 

2 popped into my head.

3 Of course, you know what is going 

4 to happen. The data are going to be somewhat 

5 ambiguous. It doesn't make an effect for 99 

6 out of 100 or something like that.

7 MR. KATZ: But it sounds like a 

8 useful task.

9 DR. NETON: It's a start. It's a 

10 start. I'm willing to try this.

11 CHAIRMAN MELIUS: It's a 

12 benchmark we can -- so we are not trying to 

13 do something. And it has applications 

14 elsewhere, which is why I think we need to 

15 put some thought into doing it, not just pick 

16 a number out of the air arbitrarily.

17 And then, at the same time, I 

18 think it would sort of help frame this 

19 situation. And I think it is the only way we 

20 are going to get by this coworker issue, at 

21 least in a way that we can -- how to say 

22 it? -- be consistent from site to site and 
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245

1 understand how to weigh different factors, 

2 and so forth.

3 DR. NETON: I also like the fact 

4 that it happens to coincide with increments 

5 of background to some degree. I mean, you 

6 have distribution in the background. I mean, 

7 a person in Denver versus a person here. I 

8 mean, so that is all kind of built into the 

9 general background. It is not a good reason, 

10 but it is another component of that.

11 DR. MAKHIJANI: Well, I'm not so 

12 sure about that. I'm not so sure about that. 

13 Because what I was going to say is that we 

14 have got to make an assumption that 

15 background doesn't cause any cancer. It may 

16 cause 1 percent of the cancers.

17 DR. NETON: Yes, but it is not 

18 DOE-related.

19 DR. MAKHIJANI: Yet, not DOE20 related, no. You said that it would help 

21 with communication to the public.

22 DR. NETON: Oh, no.
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246

1 DR. MAKHIJANI: And then, you add 

2 the radon, and so on.

3 DR. NETON: I agree.

4 DR. MAKHIJANI: You know, EPA 

5 says a certain number of cancers from radon.

6 DR. NETON: Yes, when you are on 

7 a threshold --

8 DR. MAKHIJANI: Yes, right. So, 

9 I think proceeding on the practical, I think 

10 a different approach to how to present this.

11 But, Harry, did you have a 

12 problem with where we're headed?

13 DR. CHMELYNSKI: I'm sorry. That 

14 is exactly where I think it needs to be done. 

15 This is how big of a difference are we 

16 looking for. I think you have to translate 

17 it down into risk in order to standardize 

18 that difference over sites.

19 DR. MAKHIJANI: Okay.

20 DR. NETON: So, the question is, 

21 if you add 100 millirem, would that be your 

22 lifetime dose, not your lifetime, but your 
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1 worker dose?

2 DR. TAULBEE: I would say 100 

3 millirem in one year, which would be what 

4 point in relation to the cancer where it 

5 would have maximum effect on the latency, the 

6 latency curve.

7 DR. MAKHIJANI: Some homework 

8 could be done.

9 DR. NETON: Yes. Yes, you don't 

10 want to add 100 millirem the year before 

11 everybody got their cancer because it is 

12 going to be zero effect.

13 DR. TAULBEE: Yes, do it the 

14 first day of employment.

15 DR. NETON: Yes.

16 DR. TAULBEE: I mean, the only 

17 one that is going to decrease is the 

18 leukemia, and that one you would have to try 

19 to figure out.

20 DR. NETON: Well, we would have 

21 to outline the parameters.

22 CHAIRMAN MELIUS: Yes, we don't 
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248

1 want something just very extreme, I think.

2 MR. LaBONE: Well, I think you 

3 should really carefully think very hard about 

4 how you are going to do it, and then do it. 

5 Don't play with it and iterate until you get 

6 the answer you want.

7 (Laughter.)

8 I mean, don't tinker, you know.

9 CHAIRMAN MELIUS: Design the 

10 study.

11 DR. NETON: Design the experiment 

12 upfront. I totally agree with you.

13 MR. LaBONE: Yes.

14 DR. NETON: You have to define 

15 your parameters. I am not saying that we 

16 know and then move it around.

17 MR. LaBONE: Okay.

18 MR. KATZ: But you could have 

19 several starting points in mind and could 

20 test them all. I mean, you could have more 

21 than one in mind, construct in mind, and test 

22 it.
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249

1 DR. NETON: Yes. Well, I think 

2 we would have to develop a test plan, and 

3 then maybe even get it vetted to some degree 

4 with others, just so we aren't accused of 

5 doing exactly that, like rigging the 

6 experiment or whatever you want to call it.

7 MR. LaBONE: Well, again, you get 

8 your training data set and then your test 

9 set. So, you can play with the training set 

10 and then --

11 DR. NETON: But can we do a power 

12 calculation?

13 (Laughter.)

14 CHAIRMAN MELIUS: I mean, you are 

15 going to be collecting more dose 

16 reconstruction.

17 DR. NETON: We can always get 

18 more, right? Well, I'm game for doing this 

19 experiment.

20 MR. LaBONE: In the game plan, 

21 are you game?

22 DR. NETON: Yes, I haven't heard 
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250

1 Stu say anything.

2 MR. HINNEFELD: Oh, well, yes, I 

3 mean, if the Work Group wants us to do this 

4 task, we will take it on, recognizing all of 

5 the priorities we face and then the monetary 

6 restrictions.

7 It occurs to me that we are 

8 talking about here a coworker model, right? 

9 We are talking about can we build a coworker 

10 model, which then will be applied to 

11 unmonitored work.

12 DR. NETON: No, no, no.

13 MR. HINNEFELD: No, that's not 

14 what we're talking, not this exercise.

15 DR. NETON: Oh, yes. Yes.

16 MR. HINNEFELD: I am talking 

17 about our broad discussion.

18 DR. NETON: Yes.

19 MR. HINNEFELD: Our broad 

20 discussion today was, can we acceptably build 

21 a coworker model to apply to unmonitored 

22 workers? And in order to do that, we have 
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251

1 this population of monitored workers, many of 

2 whom are probably completely monitored.

3 So, if we reach the conclusion 

4 that there is not a way to build a coworker 

5 model for those unmonitored employees, the 

6 logical conclusion is that the unmonitored 

7 employees would go in an SEC, while the 

8 monitored employees, who are quite likely the 

9 more highly exposed, will go through dose 

10 reconstruction. I mean, that is where this 

11 decision could lead.

12 MEMBER ROESSLER: And that seems 

13 like such an unclear --

14 MR. HINNEFELD: That is why I 

15 brought it up.

16 MEMBER ROESSLER: When I think 

17 about that, it is just --

18 MR. HINNEFELD: How do I go to my 

19 Director and say, "So, we have concluded that 

20 there is not a way to build the coworker 

21 model. So, these people who were not 

22 monitored, we cannot reconstruct their doses. 
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252

1 And so, we are going to recommend an SEC 

2 Class for those. But the people who were 

3 completely monitored, we can do those dose 

4 reconstructions. And so, those will have to 

5 undergo dose reconstruction"?

6 So, that is the outcome of 

7 rejecting, of saying there is no way to do a 

8 coworker model. Am I wrong on that?

9 CHAIRMAN MELIUS: Well, I don't 

10 think that we're talking about that at this 

11 point.

12 MR. HINNEFELD: Okay.

13 CHAIRMAN MELIUS: I don't think 

14 that is even on the table at this point. I 

15 think what is on the table right now is what 

16 are the best ways of doing coworker models 

17 and how does it have to be done.

18 MR. HINNEFELD: Okay. That's 

19 good.

20 CHAIRMAN MELIUS: And then, how 

21 do we deal with stratification and other 

22 issues, which, again, may mean that certain 
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253

1 strata may not end up -- may end up in the 

2 SEC or something, which is sort of what 

3 happened at Fernald. It may not be because 

4 of the statistical issues. It may be because 

5 of just lack of data, and so forth.

6 But I think we are more 

7 likely -- sort of what is the best way of 

8 constructing and evaluating coworker models?

9 MR. HINNEFELD: Okay.

10 CHAIRMAN MELIUS: And I don't 

11 think we are at the point to even --

12 MR. HINNEFELD: Okay.

13 CHAIRMAN MELIUS: At least I'm 

14 not.

15 MR. HINNEFELD: That is just one 

16 thing that worries me when I think about it.

17 CHAIRMAN MELIUS: Yes.

18 MR. HINNEFELD: And then, I 

19 always worry when we talk about getting more 

20 data because just resources being what they 

21 are, if we can accomplish what we need to 

22 accomplish without -- when I say getting more 
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254

1 data, I mean going and capturing all the 

2 monitoring data which we then have to code 

3 and go enter and build our database from 

4 additional data. That is almost always a 

5 long effort, and that almost always gives me 

6 pause.

7 CHAIRMAN MELIUS: But I think 

8 this is also a way of evaluating how 

9 much -- do you need more data? How much more 

10 do you need? 

11 MR. HINNEFELD: How much more do 

12 you need?

13 CHAIRMAN MELIUS: And then, you 

14 are going to be able to say that is going to 

15 cost "X". Is that feasible or not feasible?

16 MR. HINNEFELD: I don't have any 

17 objection to the course of action that we are 

18 embarking on. That is not what I am worried 

19 about. What I am worried about is ultimately 

20 some of the things I heard discussed today.

21 CHAIRMAN MELIUS: Yes, and I 

22 think that's sort of the resource issue. I 
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255

1 think it is sort of better than 

2 putting -- unless we have a good way of 

3 evaluating these models, then we are going to 

4 be in the situation where the Board and NIOSH 

5 may disagree.

6 And then, the letter is going to 

7 be what I described. It is going to be 

8 saying, you know, NIOSH has sufficient data; 

9 there is sufficient data to do dose 

10 reconstruction, but NIOSH doesn't want to get 

11 it.

12 MR. HINNEFELD: Go get it, yes.

13 CHAIRMAN MELIUS: Or can't afford 

14 it, or whatever, something like that. I 

15 don't think that is where we want to be.

16 MR. HINNEFELD: Yes.

17 CHAIRMAN MELIUS: I mean, it is 

18 in some sense a practical outcome of what is 

19 going on.

20 And we are not going to have a 

21 good -- "Well, how much more data?" How are 

22 you going to say it? Well, you are going to 
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256

1 go talk to John, and is it going to cost a 

2 million or $50,000 or --

3 MR. HINNEFELD: Yes.

4 CHAIRMAN MELIUS: -- a billion, 

5 or whatever?

6 I think the issue we need to be 

7 careful with here is just sort of the 

8 communications issue in terms of how we 

9 describe what this is doing.

10 But what I would hope is that it 

11 is something you can do relatively quickly, 

12 and then say we would have a Work Group call 

13 to discuss it. I am not even going to try to 

14 pin you down to a timeframe right now.

15 DR. NETON: Yes, I have no idea. 

16 It is going to require some programming 

17 efforts on our part. When I always speak 

18 with programmers, I get yelled at.

19 (Laughter.)

20 CHAIRMAN MELIUS: But I would 

21 hope we could do it relatively quickly 

22 because I don't think we need to spend, 
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257

1 should be spending a lot of resources on it, 

2 because I don't think we are trying to be 

3 that exact or specific, or whatever you want 

4 to call it.

5 MR. HINNEFELD: Are we clear on 

6 the task that we have got coming out of here 

7 in terms of using external dose and some 

8 existing cases we have, in the like 45-

9 percent range, about that? How many of those 

10 are we going to do? Actually, first, we are 

11 going to do it by the sampling method.

12 DR. NETON: Yes, you've got to 

13 plan, yes.

14 MR. HINNEFELD: Design the task.

15 CHAIRMAN MELIUS: Yes, we want to 

16 do a technical call, or whatever we want to 

17 call that to --

18 DR. NETON: Everyone might have a 

19 different viewpoint there as to what may or 

20 may not be appropriate. I don't know.

21 MR. HINNEFELD: Okay. So, the 

22 first thing we need to do is design the task.
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258

1 DR. NETON: Right.

2 MR. KATZ: And we may be able to 

3 just circulate that up --

4 MR. HINNEFELD: Yes.

5 MR. KATZ: -- and get written 

6 comments back.

7 DR. NETON: Exactly. We can put 

8 it out there.

9 MR. HINNEFELD: Okay. Okay. We 

10 should be able to do that relatively quickly.

11 DR. TAULBEE: And if we come up 

12 with a value, then your step two would be to 

13 actually for the external do a coworker, 

14 stratify it, and see if we see a difference. 

15 That is step two.

16 CHAIRMAN MELIUS: You circulate 

17 the plan. You need to implement the plan. 

18 We have a call, first of all, to sort of go 

19 over it. And then, we can talk about the 

20 next steps, which I think are just what Tim 

21 is describing.

22 DR. NETON: I was also going to 
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259

1 ask -- I'm a little confused, not confused, 

2 but I am concerned about how this is going to 

3 play out. So, we end up with -- let's say, 

4 for instance, that the ideal situation is we 

5 find no difference or no practical difference 

6 at 100 millirem with these test cases. So, 

7 then, we are going to use that as our sort of 

8 benchmark to compute or evaluate significance 

9 of difference between coworker models, right? 

10 Stratification? Is that the case?

11 So, let's say in one year, 1976, 

12 we have a geometric mean of "X" for all 

13 workers and a higher value for construction 

14 workers. Do we just compare those and say, 

15 is there a 100-millirem difference? I mean, 

16 what are we doing here? Are we just doing a 

17 statistical analysis?

18 The test is going to be the same. 

19 It is not going to be able to see -- it is 

20 not going to have much power because of the 

21 numbers, right?

22 MR. LaBONE: Yes, but if you 
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260

1 don't have sufficient power, then you fail; 

2 you basically say they're different and you 

3 stratify. So, if you don't see this 

4 difference --

5 DR. NETON: You can't see 100 

6 millirem --

7 MR. LaBONE: You would stratify.

8 DR. NETON: -- you're going to 

9 stratify.

10 MR. LaBONE: If you can.

11 DR. NETON: Well, yes, that's a 

12 pretty low bar.

13 MR. LaBONE: Yes. But you have 

14 to get the job exposure matrix, though, or 

15 something like that.

16 DR. NETON: Well, that is the 

17 other, you know, the implementation --

18 MR. LaBONE: Yes.

19 DR. NETON: -- is still kind of 

20 fuzzy.

21 CHAIRMAN MELIUS: Yes. I mean, 

22 that's why I don't think you take the one 
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261

1 case that would take the smallest increment 

2 to get over the top, and then we pick 

3 something that is more reasonable.

4 MR. KATZ: But how does that say 

5 it is a 100 millirem -- how does that relate 

6 to what you were talking about before as what 

7 is really a substantial difference? Because 

8 when you are modeling, you are dealing with 

9 taking into account all of that uncertainty 

10 of the GSD, and so on, how does that relate 

11 to that? I'm sorry.

12 DR. NETON: It is more 

13 complicated when you start applying this to 

14 internal. This is external, and Tom and I 

15 were talking. If you can't do it for 

16 external, then there is no chance for 

17 internal. But, at least if we can agree upon 

18 a value of some type as our target, and who 

19 knows, maybe it is more than 100 millirem. I 

20 don't know.

21 CHAIRMAN MELIUS: But getting 

22 back to Stu's concern, you know, if we can't 
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262

1 do it for external, it doesn't mean we throw 

2 out coworker models. I think it is sort of 

3 what is our ability going to be to sort 

4 of -- how do we go about evaluating the 

5 stratification issue?

6 MR. HINNEFELD: I kind of 

7 followed that. I kind of followed the 

8 discussion.

9 CHAIRMAN MELIUS: Yes.

10 MR. HINNEFELD: So, I kind of 

11 know what we are looking for here.

12 CHAIRMAN MELIUS: Yes.

13 MR. HINNEFELD: I did take 

14 statistics, and I do remember half of it.

15 (Laughter.)

16 DR. NETON: All right. This we 

17 can do. I think we have got a shot at doing 

18 something here that is of use.

19 CHAIRMAN MELIUS: I have a very 

20 practical question. What's the timeframe for 

21 people getting to the airport?

22 MR. KATZ: We have a range 
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263

1 of -- who's our earliest?

2 MEMBER ROESSLER: You are.

3 MR. KATZ: 6:00.

4 CHAIRMAN MELIUS: What I was 

5 going to propose is we take another 15-minute 

6 break, come back, and spend a little bit of 

7 time, some time, going over sort of what are 

8 some of the other coworker, some of the other 

9 issues related to the evaluation of coworker 

10 models that we ought to be thinking about. 

11 And it would be, again, the idea of coming to 

12 a set of guidelines to how we evaluate. I 

13 don't think these would be as sophisticated 

14 or statistically-oriented as before. But I 

15 think they do weigh into that.

16 And I have put together sort of a 

17 list here. I think we can add to it and talk 

18 about that.

19 MR. KATZ: Okay. So, we will 

20 break until 25 after, around there.

21 CHAIRMAN MELIUS: Yes.

22 MR. KATZ: I will put the phone 
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264

1 on mute, and we will back with you soon.

2 Thanks.

3 (Whereupon, the foregoing matter 

4 went off the record at 2:09 p.m. and went 

5 back on the record at 2:26 p.m.)

6 MR. KATZ: We're back. We're 

7 back to discuss other matters, related 

8 matters.

9 CHAIRMAN MELIUS: And now that 

10 Stu is gone, what would you like to talk 

11 about?

12 (Laughter.)

13 So, what I thought would be worth 

14 spending some time on is sort of what else is 

15 part of the evaluation of coworker data sets 

16 or should be part of the evaluation of 

17 coworker data sets. And I don't even know if 

18 there is any sort of technical document on 

19 this or not. I know it is not what 53 was 

20 intended for, though I think you ended up 

21 touching on it, and certainly in the back22 and-forth with SC&A and sort of what we have 
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1 talked about even here today with it.

2 And then, there is also sort of a 

3 side issue -- maybe we can get that out of 

4 the way first -- which is related, but that 

5 is the multiple sampling problem, OPOS, I 

6 guess, as opposed to opus.

7 (Laughter.)

8 And what I was thinking of doing, 

9 suggesting for that is triaging that to the 

10 Savannah River discussion. Because aren't 

11 you going to be -- hopefully, there is a Work 

12 Group on Savannah River. Is that scheduled 

13 yet?

14 MR. KATZ: Not scheduled yet, no. 

15 It is not scheduled yet.

16 CHAIRMAN MELIUS: Okay.

17 MR. KATZ: We will need one this 

18 fall.

19 CHAIRMAN MELIUS: Is it better to 

20 do that in the context of -- because you have 

21 raised some other --

22 DR. NETON: I think there is some 
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266

1 work to be done there.

2 CHAIRMAN MELIUS: Yes.

3 DR. NETON: I guess I personally 

4 would like to hear what SC&A's opinion might 

5 be, what they could offer, and what might be 

6 a better approach than OPOS. I mean, I don't 

7 know that -- I don't have a sense that SC&A 

8 is arguing that we shouldn't do something. I 

9 don't think you're saying that we leave the 

10 data as we used to and use all 50 samples on 

11 one person and the cumulative probability 

12 distribution.

13 I have a sense that you probably 

14 would agree that that is not appropriate. I 

15 don't know.

16 CHAIRMAN MELIUS: Or another 

17 alternative, I mean, again, I don't want 

18 Arjun or Tim or anybody to be put on the 

19 spot. I think my understanding was that 

20 there were other OPOS issues that were 

21 raised, came up in the Savannah River review, 

22 the recent ones, and so forth.
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267

1 And we can deal with those. We 

2 can do that next time we have the call of 

3 this Work Group, and sort of deal with them 

4 in that context. Or we can let them be dealt 

5 with directly in the Savannah River Work 

6 Group as they come up there.

7 What is the easiest way of doing 

8 it? Because I guess initially we need to 

9 evaluate it, but we need to evaluate it sort 

10 of more systematically than just as it 

11 relates to stratification. That is what I am

12 trying to get at.

13 DR. NETON: But I guess I need to 

14 figure out whether -- you know, there are 

15 implementation issues that have been 

16 identified in the Savannah River, as far as I 

17 know, but the overall concept of OPOS needs 

18 to be decided one way or the other or 

19 discussed.

20 And I have not seen an SC&A 

21 argument that says it is not valid. I have 

22 seen issues by saying they are concerned 
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268

1 about the implementation of it, but I have 

2 not seen any real discussion as to, if it is 

3 not valid, then what is better. Because it 

4 is not just enough for me to say, well, 

5 that's no good. That would imply, then, what 

6 we have done in the past is better. And I 

7 certainly don't think that is the case.

8 DR. MAKHIJANI: Well, we haven't 

9 considered the question of the alternative 

10 carefully. We have certainly raised some 

11 issues.

12 I don't know if John Stiver is on 

13 the line. But, you know, Joyce has been very 

14 much in terms of internal dosimetry and how 

15 the data are handled, and she has been very 

16 central to both the Savannah River reports 

17 that we have produced.

18 So, I think if the Working Group 

19 charges us to say, "Well, you know, you have 

20 raised some concerns with OPOS. What do you 

21 think should be done? Or do you think that 

22 individual data are better? If neither is 
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269

1 very good, then what is your alternative?", 

2 it is something certainly we can take back 

3 and look at. Or maybe we should have a Work 

4 Group meeting first, and then take that back. 

5 I don't know what you would prefer.

6 CHAIRMAN MELIUS: Or maybe it is 

7 to have the Work Group charge SC&A with 

8 doing -- I don't necessarily think it would 

9 be a very long report, but just a report 

10 summarizing what some of the concerns are 

11 about OPOS, and maybe let's not say "solve 

12 it" or an alternative, but at least flesh out 

13 those implementation concerns as well as the 

14 statistical sort of concerns about it that 

15 came up in this stratification review. I 

16 mean, I think it is already in the 

17 stratification report pretty much.

18 MEMBER ROESSLER: But it would 

19 also have to have an alternative, too, I 

20 think, because we have heard the concerns. A 

21 summary of it would be helpful, but I think 

22 we would want to --
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270

1 CHAIRMAN MELIUS: Well, yes, but 

2 I would rather discuss the concerns and make 

3 sure we, as a Work Group/Board, sort of agree 

4 that those are our concerns and that we need 

5 to address them.

6 I don't like to charge SC&A with 

7 fixing things. Because, then, we end up in 

8 the position of then essentially charging 

9 ORAU and NIOSH with reviewing the fix, and it 

10 just gets -- it is a little bit awkward. And 

11 I think we should do it stepwise. I don't 

12 think this is -- it is not like we are going 

13 to make some changes immediately.

14 Josie?

15 MEMBER BEACH: So, Jim, in 

16 following with the reports, SC&A put in their 

17 evaluation, and then NIOSH responded, and I 

18 think it would be helpful, too, if SC&A went 

19 back and responded to some of the comments in 

20 this report.

21 DR. MAKHIJANI: What occurs to 

22 me, both from what Jim said and what you just 
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271

1 said, Josie, is some of the issues came up 

2 when we actually looked at the 

3 implementation, and some of them came up in 

4 the course of the statistical review.

5 And I think it would be useful, 

6 as you said, to put all the OPOS concerns --

7 MEMBER BEACH: In a matrix or --

8 DR. MAKHIJANI: -- in one 

9 document, so the Work Group can look at it 

10 and its integrity and say this is where we 

11 are with this particular approach to 

12 compiling the data and addressing it for dose 

13 reconstruction or coworker models in general.

14 DR. TAULBEE: Yes, and I think in 

15 this Work Group it seems to make more sense 

16 because this is a mobile issue.

17 CHAIRMAN MELIUS: Okay. Fine. 

18 Okay.

19 DR. TAULBEE: Any other coworker 

20 model.

21 CHAIRMAN MELIUS: Okay.

22 DR. MAKHIJANI: We could 
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272

1 certainly do that as a next step. It won't 

2 be a huge thing because --

3 DR. TAULBEE: Yes, yes.

4 DR. MAKHIJANI: -- we are not 

5 having any new analysis, basically, to 

6 gather. And that way, we can get Joyce's 

7 input --

8 DR. TAULBEE: Yes.

9 DR. MAKHIJANI: -- and, of 

10 course, John Stiver's input, you know, the 

11 input of all the people on our team who have 

12 been involved with this issue.

13 MR. KATZ: But I think it would 

14 be doing more than summarizing what they 

15 have. They would be integrating what they 

16 have learned in this discussion, too.

17 CHAIRMAN MELIUS: Yes, yes, yes.

18 DR. MAKHIJANI: And to address 

19 Josie's point, you know, we have gone through 

20 SC&A's report, what we have discussed today, 

21 and we can integrate some of our responses. 

22 Obviously, we don't disagree with everything 
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273

1 necessarily.

2 MEMBER BEACH: Sure.

3 DR. MAKHIJANI: I thought Harry 

4 made some of that clear, but --

5 MEMBER BEACH: Well, and NIOSH 

6 brought up some points that they didn't feel 

7 like SC&A addressed in their writeup. That 

8 maybe needs to be looked at.

9 DR. MAKHIJANI: But my question 

10 would be, do you want that all in the 

11 same -- because if you want, then, an OPOS 

12 kind of framework, because OPOS is a pretty 

13 huge issue --

14 MEMBER BEACH: Yes.

15 DR. MAKHIJANI: -- because you 

16 are proposing to go back and redo all those 

17 other coworker models. So, I think it is a 

18 very big deal in terms of the amount of 

19 effort and work involved and redoing all the 

20 dose reconstructions, and so on.

21 So, my sort of tentative 

22 suggestion for your consideration would be 
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274

1 that, if you want more of a response than the 

2 slides we have just gone through and the 

3 discussion we have had for the record, that 

4 we respond to the work that NIOSH, the 

5 response that NIOSH has given and some 

6 commentary on that separately from bringing 

7 the OPOS concerns into one document and 

8 discussing that as such, so that you can 

9 arrive at a conclusion. We can do it in the 

10 same document, whatever you prefer.

11 DR. NETON: Well, I think that 

12 OPOS would be good to be summarized in one 

13 document, yes. But the other concerns I 

14 think can wait until we flesh out this 

15 practical significance issue because I think 

16 that is going to drive a lot of what happens 

17 in our disagreement. You know, these 

18 statistical tests and all this power 

19 calculations stuff is all dependent upon what 

20 this practical significance comes out to be.

21 DR. MAKHIJANI: Yes.

22 DR. NETON: And those issues, in 
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275

1 my mind, are very much up in the air until we 

2 come to grips with the practical 

3 significance. So, I don't know that it would 

4 be helpful for us to get a counter-response 

5 to SC&A's --

6 DR. MAKHIJANI: I agree with you, 

7 Jim, because, really, there are two big bins 

8 of problems. One bin is the OPOS-related 

9 bin, and the other relates to can you decide 

10 whether these distributions are the same, you 

11 know, and whether we should stratify or not. 

12 And do we have enough samples? What is the 

13 delta that they are looking for, and so on.

14 I mean, I don't have the whole 

15 universe of things in front of my eyes right 

16 now, but those are certainly two very big 

17 bins in which you can put the issues that we 

18 have raised. I agree with you. 

19 DR. NETON: I think summarizing 

20 what your current thinking on OPOS --

21 DR. MAKHIJANI: Yes.

22 DR. NETON: -- in light of what 
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276

1 we have discussed and what we have commented 

2 on, and what you have learned --

3 CHAIRMAN MELIUS: What we have 

4 seen and commented on the SRS reports --

5 DR. NETON: Right, right.

6 CHAIRMAN MELIUS: -- yes, that 

7 would be helpful and I think useful for us, 

8 as long as there is enough overlap, so that 

9 we are not -- I don't want to hold up SRS.

10 DR. MAKHIJANI: Tim has our 

11 report.

12 DR. TAULBEE: Yes.

13 DR. MAKHIJANI: I mean, there are 

14 a couple of issues with SRS, actually. One 

15 is that you have two reports from us. And 

16 presumably, you are preparing some kind of a 

17 response or I don't know what.

18 DR. TAULBEE: Jim?

19 (Laughter.)

20 DR. NETON: I haven't really 

21 gotten into them yet.

22 DR. MAKHIJANI: Okay.
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277

1 DR. NETON: Yes, we will 

2 certainly respond, but I'm not sure to the 

3 extent there is overlap, though, between what 

4 we have talked about today and what is in 

5 those reports. I mean, they are not really 

6 separate --

7 DR. MAKHIJANI: There is a lot of 

8 overlap, but there are also particular issues 

9 related to the Savannah River Site and that 

10 data set.

11 And since in the neptunium report 

12 there is a particular dose reconstruction 

13 method for using whole body data, and a lot 

14 of concerns that were raised with that --

15 DR. NETON: Okay. Well, to the 

16 extent we can answer that --

17 DR. MAKHIJANI: Okay. Yes.

18 DR. NETON: -- and then, I think 

19 as Dr. Melius starts enumerating these other 

20 issues, that may help us figure out where we 

21 are heading with the Savannah River. I mean, 

22 what needs to be described in more detail in 
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278

1 order to apply a coworker model, because 

2 right now there is no guidance. The coworker 

3 model, the only guidance we have is how to 

4 fit a log-normal distribution to a data set 

5 really. I mean, that's it.

6 And so, hopefully, we will 

7 enumerate some things here that need to be 

8 fleshed-out to provide guidance as to how we 

9 need to demonstrate that the data -- see, it 

10 is one thing to say the data need to be 

11 stratified because there is a statistical 

12 difference or practical difference. But my 

13 other opinion is, are those people that 

14 weren't monitored really representative of 

15 the ones that were monitored? They may be 

16 lower exposed.

17 DR. MAKHIJANI: Yes. I mean, if 

18 you look, I think the most recent report in 

19 my mind, if you look at that report, you will 

20 see a lot of findings are not dependent on 

21 OPOS and the concerns of that. I think you 

22 must have at least taken a quick look at it. 
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279

1 They apply generally to the data set and the 

2 approach to dose reconstruction and 

3 sufficiency, and, you know, how you apply 

4 americium to thorium, and whether you can and 

5 when you can, and so on.

6 DR. NETON: Well, we can 

7 address --

8 DR. MAKHIJANI: Yes.

9 DR. NETON: -- we can start to 

10 address that.

11 DR. MAKHIJANI: So, I mean, it is 

12 up to Mark and the Work Group as to the 

13 sequencing in which you want to do this. I 

14 mean, it is fine with us.

15 CHAIRMAN MELIUS: Hey, we got 

16 here first.

17 (Laughter.)

18 DR. TAULBEE: There is the G2K 

19 that came back, because I was tossing it to 

20 you.

21 DR. NETON: Oh, oh.

22 (Laughter.)
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280

1 There are numerous loading issues 

2 at the current time because of the 

3 sequestration. I mean, there are 

4 prioritizations going on. Right now, to be 

5 honest with you, Rocky Flats is driving the 

6 boat as well as the Kansas City plant and a 

7 few other sites that are more critical at 

8 this juncture.

9 I don't know. We can put it on 

10 the list, but we are going to have to discuss 

11 that with our contractor to see where the 

12 funds --

13 CHAIRMAN MELIUS: And as you 

14 discuss this, since we are going to Savannah 

15 River in March, my recommendation is that we 

16 aim to move this up on the --

17 DR. NETON: We will.

18 CHAIRMAN MELIUS: Yes, yes, yes.

19 DR. NETON: We will, but right 

20 now all eyes are on Denver at this point.

21 CHAIRMAN MELIUS: Well, yes, but 

22 in three weeks we can look the other way.
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281

1 (Laughter.)

2 DR. NETON: Maybe.

3 CHAIRMAN MELIUS: Or whatever. 

4 No, I understand.

5 DR. NETON: To the extent we can, 

6 we can try to address the issues that are not 

7 OPOS-related and more generic.

8 CHAIRMAN MELIUS: Yes. Again, I 

9 have read the report. I think they raise 

10 significant issues.

11 I feel like Oprah. "You really 

12 should read this book."

13 (Laughter.)

14 DR. MAKHIJANI: One point I would 

15 just like to clarify is earlier I thought Tim 

16 agreed to look at this whole question of 

17 actually how the OPOS data were compiled and 

18 how the censoring was done or not done.

19 DR. TAULBEE: Yes. We actually 

20 looked at that over lunch. And, yes, the 

21 implementation was not per procedure, and we 

22 are going to go back and redo that.
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282

1 DR. MAKHIJANI: Thank you.

2 So, that resolves a pretty big --

3 DR. TAULBEE: Yes.

4 DR. MAKHIJANI: Is that across 

5 the board or is it only in the americium? 

6 Because we only looked at the americium.

7 DR. TAULBEE: It is in the 

8 neptunium.

9 DR. MAKHIJANI: It is also in the 

10 neptunium?

11 DR. TAULBEE: Yes, it is in the 

12 neptunium, too.

13 DR. MAKHIJANI: Okay.

14 DR. TAULBEE: But they ended up 

15 applying the -- when you have a negative 

16 value and you chalk it up to the protection 

17 limit that should have been done before OPOS 

18 was run --

19 DR. MAKHIJANI: Right.

20 DR. TAULBEE: -- but they did it 

21 after OPOS was run.

22 DR. MAKHIJANI: Right. And it 
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283

1 made a very huge difference.

2 DR. TAULBEE: So, that's where 

3 we're at. We don't know how big of a 

4 difference it makes from that standpoint, but 

5 we will look --

6 DR. MAKHIJANI: In some years it 

7 won't make a difference, and in some years it 

8 will make a pretty big difference, according 

9 to the compilation that we did. Bob Barton 

10 actually did it.

11 DR. TAULBEE: Okay. But, yes, we 

12 recognize that that was --

13 DR. MAKHIJANI: So, that is at 

14 least resolved?

15 DR. TAULBEE: Yes.

16 CHAIRMAN MELIUS: Good. So, we 

17 made progress.

18 DR. TAULBEE: Yes. Yes, we did.

19 DR. MAKHIJANI: So, our one 

20 takeaway is to give you sort of an integral 

21 report on OPOS.

22 CHAIRMAN MELIUS: Yes. I'm 
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1 trying to remember what the acronym means.

2 (Laughter.)

3 DR. TAULBEE: One person, one 

4 sample.

5 CHAIRMAN MELIUS: Yes, I know, 

6 but -- all right. It is not intuitive in my 

7 world.

8 (Laughter.)

9 MEMBER ROESSLER: I thought it 

10 was one of the best ones.

11 DR. TAULBEE: Me, too, but, then, 

12 again, we are health physicists.

13 MEMBER ROESSLER: Yes.

14 (Laughter.)

15 CHAIRMAN MELIUS: Yes, but you 

16 start with a person with multiple samples. 

17 That is why maybe it should be "OPOMS" or 

18 something.

19 (Laughter.)

20 Or make one sample for multiple.

21 DR. NETON: Something like 

22 "OPOR," one person, one result.
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1 (Laughter.) 

2 CHAIRMAN MELIUS: Yes. Maybe we 

3 can translate it to German, make it real 

4 long.

5 (Laughter.)

6 You can tell we are all doing 

7 good here. It is late in the day.

8 So, actually, my list for sort of 

9 other coworker issues, I think one issue 

10 that -- I think it is very general -- is sort 

11 of when do we apply a coworker model. How 

12 much sampling data does there need to be 

13 available? It is sort of the 30 issue, but 

14 it is applied -- is it 30 out of 100 or 30 

15 out of 10,000 people, persons?

16 And again, that doesn't have a 

17 simple answer, but I think it is sort of a 

18 general guideline going forward. So, I mean, 

19 that is one of the things that I think we 

20 need to look into.

21 And then, it is for each of 

22 those -- I mean, I think we have already 
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1 talked about it at greater length here 

2 today -- sort of representativeness. What do 

3 these sampling data -- you know, how 

4 representative are they and what do they 

5 represent in terms of exposure potential?

6 And then, of those, of the 

7 different exposure potentials they represent, 

8 what data is available; what data is missing 

9 on those? I mean, I think we have talked at 

10 this at length on the sort of routine versus 

11 incident-driven, or whatever, for 

12 construction workers and others.

13 And as I was making notes, sort 

14 of doing this under stratification, but it is 

15 really part of the evaluation. I think the 

16 thing about how do we decide what to 

17 stratify, and we have already used a priori 

18 to stratify on year. That, I think, has been 

19 the general approach. And that is somewhat 

20 arbitrary, but it may make sense in terms of 

21 production and changes within a facility, and 

22 so forth.
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287

1 How do we do it based on job 

2 assignment or task? Again, it is somewhat 

3 limited by what information we have on that 

4 and what is readily available as opposed to 

5 what is maybe not so readily available.

6 And then, this question where 

7 sort of Tom and I sort of went back and forth 

8 on it a little bit. When we have limited 

9 data on a site, I just wonder if we ought to 

10 be sort of looking at the data. We are not 

11 going to be able to determine a priori or we 

12 may not recognize a priori what may be 

13 important strata or significant strata that 

14 ought to be looked at.

15 And so, I do think it takes some, 

16 in some cases it takes looking at the data 

17 and seeing what appears to be different about 

18 that data or the characteristics, what 

19 information we do have, or something.

20 Because I think it seems to me 

21 that in going through all the various sites 

22 we looked at, many sites we have come up with 
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288

1 sort of anomalies, and so forth, that you may 

2 not have expected because we didn't have 

3 complete information, particularly on some of 

4 the older sites, and so forth.

5 And so, I just don't want to get 

6 totally trapped by saying you have to have a 

7 priori strata decided on; you are going to 

8 test those. There ought to be some judgment 

9 involved in that and some attention to the 

10 data.

11 And I don't think you can look 

12 at -- I don't think any person looking at the 

13 data, to look at what is available in terms 

14 of construction or incident data, I don't 

15 think you look at that without sort of having 

16 some sense of what is in there, a judgment. 

17 You know, just who's high; who's low.

18 And so, I think you naturally 

19 pick up on that. You get it from interviews. 

20 You get it from the reports, various reports, 

21 that are done, what types of exposures they 

22 decide to -- or the processes they implement 
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289

1 greater controls on, and so forth.

2 So, again, I wouldn't throw that 

3 out completely, but at the same time I think 

4 there is probably a set of a priori types of 

5 things that you would stratify and which 

6 would be, to some extent, building or 

7 process, where they are working job 

8 assignments, tasks, and so, again, to the 

9 extent that those are available, and so 

10 forth.

11 Does that make any sense?

12 DR. NETON: It does where we have 

13 the data. But I thinking that a lot of our 

14 coworker models are just based on CEDR data, 

15 de-identified data.

16 CHAIRMAN MELIUS: Right.

17 DR. NETON: There is nothing we 

18 can do other than say this is the 

19 distribution that we have for the site. We 

20 can go back and look at the site procedures, 

21 documents, and such, to try to figure out who 

22 was monitored, but we will never be able to 
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290

1 construct any kind of coworker stratification 

2 out of that because we don't have any 

3 information as to who was who.

4 CHAIRMAN MELIUS: And again, I am 

5 not recommending this, but what if you have 

6 the situation where you have essentially a 

7 de-identified data set, but you also have, 

8 you know, it's computerized, readily 

9 available, and you also have a paper data set 

10 that has more? Do you go and take a sample 

11 of that more detailed? And we have struggled 

12 with this.

13 DR. NETON: You can always go 

14 back and look and see what we have.

15 CHAIRMAN MELIUS: Yes, which is 

16 usually what we do as a second step --

17 DR. NETON: Right.

18 CHAIRMAN MELIUS: -- in terms of 

19 what does NOCTS tell us about the data, who 

20 was monitored and stuff.

21 DR. NETON: Yes.

22 CHAIRMAN MELIUS: I understand 
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291

1 where you're coming from.

2 And I'm not thinking that any of 

3 these haven't already been done or the 

4 information isn't available or you haven't 

5 thought about it before. I think, basically, 

6 you're applying sort of a somewhat new 

7 approach to what you have already done.

8 And again, I am not familiar 

9 enough with what you have done in terms of 

10 external monitoring to --

11 DR. NETON: It is very basic.

12 CHAIRMAN MELIUS: Yes.

13 DR. NETON: The geometric mean, 

14 standard deviation of --

15 CHAIRMAN MELIUS: Okay. Don't 

16 tell me that.

17 DR. TAULBEE: But it does 

18 inherently have OPOS in it --

19 CHAIRMAN MELIUS: Yes.

20 DR. TAULBEE: -- because each 

21 person's percentage refers to year.

22 CHAIRMAN MELIUS: Yes, yes. No, 
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292

1 no, it avoids OPOS.

2 DR. TAULBEE: Yes. OPOS is no 

3 longer --

4 CHAIRMAN MELIUS: Yes.

5 So, that was the general list I 

6 had on that. Are there others? I mean, I 

7 know there are others.

8 DR. TAULBEE: I came up with an 

9 initial checklist of things that -- 

10 CHAIRMAN MELIUS: Okay.

11 DR. TAULBEE: -- I thought, 

12 thinking about the Savannah River one, and 

13 what things would help perhaps to give you 

14 all confidence of the sampling program. And 

15 that is to look at the bioassay monitoring 

16 procedures.

17 CHAIRMAN MELIUS: Yes.

18 DR. TAULBEE: Who was sampling, 

19 who wasn't.

20 DR. NETON: I'm sorry?

21 DR. TAULBEE: Look at the 

22 bioassay monitoring procedures --
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293

1 DR. NETON: Yes.

2 DR. TAULBEE: -- and how those 

3 changed over time, because they do change.

4 The incidents that have been 

5 documented, do you see construction trades 

6 workers in these incidents and annotations of 

7 what their bioassay was indicating that they 

8 did followup?

9 And the one that I wanted to 

10 really kind of focus on a little bit, or at 

11 least get some discussion on, is the 

12 population size to the potential for 

13 exposure, because some of these radionuclides 

14 that are exotics, the whole site wasn't 

15 working with. You are looking at a small 

16 group of people of 30 to 40 people that were 

17 working with it. And if you have a bioassay 

18 and it is half of that population, well, 

19 then, it is a pretty reasonable sampling for 

20 that group. Or if you have 100 percent of 

21 the people who are actually doing the work, 

22 then, even if you have a small sample size, 
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294

1 it is okay.

2 DR. NETON: That gets into, then, 

3 you should be able to identify who worked in 

4 those areas, in addition to the ones that 

5 were sampled.

6 DR. TAULBEE: That's right.

7 DR. NETON: Because, if you can't 

8 do that, then you end up in the scenario 

9 where you have to apply it to the entire site 

10 and it becomes, in my opinion, unrealistic at 

11 that point.

12 CHAIRMAN MELIUS: Yes. Going 

13 back to whatever, our significance level, or 

14 whatever we are going to call this, that may 

15 be one way. Do we apply it? How do we apply 

16 it? So, what should the application be in 

17 those instances? And if we are going to 

18 apply -- should we apply the 95th or even the 

19 50th to the entire population? Or do we have

20 30 of 40? What is fair? I mean, that really 

21 is a consideration.

22 Somehow applying that to the 
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295

1 whole, that assumption to the whole 20,000, 

2 or whatever --

3 DR. MAKHIJANI: The trouble with 

4 exotic radionuclides, I agree that 6,000 

5 workers weren't working with thorium or 

6 neptunium, and so on.

7 CHAIRMAN MELIUS: Yes.

8 DR. MAKHIJANI: They were pretty 

9 defined pieces of work that were being done. 

10 The difficulty, to the extent that we have 

11 looked at many worker records and gone into 

12 worker files, and so on, in the course of 

13 producing the reports, unfortunately, the 

14 worker files don't seem to contain -- they 

15 contain locations about the radionuclides 

16 that are monitored. So, if you are looking 

17 at thorium, you won't find any notation about 

18 thorium because thorium wasn't being 

19 explicitly monitored, even though we agreed 

20 with NIOSH that thorium would be contained in 

21 that, in the bioassay sample.

22 Or neptunium, where initially you 
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296

1 had some neptunium notation, but, then, later 

2 on, you are trying to infer neptunium from 

3 other radionuclide whole body data. So, you 

4 don't have neptunium notations in the work 

5 record. So, it is actually very difficult to 

6 know how many workers, to identify the 

7 workers who are working with neptunium.

8 DR. TAULBEE: Well, yes and no. 

9 It depends upon the facility, again.

10 DR. MAKHIJANI: Yes.

11 DR. TAULBEE: And this is a case 

12 where --

13 DR. MAKHIJANI: And maybe this is 

14 a problem for you to sort out.

15 DR. TAULBEE: There are 

16 organizational charts that identify by 

17 building. Take 235F, where they are working 

18 with the neptunium making billets, there is a 

19 breakdown of how many workers were in that 

20 building, for example. So, you do know what 

21 was the general population that was in there. 

22 You don't know how many construction trades 
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297

1 would be moving in and out. But, if you have 

2 got a population of -- and I am just throwing 

3 numbers out here -- of 45 people in that 

4 facility, and you have 30 neptunium OPOS type 

5 of results, and then you have an 

6 additional -- I don't know -- maybe 10 to 20 

7 construction trades workers, it doesn't seem 

8 unreasonable to me that the construction 

9 trades wouldn't outnumber the number that was 

10 in that facility. It would be some fraction, 

11 but that could be quite reasonable.

12 So, it really depends upon the 

13 facility. But, as Jim was pointing out, most 

14 facilities we don't have that level of data. 

15 At Savannah River we happen to because of 

16 access to their database systems, but other 

17 facilities this would be very difficult to 

18 do. I don't think I could do it for Oak 

19 Ridge.

20 DR. NETON: I would say it is 

21 almost impossible.

22 MR. KATZ: Can I ask you, Jim, 
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298

1 your plausibility issues? So, say you have 

2 monitoring on 20 -- there were only 40 people 

3 doing it -- you have monitoring on 20. So, 

4 you think that is pretty good representation 

5 for the 40. But, if you can't identify the 

6 other 20, it could have been any of the rest 

7 of the thousands --

8 DR. TAULBEE: No, no, that's not 

9 true.

10 MR. KATZ: No, I'm not saying 

11 SRS. I was just being more generic than 

12 that.

13 DR. NETON: Well, you're talking 

14 about construction trades or --

15 MR. KATZ: I'm just saying what 

16 you were saying. I'm just going along your 

17 lines. You're saying you have 20. There 

18 were only 40, but, then, can you apply it to 

19 a thousand? Is that plausible to apply it, 

20 you said, to a thousand other people?

21 DR. NETON: I'm talking about 

22 other --
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1 MR. KATZ: Whatever, 1,000 at the 

2 whole site, whatever, or 10,000, whatever it 

3 is.

4 DR. NETON: Everybody, 

5 secretaries, and --

6 MR. KATZ: But here is my -- and 

7 you probably could knock out 

8 secretaries -- but here is my question: I 

9 mean, you have two choices. You can either 

10 apply it to 5,000 people, whatever it is, 

11 knowing just because you can't identify the 

12 other 20 of the 40, or what do you do? Do 

13 you make an SEC for the whole site? I mean, 

14 that is more ridiculous in a way.

15 DR. NETON: But that is what we 

16 do.

17 CHAIRMAN MELIUS: We do the 

18 coworker model, and it is feasible, and then, 

19 we can apply something to everybody on the 

20 site.

21 MR. KATZ: Yes.

22 CHAIRMAN MELIUS: I mean, 
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300

1 whatever it is. But if we can't do the 

2 coworker model, if we reject the coworker 

3 model, then everybody is in the SEC because 

4 we can't put anybody -- yes.

5 MR. KATZ: Right. If you can't 

6 do a model, yes. What I was saying is what 

7 he was saying. You have 20. You know only 

8 40 people did it; you monitored 20. So, you 

9 think 20 is probably a pretty good 

10 representation of 40. Then, better to apply 

11 that basically, that model you make from 20 

12 of them to the whole site, even though, 

13 obviously, you know 5,000 of the people 

14 weren't involved, than to make the whole site 

15 an SEC based on --

16 (Laughter.)

17 DR. NETON: Well, we have done 

18 that. I mean, that is not --

19 MR. KATZ: Well, not that 

20 specific situation where you have 

21 had -- knowing that we have done it where we 

22 weren't able to estimate --
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1 DR. NETON: Exotic radionuclides 

2 have been, outside of thorium, probably the 

3 most popular way to get an SEC. I mean, all 

4 the National Laboratories, how many people 

5 were exposed to fission products at Los 

6 Alamos National Laboratory on a regular 

7 basis? And you say, "Well, we don't know 

8 because there were small, little pockets of 

9 research going on."

10 MR. KATZ: But, see, we don't 

11 know. That's what I'm saying; you don't 

12 know. But, if you know there were only 40 

13 people involved --

14 DR. NETON: Well, if you knew 

15 definitely there were 40 people, and you knew 

16 the names of those people --

17 MR. KATZ: I'll tell you, with a 

18 lot of the exotic cases, you don't know what 

19 that population was. You know it was small, 

20 but you don't know what it was. You don't 

21 even know the boundaries of that population, 

22 and that's different than actually knowing 
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302

1 you have 40 people in this building who were 

2 exposed, and you don't know anything for 

3 anybody else.

4 DR. NETON: Well, here's 

5 typically how this unfolds, though. I'll 

6 just be honest. Okay. We know that it was 

7 40 people, and we have a list. You know, we 

8 show here's the 20 people that worked with 

9 this, and we can reconstruct all of them.

10 But, then, you have a situation, 

11 well, maintenance workers went in there to 

12 clean up the hoods and all that kind of stuff 

13 and the trash collectors.

14 MR. KATZ: Yes.

15 DR. NETON: You have another 

16 population of worker that you just can't put 

17 in time and space in those areas. And then, 

18 it is just a house of cards; it kind of falls 

19 apart. And then, we make it an SEC because 

20 we don't know with any certainty who could 

21 have frequented those areas, aside from the 

22 fact that we know that there were certain 
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303

1 people that were more highly exposed.

2 MR. KATZ: Right.

3 DR. NETON: I mean, they were the 

4 ones that worked with it, but we just don't 

5 know who to assign it to. So, then, you end 

6 up making an SEC out of it.

7 DR. TAULBEE: Why can't you 

8 assign it to everybody who was there on the 

9 site? Or every monitored worker, everybody 

10 except all the secretaries and the --

11 DR. NETON: You know, you get 

12 some very, very bizarre scenarios. Like you 

13 say, okay, I have exotic radionuclides, 

14 curium, neptunium, americium, plutonium. And 

15 I am going to assign exposure to everyone on 

16 site for those nuclides, but only pick the 

17 one that gives the highest dose to that 

18 particular organ to develop cancer. It 

19 becomes a very contorted way of doing 

20 business.

21 CHAIRMAN MELIUS: But the 

22 alternative is also contorted.
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304

1 DR. NETON: Yes. No, that is 

2 what I am saying.

3 CHAIRMAN MELIUS: I mean, it is 

4 not an easy answer, yes. And I think it is 

5 different -- I think Tim's original example 

6 was you could identify 40. You could 

7 identify the 40 and you had the 20.

8 MR. KATZ: Right.

9 CHAIRMAN MELIUS: And then, there 

10 is an additional 20 maintenance workers, 

11 whatever, some unknown number, but defined 

12 number, but maybe a small number. I think 

13 all those situations are sort of somewhat 

14 different.

15 DR. TAULBEE: But I think if you 

16 start going through this kind of checklist of 

17 documenting the procedures, documenting the 

18 incidents, documenting population size, 

19 documenting the potential for exposure and 

20 the size of that population, I think that 

21 gives a weight of evidence of whether this 

22 coworker model is appropriate.
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305

1 DR. NETON: Well, then, you also 

2 have scenarios where you know that this -- I 

3 can't think of a specific site -- but you 

4 know that this occurred on several occasions, 

5 but if you go and look at the inventory over 

6 the entire operating history of the plant, 

7 there has been large, fairly-large quantities 

8 of the material throughout time. And maybe 

9 workers recall that this happened at other 

10 times.

11 MR. KATZ: Yes, but those all 

12 seem perfectly valid then. It is 

13 ambiguous --

14 DR. NETON: Right.

15 MR. KATZ: -- what your outline 

16 of the problem is. It is ambiguous how large 

17 the scope of the problem is. That seems like 

18 an easier matter for saying, okay, so it's an 

19 SEC. We don't know how big this problem is.

20 DR. NETON: Yes, yes, yes. I 

21 hear what you're saying. I agree. If it is 

22 a very confined and well-defined operation 
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306

1 and you can nail it, sure. But, in practice, 

2 that doesn't happen very often, is what I am 

3 saying.

4 MR. KATZ: Okay. Okay.

5 DR. NETON: I have not seen that 

6 sort of a neat, tight package very often in 

7 these 50 sites. Maybe Savannah River is one 

8 of them in certain cases. I don't know if 

9 the whole -- I mean, I hope we are right in 

10 what we have done.

11 CHAIRMAN MELIUS: So, what if you 

12 had five maintenance workers that got 

13 sampled? Do you make a coworker model from 

14 them and apply it to the rest of the site? I 

15 mean, there's lots of --

16 CHAIRMAN MELIUS: I'm comfortable 

17 with saying for the trades workers, to assign 

18 them to the 95th percentile of dose, because 

19 that is what we do for production workers 

20 that weren't monitored. We say they worked 

21 in harm's way, so to speak, working with 

22 unencapsulated materials, and I don't know 
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307

1 what they got, but 95th percentile is 

2 bounding. I'm okay with that. But assigning 

3 the 95th percentile to the whole site or the 

4 50th percentile, I don't know, it just 

5 doesn't --

6 CHAIRMAN MELIUS: What if it is a 

7 security guard that walks around the site and 

8 works there for -- you know, not assigned to 

9 a building, but he works there for 30 years. 

10 Do you come up with a probability of them 

11 being in that building?

12 DR. TAULBEE: I would assign them 

13 to the 50th percentile.

14 CHAIRMAN MELIUS: Or even lower. 

15 I mean, how long have they been there, 15 

16 minutes a day for --

17 DR. NETON: GE, even the thorium 

18 in one building for a few years on one site, 

19 couldn't figure out who went in and out of 

20 that building with any degree of confidence.

21 MR. KATZ: Right, but, again, you 

22 do not have a nicely-defined --
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308

1 DR. NETON: And again, like I 

2 said, that is more typical --

3 MR. KATZ: Right.

4 DR. NETON: -- of the scenario.

5 MR. KATZ: Right.

6 DR. TAULBEE: But, if we could 

7 have found security clearances, we could have 

8 found that data. Then, you could have 

9 defined the Class.

10 DR. NETON: Absolutely. So, yes. 

11 Yes.

12 DR. TAULBEE: Because they 

13 wouldn't be able to go into the building 

14 without a clearance.

15 DR. NETON: Right.

16 DR. TAULBEE: That would have 

17 made it easy.

18 MR. KATZ: Right.

19 DR. NETON: It's not easy.

20 CHAIRMAN MELIUS: Okay. Did we 

21 make it through your list, Tim?

22 DR. TAULBEE: That was it. That 
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309

1 was what I had for my list of things that I 

2 think -- would you all be in agreement with? 

3 We can document that, that that would imply, 

4 show that a coworker was appropriate.

5 CHAIRMAN MELIUS: Yes, yes. No, 

6 I think those are the kinds of things that 

7 ought to be evaluated. Let's say we need to 

8 evaluate those issues, yes.

9 DR. NETON: I don't want to 

10 assign us more work. But I do believe that 

11 we should probably develop some sort of 

12 guidance from within DCAS about how this 

13 works, because we have been doing it sort of 

14 ad hoc, apparently. And if we put 

15 together -- it doesn't have to be a long 

16 document, but just some sort of a TIB, or 

17 whatever, that says here is what you need to 

18 consider when you are developing coworker 

19 models beyond the fact that you can fit a 

20 log-normal distribution to the data set. And 

21 here's important things that need to be 

22 either demonstrated or discussed, or 
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310

1 something like that. I think that would 

2 help. I'm not sure it solves it.

3 DR. NETON: Do you, we, whatever, 

4 have guidance on evaluating 

5 representativeness?

6 DR. NETON: No.

7 CHAIRMAN MELIUS: Because, to me, 

8 that has sort of been the key. It is the one 

9 that we seem to have the most, I won't say 

10 disagreement, but difficulties coming to 

11 terms with.

12 So, again, a lot of it is site13 specific, but, again, I think that in some 

14 level of detail is worth fleshing out.

15 DR. NETON: Yes, I agree.

16 CHAIRMAN MELIUS: Because that is 

17 a real --

18 DR. NETON: Yes, this comes to 

19 mind. You know, we are trying to figure out 

20 right now where to fit an end date for Rocky 

21 Flats. We have an SEC. Well, when did they 

22 become capable of demonstrating of who was 
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311

1 exposed and who wasn't?

2 It turns out there is a lot of 

3 good procedures out there, we are finding 

4 now, that show that they had some very 

5 serious thought that went into who was 

6 monitored and why. This is more modern-era7 type stuff. But, after '92, for example, 

8 very serious consideration as to who had the 

9 potential to receive 100 millirem, and they 

10 were very serious about following that path.

11 You are not going to find that in 

12 the real early years, but maybe something 

13 like that that you can hang your hat on and 

14 say the highest-exposed workers were 

15 monitored, and not only did the procedures 

16 say it, but we have evidence of that.

17 Because I suspect that in many 

18 cases it is not going to be representative; 

19 it is going to be an overestimate because 

20 people that were for the highest exposures 

21 were monitored, not people with the lowest.

22 CHAIRMAN MELIUS: Yes. So, it is 
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312

1 representative of what? I mean, how do we --

2 DR. NETON: And that has sort of 

3 been our -- you know, maybe we have just been 

4 sort of assuming that all along without 

5 really documenting it.

6 DR. TAULBEE: I don't know 

7 whether assuming that, but it hasn't been 

8 documented.

9 DR. NETON: It hasn't been 

10 documented. We have seen evidence of it in 

11 the documents we're looking out without --

12 CHAIRMAN MELIUS: But I think a 

13 lot of it is how far do you have to go. How 

14 many interviews, how many documents?

15 DR. NETON: Yes. No, I agree.

16 CHAIRMAN MELIUS: And again, it 

17 is not the number. It is not going to be 

18 30 --

19 DR. TAULBEE: It is a weight of 

20 evidence.

21 CHAIRMAN MELIUS: It is weight of 

22 evidence.
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313

1 DR. TAULBEE: And you could take 

2 external monitoring at Savannah River and, 

3 then, look at our claimant population. 

4 Eighty percent of the claimants have some 

5 external monitoring data. Twenty percent do 

6 not. So, from the external coworker model, 

7 we are applying this model that we developed 

8 to the 20 percent that weren't monitored if 

9 there is evidence that they worked in a 

10 process area. If they were a secretary in 

11 one of the administrative buildings, we don't 

12 assign them. We assign an admin or an 

13 environmental type of dose.

14 But when you look at the 

15 preponderance of evidence of 80 percent of 

16 the claimants have this monitoring data, 

17 well, that is pretty significant.

18 CHAIRMAN MELIUS: And I don't 

19 know. That is the earlier statements. How 

20 much is enough? 

21 DR. TAULBEE: Exactly.

22 CHAIRMAN MELIUS: Yes, yes, yes. 
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314

1 You know, we take a sample of five and come 

2 up with a good estimate. And actually, we 

3 usually have others. But if we have that on 

4 somebody in the residual period, and so 

5 forth, we probably don't even have that on

6 some of these residual periods. But, if we 

7 did, we would be very content.

8 DR. NETON: Radioactive 

9 materials, outside of DOE, we never had any 

10 monitoring data.

11 CHAIRMAN MELIUS: Yes. You and I 

12 had a back-and-forth about one of the 

13 residual periods. It sort of depends on what 

14 kind of work they did there. Maybe you had a 

15 security guard that was going around the 

16 fence, and whether he or she ever went over 

17 the fence --

18 (Laughter.)

19 MR. KATZ: So, is somebody going 

20 to draw up a list, a sort of framework for 

21 this?

22 DR. NETON: What do you mean, for 
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315

1 the guidance stuff?

2 MR. KATZ: Yes.

3 DR. NETON: Yes, we will put 

4 together a list. We will start with a list 

5 or a topical outline.

6 MR. KATZ: Yes, an outline sort 

7 of thing.

8 CHAIRMAN MELIUS: Yes, do a 

9 topical outline.

10 DR. NETON: Things to consider.

11 CHAIRMAN MELIUS: Maybe an extra 

12 layer of detail on like representativeness 

13 and some of the other --

14 DR. NETON: Sort of an annotated 

15 outline.

16 CHAIRMAN MELIUS: Yes, yes, that 

17 would be --

18 DR. NETON: Yes, we can do that. 

19 I can have that. It won't be before the 

20 Board meeting, I can guarantee you.

21 CHAIRMAN MELIUS: Yes.

22 I think it is related to this 
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316

1 whole issue, but it is also, what assumptions 

2 are we going to make about -- you know, it 

3 comes out of the representativeness, I guess, 

4 is where I was thinking of this. But what do 

5 we assume about a monitoring data set? Do we 

6 assume that it is representative? Do we 

7 assume it is routine versus do we assume that 

8 it is the highest exposure, and so forth? 

9 Because that is really --

10 DR. TAULBEE: It has to be 

11 evaluated before you use it.

12 CHAIRMAN MELIUS: I know, but, 

13 yes, we tend to approach it with, I do not 

14 want to call it bias, but certain assumptions 

15 about it, and so forth. What amounts of 

16 information do we need to evaluate? Or do we 

17 assume that they are stratified and have to 

18 show that they are not? I mean, it is 

19 another way it came up. Now I think we have 

20 got that solved.

21 MR. KATZ: I think it is covered.

22 CHAIRMAN MELIUS: We have got 
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317

1 that covered. But I think in terms of the 

2 other, I think we have to think that through. 

3 Again, it is a point of evaluation, how much 

4 information you need, where you get the 

5 information, and then what you make of it.

6 Anybody else have thoughts?

7 DR. NETON: I'm thought out.

8 (Laughter.)

9 CHAIRMAN MELIUS: I know. I 

10 think we all are.

11 DR. NETON: Yes.

12 MS. LIN: To be clear, the Board 

13 is doing a checklist, and then NIOSH is doing 

14 an internal bound?

15 DR. NETON: No.

16 Is that Jenny?

17 MR. KATZ: Yes, that's Jenny.

18 DR. NETON: Yes, Jenny, I think 

19 NIOSH is going to develop a topical outline 

20 that sort of incorporates these items that we 

21 have discussed, both Tim's checklist issues 

22 and what Dr. Melius pointed out.
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318

1 MS. LIN: Okay.

2 DR. NETON: Sort of path forward 

3 on how we are going to demonstrate that the 

4 data -- how we are going to evaluate whether 

5 stratification needs to be considered or 

6 when --

7 CHAIRMAN MELIUS: It is really 

8 how we approach coworker modeling. I think 

9 that is really what we are --

10 MS. LIN: Yes, I got that part. 

11 I just wasn't sure what product are we going 

12 to see from the Board and from NIOSH. I 

13 don't want to be like coming back from a one14 year deployment and there's like a bunch of 

15 documents.

16 (Laughter.)

17 MR. KATZ: No, no, no. This will 

18 all be finished before you get back.

19 CHAIRMAN MELIUS: We're going to 

20 keep you on the email list. You're going to 

21 keep getting -- keep a big hard disk drive.

22 MS. LIN: Yes, well, I look 
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319

1 forward to it. I need some reading 

2 materials, and maybe they will put me to 

3 sleep.

4 CHAIRMAN MELIUS: Yes, you will 

5 be bored over there.

6 (Laughter.)

7 MEMBER BEACH: And then, SC&A is 

8 going to bring the one; that is just one 

9 action, the OPOS.

10 DR. NETON: And then, we have got 

11 the additional action to look at our NOCTS 

12 data set, look at the practical significance 

13 issue, which in my opinion is probably a 

14 higher priority than anything we are doing 

15 yet. Or maybe not.

16 MR. KATZ: Okay. I think we're 

17 set.

18 CHAIRMAN MELIUS: I have one 

19 final. My understanding is, Jenny, October 1 

20 is your --

21 MR. KATZ: It has been pushed 

22 back.
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320

1 CHAIRMAN MELIUS: Pushed back?

2 MR. KATZ: The 15th.

3 CHAIRMAN MELIUS: The 15th?

4 MS. LIN: Yes, it's the 15th, but 

5 I will be leaving my civilian post a few days 

6 early, so I can pack and drive down.

7 CHAIRMAN MELIUS: Aw, come on.

8 (Laughter.)

9 You're not going to come to 

10 Denver? You're not coming to Denver?

11 MS. LIN: No.

12 MR. KATZ: Basically, just before 

13 Denver.

14 CHAIRMAN MELIUS: I know, just 

15 before Denver, how convenient.

16 MS. LIN: Yes, I know. So sorry.

17 CHAIRMAN MELIUS: Do I have to 

18 call the Defense Department to get this 

19 delayed another week?

20 (Laughter.)

21 MS. LIN: I can't do this. I 

22 have already negotiated with them.
NEAL R. GROSS

COURT REPORTERS AND TRANSCRIBERS

1323 RHODE ISLAND AVE., N.W.

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

This transcript of the Advisory Board on Radiation and Worker Health, SEC Issues Work Group, 

has been reviewed for concerns under the Privacy Act (5 U.S.C. § 552a) and personally 

identifiable information has been redacted as necessary. The transcript, however, has not been 

reviewed and certified by the Chair of the SEC Issues Work Group for accuracy at this time. The 

reader should be cautioned that this transcript is for information only and is subject to change.

321

1 CHAIRMAN MELIUS: Oh, okay.

2 MS. LIN: But if you guys ever do 

3 a Board meeting in D.C. again, then I can 

4 book a tour at the Pentagon for you guys.

5 CHAIRMAN MELIUS: That would be 

6 cool.

7 MS. LIN: Yes, and we can go 

8 golfing at the Andrews Air Force Base. Maybe 

9 you will run into President Obama.

10 (Laughter.)

11 CHAIRMAN MELIUS: And you will 

12 see John Howard. He [identifying 

13 information redacted]. I found that out as 

14 I was going to the airport the last week.

15 MS. LIN: Yes?

16 CHAIRMAN MELIUS: He was on the 

17 Metro with me. He got off first. So, don't 

18 be surprised.

19 MS. LIN: I know, right?

20 CHAIRMAN MELIUS: I didn't know 

21 he [identifying information redacted].

22 MS. LIN: Yes, he was 
NEAL R. GROSS

COURT REPORTERS AND TRANSCRIBERS

1323 RHODE ISLAND AVE., N.W.

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

This transcript of the Advisory Board on Radiation and Worker Health, SEC Issues Work Group, 

has been reviewed for concerns under the Privacy Act (5 U.S.C. § 552a) and personally 

identifiable information has been redacted as necessary. The transcript, however, has not been 

reviewed and certified by the Chair of the SEC Issues Work Group for accuracy at this time. The 

reader should be cautioned that this transcript is for information only and is subject to change.

322

1 telling me there are actually a bunch of 

2 people working at the Pentagon who 

3 [identifying information redacted]. 

4 CHAIRMAN MELIUS: Okay.

5 MS. LIN: Are we still being 

6 transcribed?

7 MR. KATZ: Yes.

8 MS. LIN: Isn't the meeting over 

9 yet?

10 (Laughter.)

11 MR. KATZ: No, we're being 

12 transcribed.

13 MS. LIN: About where John Howard 

14 [identifying information redacted]? 

15 CHAIRMAN MELIUS: I think the 

16 Pentagon is pretty easy to identify, right?

17 MR. KATZ: We can be adjourned at 

18 this point for the transcription's purpose.

19 (Whereupon, at 3:16 p.m., the 

20 meeting was adjourned.)

21 

22 
NEAL R. GROSS

COURT REPORTERS AND TRANSCRIBERS

1323 RHODE ISLAND AVE., N.W.

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

This transcript of the Advisory Board on Radiation and Worker Health, SEC Issues Work Group, 

has been reviewed for concerns under the Privacy Act (5 U.S.C. § 552a) and personally 

identifiable information has been redacted as necessary. The transcript, however, has not been 

reviewed and certified by the Chair of the SEC Issues Work Group for accuracy at this time. The 

reader should be cautioned that this transcript is for information only and is subject to change.

323

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