Okay, so welcome to the annual current research in OEM and resident research presentations. Somewhere along the line I lost count of how many times I've hosted this there, but I did find something from 2008, so I actually was calling that online. If anybody saw it on LinkedIn, I was calling it my 15th anniversary, but it goes on much longer than that. We're always grateful to the American College of Occupational and Environmental Medicine and the resident and recent graduate group for sponsoring this session and, of course, the financial reward that goes with it, which helps to defray the cost for many of our residency programs. Everybody's heard housekeeping notes beforehand here, so I'm just going to quickly read again. Again, this is sessions 225 followed by 231, so we get a 15-minute coffee break an hour from now, and then we'll resume. It's asymmetrical. We have three presentations and then five, so come on in. Those of you who've attended in the past, it's just unusual from before. Please don't forget to silence your phone and other devices. Please remember to claim CME credit for this, all those good things there. This is also on the handout, both in the swap card app and as well as on the paper copies over there. This will be the schedule, and those of you my age are going to have trouble reading that, but we'll make it clear who's talking before. One last reminder, any questions should be asked at the front mic here, so please come up to that. The session is being recorded. Of course, many people can't be here, and this continues on for the next year as part of the extra, essentially online extra sessions for the AOHC. Please use the mic if you're going to ask a question, and we'll take it from there. So without further ado, we'll put on our first presenter, and let me get that up in here, and he already worked it out with the AV guy. Major Jean-Sebastien Chassé is at, interestingly enough, the U.S. Uniformed Services, though, for being from our neighbor to the north, nonetheless. I think we'll want to hear how that happened, but maybe not for your presentation. And welcome to Canada, and he will talk to us in the association between burn pit exposures and functional status. So welcome, Dr. Chassé. All right. Thank you very much, and good afternoon, everyone. So I'm Major Jean-Sebastien Chassé. I'm a Canadian medical officer, and I'm also an OEM PGY3 over at USUS in Bethesda. I'm really happy to be here and kick things off with a hot topic, right, burn pits? So all right, now I'm not nervous anymore. So this project was completed as part of the USUS MPH program. This is my disclaimer. I had no money, no interest in anything here, and the project was IRB reviewed. Here's a brief outline of the presentation, how it's going to be shaped like. Let's talk a bit of the background. So burn pit, as you probably all know, is just a non-controlled method of waste disposal where you dig a hole in the ground, put all your waste, and imagine all your waste in it, and set it on fire with fuel. They've been routinely used in Southwest Asia since the first Gulf War until 2018 when DOD instructed that it could only be a method of last resort. Over the recent year, exposure to burn pit smoke have become a high-profile health concern for service members and veterans. They've been labeled such names as the new Agent Orange and are drawing a lot of media attention, which is why in 2014, Congress mandated the VA to create that Airborne Hazard and Open Burn Pit Registry that was created to monitor the health of veterans, provide data for research. This is a fully voluntary, self-reported questionnaire that's completed online by service members. So in our research, we were looking for association between burn pit exposure, exposure to burn pit duties, and cumulative exposure days with a functional score outcome that we will define in a few slides. I put that in because this is how I felt when I opened the registry and looked at the sheer amount of data that's in there and the potential for association. I just wanted to keep that in mind because I've tried to turn that into this to make it as straightforward as possible. Just keep that in mind. Talking about the study design, we did a retrospective cross-sectional study based on the registry. You see here our initial and final data set. For simplicity, I didn't put all the detail of the merging, exclusion, inclusion, and things like that. Just be aware that we started with almost 271,000 participants with over 1.1 million deployment entries. And we excluded, the main reason why we excluded data was because we were missing exposure data for those deployments. So we ended up with 90% of our initial participants and just under 73% of deployment data. So this is the way we assess exposure here. On the left column is the different exposure data from our research questions. So any type of burn pit exposure, exposure to burn pit duties, and cumulative exposure days. The middle column is the question that we extracted from the registry to answer those questions. And on the right is the type of variable that we ended up with for analysis. So if any participant was reporting being near a burn pit during his deployments, that variable would become yes. For the outcome of functional status, we used five similar questions from the registry which assessed the level of difficulty for a participant to accomplish certain physical activities. Namely, to walk a quarter of a mile, walk a mile, or run a mile on a flat surface, or to walk up a hill, or walk up a flight of stairs. Each of those questions was answered from one to five on a Likert scale, one being can't do it at all, and five being the activity is not at all difficult. We summed all the answers for each participant and came up with that composite functional score, ranging from five worst to 25 best. That became our continuous outcome variable. Looping back to our design slide, this is a more detailed summary of the analysis and study design. So on the left, you have our exposure, our three exposure variable that we've just talked about. The outcome is the functional score, also we just defined that. And linking both is an adjusted linear regression model where we were specifically interested in any type of dose-response relationship. Now let's dive a bit in the results. First, let me show you our table one, which is our demographic table. This table shows all of our participants separated by exposure to burn pit or not. Three main takeaways I want to point out. First, the enormous difference in the number of participants in each group. So that means that over 98% of people that did fill the registry reported some kind of exposure to burn pit. Secondly, and as a consequence of that, over all the assessed, both groups were different for every compared variable, including the average functional score between the exposed and the non-exposed. And thirdly, our population is mainly composed of males between 31 and 50 years old, Caucasian and in the army. I wanted to show you that slide just to give you that picture and to explain what limited some of our analysis and any conclusion that could be drawn from the registry. This table is the bulk of our analysis. It shows the adjusted linear regression model between the exposure variable on the left and the functional score. This does not compare it between exposed and non-exposed because we just saw that both groups are not comparable. Here and again, a negative coefficient means that the exposure lowers the functional score as compared to the reference category. We can see that reporting an exposure to burn pit duties lowers the functional score by an average of 1.2 points and is statistically significant. Also reporting having longer cumulative deployment days increases the functional score marginally with a significant trend for the longer deployment days you have. So adding over 365 deployment days would increase the functional score by 0.28 on average, all else being equal, and that's being compared to less than 30 deployment days. Finally, and what we were most interested in, is the significant trend of a decreased functional score as cumulative exposure days increase. This means that reporting exposure to burn pits for more than 365 days decreases the score by 1.65 on average, all else being equal, with a significant trend as well. Now let's discuss what those results mean. First, even though there's multiple papers in the literature that talk about burn pit, we couldn't find one that was specifically linking exposure to function. Most paper focus on medical conditions as outcome, so that's kind of a novel, the novelty of our project. We did, however, did this as a follow-up from an unpublished paper in 2021 by Dr. Insha at USIS, who did a different type of analysis using a binomial functional outcome than ours, and their model was based on a logistic regression one. They did find a significant association between any burn pit exposure and function, but did not conclude to a dose response in their analysis. Our main association was that, first, there's a significant negative association between cumulative exposure to burn pits and the functional score. This hints at a dose response between exposure and functional status. The longer a member reports being exposed to burn pits, the worse their functional score gets. Secondly, there's a small, significant association between cumulative deployment days and functional score as well. This was a bit surprising, and after we thought about it, we thought it was due to the LT deployer effect, because deployed troops are being screened medically above and beyond any type of worker or other service members, which implies their better overall health and function. Lastly, having burn pit duties also lowered the functional score, which might be caused by the proximity and increased intensity in exposure. The main strength of this study lies in the size of the data set that we're using. Having such a big population allows our analysis to detect statistically significant differences that would be unnoticed in smaller samples. The fact that the deployment data were extracted from HR database increases the reliability of those variables, and also, everyone in the registry was exposed somehow to Southwest Asia's airborne hazards, where PM2.5, PM10, and overall air pollution in Southwest Asia is recognized as being higher in this area than in America. It's then possible that the environmental exposure, rather than specifically the burn pit exposure, might be affecting the health and functional score of service members. As far as limitation goes, there are a few as well. So we saw from our table one that our study was homogeneously composed of service member reporting exposure to burn pit. So we had no control group. We had no way of analysis, like doing an analysis between exposed and non-exposed. That explains why we did our analysis based on solely the exposed group. Secondly, the voluntary aspect of the registry limits the number of participants that actually complete the questionnaire. As of fall 2022, the proportion of eligible service members who actually complete the registry sat below 10%. So this creates a big selection bias that explains why most of our population did report an exposure, because those who completed the questionnaire feel like they were exposed to burn pits. The self-reporting of both exposure and outcome without an objective exposure data opens the door to recall biases by participants and other information biases. And the lack of individual exposure data also limits the reliability of exposure data. So to conclude, our study found a significant statistical association with the possible dose response between cumulative exposure days, cumulative deployment days, burn pit duties, and the outcome of a functional score. No causality can be inferred at this time. We can only generate hypothesis, which we've talked about. And as previously mentioned, exposure to airborne hazard in Southwest Asia might be a course of research that would be interesting to pursue. We think that a smaller, more specific study would be required to decrease the uncertainty of the associated with the registry. There are ongoing promising research about using novel biomarkers to assess exposure after the fact. That's also a course of study that could be research, because improving the reliability of our exposure and our outcome would allow stronger conclusions. I would like to acknowledge all the help I've received from USUS and the VA in the production of my project. And this concludes my presentation. Thank you for your attention. And I will take questions from the audience. Great. Thank you. It's always great to have a great lead-off speaker. And so we will take some. Any questions from the audience? Please come and use the floor mic. Anyone? A quick question while they're asking, what's the period over which these data were collected? In other words, may people have answered it younger and then as they kind of, I know you adjusted for age, but that would have been age and questionnaire, right? Could it sort of reflect some age changes despite the control for age? Right. So we use the age at the time that they filled the questionnaire in that. But yes, it's not adjusted for how long they've been exposed since the moment they filled the questionnaire. We extracted our data set in August, 2022. So we have everything before that. A long time. Yeah. So. Great. Great job, Jess. So knowing what you know now, what would you do differently in terms of trying to study this issue given like all the limitations of the database as it is? I mean, adding self-reported data and voluntary data introduced so many biases that it's hard to conclude anything. So I would grab people out of their deployment and have them complete the questionnaire and follow these people that we know came back. And it's not a voluntary self-reporting, but rather having a representative proportion of deployed members so that we can compare who was exposed, who was not exposed and have kind of that data. By introducing some clinical information as opposed to the self-reporting limitations. Yeah, I think moving forward, especially how important this has become with the PAC Act, I think that's kind of where we need to look, is look at true objective data, but it doesn't seem like the registry includes that at this moment. No, the registry asks for, do you have any L conditions, right? And the member may or may not tell the truth and may or may not know. So there's kind of that discrepancy and it's then hard to reliably assess any kind of a medical condition as outcome. Thank you. Next question. Great presentation, thank you. I suspect that rank kind of plays a role in who gets assigned POOP or PIT duties. Is there any thought on like job satisfaction or like any individuals who, service members who had been excused from service earlier as being factors in the data? So we did look at rank distribution and there was no big difference with that. That would be interesting to look at, yes, job satisfaction. Like a key thought is like people angry against the military will likely fill that questionnaire with whatever they want, right? So it's something to consider, yes. Dr. Hines. Not a question, thank you for your presentation. Just a plug, if you'd like to hear more about the clinical evaluation of these veterans, come to Wednesday's session 402 at 9.45 a.m. See you there. Great, now we have to declare commercial bias in this session. Thank you, Dr. Sheltz. Thank you very much. Appreciate it. All right, give me a moment to, where'd that go? Come on. Sorry. Apologies here. Okay. Just lost the cursor. Sorry. Sorry, apologies a minute here. Seem to have lost the cursor on here. Yeah, I know, but there's the screen up there isn't showing me what I've got here, but let's see if we can fake that. Brilliant. All right. I'm going to have to work with the screen. So there you go. There we go. I know we've got a, sorry, it's a projection versus, no, yeah, I got it. Let's see. Okay. Let's try that again. There we go. Somehow kicked in. All right. Apologies for that. They're usually, I'm like, one of the more competent people on those there. So thank you again for the waiting there. So keeping up with the theme of setting things on fire, we're going to welcome Dr. Ashley Nadeau, who's going to talk to us about biomonitoring of firefighters who are exposed to a prolonged metal recycling yard fire. Thank you, Dr. Nadeau, coming to us from the Health Partners and University of Minnesota program. Thank you so much for having me. I was going to see, okay. Yeah, so we're going to talk about biomonitoring of Minnesota firefighters after a four-year, or a four-day metal recycling yard fire. I have no financial disclosures. This was funded by the Northern Metal Recycling Company who paid the lab bills and the medical consultation fees for the Becker firefighters. And this was approved by the IRB. The objective of this study was to assess firefighters after a prolonged industrial fire. And we collected blood, serum, and urine concentrations of selected heavy metals, as well as polycyclic aromatic hydrocarbons. This is a picture of the junkyard, which included mostly cars, so tires, glass, fabric, and then also a lot of refrigerators with foam insulation. The scrapyard burned for four days straight, and this was in February in Minnesota, so there was sub-zero freezing temperatures, which made it very difficult. And also, Becker is a very small town, so firefighters from all over the cities, northern cities, had to come in to relieve the small-town firefighters. Also, they had to close a public school nearby because of the black smoke plumes that were wafting over there because of the wind. Like I said, there was a bunch of cars, refrigerators, all different types of metal, glass, foam, and insulation. And the firefighters actually contacted Dr. McKinney and asked and requested biomonitoring be performed. So he had about a day to three days to whip this study up. Just keep that in mind. So you can see our lovely snowfall, the trucks. Nobody's wearing any SCBAs. What happened was is the information that the study was gonna happen went out to all different departments involved, and there was 21 departments. Demographics, just basic, were collected. Age, gender, fire department, years in service, time at fire, duties performed. Then the biomonitoring samples were then collected by a laboratory and sent in to the Minnesota Department of Health. We tried to get a 24-hour urine, which is kind of cumbersome to get from a firefighter who's actively fighting a fire. And then also we did basic lab tests like complete blood count with differential, basic metabolic panel, and then the selected heavy metals, and the six PAH metabolites, so the monohydroxyl PAHs. Then after the analysis of their lab samples, a letter was sent to each one of the firefighters who participated, and it was interpreted based on the findings of the lab, based on any reported symptoms, and they were also invited to follow up afterwards, and they declined, which I'm gonna go into. The metal biomonitoring part included all the metals listed in this table, and if arsenic was elevated, it included speciation or fractionation. They also included RBC manganese, which is typically at low-dose, but chronic low-dose exposures. And the bottom part is the correction is only for the data analysis, but essentially if the lower limit of detection was below that we set it to zero, or we set it to the lowest detectable limit from the lab. So that's not that important. We did a descriptive analysis on this, so just describing in tables. So not as important, but maybe in the future if we do do an analysis. Then we also did this polyaromatic hydrocarbon biomonitoring, which are incomplete combustion molecules, and the carcinogenic component of soot. And we selected six different ones, and this was performed by the Minnesota Department of Health Laboratory, Public Health, Environmental Lab section. This agreement was set up later than the initial biomonitoring, so we don't have as many samples for this portion of urine samples. And here's just some of the examples of the naphthene, pyrene, and fluorine that we included. The demographics. Becker is this small town right here where the fire was, and all of these dots here are from the fire stations that contributed firefighters to go out to Becker and fight the fire. We actually had 113 firefighters before, didn't fill out the demographics survey, so their data was excluded. So we ended up with 109 firefighters who submitted lab, or submitted samples, and completed the survey, and then the 41 samples for the PAHs. This is the distribution of different fire stations, of surrounding fire stations. The demographics is interesting. There was a good distribution of different age groups for firefighters. There was actually 6.4% female firefighters, and several participated in multiple roles of active suppression, driver, engineer, command, and then there were other roles like bringing water and other certain tasks that I didn't include. And then I divided up years of firefighting into less than three, so sort of newer, three to six intermediate, so there was a large distribution, and a lot of the more experienced firefighters came to fight the industrial fire. Many spent eight or less hours at the fire, so 40%, and several spent eight to 16, which is a pretty long shift, so about 30%, and then there's some that spent up to the full four days there who were in command. The interesting part of this slide that's important to note is the days from the exposure at the fire to when their labs were submitted, because metals, heavy metals, typically are excreted within one to two days, and the average of the days from when they were at that fire to when they gave their urine jug or blood sample was six days, and some of that stretched all the way to 21 days before they even had submitted the sample, so some of the data may not be as reflective of this specific fire exposure, or it may be some other exposures along the way. We did get 99 urine samples. Again, it is cumbersome to have a 24-hour urine jug collection. We also collected the blood serum plasma, and we did the analysis of the urine within the 24 hours by volume, and then adjusted for creatinine ratio. Oh, that says creatine, sorry. This is a really busy table, but I think the main thing for this table is looking at the reference maximum, looking at the above the maximum, and then also looking at the range of above the reference range with the mean standard deviation. Oops. So for several of these, we did have some above the reference range, but it was just barely above the reference range, and the majority of the data that we did find that was above the reference range was selenium, which is commonly found in the diet. We didn't ask the firefighters to restrain from taking their supplements, vitamins, or eating seafood, or certain types of food that may give us false elevations here. Additionally, the manganese, again, that's typically chronic low-level exposure, so not like an acute exposure, and it was, all of these are just subtly elevated, but no one reported any acute symptoms, and none of them are to the elevation of, say, an actual acute poisoning, which is a good news, and that's how it was interpreted. Then arsenic, there were six that had the reflex testing with speciation, and the good news is that the inorganic arsenic was zero, which is the high-toxicity type of arsenic. Organic arsenic typically is soluble. It's found in food. It's usually conjugated with the sugar, and methylated, which typically represents that it's either fish or seafood or other dietary sources, so this is good news in that they don't have inorganic mercury. The urine monohydroxylated polyaromatic hydrocarbons, we had 41 samples. This is an interesting topic because not a lot, there is hardly any reference range that exists for this, so it's very hard to interpret data without a reference range. Essentially, none would be ideal, but we did take a look at the NHANES data and a paper that did combined NHANES data, and what I did find, again, this is a busy table, is that the firefighters were within the 95th percentile for the NHANES data, so it's in a normal range, and some of the data, or some of the firefighter samples were a little higher than the mean, but again, they didn't go over the 95th percentile, so they would not be significant statistically, just from a descriptive standpoint here, which is a good thing as well, but again, this is difficult to interpret without normal ranges or reference values. I think the discussion is the most important part because I learned a lot from this study, and that's having a protocol, if someone were to spontaneously want you to biomonitor them, and firefighters do have a lot of unique exposures, especially at an industrial junkyard, metal scrapyard, and it could be concerning. We may not see those acute toxic poisonings, but we may see low levels that they would have been exposed to, and it was very difficult to capture, given the lag time of when they were exposed to sample collection, so a lot of the elevations, again, may not relate to the fire that they were at. There are multiple routes of exposure to these heavy metals, again, some could be from ingestion from dietary sources, it could be from inhalation or skin absorption. Again, a lot were not wearing their SCBAs, but the wind was blowing in a different direction. They may have gotten soot on them. Again, no one had any acute symptoms, and none of them had significantly elevated levels of heavy metals or PAHs that were found. The exciting part of this was that the firefighters reached out, and that they wanted this community-based participatory research to occur, because of their risk that they were having with the fire, and that this raised some potential points about risk and hazard communication with them, thinking about this in the future. Additionally, they have recurrent and ongoing exposures, so it's hard to say, again, if it was this fire, or if it was multiple fires, or the fire station, the trucks, all different types of exposures that they could be having. So what's important would be a pre-exposure and a post-exposure, and then even a follow-up, so multiple data points to look at. Additionally, with the short half-life of the metals and even PAHs, usually eliminated within one to three days, it's important to have this really set up quickly. Again, the 24-hour urine, if there was another way to do this because of the cumbersomeness of that, losing samples, and then also dietary or supplement restrictions would, like a protocol for them, would be ideal. The firefighters were offered the opportunity to do follow-up testing if they did have semi-elevated levels, and none of them wanted to do it, and so they declined, but that would have been ideal. We did discuss doing a type of complex analysis related to the half-life of the compounds or the valence and the phases of elimination, but it seemed very challenging. I think, again, like Major from Canada said, we need new biomarkers, we need better laboratory testing, potentially even biopsies, because a lot of the metals go into organs if they're not excreted right away. So, again, the laboratory tests just do not separate valence of these metal compounds, and then we're also lacking knowledge of what these PAHs are capable of and what their toxic levels would be at. In addition, we didn't have industrial hygiene, and it's very difficult to have industrial hygiene for firefighters exposed to mixtures of smoke or particulates. So, in conclusion, this was a cross-sectional post-exposure biomonitoring experiment in firefighters. They had a really unique situation, and we were able to attempt to capture heavy metals and these carcinogens that are from the incomplete combustion. Again, there's a lot of limitations to this type of biomonitoring, but now we're more prepared and hopefully we can come up with better laboratory tests for their ongoing exposures, and I do think it's really important. So, thank you very much for listening to my presentation. Time for one, maybe two questions. Microphone, please, if you're able to. Excuse me, microphone, please. Yeah, thank you. Beautiful presentation and huge data and everything. You said very well, Tom Stradia, you said that, but all the lab samples were sent to the same lab, or did you use several? The heavy metals were sent to the Health Partners Lab, or ARUP, for the RBC manganese, and then the PAHs were sent to the Minnesota Department of Health, who does do some screening with the NHANES data, so they have that assay set up and validated. Dr. Wiebe? Very impressive effort. Kudos to your team, your program director, pulling this off. I just wondered, what was the final message that you gave the firefighters in terms of risk communication? We said that they are exposed to unique heavy metals and toxins, but that there is no, that they have not exceeded these levels, essentially. That they, I didn't read all of them, but essentially, they weren't symptomatic and they weren't in the range of poisoning, so we didn't have to do any treatments on them. Next, last question. Thanks for an interesting study. I think it's important that we try to measure what people are actually getting exposed to. I was just wondering, had you considered having a control group like police forces, someone that were comparable? That is a good idea. Sounds hard to get together in three days. That's why we were using the NHANES. They could be collected later. That's true. You could do it retrospectively. Great. Thank you. Good idea. More costs. Yes, more costs. Thank you. All right. Let me just see if we can get that on up here. Sorry. This is... Come on up here. For some reason, I have a little screen thing. It's kind of wonky. I don't know why here. Let me just see if that... Yeah. One of those. Thank you. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. All right. 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occupational and environmental medicine

burn pit exposures

functional status

military personnel

biomonitoring

firefighters

metal recycling yard fire

risk communication

health effects