Ranch hand advisory committee

НазваниеRanch hand advisory committee
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The other one would say that there's a level under which there can be no physiological basis for interaction. There's even a branch of chemistry that believes that you can dilute chemicals forever.

DR. STOTO: I think there's a very different issue than that.

DR. HARRISON: This is my issue, it's not your issue.

DR. STOTO: I don't think anybody really knows what this threshold is, but there is a statistical issue; that is if you take people who have a level of 9 ppt, some of those people will have had 9 ppt every year in the past, and that's just their level because of where they live and stuff like that, what they eat.

Some of these people will have had 18 ten years ago and 36 ten years before that. When we only measure them, at the time, you know, after    we can't distinguish those two groups. So we're basically saying that some of these people had a level of 36 at the time they were in Vietnam, and we're lumping them in the analysis with people who had 9, at the time they were in Vietnam.

[Simultaneous discussion]

DR. HARRISON: I want to make this something that even a statistician can relate to.

Let's imagine that we have a doorbell. let's imagine that that button requires a certain amount of pressure in order to push it in so the damn bell will ring. Okay? It doesn't matter how many fingers are on how many doorbells; if none of them are exerting sufficient pressure, then there's no bells ringing.

DR. STOTO: You're saying if dioxin doesn't have any health effects.

DR. HARRISON: What I'm saying is, we have a very good idea of how dioxin works; we have a very good idea of the molecular mechanisms of dioxin action. And so there has to be some rational    some rational level of dioxin that you can reasonably expect to see an effect, and that's the only question that I was asking.

DR. STOTO: But even if God came down and told us that 10 was the cutoff for having an effect, it doesn't mean the person didn't have 12 two years ago and 36 twenty years ago.

DR. HARRISON: I don't disagree with that at all. But if 10 is already two orders of magnitude below say the dissociation rate constant for the AH receptor, then I, for one, find this to be a trivial exercise.

DR. STOTO: No, it's not because they may have had a substantially higher level of exposure in the past --

DR. GOUGH: You're talking about two different things. You're talking about measurements and Bob's talking about effects. Biological threshold. And Bob's saying that the measurements below the biological threshold --

DR. STOTO: And I'm saying that if the measure is below the biological threshold now, it could have been substantially above it in the future.

DR. GOUGH: But not a hundred fold.

VOICES ALL: In the past.



DR. MICHALEK: There are actually three concepts here, and I think we're all talking about different things. The reason we restrict it to people 10 parts and above is we didn't feel comfortable applying the first order elimination law to individuals who had values below 10.

Because we believe    this is beliefs, we're talking about beliefs. We have beliefs. The people below 10 are in steady state and the people above 10 may not be in steady state. In fact, people way above 10 that we know are not in steady state, they're following the first order elimination.

So it's simply our attempt to isolate the sub-cohort for which the first order model is applicable; and that's why we took people above 10 in the model.

DR. HARRISON: You know, if you want to look at a very similar action, you look at exposure to estrogens in utero and the subsequent development of vaginal cancers.

So you have an exposure and then you have a biological event that takes place 20, 30, 40 years later. But the level of exposure that's biologically relevant is not the level of estrogen in some woman 20 years after the fact. That doesn't have any relevance to it; you could have castrated her at birth --

DR. STOTO: Right.

DR. HARRISON:    and she would still develop   .

DR. STOTO: No, that's a good example here, because the premise behind Model 2 here is that exposure at the time that they served in Vietnam, a body burden of dioxin at the time of Vietnam is the critical thing that may or may not be related to the health.

DR. HARRISON: Yes, and that's biologically plausible.

DR. STOTO: Yes, but by taking everybody who has a level of 10 or below and presuming that they've always had a level of 10 or below, you are biasing that statistical analysis, because some of them had a level higher than 10.

Maybe only a few, or it may not make a difference in terms of the final result --

DR. HARRISON: They may have just hit 10 ten years ago.

DR. STOTO: That's right. That's the point. There may not be a big bias; I don't know. And I would like to see    and I think from the point of view of the blue book, it's fine to do it this way, because it's an improvement over what's been done in the past. But I think that it would be interesting to, as one of the supplementary analyses, to think "Well, suppose that a certain fraction of them had in fact" --

DR. HARRISON: What if you presume that they just reached 10 when they were measured?

DR. STOTO: And do a kind of sensitivity analysis. What difference would that make to the outcome?

DR. HARRISON: Yes. I think that's a nice idea. I like that.

DR. GOUGH: Can't you do that by comparing the comparison group to the backgrounds?

DR. MICHALEK: You would do that plus you'd look for dose response, with increased dioxin levels, to see if there's a continuous pattern.

DR. GOUGH: I want to --

DR. HARRISON: Go ahead. We're still on statistics.

DR. GOUGH:    this is just off the record.

[Omitted by request]

DR. MICHALEK: What's the limit of detection?

DR. GOUGH: I don't know what the limit of detection is.

DR. HARRISON: No, he said background. He said background. Background would be something different.

DR. GOUGH: In the CDC study of ground troops, both the in-Vietnam and the out-of-Vietnam guys were around 5 ppt.

DR. MICHALEK: It looks like it's down to about 4 or less.

DR. GOUGH: It's dropped three times, by a factor of 3, according to EPI. So it's really going down.

DR. HARRISON: But these last drops are not the first order kinetics --


DR. HARRISON:    are not according to first order kinetics, and you think that they are probably due more to environmental issues than --

DR. MICHALEK: No. We don't think that's first order.

DR. HARRISON: That's interesting; how do you --

DR. MICHALEK: Consistent with the idea.

DR. STOTO: So a level of ten is going further under the tail of the --

DR. MICHALEK: If you were to reassess all of the comparisons today based on the data we have now, we would think that 10 would be not the 99 percentile anymore, it might be the 100 percentile. It might be out really farther on the tail.

The 99 percentile today, if you reanalyze solid comparisons, might be down around 9 parts per trillion.

DR. GOUGH: Oh, that's not much difference.

DR. HARRISON: Okay. What else on statistics? Because we've got   

[Simultaneous discussion]

DR. HARRISON: Let's finish statistics, take a five minute break, and then do the public session.

DR. STOTO: They can deliver their in a form other than a floppy disk this time.

DR. HARRISON: It was kind of interesting how there seemed to be some questions in there.

Nothing else then, Mike?


DR. HARRISON: Anybody else with comments on statistics?

DR. HARRISON: You're going to laugh at me for this, but I wish you would define what you mean by the term 'clean.'

DR. MICHALEK: Is that term in here?

DR. HARRISON: You know, "the contractor will clean the data, contractor will"    and I'd like to see that defined and restricted. Because that almost sounds like fudge. "The contractor will fudge the data."

DR. STOTO: It is a commonly used term in the business, but it should be defined.

DR. HARRISON: What you're really saying is, contractor will look at the input documents and reconcile discrepancies?

LTC BURNHAM: Quality assurance.

DR. HARRISON: I'm just saying spell it out a little better.

DR. CAMACHO: Where are you reading that? What is that?

DR. HARRISON: I have it down as, down on page 20, data management activities. But now my 20, actually    because I printed out a section that's different than your 20. So 3.5. I'm sorry; 3.5 and below.

[Dr. Favata arrives]

DR. HARRISON: I'm sorry, Contractor shall clean the data through an ongoing process throughout the data gathering and analysis period.

I just thought that    I understand what he means, I just thought it would be nice to say fundamentally what he meant.

DR. MINER: It's jargon; should be defined.

DR. HARRISON: Dr. Favata has finally ended her trek, trek through hell.

DR. FAVATA: Yes, it was.

DR. HARRISON: Glad you could make it.

DR. FAVATA: Thank you.

DR. HARRISON: We have Dr. Camacho on the telephone. He's ill and wasn't able to travel today. And we were just finishing up the statistical section and we were going to take a break   

DR. FAVATA: That's okay.

DR. HARRISON:    before, then a public comment time, and if there are no more -- did you have any statistical questions, that's 3.6, that's a section I don't know if you looked at or not. Just want to make sure.

DR. FAVATA: No. I have some that    my comments pertain to General Health and Neurology and then I have some comments about 3.3, 3.5.

DR. HARRISON: All right, so why don't we take like a five minute break and then we'll do the public comments. We have at least one person.

If anyone else has something they would like to    no?



DR. MINER: Joel, do you want to add the '91 report?

DR. MICHALEK: I'd just like to add to something that was mentioned before we broke.

The idea was, Mike suggested lowering the threshold, so to speak. To lower the bar on this 10 parts per trillion. Well, we actually did that in 1991. We had it in our first dioxin report. We lowered the bar to 5 parts per trillion. And if you go back to that report, you'll see we did so-called maximal and minimal assumptions on exposure and carried that to great lengths.

So that report's on our web page, too, if you want to go back and browse.

DR. STOTO: If you can just point that, send me an e mail pointing it out to me.


DR. HARRISON: But your point is that it didn't change the interpretation of the results?

DR. MICHALEK: No. In fact, what's happened since then is there has been an evolution to simplify things based on that report. That was a nine volume, 4,000-page report, which was carried to great lengths and created praise as well as frustration, because it was hard to read.

So what we've done since then is tried to simplify the approach; and that's where this 10 parts per trillion cut came from, and through committee review and just like we're having today, we have moved to where we are now. So I'll send an e mail to you on that.

DR. HARRISON: Are you back, Paul?

DR. CAMACHO: All right, move out.

DR. HARRISON: Okay, here we go. This is the open public session, and Dr. Gary Kayajanian is the lone public speaker for today.


DR. KAYAJANIAN: I published two papers on dioxin, in 1997 and 1999, which basically analyzed three studies, Caseba {ph}, the Seveso study in 1993, and the NIOSH report that accompanied the '91 publication by Finker, et al. And I drew the general conclusion that dioxin acted as a promoter-blocker. That is, you could see a reduction after five years of cancer incidence.

Now because the Seveso study was just a ten year study, and because the NIOSH study had workers exposed to lots of other chemicals, the best time period in which to see the effects of the chemical acting as a promoter-blocker was five to ten years after exposure. You have other chemicals present, but you wouldn't see their effects as cancer initiators.

This isn't to say that promotion blocking is the only thing that dioxin does. It's something that you can see in that window of time. That basically has colored my view of what dioxin does.

I had an opportunity to test some of my ideas in a paper that was published last year, and Joel Michalek was gracious enough to provide data I requested from the Right Hand study. And there were a couple of observations probably worth relating from that paper.

The first was that if you compared the background group and the comparison group; the background group was the Ranch Handers with 10 parts per trillion or less, when measured in '87. If you compared cancer incidence, either non-skin cancers, total cancers other than skin, or melanomas, you found they significantly increased, roughly six-fold increase over what you would have expected from a population of men somewhat older than the Southeast Asia populations, who had never served.

This increase was hugely significant. If you then compared the high dose Ranch Handers to the reference population, you found a reduction. So in effect, you have a cancer incidence level, a sixfold increase associated with the Vietnam tours of duty, and then with higher dioxin levels, a reduction of that level about a third, still significantly elevated over men who never went to Southeast Asia.

I've tried to develop an explanation of how dioxin works. And the model I developed generated certain unusual predictions. And the predictions that were expected was that dioxin would have, as you increased    a population as you increased dioxin body burden would have two cancer incidence peaks followed by two cancer incidence valleys.

So what I ended up doing was taking all 2275, 2255 men from the comparison group and the Ranch Handers, put them together, rank ordered them least to most dioxin, and apportioned them into seven groups, least to most. One through seven, least to most.

What you find is a cancer incidence pattern when you go from the first group to the second group. The first group has zero level, or below 1.25, below the limit of detection.

The second group has 1.25 to 2.50. This represents a significant drop.

The third group shows an increase, and that's from 1.5 to 2.5 to 2.51 to 4.0 it goes up.

The fourth group is from 4.01 to 8.0, and that goes up significantly. It goes down from 8.01 to 10.

The final two points are from the Ranch Handers that were above 10 parts per trillion. I didn't use the packaging that Joel used; I simply took a look at the data, and it seemed the first 60 percent of those in group six showed a significantly higher level of cancer.

And then the final group of 224 had a significantly lower one.

So there were five; one, two, three, four, five significant cancer incidence changes as a function of body burden.

DR. GOUGH: How did you make those cuts?
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