Okay, I'm doing my talk. Okay. Is it that time already? Oh, yeah, it is. Yeah, everybody's back. So this next session, I'm going to begin and give you a little bit of talk about some of the major studies in firefighter cancer. If you look at the Association of Work with Cancer Firefighters, there's hundreds and hundreds of articles, and most of them are complete and utter crap. And as we talk, you're going to see our—I don't want to bias you at all about our opinions, but I think you'll see what we all think about the literature that's out there and why. So in your handout, you have all these references. It tells you the exact reference, and it gives you more detail about it. It's in your handout. So I just want to say something. Could you get a little closer to the mic? Oh, I'm sorry. And actually, I got—hello. This is for shorter people. When you talk about using the—so using the literature, using statistics, that's what this is. It's like—and put your favorite word in here. Lawyers, politicians, experts use the literature, the statistics, the way a drunk uses a lamppost. More for support than illumination. Okay? And which means that—so I just had a case. It was a head and neck cancer. HPV, P16 positive, HPV positive, which kind of lets you know a little bit more about the causation of this cancer, you know? So HPV for cervical, rectal, and head and neck cancer is known not only association but causation. So I'm presenting this, and then the expert on the other side for the plaintiff comes up with an article. He comes up with an article that shows that head and neck cancer is increased in firefighters. Very interesting, except for one thing. I read this article, because I had never seen this one before, and guess what? Guess what it does not control for? How'd you guess? It's like, come on. I mean, it's like doing the lung study cancer and not controlling for smoking. And so can you find something that supports your opinion in the literature? Absolutely. There's going to be some article out there in the unpeer-reviewed Journal of Irreproducible Results that will support your position. So they're out there. What I'm going to talk about is the six or seven, sort of what I call the main ones that people go to a lot and that are better studies. I didn't say good. I said better than the others. And these are a couple of them. So one is by Dr. Ma. It was in 2006. It was Florida firefighters from 1972 to 1999, so 27 years. A lot of people, almost 40,000, and a lot of females, which is quite unusual for these studies. But one of the things that you want to look at says 1972. And some of them, you'll see, go back to 1950. Now, I've been around long enough to remember when, if you were a real firefighter, if you were a real man, and you went into a fire, you did not have SCBA. You wouldn't put on that stupid thing. And if you didn't come out coughing up black smoke stuff after your first post-fire cigarette, you weren't a man. When I was running with firefighters, riding with them back in the 70s, they all smoked. I smoked. We smoked in the ER, unless there was oxygen running in the room. So the data that's based on this old, old exposures, these exposures don't happen anymore to that extent. So now the data, right away, here's a problem with all these studies. But anyway, Ma found elevated numbers of, and you guys are going to talk about the numbers, right? About the significance of the SIR? So they found SIRs, the incidence rates. Some of them were mortality, but most of them are on incidence, in bladder, testicular, and thyroid. And a lower incidence overall. Ma found that less, firefighters had less cancer than their comparison group, overall. Especially lung and buccal. Especially lung. Now you would think that lung would be the one thing that's really higher. And this is very inconsistent across the studies. Le Master's meta-analysis. So I told you about Hoffman before, about the abstracts. His partner was Richard Bukata. And his description of meta-analysis, he would say, you know, three second graders do not make a sixth grader. So taking a bunch of data from disparate studies that are done totally different, and clombing them all together, and mixing them up, and saying, well, I've got a bigger group now, and I can come up with better data, is irrational. It's just irrational. But anyway, Le Master's came up with, I won't read all these, but these are probable. I don't know exactly what that means. And then there was a bunch of them were possible. Again, even less meaningful than probable. So that was interesting. International Agency of Research on Cancer, IRC is sort of the big thing. I mean, they've got tons of stuff. Their papers are hundreds of pages long. The one that's 2007 was talking about cancer, not just in firefighters, but in painters, shift workers, and firefighters. So it looks at all kinds of things. It's actually, I'm sorry, it's not a meta-analysis. It's a systemic review which includes meta-analyses and cohort studies and all kinds of stuff, and animal studies. There was a great article I read one time about the carcinogenicity of nickels. Now, you should get rid of all the nickels, because nickels cause cancer. There was an animal study where they took nickels and they embedded them subcutaneously in rats. And the rats got cancer. Ergo, nickels cause cancer. So get them out of your pocket. Use your Vemo. Or Zelle, whatever you happen to have. So IRC in 2007, in spite of everybody saying that this shows that every cancer a firefighter gets is due to it, only found these three. That's what they found in 2007. And this was updated in 2022. And what real change was is they changed it to a class one, which means that they classified firefighting as carcinogenic to humans. It's a very strong statement that if you're a firefighter, it might or might not give you cancer. So that was a big difference. And now they only said they found sufficient evidence for mesothelioma and bladder. Now, I will say mesothelioma does show up consistently. Now, does that make sense? It's probably one of the few that makes sense. I go into a burning building. It's old, because that's why it's burning. It doesn't have sprinklers. And what are you doing? You're tearing down the ceiling, which is the pipe, which is covered with asbestos. And then you're pulling up the linoleum, which has also got asbestos. So the exposure to asbestos for firefighters is probably not unusual. And, ergo, finding mesothelioma, which is found across several, makes sense. Of course, you know what the latency is for that. Stan, what's the latency for that? 30 years? Something like that? Yeah. So, again, this is something that makes sense. But now they only found limited evidence for the things that they before, you know, 15 years before, said were related. So they're sort of hedging their bets a little bit. Daniels was a big study by NIOSH. A member of our group, Tom Hales, was part of this. And, again, 30,000 firefighters in three cities. It was San Francisco, Philly, and Chicago. And they found some interesting things. This is what they found increased. I'm going to tell you something else they found. I want you to remember this when I get to one at the end. They also found that there was no increase in brain cancer, except in the San Francisco cohort. Where there they had an increase in brain cancer. Only in that cohort in San Francisco. Remember that little fact, okay? You all got that in there? Good. Pinkerton was an update on the Daniels and NIOSH study, but this was a mortality update. It was an update on mortality. The first one was on incidence. And they found a new excess of non-Hodgkin's lymphoma and COPD. Great. But what was not controlled for? Smoking. Okay. Does this mean this is good? That you can hang your hat on this? If I sound incredulous, it's because I am. Bukala, this is a group in the Nordic countries, Sweden, Finland, you know those people? The happy people in the world? Yeah, Finland is the happiest country in the world, supposedly. But the Finnish don't think so. So in the younger people, they found increased prostate and skin melanomas, which they didn't see in the older people. And then in the older people, they found they had these other things, which they didn't see in the younger people. Now, the mesothelioma makes sense because of the latency for that. Long, long latency. So, again, starting to see there's all kinds of different stuff, right? Cy in 2015, now this is California. And guess what Cy found? See the last one before the end? Brain. Between the Daniel study, San Francisco cohort, and Cy, nobody else found brain. So is it because you're a firefighter? Or is it because you live in California that gives you brain damage? I'm sorry, cancer. Or both. So, I mean, again, the confounding factors are uncontrollable. This literature is so hard to use because there's so many confounding factors that cannot be controlled for. Everything from exposure to duration to outside, all this stuff is uncontrollable. It's uncontrolled in any of these studies. Brantum was a systemic review. It was actually a pretty good one. It's a Canadian one. And it really sort of uses the Cochrane collaboration kind of process. So they went through 600 articles, took 11 of them, and like I think 14 cohort studies. And that's what they based their results on. Mesothelioma was really the only thing they found to be consistent. And then they said this. No conclusive evidence for association of any other type of cancer with the occupation of firefighting. But since non-Hodgkin's lymphoma and prostate had been found more frequently in firefighters in both the current review and that made previously by AHRQ, that, well, maybe that's also true. But, you know, it was very weak, weak support for that. So I did make this. I made this table. It's really pretty cool, isn't it? And I want you to look at the plotting. So the L means low probability and the S means subgroups. I don't know if you all know about this. As soon as you do a subgroup analysis after you've done the study, you set up your study, use the protocol, and then later you do a subgroup analysis, that's called data dredging. It's so meaningless. Oh, if we look at our data and we grab these two pieces, we find something. Yeah, well, any time there's always going to be something different. So that's bad science, bad, bad science. Does this look pretty random to you as far as which cancers are found? And it is. It's just a random plot, like dot plot. And I think that's the problem with the literature. There is no consistency, and this is what these guys are going to talk to you about when you're relying on this stuff. So thank you, and I'm going to hand it off to the other guys. Thank you. Okay, back to me. I'm actually going to tell you a little bit about firefighters, but since this is the public safety medicine section presenting these talks, we'll talk about other occupations in public safety and then outside. Okay, you've seen my disclaimer, my opinions only, not the federal government or any federal agency. That's what I'm going to be talking about. Okay, so this is a cancer X, okay? So I'm going to ask you a question, and I'll repeat it. But let me ask first, make sure I get it right. So I want to know your opinion. Three answers, three possible answers. The first one is, based on that, your opinion is there is enough evidence to say that firefighting caused cancer X. There's enough evidence. The second answer, option number two, there is enough evidence to say it is not caused by firefighting. And the third one, let's say, I don't have a clue, you don't have enough information, however you want to say it. Again, that's all the studies that look at that cancer X, and you see the number of firefighters with cancer X in each study, and finally you see a standardized incident ratio, mostly. So who votes for number one? Who thinks there is enough evidence to say that there is causation? So one, two, three, four, five, six, so a small number. Okay, let's say less than ten. Who says there is enough evidence to say there is no causation? Two, three, maybe, oh, so two, okay. And who says there is not enough information to make a determination? Okay, that's pretty good. Okay, thank you. We got it. Now, I want to ask these two guys now. Danny, did you vote for number three? I think I saw your hand. How about T, what do you say? What do you say? Number one, there is enough evidence to support that that cancer X is caused by firefighting. Number two, there is enough evidence to say it's not caused. And feel free to wordsmith. And number three, there is not enough evidence to make a determination. In a situation where there is not enough evidence, you have to take into account that most studies predictably for most cancers are going to be negative. And that's actually the problem. So that's true. So how do these studies outweigh negative studies considerably? So I would say that in many cases, yes, there is a kind of evidence. Okay, so Dr. Ghidori said there is not enough power in a lot of studies that look at basically rare cancer. But yes, there is probably enough evidence. Okay, what is cancer X? What's that? Okay, that's mesothelioma. That's the cancer where we have the strongest evidence. Let's skip mesothelioma for a second. But that tells you that for everything else than mesothelioma, we really don't have a lot. Because even for the one with the strongest evidence, with the best evidence, Dr. Samo told you, yes, there is strong evidence. Once you look at the number, I would say yes. I mean, there is some evidence. Now, if I had to do a case, mesothelioma, I want to know the work environment. Stan, when did we stop using asbestos in construction in the U.S.? In the 70s? Okay, so let's say you're dealing with a firefighter who exclusively dealt with residential areas built after 1970. Maybe their disease is not caused by their job. So they are always an exception. Maybe they have another job where they do pipe fitting. I think you have some consistency. But the key point here is that lack of power. I mean, the small, very small number of cases. So it's very difficult to make a determination. Yet, this is the best. This is the cancer where we have the best evidence. Yet, the vast majority of you said, we can't make a determination. So think of that next time you do a cancer causation, or you get a call for a cancer causation. Probably the answer should be, you know, I don't think I can give you an answer. Yes? Yes? Microphone. It's recorded if you don't mind going to the microphone. No. No, I'm telling you, if the jurisdiction only has... I... That's your job to find out. I would argue that the Daniel study was done in big cities with older buildings. And again, look at these studies. Look at the number of studies that did not find an increase of mesothelioma. But yeah, you have a very valid point. But depending on how the employer wants to defend a claim, I mean, I've seen that, what you just mentioned. That's not my job. I'm not going to do it. But the employer could do it. So that's what the latest IARC evaluation said. And you've seen the sufficient evidence for these bladder and mesothelioma. That, by the way, that's their definition of sufficient and limited evidence. It's in your handout. Now, what they did was a meta-analysis based on the cohort studies. So seven of these studies are cohort study. The other ones are case control, lower quality. Based on the 10th cohort studies, bladder cancer is 16% increase. And mesothelioma, seven studies, 58% increase. And that is their conclusion. Now, let's look at other occupations. Little bit more, I mean, much less studies. If you do a PubMed search, firefighter and cancer, and I just did it yesterday, I think it's 300 and something hits, hits. A lot of them are not studies on firefighters. It's just by keyword. Law enforcement, I put both the most recent and the largest studies. The top one, GU, is part of the bottom one. The bottom one is a systematic review. The populations are relatively large. You see over risk of cancer, maybe a small increase. Now, prostate, that's more interesting. You see more, more increase. Some of these populations overlap. You see Ontario. Ontario is mentioned. And you see that the author of the meta-analysis is also the author of the primary studies. She had all the studies that were included. So you get some overlap. As far as that systematic review, most studies have non-statistically significant findings. And looking at other cancers, you see mesothelioma. It's kind of interesting that all the studies have a positive association. Look at brain and GU. You see that positive association after 30 years of service. But that's nine cases. I don't think that's enough. But if you do police, that could help you reach a conclusion. Let you decide that. So we have some plausible causes. The ones that are listed without parentheses are the ones deemed to be carcinogen class 1 sufficient evidence in humans by IARC. The night shift work, that's a class 2A. CLAN labs, that's clandestine laboratories, used to be very popular. That's where chemists would moonlight on the site to make methamphetamine. Now we're dealing mostly with fentanyl. It's more lucrative. But it used to be a big deal for occupational exposures in law enforcement. And here are some explanations that have been raised. But they are not in the IARC classification. And there is one study specifically looking at radio use and the amount of radio use and showing there was no association with cancer in law enforcement. So plus melanoma, maybe consistent increase. And then EMS. EMS, I could find only two papers. They're not very good. They didn't look specifically at cancer in EMS. And you see here a proportional mortality rate that's higher in EMS in Britain. But also you see that's a while ago. And that was a survey. That's a survey. That's a non-peer-reviewed publication. But it's there. And this is purely a number of people who said, yes, I had that cancer. There is no comparison. The only interesting thing is if you are in EMS but you're also a firefighter, you're more likely to report cancer. And here are some possible exposures that exist within the job, also a firefighter. And here I'm giving you a list that's directly from the IARC. These are exposures or occupation with that class one, carcinogenicity, sufficient evidence in humans. So you see the firefighter and mesothelioma. Rubber manufacturing is causing a lot of bad stuff. Aluminum's there also in multiple places. Now, that's a different study. That's a study from the Pucalla we mentioned before. That's a pretty big study. All five Scandinavian countries, I think 40,000. I don't know if it was 45,000 people or 45,000 cancers. Now, the chimney sweep, that's historical. That's Percival Pott in the 18th century. But looking at, let's say, the past 40, 50 years in the five Scandinavian countries, you see some associations with a pretty high standardized incident ratio. I mean, I didn't know. Dennis and Sally Verygland, quadrupled risk. Physicians also probably X-ray. And the article has explanations. They are occupational cancers. And here is a list when you do that step four of the NIOSH analysis where you have to, maybe you rule out or at least consider these non-occupational causes. So you see alcohol causes a lot of cancers. Infections, you heard of PV. It's there. Smoking, even broader. Couple others. Well, that's the thing you need to rule out and or consider. And Dr. Ghirardi, you're up. With or without slides. All right. Well, I kind of like the original way that we planned to do this as a panel, but that obviously broke down. So now I'm going to actually talk from slides. But before I do, I just wanted to respond a little bit to the previous speakers. One is that HP16 as an independent risk factor for head and neck cancer needs to be interpreted. And the latest interpretation from CDC is that it's more along the lines of a co-factor. Because the effect of a viral infection with HP16 is to blast the P53 tumor suppression mechanism. So if you wipe out the tumor suppression mechanism, then cancers that are induced by anything are going to survive to a much greater degree. So that may be an example of what we were talking about earlier, where family history may be a marker for susceptibility as opposed to an independent factor as it would be, for example, about a de novo cancer that's caused by an oncogene, as opposed to a deficit in tumor suppression. Another comment that I wanted to make is that we will never know what the molecular event is in an individual. We can't know that. We can't go back 20 years and figure out something that happened at that time in a two-hit model. The best we can do is the statistics and what's available with the animal mechanism. So we can wring our hands all we want about the level of certainty. But as Fabrice mentioned, it basically comes down to a question of social equity. We are, I think, about to see heavy influence on occupational justice of the earlier model of environmental justice, which has really transformed legal thinking in terms of environmental torts. And I think we're going to see that same logic spill over into occupational justice and an increasing interest in redress and trying to sidestep a lot of these issues and basically say, well, if the evidence lines up, then we're going to accept it. And the rest is for the debating forums. There are so many issues in terms of the value. And one of them is that we have a garbage in, garbage out problem with much of the statistical analysis. For example, what the heck is a non-Hodgkin lymphoma? There are about 40 different lymphomas that qualify as a non-Hodgkin lymphoma. And we have very good evidence that many of them have different etiologies and different causes. So you talk to a carcinogenesis expert, and they will give you at least five different mechanisms for why an external agent would cause a lymphoma. Talk to a statistician, and they will say, well, we don't have enough numbers to work from. Therefore, we have to aggregate them and statistically look at the group, garbage in, garbage out. If you can't differentiate and look at them one by one, then are you misleading the public and misleading compensation agencies by pretending that you can look at them differently? So a lot of issues here. And of course, we have to understand that as deficient as it may be, with the exception of asbestos workers, the literature on firefighting and cancer risk is the most complete that we have of any occupation. So when Fabrice and Dan critique the literature, they're critiquing the entire causation literature. Because firefighting is basically a probe to interrogate or examine the entire issue of causation analysis, because firefighting is as good as it gets in terms of the analysis. And there are issues. Puccola, for example, was mentioned earlier as a study that suggested lower levels for, I think it was mesothelioma for firefighters. Puccola determined the occupational status of the subjects in his study at one point in time, and this for a country that relies heavily on volunteer firefighters who come and go. So as Dan would say, what good is that? Well, it points us in a direction, but it doesn't really get us to the destination. Now, what are we really talking about here? We're talking about frequent statistics that are designed to tell us what a group of people are going to experience going forward. In other words, what is going to happen to these people on a probabilistic basis? Specific causation is different. Specific causation is looking back in the past and determining people with these characteristics, these attributes, are or are not more likely to have this particular outcome. Those are not the same questions. In fact, if we were strictly logical and we used the methodology, and if lawyers had to be mathematics majors in order to go to law school, we would be using Bayesian analysis for this. But we don't. Instead, we adopt frequentist statistics in a situation where it may not be entirely applicable, but that's all we have, and try explaining Bayesian statistics in court sometime. Doesn't work. Now, another issue has to do with just what the International Agency for Research on Cancer is saying when it, for example, talks about sufficient evidence. Sufficient evidence in the terminology of IARC means what we would commonly understand is beyond reasonable doubt. It is the strongest possible statement of the coherence of evidence. And remember that IARC is all about coherence of evidence. It's all about the logical chain of evidence that we have that associates the exposure with the cancer outcome that is not just deemed to be causal, but for which there is evidence of causation. So if you have one missing link or two missing links in that evidence, IARC classifies it as limited evidence. In other words, group two. If it's based solely on epidemiology, it's group 2A. If it's based on animal studies, it's group 2B. But the point is that limited evidence means that there's lots of evidence. Limited evidence essentially comes down to more likely than not reasonable certainty because the causal chain is there. It's just not complete. This is widely misunderstood. And I can't tell you how many expert reports I've read that assume that 2A is a negative conclusion, that there's no causation. That's not right. That's not true. Remember that our primary purpose here is not 95% certainty. It's to try to the best we can to separate the cases into probably yes and probably no. It's basically more like the performance of a test than it is a case study. The performance of a test than it is finding an absolute for any particular individual. In the case of prostate, for example, we have so many different confounding factors, not the least of which is detection bias. And you have public safety professionals with quite good insurance being compared in most studies with the general population, many of whom do not. So there are built-in biases. And there are built-in issues in the epidemiologic data set that we have to understand in order to interpret these studies accurately. And they're not obvious when you first look into it. These are cancers that were reported by Daniels. And we've got rebuttals for some of the studies that are relatively low in magnitude and, in some cases, higher. But the point is that we shouldn't expect rigid consistency from these studies because of the power considerations, because we're looking at different populations, and we're looking at different methods. And because meta-analysis is built on epidemiology, epidemiology has its own methodological issues, erected a structure on top of that, namely meta-analysis, which basically wipes out all the differences and focuses entirely on the similarities, some of which are very few, from one study to another. And you've got a big band of uncertainty, some of which we're introducing when we rely too heavily on meta-analysis without, for example, examining such things as the exposure-response relationships in individual studies. The Daniels study also is not completely independent. For example, the Daniels study was performed on some populations that had already been extensively studied. So if you're looking back in the literature, in many cases, you're looking at the same populations in different studies at different times. We already talked in the earlier session about some of the issues with using length of service. But the point is, these are not easy studies. This is why people rely on causation analysis from experts, such as occupational physicians. It's not a slam dunk. Also, multiple studies don't equal interpretation, because hardly anybody will set out to do exactly the same epidemiologic study on exactly the same population. You don't get replication in that way in most human studies. Instead, you get a reasonable study designed to the best of the author's capability. And I would certainly question that study from Canada that you mentioned earlier, which threw in everything but the kitchen sink and was, in my opinion, not very discriminating, and you would, you look for trends in a particular direction or a particular magnitude that may not achieve statistical significance, but are you still seeing a signal that's showing that in most studies there's direction? And maybe 98, .98 as opposed to 1.0, should you throw that risk out? Well, if you have other evidence that it is statistically significant, why would you? And yet the norm is that you normally would for a scientific level of certainty. This is not a scientific level of certainty. This is a reasonable medical certainty, which is weight of evidence. The uncertainties, we've really been talking about this for very long, and the studies that we're talking about are generally getting quite old. The British Registrar General's Decennial Report that Fabrice mentioned earlier, that was a longitudinal study using PMRs to look at cancer risk in the British population, they stopped doing that about 30 or 40 years ago, and it was an enormous loss because we lost a very valuable set of longitudinal data. We don't do studies on the same population consistently over and over again using the same methodology, and as a consequence, we are always having to infer where we are in terms of occupations, even occupations as common as firefighters that have methodological issues that we can't easily interpret. We are working in an imprecise field, and it is imprecise not because of any lack of imagination on our part, but because of the methodology and because of the data available to us. And Aristotle, I think, said it best, it is the work of an instructive mind to rest satisfied with the degree of precision the topic supports, and not to bemoan the fact that you can't be exact when the methods and the data source limit us. We have to do the best we can. We come to a conclusion based on imperfect evidence, and we know that it's imperfect. We have to go with what we have. Scientific certainty in this field is not reasonable. IR classifies carcinogens, for example, by strength of evidence. Strength of evidence does not automatically translate into weight of evidence. So IARC will tell you, is there a good case that this causes that, although there may be missing pieces, and you will say, well, for group one, it's a slam dunk. It's essentially the same as beyond reasonable doubt. For group two, weight of evidence says, yes, it probably is the case. But beyond that, you're on your own. And IARC can't tell you about many important issues. When in doubt, go to the preamble of IARC monographs. IARC monographs are basically the way that the agency communicates with the world. When it concludes that there is sufficient evidence or insufficient evidence but limited or no evidence and so forth, it communicates that conclusion in the form of a book called a monograph. The single most important part of that book for our purposes today may very well be the first 40 pages, which is called the preamble. The preamble lays out IARC's interpretation of the sufficiency of evidence, and it's the same for every monograph, to be sure that people get it. However, people often don't get it, and they often come to the conclusion that limited evidence means that there's no association. That's not what IARC means at all. But IARC is telling us, and this is IARC's own words, that group 1 carcinogenesis is sufficient, and our interpretation to the court has to be that it's functionally equivalent to beyond reasonable doubt. Group 2A is defined by IARC, not by us, as probably carcinogenic. Well, what does probably mean? Probably means more likely than none. And yet, we tend to dismiss group 2A, and certainly group 2B, as being evidence. In most cases, these are actually, should be considered evidence for an association. Now we're going to get better at this, just as firefighters are getting better at protecting themselves from cancer, but we have not made the big step, which is to change our approach to causation analysis, which dramatically needs a technological breakthrough. My own feeling is that scientific certainty is not practical. We shouldn't even be thinking of that. A rebuttable presumption gets us at least halfway there, and it's better than a sheer opinion unsupported by evidence. In the environmental arena, there's been a shift in thinking about environmental causes of cancer, as well as other things, to environmental justice. We can talk a lot about what that means, and there have been sessions here at AOHC from the section on underserved occupational populations, but environmental justice basically says if it looks like a duck and quacks like a duck, then for all practical purposes, we're going to assume it's a duck. And the rationale and the legal logic behind environmental justice, you've probably noticed if you work in that arena, has really taken over in the last 20 or 30 years and drives court litigation today. I think that the same thing is going to happen in occupational justice, and that we're going to see more of these cases basically resolved by saying, look, this is the evidence. We know it can cause. We know that the individual was exposed. We're going to go with it. And that that will soon be a legal theory that will take hold, just as it has in toxic torts and environmental litigation. I may be wrong about that, but that's where I see things headed in a number of states. And I think that it's going to be an influential point of view. So I'll stop there. Because I thought this was going to be interactive, I didn't actually structure this as a complete narrative presentation. But I think I've got the essential point across. Thanks, Dr. Ghidardi. And now we have exactly 10 minutes for questions. We need to be out of here pretty soon. And while you're all beating each other to get to the questions, I do want to say one thing about the difference between association of correlation and causation. I always had a hard time explaining this to people until I saw this cartoon. It was a great cartoon. It's a little kid sitting in an airplane with his mom. And he says to his mother, he says, mom, tell them to stop turning on the seat belt sign because every time they do it, it gets bumpy. And then there's these great graphs. Perfect correlation in the graph about the number of Nicolas Cage movies and in pool drownings. So you can always find associations and correlations. Does not mean causation. I'm sorry. Go ahead. No worries. Thank you. Thank you for the outstanding presentations, Steve, fantastic as usual. It reminded me all of those talks that when we state that there is no evidence and we intend to give a kick in the middle, decision makers are going to understand, okay, there's no evidence, so it's not occupational. So they are going to switch and confuse absence of evidence and evidence of the absence, which is totally different. My question is about conflicts of interest in this field. There's growing evidence that conclusions of studies are influenced by the affiliations of authors. There's evidence that public funded studies are more likely than private funded studies that there is a likelihood of work relatedness. What's your take on that? Because you presented a lot of studies and have you done a little bit of triage with regards to the question of conflicts of interest or is it something that we will have to look at in the future more precisely or don't you think that it is a topic? What's your thoughts about it? I'm aware of secondary analysis, like systematic reviews, maybe meta-analysis that come from different sources that have their conclusions in mind before they start, i.e., I want to compensate or I want to show evidence not to compensate. I would say most of the studies, at least my table on mesothelioma, I think they're public fundings, but I tell you, when I do a report on causation, I do cohort studies with 1,000 firefighters or more. That's my inclusion criteria. Certainly, when I testify in court, the Daniel studies say, look, it's NIOSH, it's your federal dollars, probably a better methodology. Look, as I mentioned, you have that earlier NIOSH slide, we all have biases and I think we need to be aware of. I guess next time I do a report, I'll look it up, but I think most of the recent big studies are publicly funded. Big bias is who's funding me and also if I get a negative result, it's not going to get published and it won't get refunded. That's for drug studies typically. Thank you very much for the presentations, wonderful. I believe that for the evidence in occupational problems and also with cancers, always we have this factor that is undiagnosed situations. There are so many cases that we don't even know it because it's not diagnosed and happened with cancer, connected with tobacco and even in asbestos, what happens. Connected with this is maybe now with artificial intelligence, we could know more precisely the time, what, the life of the people. So how do you see this, the factor of undiagnosed because we know that these cancers are maybe major undiagnosed and the future about artificial intelligence. This talk is going to be very different in 10 years for several reasons. First of all, the stuff that's going on with genetics. What's going on with genetics in cancer? So that's going to change everything. AI is going to change things. And causation issues will be better. And so, yeah, in 10 years, this talk is going to be very, very different. Thank you. Thank you, Hiram, Ida, and Mike. Just a small comment about the IAC grouping. We tend to forget the group four, always 1, 2, A, 2, B, and 3. Group four is actually components that are safe. And we, unfortunately, there's only one component in that group, which is a nylon capsular pram. And of course, it takes so much funding to actually show this is not a risk. And I just think it's sad that there's not more focus on group four. Just a comment. That's funny about the safe stuff. There was this group that went into Berkeley College. Smart kids, right? They're all smart. They're at Berkeley. And they're passing around a petition to ban dihydroxide because it causes billions of dollars worth of damage every year, thousands of deaths. And we have to ban dihydroxide. And the kids were signing this petition like crazy, H2O, dihydroxide. So, you know, how do you spin it? Daniel's study did not control for the lifestyle factors. It even said that colon cancer was increased, but it was most likely due to alcohol and cigarette and all that. So there was no control lifestyle or obesity was not controlled in Daniel's study. Smoking is the big one. Smoking. There was an indirect control through COPD, but look, it's just one of the better studies. It's not a perfect study. Great. Well, thank you all for coming. And one last. Oh, wait. One last thing. One last. One last. Don't get all excited now. I have one minor comment. I know you were making some interesting comments about association is not the same as causation. Well, you know, basic occupational medicine 101, we go to Bradford Hill principles, right? And look for those seven different nine factors. And we bring everything together to be able to make that judgment. And I think IR does that very well. So you know, not making flippant comments about association is not the same as causation. I think we do have the scientific rubric to be able to figure that out. You? No. You're the one signing your report. No, but what I'm saying, exactly. That's what I'm saying. That we have a way of figuring that out, right? Just because there is an association, but we have a way of figuring out if the weight of evidence is towards causation. At least the IR papers, monographs, will not answer the specific causation. They can, they could answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. They can't answer the general causation. Yeah. So, yeah, sorry about the impulse control, everyone in the room. The talk is very interesting. The issue of causality is really interesting. And why we care about it is very interesting to me. Because I'm sort of sitting here thinking, all right, here we are in the United States where we have a health care system that does not necessarily provide care for everyone under all circumstances. And we have somewhat a social safety net, let's say, that's more holes than net. And if we didn't have those things, how would that impact our concerns about causality? So coming from another system, France, and I certainly wasn't deep, I mean, I wasn't an expert in France, I'm aware of personal injury litigation, but workers' camp, no. I think the answer might be we don't really, we care about causation or association. And what T said several times, this is about prevention. My thing to firefighters is stop studying does this cause this cancer or that. Let's put all that money into better prevention. And let's talk about decontamination, let's talk about better PPE. That's what this should tell us. If we say, well, this job is, you know, type one, going to cause you cancer, let's do something to change that, not to try and prove causation. And I think that should be the point of these studies, but it's not. Because the politicians and the lawyers get involved and mess it all up. Thank you. You like that answer? Yeah, it was good. You did, you did. Yes, sir. You smiling? Laughing? So on the issue of prevention, okay, yes, let's, for firefighters, let's invest in prevention, all right? When a firefighter goes into the knockdown or other jurisdictions, overhaul, most of the time they're not wearing PPE. Most of the time they're not on oxygen. They're wearing it, they're just not using it. No, they're not using it, okay? Let's be frank. They're not using it. When a contractor sends a group of workers to go into a building where there might be asbestos, they don't know, they're in full Tyvek's suit. They have respirators. So we, as physicians, goes back to my original slide, NMJ, this is a fire chief's problem. If the fire chief says, you don't put that on, I'm going to write you up, and I write you up three times, you're going to get fired, guess what? They will wear it. They will do it. Like the contractor says, you have to do it. That's it. Period. End of discussion. So it comes from above. And it's about that. So anyway, thank you all. And one last thing, remember what your mother told you when you're filling out your reviews of this. If you don't have anything nice to say, don't say anything. Laughter.

firefighter cancer

causation

challenges

complexities

literature evaluation

confounding variables

bladder cancer

mesothelioma

prostate cancer