We have three speakers today. I am Fabrice Czerniecki, Chief Medical Officer, Transportation Security Administration. If you have flown lately, you've seen some of our employees. They wear blue shirts, and they will assist you. We have Dr. Gidari, who is a past president of ACAM, and Dr. Sammo. Dr. Sammo is actually my former boss. He's at Northwestern in Chicago. So I'm going to start. What we're going to do this morning, the first session, I'm going to go over, I would say, the theory of a causation analysis, starting with what some people call the Hills Criteria, going over the six-step analysis from NIOSH. We'll go over the critique of that method, the presumptive laws, and the difference between legal and scientific causation. The next session will have specific examples, mostly around firefighters, public safety in general. This is a presentation sponsored by the Public Safety Medicine Session, but we'll also talk about other occupational and non-occupational cancers. So let's talk about the Hills Criteria. Austin Bradford Hill was a British epidemiologist and biostatistician. I first have to give you, we don't have, I don't think we have conflicts. I mean, you'll see my other speakers, but I have no relevant financial relationships. That's a new word. But I'm a federal employee, and I have to tell you that what you are hearing today from me, that's my personal opinion and not the opinions or position of the U.S. government or government agencies. Okay, done with that. And I don't do any cancer causation at work. So what you are going to hear from Dr. Hill, and I'm going to quote his 1965 article as much as possible. I'm not giving you my opinion. I'm giving you his writings. He never meant his paper to give an exhaustive list of criteria. So this is the word he used. These are aspects. You know, having done that for a couple of years, I think that's a good way for you to analyze the literature. It's a systematic approach to the literature, but these are not required criteria. And I'm going to go down all of them. By the way, you have a handout. So if you go to, I guess, Swapcard, you'll get the slides, but also you'll get a handout with, at least for my piece, with all the references. So you'll have a link to that article of Hill from 1965. It's on the internet, free access, full text. I highly recommend you read it if you are involved in causation analysis. I mean, I'm rereading it every time I do some work on that, and I'm still learning something new each time. So I'm going here by the order of what Hill thought were the most important first, and understand that other authors have different classifications and different ideas. So first he talks about strengths of association, and he starts by talking about the squamous cell carcinoma of the scrotum in chimney sweeps, but a little bit over 200 years ago. And you see the risk, actually it was a mortality, it was 200 times higher. And he said, yes, that's pretty convincing. But then he goes down to other diseases, lung cancer and heart attack in smokers, and he said, that's not as convincing. Now put that into perspective in occupational cancers, glyphosate. Would you ever see that type of related risk in occupational medicine studies? I mean, I'm sure it happens, but it's not, at least in firefighters, it doesn't happen. Or law enforcement. It's pretty rare. So that was the first, the strength of the association. And by the way, you will hear the word weak or strong. This is arbitrary. There is no, that I am aware of, there is no scientific threshold that your risk is too strong, your risk is 1.5 is strong, or your risk is less than 1.5, it's weak. But just to put things into a storyboard perspective, what Dr. Hill called strong was pretty high. Not something you're probably going to find. And then the next one that he thought was the most important was consistency, and consistency is, are all the studies showing the same? So in this case, smoking and cancer, 36 studies, they all agree. And how often do you see that? So keep that in mind when we look at specific examples. Specificity, are you looking at one specific cancer versus it's increasing all cancers? The example he gives here is what if smokers have a 20% increase in cancer mortality in all cancers, but for lung cancer is 1,000%. That is specificity. And specifically call that specificity the magnitude. So the strength of the association was much higher with lung cancer than the other cancer. Okay, temporality, third most important, but you typically get that. I mean, did you study, look at exposure before there was diagnosis? That I don't think it's ever a problem. Okay, biological gradient, I think is a very strong argument. Hill has that as number four. This is, the more exposure you have, the more risk of bad outcome you have. You see the example of smoking. It is rarely found, unfortunately, in occupational studies. And typically what you have, you have a surrogate of that exposure. So think what you get for firefighters is how many years they work, how many runs they have, maybe more specifically how many fire runs they did. Okay, not very common, pretty powerful argument when you have it. Okay, now from now on, this is more for historical purposes. This is probably not something you are going to use when you do causation analysis, but I think it's useful to know the next criterion, what's called plausibility. It means can you explain it? But as Hill mentioned that, that really depends on how much knowledge we have at that time. So the fact that you can't explain it today doesn't mean it's wrong. And the quote he gave, as Sherlock Holmes advised Dr. Watson, when you have eliminated the impossible, whatever remains, however improbable, must be the truth. Hill, in his article, so he gives you each point to analyze and then he destroys it. He gives you the argument, then he gives you the counter argument. So you definitely have to have, you know, you go down the analysis. I think you need to meet some of these so-called aspects, but you certainly don't have to have every single one of them. Coherence, somewhat similar to plausibility, is that this makes sense based on the rest of the data we have and specifically talks about animal models. Experiment really happens in the occupational setting where you introduce a toxin, but you could remove it. So what if you create a system where you have, that's what you do for chimney sweeps. You don't clean yourself, I don't know how they did that at that time, but clean yourself after cleaning the chimney. And by the way, wear clothes. A lot of the kids who did that, they were naked when they were doing that type of work. So wear some type of protective equipment, do some type of hygiene. So I think it was the Netherlands that did that back in the 18th century and they saw the mortality from a scleral cancer go down. So that's an experimental evidence. And finally, analogy is based on other drugs that are similar. I don't think you'll see that very often. Now I have to tell you that ACOM put together a clinical practice guideline on work relatedness. It's in your handouts and they define things a little bit differently. First they call them criteria and they say four were so-called the most important. And that's the ones you are probably going to use when you do a causation analysis. And by order of importance, temporality, strength of association, dose response, and consistency. Keep in mind that Hill had consistency as number two. So these are not, this is Dr. Hill, these are not rules of evidence. Now we call them viewpoint. I think it's just a way to analyze the literature. Now this is for the literature. This is not about a Dr. Ghidadi, you know, who got exposed to smoke, you know, during his long career as a firefighter. This is when you analyze the literature. Now let's see what Nyosh says now to determine work relatedness for a worker. They put that together back in 1976, update in 1979. I think it's a great paper. It's your tax dollars at work. This is available on the internet. You'll have the link in your handout. Please read it. It's pretty comprehensive. I think that method is underused. I mean, I read a lot of reports from physicians or epidemiologists who are told, you know, please tell me if, you know, my patient, the claimant, the plaintiff, my client, did the job cause their disease X. And I'm not going to tell you that's the only way to do it, but that's a pretty good way to do it. And that's certainly the only way that I am aware of. Now ACOM has a slightly different version of their work relatedness. The clinical practice guideline I mentioned, but it's based on the Nyosh guide. Now what's interesting is, let's get on the next one. So Nyosh originally did that as a prevention tool. So in order to prevent a disease, you need to know what caused it. Now they did mention that yes, for compensation, you could use it. I've only seen that used in litigation. So let's see, wait, skip. I think also it's a great way to write your report. And if we have time at the end of the second hour, we'll talk about report writing. Also a great way to teach people how to do a causation analysis. Okay. They tell you not to skip steps. First step is, does the worker have the disease? I mostly do cancer cases. And you would think having a cancer diagnosis is pretty easy to establish. No, first you need to know that the person has cancer. And I would say 5% of the time, that's not established. Seeing COVID-19, what type of diagnosis you want to accept before you start your causation analysis on COVID-19? And I heard that some unnamed states on the West side of the US, a lot of the claims, there was actually no evidence. There was no diagnosis. There was no test, positive test. And what test would you accept? You want to take an antigen test? You know, we'd take a PCR. But anyway, you as the evaluator, you need to be convinced that the worker has the disease. And then more particularly for cancer, the type of cancer, because it is not, you know, you have an oncologist and then you have a tumor board and pathologist. And I have seen discrepancy. You can't do an analysis if you're not sure of the diagnosis because the epidemiological data you're going to use is different. And sometimes I had to go back to the lawyer and say, I don't think that cancer you mentioned, but if that's the question you're asking me, we'll go that way. But I'm going to say, we'll make that assumption. I don't give my opinion. I'm not a pathologist, but I'm going to say there is disagreement on the diagnosis. Okay. That's what NIOSH had. They had a comprehensive evaluation as the step one. But step one, if you remember one thing, is establish evidence of the disease. So if it's fibromyalgia, you need to use, or even major depressive disorder, you need to agree on diagnostic criteria and you need to ensure that that worker is meeting the diagnostic criteria. Certainly easier for cancer. Step two is the epidemiology. I recommend and ACOM recommends that you use the HILS so-called criteria or viewpoints. NIOSH doesn't tell you that. But what NIOSH tells you, what I bolded, is you cannot be one-sided. And the vast majority of reports out there, they are one-sided. So, doctor, so give me the five articles that show that firefighter ex-lymphoma was caused by firefighting. Exactly. Thank you. He's not giving me the 50 that showed the opposite or the 200 that said, we don't have a clue. And that's, you know, that's probably where most of the data will tell you is we don't have a clue. And, and Dr. Guidotti, who is a smart man, will tell you what type of lymphoma. That's step one. So whatever you find or whatever is available you need to include it. Don't be one-sided. Step three is, was the worker actually exposed? Now, that's a tough one. Putting things back into our perspective of public safety, we're just going to assume that the person was a firefighter. You know, it's true that they were a firefighter and that's a surrogate for exposure. But ideally, you want a personal exposure for that worker with actual IH data or exposure for the class of worker or for the work location. But understand that you rarely get good evidence on the specific exposure. You can certainly prove that the person was a firefighter or police officer. Now, what if I tell you, yeah, engineer Guidotti was a firefighter except was an engineer. He didn't get in the burning building at all, ever. Or, you know, I've been a command staff, I mean, a friend of mine. She didn't leave the office. She was a chief. And by the way, she was a paramedic, never been a firefighter. Yet, in that state, the presumptive laws for firefighters applied to my friend. Again, not your issue, but just be aware of that. Exposure can go a little bit beyond the obvious. Step four is basically the other factors that could have caused the cancer. And the NIOSH paper, they split that into two. When you have the pre-existing conditions. So, you know, I had back pain all my life, but, you know, my job really made it worse. Maybe, maybe not. And they give you the example of, you know, the slaughterhouse. Love that, abattoir. It's a French word. And I tell you, you know, everybody gets arthritis, and it's probably edge related. But the courts, in this case, usually they rule that it's job related. Why? We don't have a clue. And you, as the, you know, medical testimony person, basically accept that. You know, answer the question, help the judge, but understand that they're going to make your decision completely independently from the science. Just, you know, just accept that. Don't take it personally. So, that's the first one is these pre-existing conditions. Corneal artery disease being another one, you know, how do you see the difference between job stress, but by the way, hard work they do, they actually have less sudden death. Why? You know, that could be a, you know, healthy worker effect, but, you know, maybe physical activity is good for you. Maybe that's not stress. Okay, and then you have the other piece, which is the, I call them the non-occupational factors. Now, it doesn't call them that way. And at least until recently, what did firefighters do outside of firefighting? Okay, construction work. Construction, I mean, they work. They have another job, at least in my, in my place, you know, when we wanted to find off-duty firefighters, we had to go to the home depot. They were there, you know, buying supplies, moving supplies. You know, interesting that, you know, they come to the office and they said, you know, I've back paid, I can't lift anything, but then you see them on a construction site, you see them moving four by four. So, look at their side jobs. Hobbies could be an issue, and also these personal factors like, you know, you see they're smoking and they have coronary artery disease, or they have lung cancer. Is that the job? Is it smoking? That's something you need to consider. Not giving you the answer, but you need to consider that. Step five, called the validity of the testimony. Practically, what it means today, is there a conflict within the medical records or between the, what the employee tells you and the medical records. So, what is the validity of the testimony? Or between the, what the employee tells you and the medical records. But what Nyosh told us back in 1976 is, as the judge basically, what are the qualifications, like board certification, of the witnesses, and the three types of witnesses are listed here, physicians, industrial hygienists, and epidemiologists. But you do the evaluation to tell you, stop, stop at the end. I said, usually my report, step five, this is not an issue. But once in a while, I'll say, you know, the guy said he never smoked. Dr. X says he smoked for 20 years. Is that a problem? I mean, I don't know which one's telling you the truth. I'm not assuming. You know, if you write a report, don't call, I mean, if you call people liars, I mean, I recommend you don't do that. If you do it, you're on your own. And same malingerer, they actually, you know, some of my colleagues say, yeah, yeah, you, especially psychology, say, yeah, it's okay to do it. You need to be on very solid ground before you start using these, these words, I think. But certainly take a step, stop and say, yeah, I have an issue, or no, I don't. Here is the, or the other thing here, if you have missing diagnosis, so case in point, comes, you know, guy come see me, he's a fire captain, he's not in his prime, and he has disabling back pain and probably sciatica. Okay, fine, fine. You know, can I prove he has it? No, but let's say there's consistency there. The problem, there is no injury, you know, no real injury. So I ask him, before the date of the event, which was something that I was doing that in my car, I hear a guy say, he was typing on the computer in the car and that caused permanent disabling back pain. Okay, fine. So I ask him, did you ever have back pain before? Okay, he say yes, because they know that's kind of, if you tell me you never had back pain, you're not credible. But have you ever had back pain that led you to get medical treatment, get an MRI, get x-rays, get you to take time off work? And he says no. So in my report, I wrote here, step five, you know, I really would like to get medical records before the incident. And guess what? That guy's been disabling back pain all his life. So that could be something in step five. If you don't have the records you want, I think you need to spell it out. And hopefully I'll give them to you. Okay, that's the medical qualification, a witness is not an issue for you. What NIOSH tells you is everybody has some bias and please recognize it. I get that. And finally, you conclude. They give you all these seven questions and say if you answer yes, you're good to go. Pretty difficult, pretty difficult to get there. And that's the end for me. And now, Dr. Ghidadi, you're up. Okay, well, the reason that I'm sitting down here is that I was under the impression that we were going to be doing a panel discussion and that there would be a table here. But the hotel didn't set up the table. So that's why Dan is down here and I'm up here and Fabrice was at the podium. However. Does that work? Okay, I was just explaining why the seating arrangement is wonky. Let me just comment a couple of things because we originally planned this to be a give and take and an open discussion. To point out that the Hill Criteria actually serves another purpose. The use of the Hill Criteria allows you to evaluate the expert on the other side if you are serving defense counsel or the applicant counsel. And that is that the Hill Criteria are very easily misused. And the way that they are misused is indicative of where the quote other side close quote is coming from. If an expert tries to apply the Hill Criteria to an individual case, then the chances are they may not even be honest because the Hill Criteria so-called are intended to be applied to a body of evidence. Always more than one study, always more than one type of analysis and indication of an association. So when Hill put together these criteria, again, as Fabrice said quite accurately, his model, he was concerned mainly about cigarette smoking. High risk, it was an issue at that time, it was the forge on which many of the tools of modern epidemiology were made. But he himself was also very interested in lower level effects, lower risk levels. And he warned everyone at the time in 1965, not to take these as criteria as evidence that they were only indications that further work needed to be pointed in a particular area and that the issue merited further investigation. So, and he also was very clear that this was a high level judgment, a decision to look further into the issue. It had nothing to do with applying the evidence to a particular individual. So if an expert tells you that in an individual case, it doesn't meet this, that, or the other criteria for causation, that tells you more about the expert and that that individual may not be trustworthy. Another issue has to do with the reliability of the Hill criteria. Hill was brilliant. He was not only brilliant, but he was quite a humanist. He was very human individual. And on the one hand, he was very rigorous and was one of the founders of clinical trials. But on the other, he is famous for his quote, behind the statistics are tears. He was a very, very sensitive individual. And he pointed out that the criteria are guidelines for thinking about this, but they are cognitive criteria to engage your thinking. They aren't a checklist. They should never be used as a checklist. They should never be assumed to be required to prove anything with one exception, and that's temporality. Temporality is by far the most stringent and the one essential criterion that the Hill articulated. And then there are series that are quite weak. One of them being plausibility and coherence of evidence, which only comes after you have additional information. And that means that a first case or a novel situation of an epidemiological association never has that. It never has it because the studies haven't been done. So if you're dealing with a new association or a new issue, you can't possibly satisfy the Hill criteria. So anyone who insists that they be satisfied, blowing smoke. So be very, very careful and aware of that and also aware that Hill did not work exclusively in the, within epidemiology. He also was well aware of work that was being done in cancer causation elsewhere, and designed his recommendations, his aspects, to dovetail with scientific evidence from the laboratory. That was by intent. Most of the logic of compensation is based on tort law. Tort law is, has a requirement for the balance of probabilities. In other words, the, what is more likely than not to a 50% level probability? Not 95% level probability. If you're talking scientifically, you are essentially accepting that you have to be 95% certain because that's what a P value of 0.05 means. That's what the confidence interval is calculated on. So if you take that same mindset of the scientist and you bring that into compensation, you are actually outside of the law. The law expects you to, and in many workers' compensation acts, it's explicitly written into the law, which is kind of redundant because compensation law came originally from tort law. You should be thinking in terms of 50% plus one, the weight of evidence. That's how the guidelines were developed, except in Vermont, which basically says, if you only have 50%, the applicant gets the benefit of the doubt. So, the system fundamentally, and most experts err, by insisting on 95% probability and throwing out any evidence that is not, that doesn't have a P value of 0.05. In other words, conventional confidence intervals. If you have no other evidence and you have an indication that, that more likely than not that there's a causation, you actually, by your rights, should be concluding that that is the, should guide your conclusion, not 95% scientific certainty, which in our field is almost impossible to achieve. And that makes presumption and fair adjudication almost out of reach for many individuals, especially for a case in which the evidence, the scientific evidence is relatively limited. In terms of exposure, this is really complicated because our exposure today is much better documented than in the past, in part because of work from pioneering groups such as that at the University of Ottawa, who have done a great deal of work looking, for example, at exposures of, say, PAHs and so forth on the personal protective equipment that firefighters are actually wearing on their skin and in their urine from transcutaneous absorption of PAHs. And we now know that the exposure is considerable, considerably greater than we thought. At the same time, as my colleagues are fond of pointing out, the exposure levels that we are hearing about anecdotally and that we are observing in the field when we look at firefighters, it is absolutely true that they are better protected, that their time spent in harm's way is much reduced, and so forth. You can only imagine how bad it used to be. But that's not an argument for firefighters 20 or 30 years after the fact, today, not having sufficient exposure to develop cancer. And that brings up another issue that is particularly true for firefighters, but also, I think, true for many other occupations. And that's that length of service is not a particularly good indicator of exposure. Unfortunately, it's just about the only one we have. The only one we have, in most cases. But many things have happened over the years in firefighting, and most obviously with firefighting. Not only is it a question of cumulative exposure, but the level of exposure has changed dramatically in firefighting. And with personal protective equipment and so forth, the firefighters are much better protected now, notwithstanding that they still get appreciable exposure. In addition, firefighting technology has changed. Most recently, the use of foam, which has introduced a new hazard in the form of PFOS, but has also reduced hazards in a variety of other settings, such as during overhaul. So, when they're wearing PPE. So, and in addition to that, you have many other things that are happening with regard to health protection of firefighters. And firefighters are much more conscious now than they ever were before about non-firefighting cancer risk. So, you see many firefighter-led initiatives today going right down to the fire hall regarding healthy lifestyle. So, it's a complicated thing. And duration of service is only a metric. It co-varies with age, it co-varies with cancer latency, and so forth. So, yeah. So, it's not an ideal metric by any means. I think I'll stop here. And because these slides were, oh, okay. Yeah, I should disclose, shouldn't I? I edited that book. That book is what we knew as of 2015. That was a while ago. There's been a lot since then. So, and the book's not for sale at this meeting. And I think that's all I have to say about that. The rest has to do with responses to individual questions. Yeah. Thanks. So, quickly, legal and scientific causation. Oh, I need my paper. To give you a quote. Okay. Quoting a guy I didn't know, Nigel Lawson. He was in Margaret Thatcher's government, a couple jobs. Pretty bright guy, I think. So, let me ask you. If I work harder than this guy, and we have the same job, should I get paid more? So, raise your hand if you think that's just if I get paid more. We do the same job, I just work harder. Let me see. I work harder, and I'm commensurately better rewarded. Is that justice? Raise your hand if you say yes. Good point. Okay, so that's how you define it. Nigel Lawson said, okay, that's justice. You work harder, you get paid more. But then he said, if that extra reward is taken from you in taxation, that is social justice. And obviously, we don't want to make a joke out of that as a conservative guy. But, oh, he moved. He said, the problem, social justice, is not very well defined. It's basically whoever you, the person using that word, say it is. We don't do that. If they ask you to do causation analysis, that's not a factor in your analysis. But understand, the trier fact, the judge and the jury, they're gonna use that. However they think the outcome should be for other reasons. But Mark Melhorn, who is the lead author of the AMA textbook on causation, that's how he wrote it. So if the job causes 100 illnesses, the employer shall pay, shall compensate 200 illnesses. So 150% are not caused by the job. And that's pretty much jurisprudence in the US. Is that okay? I don't know, not my job. But let's change it. Let's say the job is causing 100 illnesses, and now there are 120. So there are 20 that are non-occupational, 100 are occupational. Is that fair for the employer to pay for that? What do you think? Yeah, probably. Okay, now let's flip it. The job causes one illness, and the non-occupational causes cause 100 or 99. Is that okay? And I'm going back to, it's not a medical issue. Now if they ask me for causation, I'm going to give that number. You know, more likely than not, the job did not cause that, you know, because it's one out of 100. But that's where it becomes tricky because you're not part of that social justice process. This says that number, that magic number, which in the US is typically 50%, but not always. That is not a medical decision. Dr. Samuel will talk about presumptive laws, but also be aware that depending on your system, you could be dealing with two different sets of criteria. So I'm working with a state or with kids in a state where they have both a determination on the disability pension reasons and workers' comp, and they are different, and the process is different, and the determination is different. So practically, you need to have a good relationship with whoever is retaining you. You need to educate the person, and the very first step is, you know, before you can find out whether you have the knowledge to answer, you need to be able to understand the question, and that is not obvious. You need to understand, and obviously, you need to have the knowledge. When you write your report, use the wording, you know, that the system wants you to do, but your role is probably to do a scientific causation analysis, not to go over the, you know, it would be nice that the person be compensated. So be an advocate for science. I want to give you some examples on the correct wording. You heard more likely than not, and I think we'll go back to that in one second. Most states required something like what you write in your report. This is at, well, my conclusion, my opinion, at a reasonable degree of medical probability or medical certainty. Case law has said that means the same, has more likely than not. Ideally, the state, they want the word hereditary factor. They don't want family history. Again, that's the magic word. Ideally, the retaining party, they should be asking you the question instead of having you find out. Words like probable and possible, usually, at least in the US, you can't use them to reach a conclusion, and there have been cases where the physician writing the report said this is, you know, likely, possible, probable. Usually, for the sure fact, that means nothing. Usually, when you do a causation analysis in the US, they ask you for more likely than not. Case law, and I gave you the reference, does mean relative risk of two or more, but that's not always the case. If the question is any contribution from the occupation, then typically, they will ask you to do an apportionment, again, not always. So, go back to high-stress occupation, assuming high-stress causes coronary artery disease, which I think remains to be proven, plus smoking, so you compare the two related risks, and you can come up with a proportion of compensation, one from the smoking, non-occupational, and the other one, occupational. But this is something that the retaining party should tell you. Your job is primarily to do some type of scientific analysis. And I think now, Dr. Samuel, we're going to talk about presumptive laws. And we will have time for questions at the end, or now, if you want. Don't jump. Hi, y'all. So, I'm going to talk about presumptive laws. And what a presumptive law is, is that the government, the society, has decided that, I'm going to make this go forward, that whatever it is, is presumed to be causative of whatever condition you're talking about. We're talking about cancer. And so presumptive law says, because you're doing this job, we presume that you have cancer. So in my humble opinion, I think this is, for firefighters, basically we're saying, we're sending you into this horrible burning building, being exposed to all this nasty stuff. So we're going to, as part of your appreciation for what you do, and compensation for what you do, we're going to presume that if you get cancer, it's due to your job, and it's covered. That's what the presumptive law is meant to do. This was mentioned before. This is not a medical decision. This is a societal decision. Politicians and the public have decided this is what they want to do. So basically, this is one of SAMO's rules of dealing with bureaucracies, NMJ, not my job. It's not your job to say whether this is right or wrong. Society has decided it. We, as medical professionals, don't get to vote. So that's where it starts. When you look around, most presumptive laws are for public safety workers. Most for firefighters. Some states also. And again, this is state to state. If you go to the International Association of Firefighters, the union's website, they have a really good chart that lists every state, and good links to the actual state's documents. So it's a good place to go to get the information. IAFF, International Association of Firefighters. So most are for firefighters. Some states also include police, some also EMS. EMT is paramedics. But mostly it's for firefighters. I really couldn't find any presumptive laws for pretty much anything else. So that's great. I think it's simple, right? It's simple. You're a firefighter, you get cancer, you're covered. Then come the lawyers and the politicians. So what was simple is no longer simple. So they had things called varying criteria for inclusion. What do you have to do in order to be included in this group of people that's covered by the presumptive law? So one is you have to have a minimal term of service. And it runs from about three years to 10 years. You have to have done this job, firefighting or EMS or policing, whatever it is, for a minimum amount of time before you can be included under this presumptive law. Makes sense if we're talking about exposure that one time exposure may not do it. But how do we know that? We know there's things that you get a one time exposure may cause a horrible thing. So again, this is not about science. Then there's a minimum age for the onset of the cancer. For some, it's as low as 55. So if you're over 55 when your cancer shows up, you're not covered. Most of them are 65. Some go longer, some forever. So again, varies from state to state. You've got to look at your own state. Next thing is that they all say that you haven't used tobacco products usually for about 10 years. It doesn't matter what your cancer is. Now, we know that smoking does increase the risk of all kinds of things, including some cancers. But not all cancers. But that's an inclusion criteria or an exclusion criteria. Another thing is that you had a history of physical at the time of hire which showed no sign of cancer. That makes sense. In other words, you didn't come on the job with the cancer. So therefore, obviously, the job couldn't have caused the cancer. And some of them say that you need to have annual physicals that don't show cancer for three years, five years, seven years, depending on the state. So these are inclusion criteria. And that you have no other job that might cause cancer. Now, as Fabrice said, when I first started taking care of firefighters in 1827, no. It wasn't that long. It was a long time. It was 1975. So what's that, 48 years? Every firefighter had a side job. Every single one of them had a side job. Most of them were in the trades. So did they have another job? Probably. Did they smoke and things? I mean, so there's all kinds of other issues that you need to know. So that's about the inclusion criteria. Enter the politicians again. Every single state, you look at this map and the colors are all different. Every single state decides which type of cancers are covered. And when we go over the literature, you'll see why that somebody looked at, you know, it's like the blind man and the elephant. The elephant's like a rope. The elephant's like a tree trunk. The elephant's like a hose. You know, it depends which article you look at that makes you decide which cancers you're going to cover. We're not going to cover all of them. Which cancers you're going to cover or not cover? So every state has something different, right? It's not presumptive that if you get cancer, the presumptive is if you get this cancer or that cancer, then you're covered. But if you get the other cancer, you're not covered. And it varies from state to state. Doesn't make sense scientifically, but once again, what's the rule here? NMJ, right? Not my job. Then it says, as Fabrice talked about, you need to be exposed. You need to know the exposure. But how do they say this? This is a very common one. The cancer, well, I know this because this is in Illinois. The cancer involved must be a type which is caused by exposure to heat, radiation, or a known carcinogen as defined by IARC, International Association of Research on Cancer. Can anybody think of a cancer that does not fall into this? So is this a type of cancer that might be exposed? Well, yeah. What cancer isn't caused by something? We don't know what it is, but it is. Something's causing it, right? Could be genes, could be this, I don't know. That the cancer is found by research to show a higher incidence of occurrence in firefighters. Next hour, we're going to talk about the literature on this. And this is not an easy question. And you really need to come to the next hour. This is a teaser to make you come back again. But this is a crux of when someone comes to you and says, is this cancer caused by my job? It's going to be based on the literature, right? And similarly, the firefighters exposed while employed to a known carcinogen, which is reasonably linked to the cancer. Well, Fabrice talked about that. It also is that, well, what's the exposure? I'm sorry, T talked about that. I mean, yeah, we walk into this nasty thing, but how much exposure? Were you wearing your gear? Were you not wearing your gear? So all of this can change. And really, to really screw it up, they added this thing about rebuttable. So most states say that this is rebuttable. In other words, OK, we're doing this great thing for firefighters and EMS public safety workers, and we're going to give you coverage if you have cancer, unless these things apply. So it's rebuttable. So in other words, trust me, as an expert that does this, this is job security. Because someone's going to try to rebut it, and they're going to call you in to testify. Great. Assuming it's related, lest the city demonstrates by the preponderance of the evidence that cancer was caused by other means. That's pretty vague. Is it your family history? All kinds of stuff. This presumption of coverage may be rebutted by preponderance of the evidence. The evidence may include. May include. I used to get these tapes called Emergency Medicine Abstracts. And one of the guys in there, Jerry Hoffman, I'll talk about his buddy later, always said whenever you see the word may, insert may not. When you see the word might, put in might not. Because it makes the same sense. It's using the same data. Talk about spin. Gives you the same sentence. Using the same data. Anyway, evidence may include the use of tobacco products, physical fitness, weight, lifestyle, hereditary factors, which we all know under GINA, you're not allowed to ask about that. The fact that every male in your family had a colon cancer by the time they were 45 has nothing to do with it. Exposure. And exposure from other employment and non-employment activities. Another rebuttable. Shall not be deemed to result in a firefighter's employment if the employer assures a preponderance of medical evidence that it didn't occur on the job. So that my side job, I was a pipe fitter. You know what pipe fitters do? They take off old insulation on pipes, which was all asbestos, and put on new stuff, which is some other nasty crap. So it's that kind of stuff. So this is rebuttable. So this whole thing about presumptive law is very sweet, very nice thought. And I'm all for it. I mean, I'm saying we send these guys into horrible things. And if society decides this is what we're going to do, great. But then get rid of all the rest of this. But again, you're going to get stuck in the middle of this because all of these other things, they're going to ask you, what's the preponderance of the medical evidence? So that's all I got from that. Oh, yeah, when people tell me to stop living in the past, my first thought always was, but the music was so much better then, wasn't it? Thank you. Thank you. Thank you. Thank you. Please, please, please, no applause. Just throw money. I'm told to stay here. I don't know why. The what? Oh, we can do Q&A now. Anybody got a Q? Oh, and please use the microphones around somewhere because I think this is being recorded and whatnot. And make it look like you're paying attention, that you really do have questions. Otherwise, you get a longer break. You guys want to say anything? Clever? Yeah, Dan, you mentioned family history as part of the rebuttable criteria. The most, really most, of the heritable conditions that we have or that we know well in cancer biology are actually failures of host defense mechanisms. They're things such as the P53 mechanism and so forth. Their deficiencies in DNA repair, they're that kind of thing. So when you have a family history of cancer, that is more likely than not, actually, to mean that the individual has a host defense problem. Well, if you have a host defense problem and you're challenged by a exposure to a carcinogen, by a exposure to a carcinogen, of course, you're going to have a higher probability of getting that cancer. So does a family history of cancer actually, aside from the oncogene-related effects, which are de novo cancers, can you actually say that a family history of cancer rules out that the new cancer, in fact, was induced by a carcinogen and treat them all the same? Or are you basically looking at a cancer amplification effect in an individual who has, to use a legal term, a thin skull and who is, by law, you're not supposed to consider that to be disqualifying? And by the way, anybody who asks the questions, you get an automatic pass. The rest of you have to take the test. Please, go ahead. All right, well, thank you so much. This is a wonderful presentation. And I'm relatively new to this, so everything I hear is new and helpful. But I'm sometimes asked to address issues of major contributing cause versus the mechanism of injury. And there's a component that's layered on top that I'm asked about clear and convincing evidence to make a determination if there was clear and convincing evidence. And I'm not sure within the criteria exactly if that falls, which bin it falls in, and how far should I go. The guy who was able to climb up is going to answer that. This is typically, no, sometimes it's driven by statute, but usually it's driven by case law. And you need to ask the lawyer who is retaining you. Personally, I would say, I have no idea what you just said. OK, very good. Thank you so much. I work in federal human resources. We have five levels of evidence. But we're very good at defining things in a federal government. So I would ask, my guess, this is more likely than not, but especially looking at foreign systems, I mean, non-US systems, I would really ask what works in your country. Give me a question that I'm able to answer. Thank you. I don't know what clear and convincing. I mean, actually, by the end of the second hour, I think you'll have the answer, which is we don't have it. OK, Dr. Tarrasso. Teaser, teaser again. Thank you very much for a great presentation. I never miss your talks, because every time I come to your talks, I learn something new. So I'm going to pose a hypothetical to you on causation. So you want me to speculate? Yes. With a reasonable degree of medical certainty, yeah. So the hypothetical is a firefighter who has more than 15 years of service, develops a rare cancer. You may see 100 of these a year, mostly in young individuals, according to the epidemiology, in their 30s to 40s, mostly men, propensity mostly for men. Again, but so small numbers, you can't really assign causation to gender sets. So he develops a cancer. It's relatively rare. And as a backdrop, it's California. So the workers' comp laws are built on a house of cards. And it's very wacky. So the threshold for industrial causation and industrial causation in California is very, very low. The South Coast framing case law established the threshold as, to use their analogy, the crumb that remains in a pie dish as the industrial contributing factor makes the whole thing industrial. So his department accepted his cancer as work-related. But now the retirement board has to make a decision as to whether the cancer is work-related or not. The only available information on the cancer is that, so there's no known exposure that causes the cancer. There are animal studies. The cancer is studied in rats. And a particular substance is used to induce the cancer in rats. This substance is also one of the byproducts of combustion of diesel fuel. What's your question? The answer is, I think the answer is NMJ. That's the answer. What would you say is? That's your answer. Your job is to advise the pension board. The pension board has to decide, not you nor me. I would tell them exactly what you just said. Here is what the best evidence shows, or what the totality of the evidence shows. No human arguments. Maybe arguments in whatever animals. At what dose, if you went to the glyphosate lecture. Present the data. Go in depth if you can. The decision is not yours. But they want your opinion. Then that's it. Personally, I would say, look, I have no evidence. I mean, I haven't seen the evidence, but I would say I've reviewed the evidence. There is no evidence that firefighting caused the cancer. Our job is just to say, here's the medical data. Do with it what you will. Not my job to make this decision. I'm not the decider of facts. Next hour, we might give you some examples. Fabrice, could I take a crack at that? Until about 1925, 1930, we actually relied almost entirely on toxicology for this kind of question. Back in the courts in those days, the expert witness was much more likely to come from a research laboratory in tox. And that was the level of the discourse that they had at that time. It was only really since the 40s and 50s that epidemiology has come to be the standard proof that the legal system relies upon. My own feeling is that if the animal model fits, then that's the best available evidence. That's the context in which it has to be provided. But it's also a factor that one thing that gets lost in these discussions is power. The statistical power of a study, of almost every study for cancers that are less common, for example, than lung cancer, is so low that, of course, you're going to have many negative studies. Of course. The statistical power that you try to achieve in designing an experiment is about 80%. Most epidemiologic studies are closer to 30% or 20%. What does that mean? It means that 70% or 80% of the studies predictably, statistically, on a random basis alone, leaving aside bias, are not going to find an effect that's actually there. So I am not impressed by negative studies on relatively rare cancers. I have been asked to. I'm sorry, we're beyond the time. So you're free to, no, no, you stay, you stay. You two stay. You're free to go. And come back. We are staying here, so we can still answer your question. We're stuck here for another hour and 15 minutes. Just tell me if you want to ask your question here or come to the podium. Up to you. I have been asked to see if there is causally related to the exposure. As a firefighter, there was only two that I really was sure. One was a passive smoking. And one, this person was actually doing the boat. He was on the boat for his work. And he developed skin cancer. That is the only two times I was. Passive smoking. Sounds very reasonable.

causation analysis

occupational cancers

firefighters

presumptive laws

compensation

state variations

inclusion criteria

risk factors

scientific literature

evidence-based analysis