Do you know what, I might actually get started, because this is probably a niche area. Those who are going to be interested are probably going to be trickling on over anyways. And the first couple of comments are very introductory anyway. So welcome to the COVID-19 workers' compensation experience. I apologize if this is traumatic for some of you. I know this is going to bring back a lot of memories that you experienced in COVID-19. So if you choose to run out of here screaming, I would not judge you, I promise. So a couple of comments. So I have the fortune of being a stakeholder on the WCRI committee in my state. So WCRI, for those who do not know, is Workers' Compensation Research Institute. So they basically do research across the United States, but they're also active in other countries like Canada and New Zealand. So they do research on issues that are important in workers' compensation, like worker outcomes. So being on this stakeholder committee, we get the privilege of letting them know what are important issues in workers' compensation, what areas to target their research. And also, when the preliminary data come out, they often get it in front of the committee for feedback, and they may change whether the research concepts, ideas, or sometimes the data based on recommendations. So that helped a lot going through the pandemic, just having that feedback and that experience. So those are my disclosures. I will not be discussing any off-label use of medications for COVID-19 today. So we'll talk about some of the unique challenges posed by COVID in causation determination of occupational illnesses. Just by show of hands, how many of you got involved truly in treating acute COVID-19 among your workers? How many? Just by show of hands. Okay. Got it. How many of you were involved in causation determination? Oh, it's quite a few. And then who did like a lot of impairment ratings for the COVID? Okay. And then just last question. How many of you are with insurance companies? Doesn't have to be workers' comp, of course. Okay. Great. So we'll look at some of the COVID-19 workers' comp data, and that includes both COVID cases as well as non-COVID cases, and then some of the post-COVID claims. And then there was an interesting emergence with the use of telemedicine that we all saw, right? So we wanted to see how that panned out through till 2021. So stay tuned. So I'm sure a lot of you are probably hyperventilating at this point. You see this occupational risk pyramid for COVID-19, which by the way, I'm sure a lot of you know, we stole this from the influenza experience, right? So you recall that very high-risk workers were healthcare workers, laboratory personnel, the high-risk healthcare delivery and support staff, medical transport, and so on. Medium workers, medium those workers who had frequent close contact with people who may be infected, and then lower risk, those with minimal occupational contact with the public and other coworkers. So this is not to go through the risk assessment right now, but I'm sure a lot of you like me were glued to the CDC website, you were glued to your state health department websites to get these algorithms to help you figure out, you know, what was the level of risk after an exposure to determine, you know, return to work. Is it no work? Is it exclude from work or test and monitor? So you may recall the one unique thing about this experience with the pandemic, it created the perfect storm for occupational and environmental medicine specialists to showcase their talents, right? So a lot of what we do was sort of at the forefront. So in comparison to other claims for occupational disease, where there's typically a unique work exposure, right? Infectious disease is always a little bit more challenging, but even managing, say, a needle stick, you know exactly what happened, when it happened, the source and so on. If you have a TB exposure, you know the source of that. But COVID-19 was a little bit more challenging because it was in the community. How do you decide if this was truly work-related? So a lot of us got involved in causation analysis for organizations. I'm in a hospital system, very large, 35,000 employees. So we had a lot of exposure, a lot of cases. And then once a lot of our clients got wind of it, which were a lot of the assisted living facilities, they wanted to get some support with determining causation for their cases and so on. So you remember that establishing the diagnosis is critical. You had to define the workplace exposure, frequency, duration of contact with bodily fluids and so on. Hygiene was important. PPE was crucial. In fact, a lot of our sites that had the higher cases, including deaths associated, was associated with those that did not have adequate PPE or had issues with the fit testing, at least initially. Once we got supply, we had a significant difference. I'm hospital-based, clearly, but some of the non-COVID data that I do show you are from other industries as well. So this is from the AMA guides, and I'm sure you're used to this language. So elements of causation analysis without the work-related exposure accident. Is it medically probable that the patient would have the current diagnosis and require the treatment? So the other thing that COVID did for us, in addition to taking a proper occupational history, right, was taking a travel history and social history. So we had to really think about travel, and some of our cases that we ended up looking at, we had physicians who, you know, they came down with COVID, but they traveled to a different state, you know, interacting with family, friends, and so on. So in terms of determining the work-relatedness, so we document exposure. We determine that the disease is related to the exposure, and there are a lot of policy variations across the state. I'm sure you recall that, and in some places it was a site that was community acquired, or there were presumption laws, which we'll talk about in just a little bit. So a lot of the cases, or a lot of the states required a positive COVID-19 test to make a particular case compensable in workers' compensation. But some of the earlier cases, you recall, had presumptive diagnoses of COVID-19 if the test was negative, and, you know, the overwhelming symptomatology was in that indication. So we also thought about case involving medical treatment, days away from work, and then the state variations. So our job as OEM specialists, right, so we had to really, we were at the forefront of this, establish the diagnosis, determine the exposure timeline. A lot of times you could not identify the specific patient or client contact from which the exposure arose, but this is where we got into the understanding or statutory presumptions that were across states. And then there were specific considerations in certain workplaces, like corrections, jails, meatpacking, retail, which we kind of all saw play out. And then we made our decision based on whether or not the exposure was greater than that of the general public, and was it likely to result in COVID-19 transmission in the workplace. So just a few comments on presumption. Wasn't always possible to determine where the exposure came from, so think about your first responders, police, even some firefighters in some instances, healthcare workers. And some of the judges would take into consideration some of those specific workplace factors when making those decisions. So if traditional workers' compensation criteria were met, were not met, that's where an OEM specialist was able to help you to sort that out. So in occupational disease determination, the burden of proof actually fell on the workers, but with the presumption laws, that sort of shifted the burden to the party against which the presumption applied, which was the employer in a lot of the cases, compared to irrebuttable presumptions, which we will not get into a lot, but that established a legal conclusion regardless of the evidence presented against work-relatedness. So is there a legal presumption of compensability? That should shift the burden to the employer, as we said, when there's presumption of compensability, the employer has to prove that the employee did not contract the COVID-19 at work. This was the case in New Jersey, so we were able to assess the cases on a case-by-case basis, and the legal standard was more probable than not. So if there is a presumption in the state, then the employee had to prove that they belonged to the presumptive class, they had to test positive for COVID, and this was true for a lot of the claims that we were looking at, for the claims to be considered compensable, they had to test positive. They didn't have to prove the specific source of the exposure. So main classes, we kind of established that, was healthcare workers, public safety officials, but keep in mind also firefighters, police officers. So we were on the East Coast, right, watching, kind of watching things play out on the West Coast, you know, California is its own country, so we saw California's executive order came out April, May 6th, rebuttable COVID-19 presumption. So basically, they said that a claim was presumed to be work-related if they had tested positive within 14 days after the last day worked, the day of injury was within that certain time frame, which was the public health crisis, as long as they worked outside their home at the employer's discretion, and the diagnosis was made by a physician holding a California license and the like, and New Jersey kind of copied. So just a little bit, little taste of the California workers' comp experience. So initially, I didn't follow through with the California data, but I thought this was pretty interesting, because initially about 27% of claims were accepted, 35% of claims were denied, and then 36 were under investigation. But curiously, of those that were denied, majority, almost 70% were due to negative COVID tests. Lack of exposure at work was another big factor, but at all other, employee had not been exposed at work, there was a lack of diagnosis or symptoms, the employee had been teleworking, or they refused a COVID test. So those are some of the reasons why the cases were denied. So this is New Jersey now, so we're back to the East Coast. So periods defining the public health emergency, and not to read the periods off, because you know what they are, and that went through a couple of the waves. But the first hit in 2020, and then we had another wave in 2022, as you recall. So this was all rebuttable presumption, the bill came out, the state Senate Labor Committee advanced legislation to create rebuttable presumption that the COVID-19 diagnosis in the essential worker was work-related, and this was important so that employees could get their benefits, so that they wouldn't have to use PTO time to cover the illness. So permanency was a little bit more complicated, because just because a case became compensable, it didn't mean that the employee was eligible for permanency benefits, and there the burden of proof rested on the employee in that scenario. So we're going to take a little bit of a deep dive now, so I mentioned earlier, so Workers Compensation Research Institute, they are independent, they're not for profit, but they are funded by their members, we'll talk a little bit about some of who the members are, employers, insurers, governmental entities, MCOs, they do have representation from physicians, other healthcare providers on their committees, and they do research based on a variety of reasons, and some of the things that could trigger them might be, you know, what's going on in the state? Is there legislation in the state? In this case, of course, the pandemic was a big driver for that. But they don't take position, they do consult with their stakeholders on what are the important issues, and when the preliminary data come out, they do have us review the data. We meet about twice a year, it was in person before the pandemic, during COVID it was virtual, and then after COVID it was, well, not after COVID, I don't know if COVID is over really, right? Hopefully it's endemic now, but now it's like a hybrid type thing going on, so it's interesting how that evolved, but we'll give them our feedback on the data. So some of the studies that you'll notice, preliminarily there might be, you know, one set of data or information, but then they continue the study, so you might see some variations in some of the numbers, and that's one of the reasons why. So, you know, policymakers have an interest in this information, it might help to inform policy, and in states where there are major legislative changes, they might target studies surrounding that. So we recall the massive economic shutdown, right, at the very beginning, you know, what were some of the impacts of this with non-COVID claims, right? What were some of the access issues that might have come up? And those were important for informing policymakers and stakeholders. So the first one we look at was the composition of claims, right? So this was across 27 states, so not all the states are involved, so there's various reasons why different states are involved at different times, but, for example, New York wasn't involved initially in some of the studies, but later on that data was made available. So first we looked at all paid claims for quarter one and two of 2019 and 2020. Why 2019? We're looking back to see what happened the year before, right? And we used the injury codes related to the pandemic to gain that information, that comparison. So first we're looking at, with the COVID-19 claims, so I try to separate these out so I don't totally confuse you, but so with the COVID claims, so how did the prevalence of COVID compare across the states in the first couple quarters? What factors were behind the variation? And then for the non-COVID claims, what was the impact of the pandemic on the number of claims? And then how did the share of the greater than seven days lost time, which is a critical number in workers' compensation among all the paid claims, what was experienced there? So with the COVID-19 claims, so what we saw here was, this is the spread across the country, so the experience was not equal across the country, right? Remember those very heavy hit states initially, Massachusetts, New Jersey, New York was noticeably absent from this particular data set. We do see New York come in a little bit later, but we could see even New York, New Jersey and Pennsylvania, as close as we are, quite a big difference there. So this obviously is just the spread based on how the data is presented. Let's look at California. California, under 10% at this point, and this cross section was June 30th. So keep that in mind. So this is now percentage of COVID-19 lost time claims among all paid lost time claims. And that's a big spread again, we're seeing there. Heavy hitters, Massachusetts and New Jersey, again, pretty close, neck and neck there. And then Pennsylvania, around 30%, and California around 20%. And so we see now, what we're seeing here is the severity of the pandemic and over 1,000 deaths per million, we see in red there. So again, the heavy hit states, Connecticut, New Jersey, Massachusetts, and then we see in yellow there, we have 500 to 1,000 in teal, if you're not colorblind, 100 to 499, and then green is less than 100 COVID-19 deaths per million population. So how are the presumptive laws coming in? So there were clearly incentives to report a COVID-19 infection as a workers comp claim, for one reason or another. We talked about the presumption laws that protected workers, but there are laws, regulations, rules, practices that influence how a claim was being reported. Some of the reasons for a variation, where the pandemic struck hard and early, presumption laws, Massachusetts had something called pay without prejudice. And then also think about your denominator, right? So what was the impact of shutdown, social distancing, and working from home? And then the industry mix had a variation there as well. So perhaps not surprising, this was kind of all over the news, and I'm sure you see the big spike there. And so high-risk services were hit hard and heavy, lower-risk services, and then I'm sure you probably got a little bit of wind. Construction kind of fell off for a while, as did manufacturing, particularly in those heavy hit durations. So again, this was all over the social media news. So hospitals and assisted living facilities were hit pretty hard, as were physicians and dentists' offices. So they got pretty beaten down there. So we just said that. So the non-COVID claims, what was their experience? So majority of states had at least 30% decrease in the number of paid non-COVID-19 workers' comp claims, and that did not align with employment, just for comparison. And all claims, injury dates, they compared 2019 to 2020 again, so you get that nice comparison. Lost-time claims, so just take my word for it at this point, so higher share of claims with greater than seven days lost time in 2020 compared to 2019. And non-COVID claims were more likely to have greater than seven days lost time in 2020 than the prior year, and COVID-19 claims more likely to have greater than seven days of lost time than the non-COVID claims. So I didn't end up sharing a lot of our hospital data for a lot of reasons. Some are still in litigation. We still have some post-COVID claims. This particular, the frequency data was very similar for us. So I figure Workers' Compensation Research Institute, they publicly make their data publicly available, so I felt more comfortable with revealing this information. So what were some of the pandemic-related factors affecting medical trends? So you recall when elective surgeries and invasive procedures were suspended around March to May, and that was per executive order, but then what happened? We saw telehealth services expand, and this sort of mirrored what was going on in the rest of the country where they were opening up telehealth services to make sure that people had access to care, right? And then we had a drop in the dollar sign, the number of claims with ER services. People wanted to avoid the ER, and a lot of reasons, people were afraid of getting COVID, but they were also perhaps wanting more individuals that were sicker to have access to care. Maybe telehealth helped facilitate some of the barriers to care. New Jersey at that same time reported a large decrease in utilization, and we just see there, just to add some color, we have in yellow there, we see the emergency department visits compared to your outpatient visits in darker green and the inpatient admissions. Inpatient admissions look like it was pretty consistent, because if you're sick, you're sick, right, you have to present for care. So with regard to non-hospital and hospital outpatient services, we see a decrease there across various services, physical medicine, major radiology, ER visit, prescription drugs, major surgery, and ambulatory surgical care, and so on. And this was similar across the other states. So even though, because I'm sitting in the New Jersey meeting, of course, we're getting a lot of emphasis on the New Jersey data, but we do have some comparison on what the other states were doing as well, including Pennsylvania, that's pretty close by. And hospital inpatient, again, we saw, yes, there was some shift, but not a lot, because if you're sick, you're sick, you're going to be presenting for care. So what we did see, too, was a decline in major surgeries, and we saw that drop, and they had data as far back as 2015 to look for that comparison. And this, too, was similar across the other states. So we saw a drop in utilization, no surprise there, in non-hospital and hospital outpatient services as well as in major surgery. So shifting gear a little bit, so we saw a decrease in medical services used by the general public. Well, what was the experience with workers' compensation claims, right, what did that utilization pattern look like? And then look at the hardest-hit states, we said, you know, Connecticut, New Jersey, New York was heavy hit, but a lot of that data is not here. And then compare the impact of the pandemic for different medical conditions. So they helped us to look at traumatic injuries, you know, not-so-traumatic injuries, what was the experience there? And then we were able to compare to 2019 to see what was happening the prior year. So in this particular study, 27 states, but those 27 states represented as high as 68 percent of the workers' compensation benefits paid in the United States, claims with more than seven days, lost time again. In this particular study, they did not distinguish between in-person and telemedicine services. We do have some telemedicine data coming up, but in this particular study, it was all comers. COVID-19 claims were excluded from this particular analysis, and claims with injuries from 2019 obviously compared to 2020, but they're looking at payments from medical services or income benefits. There's limitations, of course, in the database, because it's what you have, right, to look at the data, and so it's not complete in some instances. These were private sector employees. There were some local public employees like police and firefighters, but federal and state government workers were excluded. And what were some of the source of that data? National and regional insurers, claims administrators, state funds, self-insured employers. And some of the considerations, what impact did the COVID-19 pandemic have on the timing of medical treatment for workers? There were concerns regarding delays in medical treatment. I'll give you the answer to the question. There weren't a ton. There were some areas, so I'm going to hit the highlights for you. And they made sure the injury composition was similar for those studies. So I've already given you the answer to the question. But in terms of timing of medical services by medical conditions, I'll show you how they broke it down. For traumatic injuries, those were considered to be fractures of upper or lower extremities, and then lacerations and contusions were lumped together. So those were all your traumatic injuries. And then soft tissue conditions were categorized by spinal, so spinal sprains and sprains, or non-spinal, so your other sprains and strains. So a simple classification, but just so you know what they were using. So workers, were they avoiding medical services during their early months out of fear? Maybe there were some access issues. Maybe telehealth helped with that a little bit. Maybe people were just being considerate and saying, make sure that sicker people had access to care. So the hardest hit region, not surprising, perhaps, you saw the bigger changes, right? And again, these were Connecticut, Massachusetts, New Jersey, and we saw a significant drop in ER use. And going across, what you're seeing are the quarters. So just if you wondered what's going from one box to the next, and that's what that is. So 8% drop in ER use, 6% drop in physical medicine services, so those are like your more procedural type encounters, right, compared to say evaluation and management, which are your more cerebral or cognitive encounters. There was a 3% drop in major surgery and pain management injections. And even though the number sounds small, that was considered a significant change because you had about 13% to 16% of lost time claims had either major surgery, pain management injections. So the numbers were not that small. So in the hardest hit region, we had some delay in minor radiology. You might see the numbers kind of bouncing around there, but they were not statistically significant. So I'm just highlighting what was statistically significant for you there, just to make it a little simpler. And then, yeah, don't hyperventilate. So I hyperventilated when I saw this. So it says, you know, three day increase before major surgery, other strains and sprains. So I'm sure you're thinking, why are other strains and sprains going to surgery, right? But I'm glad they had this disclaimer in the discussion because most professional organizations and evidence-based guidelines, including ACOM, by the way, do not recommend, you know, there's no strong medical evidence for surgery with regard to back conditions. And then MRI for spine sprains and strains is not typically recommended. So yeah, don't hyperventilate yet. So they did acknowledge that in the study. The other thing to point out is perhaps, you know, the ICD-9 codes, we all know, we've all been there. A lot of the times when somebody present initially, you know, there's a lump sum, you know, dumping code that's assigned. And then as you move farther into the case, you get more and more specific, you know, diagnoses, hopefully. And you should get more and more specific codes, ideally. That's what's supposed to happen. Not always. And I suspect that's a bit of what you were seeing there. So I'm not going to delve a lot into existing claims, but just remember that they were there because you have now these existing claims from 2018, 2019. What was happening with those cases, right? Because they still needed care more likely than not. So we could look at the time from injury to major surgery, pain management injections, neurological, and that's your neuromuscular studies. So like your EMGs and the like, major radiology. So the time there was affected. Traumatic injuries, thankfully, was not affected. So I think we're all relieved to hear that, right? So just a little look at the data there. So in dark green, you have quarter one. So your quarter one was fine, right? We were all okay quarter one. So no issues there. And then get into quarter two there, you see some changes there for your physical medicine, your neurotesting, pain management, major surgery, and so on. So we started seeing some impact there. So with regard to telemedicine, we saw that huge jump there. That's quarter two that you're seeing there. Can't miss it there. I don't think you need money to point that out. So that was about 10% jump in use of telemedicine. And we saw that when they expanded the use of telemedicine services. And then March to June, we see, we're just showing off New Jersey there. You can see 9% use of telemedicine. Then we see New York kind of jumping in there, right, at the very end. But look at New York, that's like 25%. California is about 15%. And then, but looked at what happened July to December then, you know, once things opened up a little bit better, right, services started to come in again, then we started to see a drop in use. But the drop down to about 3% there, the comment there is that even though it dropped off, it was higher than our pre-pandemic level. So there's, that's a little bit encouraging, right, because, you know, maybe there is utilization there. We have some more data to show, but I just thought it was curious how California just kind of sort of held its own there at 12% even towards the latter part of the year. So that was a little interesting. So the rate, the spread was about from 1% to 11%, right. The average was about 4% use of telemedicine and reimbursement. We talked about the reimbursement because the reimbursement became critical because one of the challenge with, you know, making telemedicine actually work is the reimbursement because if it's not a fair, equitable reimbursement, that's going to really hamper its use. And we clearly see there's some utilization in the workers' compensation arena and perhaps, you know, definitely in other areas. So New Jersey had about two telemedicine visits for evaluation and management, the more cognitive cerebral encounters. And that was similar across the board in a lot of the states we saw that was pretty consistent. The ratios are slightly different, but you could see that there. So this just kind of showed you what was happening in the different, you know, March to June, July to December, January to June, and the various states. And that was kind of reflective of what we saw before. So in regard to prices, this is pretty critical. It was pretty consistent. So you saw on the top, sorry, wrong button. On the top there, you see going across, you see in dark green, those are the telemedicine encounters and the pricing associated with reimbursing providers for doing those encounters. And then on the bottom there, you see in yellow, we have those in-person visits. And those are pretty consistent and a lot to think about there. So they kept the prices consistent. However, what emerged from this was that if you saw a provider initially using telemedicine, the majority of those would continue to be seen via telemedicine. And in the preliminary studies they threw at us in the stakeholder meeting was about 65%. So they got kind of excited. They followed that out to 2021 to see what was continuing to happen. And the numbers held pretty high. They're about 58%. So if you started your care in telemedicine, it was very likely you were going to finish your care with telemedicine compared to those who started with initial in-person visit, only 3% of them had telemedicine visits subsequently. So we had the numbers there for you to show that experience. So the other thing they were interested in is, well, what happened after 2020? So I'm in these meetings, right? These meetings kept going virtually even during the pandemic. So they're sitting there talking about costs and indemnity. And all we can think about is, well, what's happening with the pandemic, right? So we said, well, we have to get this information about the pandemic and so on and so forth. But they were able to follow the data all the way out through to 2021. So now we're starting to see what was happening with telemedicine through until 2021. So what happened, even though the numbers dropped off, the numbers stayed higher than pre-pandemic levels. The pre-pandemic levels were like 0.2%, 0.3% in that area. So we're still staying pretty high at about 3%. However, physical medicine was closer to the pre-pandemic level because those are your procedural hands-on encounters, right? E&M services, definitely there is an opportunity there for utilizing those services. So just a comment on strains and sprains. So this is not the telemedicine talk, right? But of course, you all know that there are some encounters that are going to be conducive to telemedicine and some just won't be. Thankfully, our traumatic injuries, those can be seen in appropriate settings for that. But strains and sprains, because of the ability to assess now using telemedicine, we can do physical examinations with some limitations. So you can assess if you have a minor sprain strain, you can initiate care and that can help with access issues to get a worker in to be seen, for example. So there's definitely utilization there. There are limitations. I think we have acknowledged what those are, but I think there's definitely a role for this in the future moving forward. Of course, your more traumatic injuries, those had opportunities to be seen elsewhere as was appropriate. So we suspect, though, that the telemedicine use may have helped avoid some delays in care, right? Care was sooner in some states as a result. In fact, you saw that in the data. Some people were able to get in sooner because they had telemedicine options, because it's still hard to get in person to see a doctor even before the pandemic. But what was interesting, there were more visits associated with the telemedicine visits. And then those who had some mix of telemedicine and in person, they ended up having more visits overall. So definitely something to watch, but we suspect that utilization of telemedicine will remain at the pre-pandemic levels. So we'll see what happens there. So it's certainly an opportunity for them to monitor. And you guys know, it doesn't stop here. They'll continue to monitor some of this information and data. But also, there's a lot of legislative actions at the federal and state level to streamline the process of delivering services via telemedicine to regulate reimbursement so it's more equitable. So I definitely think there's a lot of opportunity there. So let's take a look at some of the post-COVID claims as well. So this CDC, how they describe it is post-acute sequela of SARS-CoV-2 infection, right? But we saw a lot of terms, long COVID, post-COVID, post-acute COVID, long-term effects of COVID, long-haul COVID. When I first heard long-haul COVID, I said, oh, truck drivers are getting COVID? Yes. So a lot of different terminology. So the CDC is the one I have on top there. Some studies that were done preliminarily showed some pretty high numbers, like 52% I think it was Chan et al, 52% of individuals who were in the ICU due to COVID ended up getting long COVID. So the numbers were very high. So the scientific consensus is still out, of course. There is a long COVID talk later in the program. So this is not the COVID, long COVID talk, but there is some data here that you might find interesting. Of course, this is still developing and likely to evolve as we do get more data on this. Timing of symptoms is important, as only conditions observed in the acute period was counted. The WHO defined it as occurring three months after COVID-19, lasting about two or more months without alternative diagnosis. So one of the things that you saw, for example, somebody is in the ICU, they could be in the ICU for quite a prolonged period of time, right? For COVID, what are we seeing? Are we now seeing, are we seeing the post-intensive care symptomatology, or is it truly long COVID? So there was a way they tried to clarify some of that by extending the period out. So some of the symptoms, I won't bore you with those. You know what they are, brain fog, the anxiety, the neuromuscular challenges, but it's perhaps more helpful to lump them system-wise, right? So pulmonary was huge, interstitial lung disease, reactive airway disease. And we were dealing with the long COVID cases, dealing with the causation assessments, dealing with the impairment, and the return to work and fitness for duty, which is another interesting part of it. Not covering that today, but that was another interesting experience. But we had a lot of POTS cases, curiously, like several, quite a few AFib cases. Psych was huge. We saw PTSD, anxiety, depression, as I'm sure you did. So I've put these in this particular order because this happened to be also the order we saw with how the frequency of long COVID cases. So pulmonary was massive, like probably 60%. Cardiovascular was not far behind, and psychiatric. So we heard about all the mental health challenges associated with COVID, but that was not far behind. Then, of course, neurologic, the sleep dysregulation, the altered cognition, the brain fog, which we're still struggling with, memory impairment. We all kind of saw the hematologic challenges, the VTEs, and the hypercoagulability PE, and so on, and renal challenges. So in this particular study, this was involved in 31 states. They're using medical billing information. So the workers were confirmed with COVID-19 to ensure that it was a compensable claim, right? So some employees, I just put that in there as a reminder, that were using sick time. They were using group health. They were using employer services outside of that. I also wanted to make another comment that this is not the group health experience, right? Not at all. So this is quite different. But I just put that in as a reminder that some employees may still have gone to some of those services. So you may not be getting complete information, right? So we're looking a lot at the first wave data. So your post-acute care is like 30 days out from infection, but they narrowed that down even further to 90 days to ensure that you're really getting those long COVID cases, right? So here, they're trying to identify the illness date for non-hospitalized workers, or that's the hospitalized discharge date for your hospitalized workers. That's your start point there. And the start point of the post-acute period is a whole month out, right? So then your whole month out. But then you get your three months in at about this mark. That's where you're starting your three-month post-acute period. So some of the considerations were, you know, most people, they got better. The good thing about occupational medicine, we have healthy patients, right? So they get COVID, they get better, they get back to work. So that's what the majority, over two-thirds, were like that. So how often are the workers getting care beyond their short quarantine period? Of course, we know that that quarantine period kind of changed over the course, but at the very beginning, what was the prevalence of long COVID symptoms among workers with COVID-19? What are some of the industry and worker characteristics? Some of what we saw earlier kind of panned out, and we'll see that again. And how do the rates vary across the states? And some of those same factors were coming back in again that we saw earlier. So this was a bit of a repetition, but it was interesting to see the consistency there. So the workers' compensation is uniquely suited, you know, people have to go to work, it captured the patient experience. They could tell, you know, who was getting hospitalized, who was getting ICU care, you know, within the limitations of databases, obviously. We had some that were ill, but not hospitalized. Those with no medical care, not as well captured, but we got some information there. And then variations in the percent of patients developing long COVID, it depended on your sample of workers, right? So there were some variations there. And the numbers across the literature was also varied. So some of the sample characteristics I thought it's worth mentioning, about 80% of claimants worked in hospitals or facility living establishments. So I'm surrounded by assisted living facilities. So we were just getting hit, you know, by the hospital workers and by the assisted living facilities. And then dentists and physician's offices, majority were women. So it was 80% of that 80% that were women. Of course, a lot of women working in hospitals, as you know, and they were a part of the hospitals or other healthcare facilities. So they were able to get tested. So that could have skewed things because the more likely you are to have access, the more likely you are to test, right? So very low numbers. We've seen, for example, food industry, you know, we had to eat, things had to stay open. Average age was about 42. And then just another consideration just in terms of those who are age over 65, they face more severe consequences because of the comorbidities, which we all know about. So here's the spread on that. So about 7% of workers with COVID-19 claims developed long COVID, just to give you the answer to the question. Long COVID prevalence was highest among workers hospitalized during the acute stage of the disease. Some workers with limited medical care, you know, early after the infection developed long COVID symptoms. So I thought this was odd because we haven't seen, we haven't emphasized Arizona a lot. And then suddenly they just kind of popped out. And New Jersey and Massachusetts, which were pretty heavy hitters, they were kind of, you know, buried in there somewhere. So that kind of struck out. And California was also pretty high. We see the number right up there, pretty high up there. So we'll do a deep dive. So we see lungs as body systems affected was about 63%, so pretty high numbers. Heart was pretty close behind there to almost 30%. Mental health was about 10%. Then the other, they kind of lumped everything else together. So a majority of our workers received only indemnity benefits, of course, no medical care. These are your healthy people. They do their quarantine. In fact, they're probably twiddling their thumbs at home, can't waiting to get back to work. So that was the highest, 67%. Then the other 33%, they received some kind of medical care, but some had no indemnity payments. Maybe they were, you know, took sick leave or, you know, went under group health or whatever they did. And then those who got medical care and indemnity benefits. Thankfully, fatality was less than 1%, but some of us who were in healthcare definitely experienced some of that experience. So this is showing you the trend in percentage workers who receive care in each month since the onset of COVID. So that's month one. We see a huge spike there, and then things drop down by month two. And then we see things kind of almost plateau, but it's not completely going away. This followed the data probably out to about 12 months, but things didn't totally go back to zero. So we do have quite a few percentage, so this looks like about 3% of people who were still having long COVID, they were still requiring medical care, you know, post COVID for what we're thinking now is the long COVID symptomatology and sequelae. So this showed you by, you know, types of care. So ICU there in the light green, and in the darker green, we have hospitalization without ICU care. Then we have the two or more days of care, and then the one day care, which, you know, even though it is a low number, it kind of didn't, some of them did not get back to zero there. So this just showed you, you can see here about 3% were hospitalized, right? So you have your hospitalization, your ICU care totaling about 3%, but they looked, that was the majority of those who developed long COVID. So we saw that coming out as well. And then the claims with medical care were about 33%, and then those who ended up getting, you know, some kind of post-COVID, the long COVID was about 11%. So that was very interesting there. So this just showed you the different types of services. So E&M we said was always higher, but there were also experience with laboratory testing, epidemiology, prescription drugs, and so on, and so on, and other services. So about 3% we saw required some kind of hospitalization or ICU care. About 15% required only one day of care, and 4% of workers with COVID received care in months three, and workers hospitalized were most likely to continue receiving care. We saw that. That was huge. Huge numbers. I think that was like 74%. So here they looked at, there's a lot more worker characteristics here. So they only did two gender characteristics, which is probably limited by the data set they have. That's the kind of thing I might bring back to one of our stakeholder meetings to address some of those challenges, but they may be limited by the data that they have. We also see some age breakdown now. So now we're seeing those who were, you know, 15 to 24, about 11% of our claims. Those who are 65 and over, about 4%, but representing, you know, half of those with medical indemnity costs associated, perhaps not too surprising. Not a lot in terms of type of area, metropolitan versus micropolitan. In terms of industry groups, so we are seeing again, so this is emerging again, so we see those who are in like assisted living facilities, I think that's what that is, and hospitals, physicians and dentists. Those are still pretty high there. Food workers and our construction kind of fell off the curve there. And then we see the same concerns with the costs in terms of medical indemnity costs to the right there. So very interesting. So yeah, that was the answer, 7% went on to develop long COVID among all your workers' compensation claims. Those with any medical care, about 20%. ICU care was 74%. Workers hospitalized, about 44%. So those with two or more care was about 19%, and then one day of care was about 5%. So the costs, I wasn't going to delve too much in costs, but I know you can't possibly have a workers' compensation talk without costs. So the higher average medical payments, about $25,000 per average claim. Higher average indemnity benefits, about fivefold. So those who ended up developing long COVID, right? Longer average duration of temporary disability, threefold. So you're talking about that your bill is probably going to be like $50,000 for your average hospitalized workers and about $150,000 for your average ICU care. So certainly not small numbers there. So in terms of worker characteristics, I don't think this takes brain surgery to figure that out, right? But likelihood increased with age, including your comorbid conditions. Rural urban dwelling didn't have a huge demonstrable effect, and gender had no effect. So with our experience, it was what you're seeing there in the pockets of where we had our huge spikes in our financial information, which correlated with our claims numbers as well was in the millions. This was particularly the onset 2020, the really bad year, right, was where we had issues with our respirator program. So either they didn't have supply issues at the very onset, I was on some of these special pathogens calls where they were talking about supply, supply, supply. We're having issues like everybody else. That wasn't unique to us, certainly, but we're having issues with some of the sites that had not gotten their respirator fit testing up and running timely and being compliant. So we had some of those compliance issues. So we got the just-in-time fit testing in place to try to get the fit testing done. And you may recall, OSHA gave us a little bit of a breather where you didn't have to be compliant with your annual fit testing just for that window for the pandemic. So we had a little protection there. But that's where our spikes were. I mean, sorry, not hard to miss that. But then look at what happened in 2021. We cleaned up some of our sites, right? So you saw the sites got under control. Then you have things reemerging in some of the other sites. So then we had to kind of shuffle around to get those sites cleaned up also. So now we're being very aggressive with getting our respirator fit programs in place and so on. So our data is not completely clean yet. This is just a preliminary data. I'm like, yeah, just give me some data, right? So my typical performance improvement meetings is usually spring, so like right around now. So I'm just starting to getting some of my data to look at. We do have a TPA that kind of helps us with a lot of that. So just really preliminarily. So we do have, this was 2020, some of our last time claims compared to our medical-only claims or record-only claims. And those are the cases that we pulled. So the majority of cases have closed right now, I think about 80% in 2020. And by 2021, we're pretty close to 100. We may have some outliers, and we do have some of our post-COVID claims that are still being assessed. So our costs are around $9 million at this point. So we're definitely taking a critical look at this. This is 2021, so much lesser in 2021. And then our closure rates. So this just showed you the closure of our cases, you know, first in 30 days. What happened in 60 days? What happened in 90 days? What happened in 180 days? And by the year, most of our cases were closed. And they just gave you the breakdown by last time claims, medical-only claims, and those that were record-only claims. And then 2021, we're still doing pretty good. So 2021 was a little better year, so we may still have some stragglers going on there. So this is just to show you the numbers. So we saw very similar. So with our non-COVID claims, right, for the prior year, so we had 2019 data. Ours is not as pretty or clean yet. You'll get nice graphs pretty much later on from my presentation to the PIC committee. But in 2019, we saw, yeah, 226. And then in 2020, we see our numbers of non-COVID claims. Ours went up, actually. And then in terms of 2020, yeah, I was just highlighting that number there for you. And then in terms of our total incurred, like lost time claims, total incurred, and our lost days. So that's what that looks like. I'm just going fast so you don't take any pictures. So data, data everywhere, not a thought to think. So I won't re-summarize everything because I think I was summarizing as I went just to make it easier. But I'll open it up for discussion because I figured there's some questions you might have or maybe you want to share your experiences. So I'll open it up to questions. But use the microphone. You know, be succinct and clear with your questioning. Go ahead, George. I see you're first. Hi, Dr. Taylor. Thank you for that great presentation. I have a question. Do you have any data on the amount of long-haul COVID that they were denied? Because as we know, a lot of the long-haul COVID had pre-existing conditions like anxiety and depression. Was there a denial? Do you know the numbers? Yeah. So this data set, they're looking at the cases that were accepted. And they're looking at the cases that were not accepted. And they're looking at the cases that were accepted. And they're looking at the cases that were not accepted. And they're looking at the cases that were not accepted. And they're looking at the cases that were not accepted. And they're looking at the cases that were not accepted. And they're looking at the cases that were not accepted. And they're looking at the cases that were not accepted. And they're looking at the cases that were not accepted. And they're looking at the cases that were not accepted. And they're looking at the cases that were not accepted. And we saw that to require that, we had to have a positive test and we had to meet the compensability requirements, unfortunately. So I don't have that particular data, but we had the experience because we had to do the causation assessments ourselves to determine if it was related, work-related or not. So we went through all of that. So we denied some of our claims. Actually, I think one of the numbers there, we did have the percentage that we gave. That was just our data. So there was a percentage that we denied. And that's because either the exposure didn't happen at work or they didn't test positive or it wasn't consistent. But there were definitely some cases that were denied. I probably could get our numbers pretty soon because, like I said, I'm going to the PIC committee pretty soon. So I'll have that. But that's our personal data. I don't know what the rest of the country was doing at the same time. But I'm sure that kind of study is going to be emerging. So if I go back to the committee meeting, that's the kind of thing I could say. This is important. People want to know what percentage of claims were denied because there were clearly some. If they didn't meet the criteria, we saw California's preliminary data. But we could probably follow some of that out to see what their experiences were. But yeah, great question. Yeah. I'll jump back and forth. Go ahead. Thank you. Just an observation about telemedicine and workers' compensation claims for strains and strains. I'm with the Workers' Compensation Board of British Columbia. And I had a look at 38,000 claims for strain strains starting way before COVID. And afterwards, I looked at outcomes as it relates to whether the care was delivered with telemedicine or in-person. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. And I had a look at that. Great question. Great question. Great question. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you.

workers' compensation

COVID-19

WCRI

occupational illnesses

telemedicine

compensability

medical trends

long COVID

telehealth services

closure rates