First, I'd like to welcome everybody to our session this morning, Residents to the Rescue, being done by Dr. Victoria Wells-Wilson. Unfortunately, due to unforeseen circumstances, Dr. Wilson has to do this remotely, so this is an exception. Just a little by way of background, Dr. Wilson is a professor at the University of Cincinnati College of Medicine in the Department of Environmental and Public Health Sciences, as well as in the Department of Family and Community Medicine. She's Director of Occupational and Environmental Medicine and the OEM residency there. Her interests include global health, justice, and joy. I love that, joy. Welcome Dr. Wilson, and again, thank you for everything that you're doing. I'm going to leave it to you to take it away, okay? Great. Well, good morning, and thanks for coming. This will be an interesting experience. I had planned to be there in person, but I lost my brother three days ago and have been dealing in Cleveland with his death and apartment, and just couldn't make it to Philadelphia. I appreciate your understanding and letting me do this by Zoom. Let me start by sharing my screen, and this should be the right one. Let's see if this works. Let me go back to the beginning of my program, there, and one more change, and that's going to be making this bigger. Let's see, view, hold on. I think if you go to the bottom, yeah, there you go. Is this it? How about here? How's that? There you go. You're good. Great. Good morning. This is Residents to the Rescue. You'll hear throughout the next hour how the response to the COVID pandemic evolved over the last three years from the perspective of a university, which is the University of Cincinnati, with its risks of congregate living in the dormitories, and a large health institution that included both inpatient hospitals as well as outpatient clinics. That's the UC Health System, and how the residents made such a huge difference in keeping our hospital doors open and our patients safe, particularly during the Omicron surge of last winter, and we'll be talking about that. Testing for COVID has been part of the way that we've protected our communities, but there's the good, the bad, and the ugly that I want to go through. Okay, just a second. I have no conflicts of interest. I have no legal, moral, or financial relationship with any organization beside my employers, the University of Cincinnati, and UC Physicians. I want to thank the residents themselves. Our residency is small. We had four residents at the time, Taylor Buckley, Wally Jahangiri, Alexi Kraynev, all of whom are going to graduate this June, and Tyler Nickel, who graduated last June. Two students also helped with this presentation, doing some research for me and with me, Ashweta Mappara and Dania Abu-Haleja. My colleagues who helped write much of this talk you'll see in the algorithms that I present include the UC vaccination team, the COVID-19 response team, and the UC Health core team. What's the good? To describe how valuable the CDC algorithms have been in preventing COVID-19 transmission, I want to review the bad, that there are lots of limitations in depending on the COVID-19 testing for decisions about quarantine, isolation, and return to work. The ugly, COVID-19 has definitely challenged our progress in the total worker health arena. And finally, to go back to the good of how our residents helped mitigate the costs of the COVID-19 pandemic in our university and hospital systems. We had lots of sources of information over the last three years. My gold standard since graduating from med school has been the Centers for Disease Control and Prevention, and specifically the National Institute for Occupational Safety and Health. I don't have to define that for this audience. The infectious disease centers have been obviously part of the CDC response. More locally, the Ohio Department of Health had interpretations of CDC recommendations. Cincinnati unfortunately has two health departments. We have the city health department and the county health department. And yes, there are reasons for that that are good, but as an academic who actually has worked in both of those other systems, I wish they would merge. The group that ended up taking the helm, you might say, of how Greater Cincinnati would respond to the epidemic was the Health Collaborative, which is really an organization that brings the health systems together, the hospitals, the ambulatory care centers, and that ended up being kind of the leader of how vaccines would be provided, how testing would be provided, and so on. In my world, the fact that the University of Cincinnati and UC Health and UC Physicians are three separate entities played into the complexity of our response. And let's not forget the OSHA emergency rules that we've been following and waiting for changes as we speak in terms of record keeping, primarily. They were, just a side note, if you were in a health facility at the time of the outbreak of the epidemic, all of a sudden, many institutions realized we were out of compliance with respiratory fit testing and that many of the health care workers who needed N95s had not been tested. That was one of the early interventions to make sure everyone who should wear an N95 was wearing an N95. Okay, here's an example of what CDC would provide for organizations, and this is an antigen test algorithm for community settings. So we had a different algorithm for the health care setting. And I just want to go over this a little bit, not in detail, because they changed over time. What I really want to point out is how complicated the decision trees were and are. In other words, we cared a lot about whether the individual was asymptomatic or symptomatic. We wanted to know, of course, we cared about the result of the test. Was it negative or positive? Had the person been exposed? And we can talk about what the definition of exposure is and how that changed over time as we learned more about the virus and transmission. Whether the person was up to date on vaccines and whether or not to quarantine in the case of a negative test. And when the test was positive, again, what isolation would mean. You'll see these small superscripts. Here's number four, here's number five, and let's even do one. Here, asymptomatic, what do we care? Well, here's for asymptomatic employees. Let's see, let me move this, if I can move this down here. We had even additional guidelines from CDC about testing. So asymptomatic, if testing after a suspected exposure, test five days after last close contact. You can click here and you can find out how CDC is defining close contact at the time. For those who are traveling or have recently traveled, please refer to CDC's guidance for domestic travel, international travel during the epidemic. And here are the precautions. Again, I don't need to go through these except to show the level of complexity that we as the healthcare team assigned to keeping our workforce working, we're dealing with. This again just refers to the superscripts in the previous algorithm. And again, here are treatments, and I won't go into that right now. I mentioned that each institution has different priorities. At the University of Cincinnati, we had a low-risk population, meaning 45,000 students. Talk about a healthy worker effect, you have a healthy student effect, marvelous. However, poor rural followers and congregate living, both in terms of dormitories and then just terms of intimacy that young people have and we older people might have less of. UC Health, this is a health system of four hospitals and many ambulatory care sites with 13,000 employees, and obviously, the risk to them of exposure from patients was much higher than at the university, as well as the importance of keeping the workforce working in a way that at the university, if professors were missing classes, students didn't die. If doctors and nurses and food handlers were not available in the hospital, people could die. And so, the importance of keeping people at work is very different. UC Physicians is a group of just under 3,000 people who actually is the corporation that provides the physicians and other providers, the advanced practitioners to the hospital. Exposure status would determine some steps in the algorithm, whether positive, negative, not determined, or unknown, same with symptoms, positive, negative, not determined, or unknown, and which symptoms. We had major symptoms such as cough and short of breath and fever. We had minor symptoms such as sore throat, conjunctivitis later, a diarrhea at one point, headache. Again, CDC would guide us on algorithms like one major symptom constituted symptomatic or two minor symptoms constituted symptomatic, but again, there's a lot of gray area. Dealing with unknowns, obviously, is a problem. The UC Health System, for its employees, required reporting, and we used Red Cap, created a questionnaire that they had to fill out if they were exposed or had symptoms or had a positive test. At the University of Cincinnati, again, employees are less critical, and therefore, reporting for them was merely strongly recommended. Our enforcement for required was incomplete. We occasionally would report to managers when an employee was supposed to be isolated or quarantined because the employee was being a resistant, but mostly, we relied on the honor system, and meanwhile, at the University of Cincinnati, the system was transferred from Red Cap to Salesforce at great expense, a much more robust system, but at a cost of at least a million dollars for everything that we bought. That was a big question. How much do you spend on data versus testing versus vaccines versus public health people like me going to tell people to get their vaccines, et cetera, et cetera? Very interesting questions. The exposure criteria were also complicated. What length of time were you with the person? How close were you to the person? Was the person coughing or sneezing? Were you wearing a mask? Was he wearing a mask? And so on. We came up with criteria, again, derived primarily, this is a University of Cincinnati algorithm very much based on CDC guidance, and we had a, at the university, we had about, trained about 120 contact tracers to ask students and employees questions like this. And just more additions and exceptions, roommates are automatically considered contacts, but not a housemate, apartment mate, or suite mate, questions like that. And at the beginning of the pandemic, you can imagine how irate parents would be when I would tell their child, you have to be quarantined. And we had actually taken over a nearby hotel for our quarantined students. Well, that was interesting because, of course, the quarantined students weren't sick, and they had tested negative, and their parents were irate. Why is my kid having to live off campus for five to 10 days, depending on what time in the epidemic it was, and so on, just to protect other people because they might be incubating? Lots of discussions, and then, just to remember, healthcare workers, because they were considered so crucial to keeping our systems going, they had different exposure criteria, primarily being that we would consider a worker not exposed if he or she had been wearing appropriate goggles, gloves, and an N95. Let me just keep going, doing just fine. Okay, here are some of the symptoms. Here were the, this was early in the epidemic. The major symptoms, you just needed one of those to qualify, a new cough, shortness of breath, difficulty breathing, loss of smell, or loss of taste. These are minor symptoms, not in the sense that they don't bother the patient, but they're not as specific as these are for the SARS-2 COVID-19 variant of the coronavirus. You can see, again, many coronaviruses cause fever, myalgia, chill, rigors, sore throat, headache, nausea, diarrhea, fatigue, congestion. That was the word I was looking for earlier. Okay, and then here are the release criteria for release from quarantine or isolation that we had to deal with. People who were, we had to define what up-to-date on the vaccine meant and unvaccinated and partially vaccinated were treated similarly. Full vaccination were treated differently. And then we would come up with our algorithms for, for example, here exposed and asymptomatic. Next we'll see exposed and symptomatic and so on. And so on, not exposed and symptomatic. My purpose, again, just to show you the complexity of the decision trees that we were dealing with. What vaccines did they have? And we would have to make sure that the vaccines had been taken in appropriate sequence and that once boosters were available, that they had been appropriately boosted. Over time, more and more vaccines became available. And these were, at the University of Cincinnati, we would accept any of the FDA authorized vaccines and also the World Health Organization's emergency use listing that would change. Our university has lots of international students and employees, faculty, and the benefit of the COVID shutdown was that travel was very much limited. And so we didn't, it's not as though a lot of those students or faculty got, could get their vaccines back home, but we did have to revaccinate people who had received vaccines that were not approved by the WHO's emergency use. And again, here's just an example of the decisions. So we changed the algorithm based on whether or not a person was vaccinated and whether testing was required. All right. And just the CDC resources had wonderful links and here they are, they're available to this day. And I, again, I thank CDC for its leadership and its ability to try to weigh this balance that changes as we learn more between what I would call liberty and preventing the spread in our communities. And our community might be just our family, our community might be all of Cincinnati, but that balance is something that each of us as individuals recognizes. Some people are very community-minded and would never want to hurt another individual and other people are very individualistic. You know, I call them cowboys. I don't know if that might be politically incorrect, but kind of I'm my own person and I, you know, I can do what I want. We've all learned a lot about that over the last three years. This is the UC health algorithm. And as you can see, very complicated. And here are with lots of definitions, mild illness is different from moderate, from severe illness, critical illness. If the individual worker, this is all for employees, okay? Look up here, guidance for UC health, employees and clinicians. And this is the one that's, this is the exposure algorithm. We have another algorithm for symptomatic employees and more superscripts, what are the exposures? Definitions, is the person severely immunocompromised? And what does that mean? Do you have, and again, over time, we'd actually write out what medicines we considered immunocompromising, how much prednisone for how long would be considered severely immunocompromised. So, having thanked CDC for helping us make these rules, a lot of our decisions depended on the tests that we performed, and I wanna give a few minute review of predictive values. And the predictive value of a test matters because we're going to be making decisions about what happens to that person. And I had hoped to be in person and have some interaction, but I will just answer my own question that there are three criteria that determine predictive values. The sensitivity of a test, the specificity and the prevalence of that disease in the population. Sensitivity is the ability of a test to correctly identify positive specimens. Specificity is the ability of a test to correctly identify negative specimens. And how sensitive and specific are the tests for COVID-19? And as you can see, these are the tests that were most earliest available, sorry. Nope. There we go. And as you can see, the sensitivity peak for the PCR test was 100%, and so was the specificity for the PCR tests. However, in other conditions, the sensitivity and specificity would fall as far as 70% for the CDC early test and 95% also for the early test. We relied on PCR tests for, again, the first few months. And in May, a few months later, we began getting the antigen tests, which are faster and less expensive. A cost to that, though, is less sensitivity. Here's the sensitivity. The highest we get on these three is 97% down to the Binax, down to 64%. Specificity can be as high as 100%, great, and as low as 97%. How about prevalence? We know that the prevalence also changed over the course of the pandemic. And just to remind us of the definition, it's the percentage of people in the population with the disease or characteristic being studied. Where sensitivity and specificity are determined in the laboratory against a gold standard, prevalence is calculated in the population through testing. Obviously, what we're looking at today and the example I'll use is COVID-19. And again, remember the prevalence is gonna be different among asymptomatic populations and symptomatic healthcare workers and community members, exposed persons versus unexposed, and so on. So let's just calculate, well, if an employee comes with a positive test, what is the predictive value that that really represents a true positive? Okay? And the negative predictive value, what is the probability that a negative test indicates a true negative? Because that's what we really care about. This is a graph, a table that I think I got from the CDC website, but just shows again, the truth is yes, and if the test detected it, it's a true positive. If the truth is no, the disease is not present and the test is negative, that's a true negative. Contrary, we have false positives and false negatives. I will assume you all understand that. Now let's take a case. Here is a test that has a reasonable sensitivity of 70%, specificity of 99%, and in a population where the true prevalence of COVID-19 is 10%. Excuse me. So the sensitivity of the test is 70%. Sensitivity, the ability of the test to correctly identify the case, and that is represented by the seven out of 10, 70. That means three though, were incorrectly, they had a negative test, even though they were true. Meanwhile, 99% specificity means that 89 out of 90 people without the disease tested negative. Only one tested positive. That of course was a false positive. The total positive tests were eight, the total negative 92, leading to a positive predictive value of seven eighths or 88%, and 89 of 92 is 97%. Very good. In this media, prevalence of 10% in a population is actually considered on the high side. We often reported during our huddles, which I'll talk about in a minute, what was our positivity rate in the population and, excuse me, among asymptomatic people, I can't say never, but rarely got above 10%. Among symptomatic people, it would be much higher. All right, let's take another example. And here I just am changing just the specificity of this test, that now it's only 90% instead of 99%. And so if you look at our chart again, where in truth, 10 people, these 10 people have COVID in our population of 100, that's our 10% prevalence, and seven were identified by that sensitivity of 70%, but with a 0.9 specificity, only 81 of our 90 negatives were correctly identified. Nine had positive tests by mistake. And now if you calculate the positive predictive value of seven over 16, you get 44%. And so at this point, we are, how do I say, calling a positive a true positive, we are wrong more than half the time. We're correct only 44% of the time. Luckily, if you have a negative test, we still have a predictive value of 96%, that great, 96% of you who test negative really are negative. This is part of the bad news about the realities of testing. In an asymptomatic population, we'd often get down to a 5% of prevalence. And again, with the antigen testing having as low sensitivity as 70%, again, specificity for the antigen testing is usually way up at 99%. You still, with these numbers, end up with 78% positive predictive value, 98% negative predictive value. Again, if the sensitivity increases, that's good news, at the cost of specificity, down to 95%. Again, with these numbers, we have more false positives than true positives. In a very high risk population, such as a symptomatic group, especially exposed and symptomatic, now we can get some good positive predictive value, even in the situation where our sensitivity is not so high, still we can get, and because our specificity is high, almost every positive test is a true positive, 28 out of 29, 97%. Now, of course, we're missing a lot of true positives because, excuse me, we're missing a lot of positives because they have a negative test. And so our negative predictive value goes down. This is a neat chart that shows some of the issues that you care about in terms of choosing which tests to require your employees or patients or worker students to take. In terms of managing the individual cases is one of your criteria. Another is detecting outbreaks of disease, where you care a lot more about timeliness than elsewhere. And then you also use these same statistics to evaluate what's happened and plan for the next pandemic. And in the last three years, we've been doing a constant re-evaluation to see if what we're doing is correct, if our tests are good enough and so on. So now let's bring it to the situation in Cincinnati last winter. I'm gonna talk about the epidemic curve. UC Health at the time was under critical staffing and our residents, as most of you know, have other responsibilities. They're earning their master's degree in public health, so they have to go to class and do all those assignments. They're working on their capstone or thesis final projects, doing research for that. I think two of our residents were moonlighting at the time, and I know there's controversy about that, but we do allow moonlighting as long as other criteria are met, that their major commitment are the 80 days per year that they're working and learning in clinical environments. And part of that was staffing the UC Health Employee Health and Safety Clinic, which serves the employees of University of Cincinnati, UC Physicians, and UC Health. So they had a lot of commitments, and I do wanna tell you a little bit about our process at the University of Cincinnati. We, as I said, we have the four hospitals, many outpatient facilities. We had a huddle every day that had reports from about 20 departments, labor and delivery, ambulatory care, from the quarantine, public safety. Employee health was one of the reports, and our director would respond every day with how many people are out for COVID, how many are out on quarantine, et cetera. And every day, the quarantine began with the epidemic curve that was also updated daily from the Ohio Department of Health for our region in greater Cincinnati. Interestingly, at the beginning of the pandemic, we were meeting every day, 4.30, and literally hundreds of people from the thousands of workers would be on these huddles both presenting and learning as we changed rules depending on what we were learning. And then in 2022, after about two years, it went from every day to every other day, then it went to Monday, Wednesday, Friday, then it went to just Mondays, and now we only do it monthly. Things just are not changing that much, and we're relying on email more. This is what the epidemic curve looked like in January of 2022. On the y-axis, we have the beginning of the epidemic here in the winter, the January, February, March of 2020, going through October. This is the Delta surge that came in our first winter, then we had a wonderful reprieve, the vaccine starts being available actually way back here, and so people get vaccinated and things slow down for a while, but bad news, we begin getting the Omicron surge in January of 2022, and this is due in our area both because Omicron was not as sensitive to the vaccine as Delta was, and we did not have high penetration of vaccine usage. I've already talked about how employees had to report, and there were difficulties in the system, and the changing rules because, for example, at the beginning, we needed to conserve masks so we didn't want low-risk people to be using masks. The virus mutated, and so symptoms and infectivity and severity changed. The population changed, employees, communities, and even the residents' attitudes was an issue about the change. Mostly the science itself changed as we learned that the fomites were not important. Young people without comorbidities, not so vulnerable. The tests changed, vaccines, treatments changed, and sadly, and this is the ugly, and I'm only going to spend a second on this because the challenges to total worker health are so many. This is not meant to do anything but show you how embracing and holistic the total worker health paradigm is, and I'll just quickly run through the various ways that the attention on COVID-19 meant less attention on other aspects of everything from ergonomics. At the University of Cincinnati, Dr. Davis led a wonderful program of checking out how we at home, and I'm working from home right now, and I did the right thing and bought a riser so that my lap, so I'm not looking down all the time, I'm looking up, and Dr. Davis would be very proud of me. But I know my husband, who also worked from home for many days during the early part of the pandemic, he was hunching over his laptop. Ergonomic factors, just as an example. How our workplace environment is constructed and used, what about community supports, how was this impacted by COVID-19? One of the motivations of faking tests, and again, when we went to antigen testing, the issue that if a worker was absent because of COVID-19, benefits were different than if they were absent for another upper respiratory disease. And so keep that in mind as we're going to be reviewing the accuracy of tests. Part of it comes from, hey, does the reporter, be it a laboratorian or an individual patient or a doctor, do we report what's true? And it can be a mistake, oh, I think that's a positive pink line, oh, I don't think that's a positive pink line, like, you know, view observer bias, but it can also be faked, meaning, oh, I want to see a red line, I'll just say it was red, even though it wasn't. And I've already mentioned the issue about paid time off. Leadership and how leaders included the people on the ground dealing with COVID-19 patients, very affected. Burnout, I just want to, I've seen it, and part of the problem during, especially over the last year and a half of the pandemic, I think, has been the burnout of health care workers, as a physician, for example, my own burnout is partly because I used to be an expert at many things in medicine. And now I'm not. Now I'm faced with a disease that nobody knows, and certainly I don't. And so I have to be always hedging what I'm telling my patients, what I'm telling my community, what I'm telling my residents, because I only know a fraction of what is known. And what is known is only a fraction of what needs to be known. It's not a pleasant place to be professionally. Policies, obviously, I've been talking about the policies all along and work arrangements, freelance, virtual, et cetera, and the workforce demographics. We I just want to call out again the opportunity that I feel CDC did take well, which is that persons of color are still have less access to health care information. And we've done a good job, but not a good enough job in terms of making sure that marginalized communities are brought into the fall of public health. OK, now, having finished the that sad part about how devastating COVID-19 has been, I just want to tell you that the residents at the time were Tyler, Alexi, Taylor and Wally. And I have just about a few more minutes to talk about their coming to the rescue. The nurses had been working overtime in employee health to do to implement those algorithms, talk to the workers, talk to the workers, families to keep keep infected and infective workers away from the workforce, bring them back as soon as possible, because, again, we were under critical staffing. And these four residents were volunteered and agreed to basically do the notification and decision treaties for the thousands of employees who were infected and exposed to COVID-19 during the Omicron surge. So let me just and it was January of 2022 when, as you can see, on January 3rd, we see this peak. And as I say, the nurses are exhausted. The residents start doing the notification. And I'm just going to go through a whole series of epidemic curves and showing you what happens over the next year. So a week later, we're still rising. No sign of decline. A week, nine days later, again, still high in terms of what's going on. A week later, a little dip. Is that going to stay? Let's see. The next week. Looking good. Way down. The next week, it stays down. Next week, it stays down. Next week, it stays down. And now we're sorry. That was the end of February. Now I'm going to jump a month to the end of March. The end of April. The end of June. The end of September. The end of December. Thanks to those residents, our hours of work, we were able to keep the hospital open. Of course, there were so many reasons. Omicron was less contagious than Delta, less severe. More people had been vaccinated by then. There were many reasons that the Omicron surge was less, was not as prolonged as it might have been. But I like to give credit to those residents who worked so hard to make sure that workers who were safe could come back to the hospital to work. So, we have five more minutes for questions. I don't know if there's anyone there. There are people here. If you do have a question for Dr. Wilson, we ask that you come up to the microphone as this session is being recorded. So, any questions for Dr. Wilson? No? Oh, we have one. Hold on. Thank you. Is this working? Yes. Yes. Thank you for your presentation and my sympathy on your loss. Thank you. Thank you. Many of us didn't think you would even be presenting this morning. But a couple of things came to mind during your presentation. One is based on CDC recommendations. At one point, as you mentioned, the recommendation for quarantine went from 10 days to five days. And I'm wondering if you have a, at this point in the course of things, if you have an opinion regarding the five versus 10-day quarantine. Great question. And the way we handled it at UC Health was that people can come back after five days with a negative test. And we at UC Health Employee Health and Safety actually did the test. So, and we, day zero was the first day of symptoms. And so, at day five, if they were afebrile for 24 hours and symptoms getting better, they could come to our clinic. We, of course, are all, you know, fully masked and so on, because we know some of them are still going to be positive. But we were doing a rapid antigen test right there. And we set up a kind of a, I don't know, a maze where the employees would go. They would literally wait for the 15 or 20 minutes for the test. And if it was negative, they were allowed to go back to work. If it was positive, they were not allowed to go back, and they had to go back home. And just for practicality, we advised waiting two days rather than coming back every day for another test. And one of my colleagues is actually studying, calculating kind of how many person days at work we gained by providing that early on. Now, we moved to accepting at-home tests with people telling us, oh, my test is positive or negative. They have to send in a picture. And it's been an interesting experience. And we've pretty much still followed that rule that they can't come back until, oh, for 10 days if they keep testing positive. What's interesting is, what about the person at 10 days who's still positive? We often, I as the medical director, talk to the person. And the first question is, excuse me, whoops. The first question is, how do you feel? Okay. So they say, oh, I'm great. I'm great. So my best guess, and I wish I were there to hear other people's, is, okay, as long as you're still positive, antigen positive, I think you're contagious. Yes, the PCR tests could stay positive for 90 days. And I don't think, and I'm pointing to nasopharynx versus anterior nares, that I do not think a positive PCR at 10 days is the same as a positive antigen. That again, and correct me, I wish I were there to learn more, but that I think you're still contagious. Our rules are though that we want you back and you're less contagious. You're certainly not coughing and wearing N95 as long as you are still testing positive. That's been my walking the line between keeping my workforce and not having everybody out for as long as they stay positive and protecting my patients who may be getting exposed to that person who's still positive at 10 days. Thank you. My second question was, when did you relax the mask wearing guidelines for the non-clinical settings? Great. Okay. We have for non-clinical settings, we relax that. Wait, let me just think for a second, because we, just a week ago, maybe it was two weeks ago, was when we finally said, no, we said in non-clinical settings, it was closer to, I would say two or three months ago. We are now saying, okay, what changed a week or two ago was even in the clinical setting, we are not requiring masks anymore. Okay. So it's only if under certain conditions, like if you're working with a COVID-19 patient, if they're immunocompromised, if you're immunocompromised. So there are some criteria, but for, I would say that we had relaxed in terms of non-clinical places, about three months ago, we started being able to have lunch together, for example, because it was a non-clinical setting and we could, you know, eat together. And this was, I would say, peer pressure in Cincinnati, that the other big hospitals were going to use masks appropriately, according to our old infection control rules. And if you email me, I can, I can even find out more. I can go back because of course we, it's all on the emails to all the employees as we would change those rules. So I can be more specific. Well, thank you again for a great presentation. Thanks for coming. Thank you. Any other questions? All right. No more questions. Thank you, Dr. Wilson. Well, thank you all. And I'll be, I'll be visiting some of the classes today, the lectures, and I look forward to next year already seeing you all in person. Great. Thank you so much.

Dr. Victoria Wells-Wilson

University of Cincinnati College of Medicine

Residents to the Rescue

COVID-19 response

Omicron surge

COVID-19 testing algorithms

total worker health

epidemic curves

critical staffing

mask-wearing guidelines