Hi, everybody. Welcome back to the Council Medical Specialty Societies webinar series on how to use clinical registries to address the pandemic. I'm Helen Burstin, the CEO of the Council Medical Specialty Societies, an organization of the 45 medical specialty societies in medicine. We're really delighted to have this be our final six of six webinars. We'll focus on using clinical registries to address disparities in COVID-19, something that has clearly been around with us for a long time, but has been very much uncovered in more detail, as we'll hear from all of our speakers today. Really pleased to have had the support from the Gordon and Betty Moore Foundation for this webinar series, which has allowed us to make the webinars all free. All the recordings are available as well as the slides on our website so that you can to share that and continue the learning across your own organizations. We are really pleased we've also been able to do this in collaboration with the AAMC. We feel it was important since we began to see so many specialty society academia and health system collaborations around the pandemic response, that we wanted to make sure that we were keeping groups connected. We'd also invite you to join the conversation online using the hashtag COVIDregistries and follow us at CMSSmed for any further updates. A couple of housekeeping details before we get started here. On your webinar today, you will see there is a Q&A section of the webinar where you can enter in questions. You can enter them at any point during the webinar. We won't address them until the Q&A session at the end. We'll queue them up and have them ready for the speakers when we get ready. There's also the opportunity for you to download the PDF of the presentation as well. You'll also get a short evaluation at the end and I would love your feedback as we move forward and think about other ways to keep bringing this content to you. Just a quick review of where we've been before we launch into this one. Since this is our final sixth webinar, we wanted to explain the logic of how we came to this point. We really wanted to see how we could use, in many ways, recognizing COVID-19 as a catalyst to bring registries to the next generation, to think about how they could both move in terms of accelerating the availability of electronic data sources, merging with other data sources. How do we leverage technology to really have it be more point of care? We had a remarkable webinar completely focused on cloud-based platforms and analytic tools with representation from folks at both Verily and all of us, and really excited to have seen the growth and the building on of where we think all these are directionally, where we think clinical registries we hope will go and the evolution of them in the future. We also had our most recent webinar was one of these two cross-cutting webinars like today's, and it was a patient-led webinar and it focused on patient engagement and inclusion of patient-generated COVID data. We had several of the long-hauler COVID patients talking about their development of clinical registries and how those have been used to really understand what's happening in the COVID space, and really, I think, an important lesson for all of us of the importance of patient engagement in all of our research work, as well as all of our clinical registry efforts. Finally, today's registry will really focus on how we can think about how clinical registries and data systems broadly can be used to ensure that we're ready, either for this continued pandemic, the next pandemic, but also just really to have the right information we have in hand to be able to always look to see where there are populations who are falling short, not achieving best possible outcomes, and really think about how we can both have those data ready on hand, collect the data, have them available, but probably most importantly, and I think you'll hear from all the speakers today, is how do we actually use those data to really drive improvement moving forward. So a phenomenal panel today. I think one of the few small silver linings of the pandemic is the ability to get four extraordinary people like you on a webinar in the middle of the day without travel and hotel, and it's really been a gift to all of us. So I'm going to introduce all of you quickly, and then we're going to launch into this. So first is Kirsten Bibbins-Domingo. Kirsten is a professor and chair of the Department of Epidemiology and Biostats. She's also the Lee Goldman Endowed Chair in Medicine and Vice Dean of Population Health and Health Equity at UCSF. Bill Wood is the chair of the Data Hub Oversight Group at the American Society of Hematology. Ash is one of our members, and Associate Professor of Medicine at UNC. Clyde Yancy is the Vice Dean of Diversity and Inclusion and the Magerstadt Professor of Medicine and Professor of Medical Social Sciences at Northwestern and the Chief of the Division of Cardiology there as well and at the Feinberg School of Medicine. And then finally, last but not least, Eliseo Perez-Estable, he's the Director of the National Institute of Minority Health and Health Disparities as part of the NIH. We're going to launch into this with an overview of the importance of place and disparities by Dr. Yancy. So, Dr. Yancy, I'll turn to you. Well, thank you very much, and I am pleased to be a part of this panel. Please confirm that you can hear me. Excellent. Let's go forward. I think it's evident to all of us that the COVID-19 crisis has really exposed the extent to which disparities are still evident in our health and society. I, in particular, am a fan of the American Public Media Research Laboratory website that aggregates data by state every eight to 10 days that keeps track of the incidence of coronavirus and the mortality attributable to COVID-19. I'd like to share with you the most recent update as of August 18th as we go forward. These data demonstrate that through August 18th, one in 1,125 Black Americans has died, one in 1,375 Indigenous Americans has died, one in 1,575 Pacific Islanders have died, one in 1,850 Latino Americans, one in 2,450 White Americans, and one in 2,750 Asian Americans. This is an extraordinary burden, particularly for persons of color. These data continue to reflect information collected from 45 to 50 states with race-ethnicity data seemingly accurate on 93% of the deaths. We can go forward. I think what we're learning now is that consistently throughout the entirety of this crisis, we have articulated a message that continues to refrain. Black Americans have a very high rate of death compared to Whites, but Indigenous people as well, the same for Latinos. The most startling thing that happens on this website that I continue to follow again about every eight to 10 days is a tabulation of the number of excess deaths that are affecting persons of color. That is to say, if everyone with COVID-19 had actually succumbed at the same rate, there would be an additional 19,500 Blacks, 8,000 foreign Latinos still alive. This is a very dramatic, very sobering use of, as Dr. Burson said, broadly speaking, data sets, databases that really are informing us about this crisis. We can go forward. If we now look at this in a depiction that gives us a graphical representation, you can see how much more likely Blacks, Indigenous, and Latinos are to die due to this condition. If we continue to go forward, we begin to understand at a local level just how pernicious this problem has been. I got engaged with the analysis of what was happening by virtue of the events in Chicago. 30% of the population in Chicago is composed of persons that self-identify as Black, but 50% of the cases and nearly 70% of the deaths involve Black individuals, and those numbers remain extant even as we speak. Introducing a concept that Dr. Bernstein already challenged us with, that is importance of place. What was so striking here in Chicago was that the majority of these deaths were concentrated mostly in just five neighborhoods on the city's south side. Later on in this webinar, Dr. Bibbins-Domingo will tell you much more about the importance of place. We can go forward, please. There are a number of statements that I and others put into the literature, but we basically argue that COVID-19 really has been that bellwether event that triggers the need for us to fully and comprehensively address healthcare disparities. If we go forward yet again, we'll see an important set of statements put forward in the literature by William Owen, who argued that, yes, it is true, people must make good choices, but they must have good choices to make, making the point that it is the presence of adverse social determinants of health that so jeopardizes individuals. If we continue to go forward, there's yet another important statement, this one from David Williams and Lisa Cooper, that really highlights the difficulty that we're seeing that has been exposed by COVID-19, not just higher rates of uninsurance, but in a very troublesome way, rates of underinsurance. What is so important is the conversation is deepened because it's a discussion about the real effect of segregation. If we move forward, we'll understand that segregation really has marginalized communities and subjected them to a consistent disinvestment, and it's been this disinvestment that has put them at such particular harm. They've argued that what we need going forward is a different kind of herd immunity, one that makes us immune not only to the virus, but immune to these negative social determinants. Let's go forward yet again. I wanted to be certain that as we begin to think, broadly speaking, about how data can inform our logic, we've already shared with you how local public health datasets, national public health datasets, have made clear that there is a disproportionate burden of COVID-19 in persons of color, but what about in other countries? This was published in the Lancet Respiratory Medicine, May 8th, 2020, and it is an aggregation of a categorization known as BAME in the UK, Black, Asian, and Minority Ethnic. What's important about these data are that these data are describing the impact of COVID-19 on healthcare workers, and it started from the deaths of 11 doctors, all of whom came from BAME communities. This really was their bellwether event to say that even in those persons with resources, seemingly, there was this disproportionate burden. Go forward yet again, we can see another group of healthcare workers in the UK where there's a strikingly high likelihood of death if one is self-identified as Black, Pakistani, Indian, and this is for men and women. So again, datasets are informing us that the problem is much more dramatic and much more onerous than even the raw data might have shared with us in the past. Let's go forward yet again. I am particularly sobered by these data as I've been working with colleagues in Brazil to help understand the burden of COVID-19 in those communities of color. It turns out that Brazil has had more infections and more deaths than any country other than the United States, and Latin America is becoming an epicenter for this pandemic, and again, we know this once more because we've been able to use large datasets to inform our insight. We now know that Brazil has had over 3.6 million cases. Listen to this. That's more than the entirety of Europe, 115,000 deaths. If we go forward, here's yet another depiction of how these available large datasets can really help inform our understanding about COVID-19. This is from the Johns Hopkins University. This is their COVID-19 dashboard, and I don't need to tell you that red depicts the hotspots. You can see where the areas of greatest concern are aggregated. This at least lets us understand how best to direct resources, but it's clear that the Americas continue to have a problem. We can see that South Africa has a pretty substantial problem. We can see that India has a really uncontrolled and growing problem. Go forward yet again. It's important as we go into the rest of this discussion that we have a couple of reasonable definitions so that we can all speak the same language. If we can go forward once again, just reminding you that when we're talking about these data, we're talking about these data not just in a context that they describe differences between persons of color and those that are European or white, but that we're identifying differences that are uniquely present because of operational deficiencies in healthcare systems, because of discrimination, because of biases, because of this consistent underinvestment or disinvestment, as has been described before, and yes, because of evidence of systemic racism, but it's been these kinds of influences that have qualified the differences that we're seeing using these large data sets. It's not just differences, but disparities. Move forward yet again, please. This is the circumstance. In many of the communities where we are seeing just an incredible burden of this condition, they fall into the far right category. I'm thinking about the five communities on the south side of Chicago where the deaths have been so terribly clustered. Their reality is that because of the marginalization and because of the disinvestment, they have so little access to favorable qualities of health and thus are vulnerable to something like COVID-19. Move ahead, please. So here's a depiction of what qualifies as social determinants of health, and the list will be on the next graphic if you go forward yet again. You can see the usual contributors to what we describe as a social determinants of health. Go forward yet again. You can see how these social determinants of health impact the totality of one's health circumstances, and it really recognizes the necessity for us to pay particular attention to these important variables. Please go forward yet again. So the last thing I want to share with you is yet another effective use of large data sets. This time, the United States Census. There are 15 variables that are collected in the United States Census that allows a score to be given called the Social Vulnerability Index. Scores close to zero imply lowest vulnerability, and scores at one imply highest vulnerability. Please go forward yet again. This is Chicago, and this is where I live and work, and where I'm positioned right now is downtown Chicago in Streeterville. You can see the vulnerability index here is 0.08. It's as low as one can get. And now when you look at the same calculus done just 10 miles away on the south side of Chicago, you can see that not only is the vulnerability indicative of significant risk, but we know that the life expectancy between these two areas just 10 miles apart is about 20 years, and we especially know that these areas with the highest vulnerability are the areas that have really withstood the brunt of COVID-19. Please go forward yet again. So these are those same areas, those same zip codes to which I referred again, where there is the density of the COVID-19 cases, if we can go forward yet again. Once again. We can see the overlay of deaths due to COVID-19 that uniquely populate in these areas. This is, again, another effective use of a large-scale data set that is informative and lets us know where the risk for this condition really resides. I'd like to yield to the other speakers at this point, but I hope that what I've done is to remind you of the extent to which we're seeing disparities in COVID-19. I hope that I've been able to introduce to you a burning platform that says we must address these disparities because this disproportionate burden is a gross example of something that is terribly inequitable, and I hope that I've also been able to introduce to you that place is important, but what's important about place are the characteristics of place that qualifies as social determinants of health, either providing great durability when there is a calamitous event or great vulnerability when there's a public health crisis. Thank you very much. Thank you so much, Dr. Yancey. Great. Thank you very much. That was an outstanding overview of the really important issues that we're faced with with the pandemic. It sets me up nicely to talk about our work looking at place and health. Next slide. So what I hope to convince you is that as you think about your own electronic health record data or clinical registry data, that geocoding location for the individuals that are included is a critically important piece of information. It is important not only because knowing where people are can, I think, open up pathways to many other types of interventions that are more place-based, and because being able to link data via geocodes actually expands the way we can think about the factors that are contributing to health and disease in particular individuals. So this is the Population Health Data Initiative that we embarked upon when I became the Vice Dean two years ago. Our goal here was to geocode all of the addresses within our clinical centers to link these to existing population-level data sets that have neighborhood-level characteristics, and then to think with others collectively in our health systems and departments of public health about how we could use such linked data to think about research quality improvement and other types of programming. Next slide. We focused on several chronic conditions as a first focus because we were partnering primarily with our Office of Population Health and Accountable Care. We reasoned that several of these were both high priorities for our health system as well as were very amenable to thinking about how context might be important both for understanding a particular clinical trajectory as well as thinking about particular interventions. So hypertension, diabetes, asthma, conditions associated with older age, as well as opioid use and misuse. Next slide. So one of the things that we did, we were focused on our clinical data, but we have many faculty who really had been doing this work, geolocalized work for a long time. We housed the Bay Area Cancer Registry, part of the SEER Registry for cancer cases in Northern California, and several of our faculty, Scarlett Lynn Gomez, most notably, have been involved in thinking about how place is related to underlying cancer risk. She's developed this measure of neighborhood socioeconomic status, similar to measures that many use, but allow us to basically peg the measure to the particular variations across state or across region. This is a map of San Francisco showing neighborhood census tracts by socioeconomic status. You can see here on the eastern side of the city, we have more neighborhood census tracts with lower socioeconomic status. That's different on the western side, and it's overall high relative to California where this is pegged to. Next slide. When we mapped our diabetes cases, our poorly controlled diabetics onto this map, it showed us several important features. First is that those people who are controlled diabetics, they actually don't cluster. Using our statistical definitions for what clustering is, that there's something about these cases being close together that is not what one would predict. However, uncontrolled diabetics do cluster. That was the first interesting finding. Now, you might predict that uncontrolled diabetes would cluster in areas with lower socioeconomic status, and certainly that's what we see in the right-hand side of the graph on the eastern side of the city, but we also find clusters in other places in the city, actually fairly large clusters that are not explained by just individual families or households. It was revealing to us and to our colleagues in population health that we were learning something different than just the way we might segregate our data by just individual characteristics of race, ethnicity, or of income. That place was telling us something else and might actually cause us to think about the neighborhood characteristics or about other characteristics of individuals that are localized within these clusters. Next slide. So we, of course, we are only one of several health systems within our area, and to sort of start to build out a population health sense of the area, we had to think more broadly with our other health systems. This is one where we provide the clinical care. This is the safety net setting in San Francisco. Next slide. So we did the same type of geocoding with the safety net setting and have been using it to think for how priorities across both health systems where we, UCSF faculty members, provide care can help us to think in a more global way about how we achieve mutual goals. In this case, we're looking at how uncontrolled diabetes, excuse me, poorly controlled hypertension clusters across San Francisco. And you can see here, clusters, this is a very common condition, and these are census tracts defined by their number of patients across both systems that have a poorly controlled hypertension. One of the things that doing this has allowed us to start to look at is where we can think about community level interventions for blood pressure control. If you are someone who follows this chronic condition of hypertension and disparities, you know that we have a nice New England Journal of Medicine paper suggesting that the benefits of a barbershop intervention to improve blood pressure control achieves levels of control that are compatible with some of our most aggressive medication regimens for blood pressure control. The scaling of that type of intervention though, depends upon thinking in a very place-based way. And in our setting would require thinking across health systems to actually implement. And this type of mapping across health systems allows us to do that. Next slide. We, of course, even across these two systems are just part of the systems that we represent, part of the work in San Francisco. And so our Department of Public Health is very interested in the networks and engaging all of our health systems to contribute this data. Next slide. And receive funding from the Bloomberg Initiative to continue to do this work. This is, as you know, essential to a Department of Public Health. Oftentimes, the things that they're charged with examining that relate to chronic conditions are data that only sit within health systems. And in the absence of a more systematic survey, Departments of Public Health have very limited ability to engage in place-based or public health types of interventions. It was of relevance to our city because we had already embarked upon a soda tax. We had revenue for a soda tax. And it forced us to think more across the city about what we would do with those revenue to actually start to achieve our goals of both preventing diabetes and a secondary goal of also achieving better control amongst diabetics. And that can really only be done with partnerships where public health invests in more community-based and place-based interventions. But the benefits can be measured by the data that we collect as a part of our clinical activities. Next slide. So we were doing all of this great work and then of course COVID happened. And so a lot of the diabetes work is on hold. But I think this way of thinking has allowed us to shift into other types of strategies that are specific to COVID. So this is the map of San Francisco again, now mapping on our cases in the last two weeks. If you look, and San Francisco actually has fairly muted numbers of cases. We're a city that has not had an overload of our healthcare system with COVID-19. This is the number of cases, 165 cases per 100,000 in San Francisco over the last few weeks. But there's a tenfold difference in the rate of cases across the lowest zip codes to the highest zip codes. And again, you'll see the familiar pattern on the eastern side of town where more of the cases are. Next slide. One thing that we have been doing is we realized early on in the pandemic that where we had tests in the city did not match where we thought the need was based on our mapping of where cases are. And so one of the things that our contribution as a health system and an academic institution is try to provide more data about where the pandemic is based on what we are seeing in our healthcare data. And this is a study that led by my colleague, Diane Havlier, who is the Chief of ID at San Francisco General Hospital. When we realized that the cases we were seeing in our hospital were all amongst Latinos, really taking testing to Latino communities where there hadn't been before and doing it in a way to show both the burden and the additional contributing factors to the high transmission. Next slide. This was testing done in April. And we focused on one census tract within San Francisco. We did this deliberately because our goal was not just to provide the service of testing, but also to provide the data to inform the public health response. We chose the census tract because this is a tract that is predominantly Latino. You can see here 61% Latinx. Next slide. This census tract also has other features that are notable to try to understand transmission. This is a poor census tract with poor families. So these are the darker areas are areas where there is a high degree of poverty with families of children that have children under the age of five. Next slide. This is also an area in town that has a high degree of overcrowding. The number of people living in a particular room. These are all data that we have linked to in order to show the type of risk that we're interested in understanding because these are elements that are important for driving the types of cases that we're seeing. Next slide. So, Dorit, this is, you have to remember what things were like back at the end of April. What we showed in this work that Diane and her team led was that they tested 4,000 people over a four-day period, really showed a prevalence here, a PCR prevalence that was about 10 times the overall community prevalence in San Francisco at that time, a much higher prevalence among people who were actually working, really highlighting the importance of occupational exposure here. Next slide. Importantly, while this census tract is predominantly Latino, it actually is also a very gentrified area of the city. And the most recent data suggests that about half of the census tract, about 44% of the census tract is Latino and about half, 44% are white with the rest in other race and ethnicities. This is what reminds you that place matters, but also individual, but also how different we are even when living in the same place. Although this, we tested, our testing was roughly divided in proportion to the representation in the census tract, the positive cases were only seen amongst Latinos living within that census tract. These are all, were among individuals who could not work from home, who were actually out working during a period when San Francisco was still in a strict shelter in place. And most of the people who were positive were asymptomatic. Next slide. This was before we had widespread asymptomatic testing. Next slide. So the importance of doing this type of testing, it's led us to realize that, so in response to this testing, actually the city really shifted its testing resources to the previous slide, really shifted its testing resources back to the areas where we showed high transmission. It shifted its testing strategy to asymptomatic testing, which was new at that time. And actually led to a lot of protections within the city for what we've called the right to recover program of protecting wages, protecting jobs and giving people resources if they test positive and need to isolate. Those were all things that we identified based on this study, both of a cross-sectional look at the prevalence and also the transmission patterns that we learned, which were, which really helped to underscore that people are, the Latino workers were being exposed during workplace, through workplace exposures. We're bringing the virus back home to overcrowded living settings. We're transmitting the virus to other members of the household, including children. And really, I think, led many of us who've been working in this area and also our Department of Public Health to rethink how we could basically target our resources to both the communities that need, but also to the factors that are really leading to increased transmission in these communities. So this is our latest strategy where we've been testing in the BART stations. This is our rapid transit stations here, trying to both understand what the transmission rates are amongst people who are going to work and who are entering BART stations and thinking about a strategy for ongoing surveillance here in communities where there's high risk, but also people having to work and continue to work outside the home during this time. Next slide. So I think if just to leave you and summarize here that what all of this work has highlighted for us is how important location is, that we sit on a wealth of data within health systems and within clinical registries, that understanding and geocoding and using those geocodes to link to other types of data can tell us about the conditions that we all built these registries and clinical programs around, but can, of course, also help to understand how we can contribute this information to drive our public health response as well. And with that, I'll turn it over to the next speaker. Thank you so much. That was extraordinary. The place aspect comes through really clearly there and love the fact that actually changed your testing strategy for the city. Delighted to turn it now to Dr. Bill Wood, who'll give us the perspective from ASH. Bill. Sure, thanks for the opportunity to join this webinar and to present on behalf of the ASH Research Collaborative. It really has been a pleasure to listen to these excellent presentations by Dr. Yancey and Dr. Bibbins-Domingo. I hope over the next few minutes to introduce those who are tuning in to the ASH Research Collaborative to the Data Hub project that we have developed within the ASH Research Collaborative and importantly, to illustrate the concept that medical specialty societies are very well positioned to collect data and develop interventions to potentially address disparities within specific medical contexts at scale. And while the Data Hub is something that we've been developing over some time, the COVID-19 pandemic certainly illustrates the type of use to which this infrastructure can be put to help make a difference in the lives of vulnerable medical populations for whom we care. Next slide, please. So just to introduce briefly the ASH Research Collaborative for those who may be less familiar. Sorry, let me grab the light here. The ASH Research Collaborative is actually a separate organization that stands alongside ASH. It was established by ASH, the American Society for Hematology in 2018. And there are currently two programs that are major programs of the ASH Research Collaborative or I'll abbreviate it in this presentation, ASH-RC. And those two programs currently are a clinical trials network. And that clinical trials network is specifically in a setting of sickle cell disease. It's not to say that there may never be clinical trials networks in other hematologic conditions, but right now the CTN is in sickle cell disease and also a program that we call the Data Hub. And the Data Hub, which I'll talk more about in just a moment, is a program that is intended to encompass a whole variety of malignant and non-malignant hematologic conditions, but not limited to sickle cell disease. And the overall goal of the ASH Research Collaborative is to improve the lives of those affected by blood diseases. And we intend to do that in a variety of ways as we'll talk about through the Data Hub specifically. Here we work to take data and we use data to develop insights and develop knowledge. And from there, we look to take action. Next slide, please. So I'll talk about a few of the programs that we have developed within the Data Hub. I'd like to speak for just a moment about our initial disease areas, why we chose those areas, and then an additional program that we developed coincident with the COVID-19 pandemic, which is the COVID-19 Registry for Hematology. We intend for the overall structure of the Data Hub program to be replicated condition by condition. And we wanted to start somewhere with a footprint in both non-malignant and malignant hematologic diseases. And we very carefully selected two initial conditions of interest, one of which is sickle cell disease. This coincides with ASH's longstanding interest in this condition for a variety of reasons, having to do with advocacy, having to do with disproportionate impact on vulnerable individuals in the United States and elsewhere, and having to do with the science in this area and the pipeline of therapeutics that will soon become available to help individuals with this condition. We also wanted to focus on a malignant condition as well. And so here we chose to focus on multiple myeloma, which is also an area that is importantly affected by disparities in access to therapy, with several recent studies demonstrating disparities in access to novel therapies in autologous stem cell transplantation, as well as clinical trials enrollment, which has been an area of particular interest for professional societies, regulatory bodies, and others in recent years. Next slide. So in each of our programs within the Data Hub, we aim to aggregate data and then to use those data for various purposes. And where does this data come from? So within each of our programs, we develop a network of sites that are contributing data to each particular disease area. The networks of sites may or may not be fully overlapping from condition to condition. Many times there are different centers that have particular interests or particular representative populations in different conditions. And so there may be some non-overlapping centers. At each of these centers or sites, we actually do direct EHR integration to obtain structured data. And those data come in basically in whole to a vendor that we work with, Prometheus Research through IQVIA. We also have the ability to bring in a variety of other types of data to our program, including various types of specialized unstructured data, including genomic, molecular, and other testing features. And we can bring in data entirely from different types of health systems and consortia, including cooperative groups. We also have the ability to bring in closed clinical trial data sets. We spent a lot of time recently thinking about how to make our programs patient-centered. And we have plans to build out those plans and we have plans to build out patient-reported outcomes and other forms of patient-generated health data from wearable sensors and others that we will aggregate within the Data Hub. We take these data in the Data Hub. We basically run them through various disease-specific models that we've developed around the structured, unstructured data. And we use those data in a variety of ways. We are looking to, importantly, improve care and outcomes at the site level. We are able to facilitate quality improvement initiatives. We work on developing support for clinical guideline implementation. And ASH has developed a variety of clinical practice guidelines in recent years across disease areas of interest. And we are developing a suite of personalized analytics and decision support tools that can be used at point of care. From an evidence generation perspective, we can help to identify cohorts that can be used for the clinical trials network and other studies of interest that investigators or groups of sites may want to run. We can develop evidence for the purpose of pre- and post-market research for various stakeholders. And importantly, we're facilitating academic research as well. Next slide, please. Importantly, we see this effort as one that's well-positioned to address disparities now and into the future. In particular, when we think about our initial two programs, sickle cell disease and multiple myeloma, these are areas where disparities are critical to improving health outcomes of these populations, how to recognize and address disparities in various issues relating to access and quality of care. For those sites that are part of the sickle cell disease clinical trials network, there actually is a requirement to participate that includes implementing a community advisory board and fully understanding the needs and interests of particular communities within which that site is implementing its activities. We wish to, throughout all of our disease programs, increase awareness of clinical trials and do our best to reduce barriers to participation from traditionally underrepresented groups across different clinical trials and different disease areas. We have an interest in developing learning networks, and we are working with academic leaders in this area to understand how those types of consortia of interested sites can be built, developing site dashboards, developing communities of interested practitioners, patients, caregivers, and others to contribute to care improvement and improved outcomes. We talk about how to improve shared decision-making, and we use the data coming to the Data Hub to facilitate this at the patient and provider level. And importantly, for a variety of stakeholders, we generate real-world evidence to, again, better characterize disparities and to hopefully galvanize opportunity to address those disparities at the sites within which we work. Next slide, please. Importantly, this is an opportunity to do this work at scale, and I think this is where, hopefully, this is an illustration of the power that medical societies can have. Ultimately, we expect that 110 sites will be participating in our Sickle Cell Disease Data Hub based on the interest and ongoing activities that we have to onboard sites, and ultimately, this has the potential to represent about 50% of the U.S. sickle cell disease population. This includes, as you might expect, a variety of large academic institutions and community centers that are seeing patients. All sites that are sending data to the Data Hub will include data, some amount of data, including limited data on all patients. We do have a patient portal for longitudinal engagement for the collection provision of patient-reported outcomes wherever patients might be. Next slide, please. And in looking towards the future, we can see several opportunities to begin to do the kind of work that our previous speakers have talked about during this webinar so that we can make a difference in the lives of those who are affected. We are working right now on integrating COVID-19 phenotypes into our data set so we can have a better understanding from a denominator perspective and at scale about who is affected, why, and how. Additionally, this falls within our overall approach towards developing computable phenotypes across hematologic conditions in line with the state-of-the- art across data science programs. We have a keen interest in social determinants of health and are working to build metrics into our data collections that we can better understand, again, where there are opportunities to improve care delivery and where there might be gaps based on the real-world evidence that's being generated. This gives us the opportunity to consider interventions. We're looking at opportunities, for example, to increase and improve testing for COVID-19 in our medically vulnerable populations, including those with sickle cell disease and multiple myeloma. We also have the opportunity to develop and run pragmatic studies through these groups. Ultimately, moving forward, we will be onboarding additional hematologic conditions, both malignant and non-malignant. These two that I described are only the start, and we hope to encompass the whole variety of conditions that hematologists treat. And critically, as I discussed briefly with sickle cell disease and our community advisory boards, community engagement will remain a bedrock of what we do in all disease groups moving forward. Next slide, please. I wanted to mention for just a moment the COVID-19 Registry for Hematology, which is a program that the ASH Research Collaborative Data Hub stood up towards the beginning of the COVID-19 pandemic. The goal here was to provide hematologists with near real-time information using data collected from around the world. We did this in response to what we were hearing from our hematology community, which is that we need to understand better what type of risks our patients were experiencing and whether or not this should impact the care that was being delivered. Next slide, please. To do this, we launched a registry on April 1st of this last year. This was a largely provider-entered patient-level data registry with case report form-based data. Patients that were included needed to be COVID-19 positive and have a past or present malignant or non-malignant hematologic condition or a COVID-19 related hematologic complication. This was central IRB reviewed with an exemption, and we, through this, aim to provide publicly available real-time observational summaries, which are provided on our website at www.ashrc.org. Next slide, please. In doing so, we now have the ability to filter across hematologic conditions for those interested in looking at our publicly available data summaries, which are updated on a daily basis. As our case number increases, we're adding additional metrics from the types of data that we're collecting. An example data collection form is included on the website as well. And because ASH and the ASHRC are societies with global reach, we are in fact collecting data from around the world and have a large amount of data that continue to come in from Brazil and India and other affected countries from around the globe. Next slide, please. As one might imagine, as the Data Hub becomes more mature, in particular sickle cell disease, multiple myeloma, and other related conditions, we'll be able to do what we started doing in the COVID-19 Registry for Hematology and integrate this into our usual Data Hub activities, which will significantly increase our reach to better understand the impact of COVID-19 on our medically vulnerable populations. I appreciate the opportunity to speak about what we've been doing. I look forward to the discussion and the Q&A. Thanks again. Thank you so much, Dr. Wood. I'm not sure if we actually are going to have Dr. Perez-Estable in photo or only in audio. Only voice. Only voice. We'll take you either way, Alessio. I tried to get the go-to meeting camera to read it, but it wouldn't, and my IT person said that he had the same issue. So let me try and summarize or be brief in my comments. I appreciate Helen for the invitation, and it's great to listen to the three sort of data-driven presentations, because mine is not going to have any data or any of our own research, since we fund the research for you to do this wonderful work. Next slide. So I'll just, for the audience's sake, not for the panel's sake, just start with some definitions for us. These are NIH designated populations with health disparities, all race-ethnic minority groups as defined by the census, poor people of any color, underserved rural residents, and as of 2016, sexual gender minorities, primarily for NIH research purposes. A health outcome that is worse in one of these populations in comparison to a reference group is what we define as a health disparity. We also embrace the unifying concept that social disadvantage that results in part from having been subject to discrimination and being underserved in health care contributes to health disparities in all of these groups. Next slide. I wanted for my colleagues to repeat this every time I speak now, that the social construct of race, ethnicity, and the demographic fact of socioeconomic status, however it is, may be that you measure it, affect health outcomes until proven otherwise. And many clinical researchers and many, no, stay in the same slide, many clinicians do not collect this information adequately, definitely not socioeconomic status of this. One message I want everyone to get across today is that it's fundamental importance in human research as much as blood pressure and body weight and genes. We know they predict life expectancy and mortality that are not fully explained. We know that certain groups, African Americans, have more strokes when compared to whites for the same systolic blood pressure level, data from the regard study that many of you are familiar with. Among persons with diabetes within a staff model HMO system, all racial ethnic minorities had less heart disease than whites and also all had more end stage renal disease. Next slide. This is a brief graphic to illustrate the importance of socioeconomic status as data from 2016 linked to IRS reporting for U.S. household income. And as you can see, if you are at the poverty level, a household income of $25,000 or less, you're three times more likely to die than if your household income of four, household of four income is over $115,000. And 115,000 is well off, but certainly not wealthy. Pre-COVID, the U.S. median household income was hovering around $62,000, $64,000 to give you a sense of this robust relationship. It is as strong as blood pressure or tobacco smoking or BMI. Next slide. NIMHD has staff developed this framework to sort of capture conceptually how we view minority health and health disparities research. I think in this particular panel, we focused a lot on place and community and societal factors and how that links to individual data, all interacting with the healthcare system. And so we use this framework to capture where their particular effort is directed, but how all these factors really do interact. Next slide. Now, social determinants of health have been mentioned by all of you. We, of course, consider these to be critical. I would emphasize that currently at NIH, there is a great interest in more interest in the health of the population. So, in more interest in social determinants of health than I have seen in five years, I've been here. We, of course, include demographics, including family background, and not just for immigrants, but for all, where one lives in urban or rural areas, cultural identity, religiosity, language proficiency, literacy, and numeracy. These are just example, not exhaustive. All of these are individually derived. We also want to focus on structural determinants, housing, green space, broadband, economic opportunity, the presence of access to transportation, access to quality schools, healthy food, and public safety or criminal justice issues. NIMHD completed a phase of its progress on harmonization of social determinants of health measures. I put the link in here to the Phoenix Toolkit. We have a page on that on our website now. We endorse core set of measures as well as secondary or specialty set of measures, and we encourage all investigators to use these standardized measures in order to be able to have better comparisons across studies. We plan to continue this work and collaborate with our colleagues at NIH to continue to expand this set. Next slide. I have to say how critical racism is in us considering our research agenda. At NIMHD, this has been true since it started. This is just data from the Kaiser Family Foundation survey that updated in 2015, asking respondents in the past 30 days, were you treated unfairly because of racial or ethnic background in store, work, entertainment place, dealing with police, or getting health care? You can see the rates. If 53% of African-Americans and 36% of Latinos say yes to this in the past month, there is no way that anyone can say we're beyond racism, we're beyond discrimination. Oh, it's been 200 years. This is real, and it's become very real in the last several months, as we know. We do a lot better in health care, but not good enough. And the issue of trust and unconscious bias have all been looked at as possible pathways to this. Next slide. Now, in thinking about racism, most of the research, most of the empirical research has been done on the interpersonal level. So, ask someone, have you been discriminated against like the prior survey? And there are outstanding measures with excellent psychometric properties that have been developed on this. But the topic of structural racism, I think, has arrived as a mainstream issue to address. And I can guarantee you that we've been having many conversations at NIH about this issue, both how it affects us as an agency, your institutions, and then society in general, given what has developed in our country over the last six months. And the recognition that this is a construct, a concept that needs to be incorporated in our research agenda, has now arrived. Now, internalized racism of how people respond, how they sublimate, how they accept this cultural or biological inferiority has also been studied to some extent. And then there are other novel constructs of perceived societal discrimination, not how you feel, but how do you think it is out there, and some interesting work done with adolescence or secondhand effects of racism. So, if an adult is victimized by racism, how does that affect his or her child? Next slide. Now, clinical registries in research to address COVID-19, so what is needed, I think you heard some outstanding efforts on how to move this field forward. I would start by saying we all need standardized measurement of demographics and social determinants of health. It's great to use your favorite measure, but we have to sort of agree at some point to use the same measures, and I think we're getting closer, although I would be hesitant to say we're there. NIH does have some leverage in grantees, but it does take the willing to move this. We need to address structural social determinants of health, and these need to be incorporated into clinical management or clinical thinking. The data that I saw from both Chicago and San Francisco really begin to get at this in a substantive way. Track test results, symptoms, and clinical cases. So, this whole idea of this pandemic and what happens, we're at the beginning of it, unfortunately, and the consequences are yet to be fully defined, and we know from past experience that we need to create robust data systems. We have the capacity to do this and to be able to track individual cases as well as populations and communities. We need to have communities participate in trials. There are two or three vaccine trials that have been rolled out. We need to make sure that we have a science-based response to this pandemic, and we cannot find out what works for treatment or what works in terms of prevention if we don't have participation in clinical trials by the very people who are most affected by this. Unfortunately, there's a high level of mistrust that has been generated in science as well as anything that comes from the government. And then, as I said before, recognizing and managing structural and interpersonal discrimination is critical in all of our research in this context. Next slide. So, a brief summary of NIH programs to address COVID-19 disparities. Many of you are familiar with this. They're all in process. We started in late March, early April with a group of sort of intense meetings orchestrated by Dr. Collins asking for what is the science response going to be. And, of course, preclinical molecules or what's the vaccine candidates, what are the therapeutic candidates, what are the clinical issues. But a group of us also said, well, there are social, behavioral, and economic issues as well. And we formed a coalition between NIMHD, the National Institute of Mental Health, National Institute on Aging, OBSSR, the Office of Behavioral and Social Science, as well as a number of other institutes that joined us. And we have funded 52 supplements or in the process of funding 52 supplements to grants in these topics, as well as individual ICs have funded supplements. And we will continue to do this. With April supplemental appropriations that came to NIH of $1.5 billion, Dr. Collins stood up the RADx program. This is Rapid Acceleration of Diagnostics program. The underserved populations got $500 million of this after recognizing the dramatic disparities that have been unveiled by this pandemic. Now, congressional money comes with a label, and this label is testing. But we were in the process of evaluating, reviewing, and deciding on the funding of these supplements to large center grants and networks that were submitted. We had a robust response. We plan to fund 25, up to 25 of these, and also fund a center for data collection and coordination, so a data hub. NIMHD will house the data collection center, and the supplements, of course, will be based on which institute the original grant is on. We have a second receipt date that is next week for smaller proposals, but we will fund another 25 in that category. I want to emphasize the importance of data harmonization. And again, this is a requirement of common data elements. This effort around COVID has created a lot of momentum on this space, led in part by the National Library of Medicine, but many of us involved in this at the IC leadership level, is to make sure that we standardize these measures and then in some way, quote, unquote, require use of these common data elements, which I think we hope that investigators will understand the importance. And finally, the third program is this Community Engagement Alliance Against COVID-19 Disparities Initiative that NIMHD and NHLBI have been co-leading. This has been a rapid development, primarily driven by the concern about lack of diversity in the clinical trials that have been launched. We may be too late for the first one or the second one, but our goal is that this is not going away anytime soon, and this community engagement and promoting of diversity to address the mistrust in science is an important component of advancing the science response to COVID-19. Next slide. So, just some parting words. Linkage of clinical registries to population-based data is critical. This is when they're most useful, I think. You can use census imputation if you don't have primary data. I think maintaining unique personal identifiers in a confidential manner so we have the option to follow these patients over time, these persons over time, and I want to use the term in perpetuity. We have to think in those terms. Leverage the data that CMS has, which is a big, big challenge, including state Medicaid, the Social Security Death Index, and link to clinical data. The C or Medicare linked data is a great example of how this can work to our advantage. And then use the standard consent for future of the identified data and data sharing. We don't need to go back to the individual persons to ask permission to analyze their data unless we want to collect more information from them, and this is part of the consent process. Last slide, I think. Next slide. Yeah, just a promo for our special issue in American Journal of Public Health. It's available free on the website as a link where we outline our research strategies, methods, measurement, etiology, interventions. This is pre-COVID, but I think the overarching high-level principles still apply. And then the last slide of how to communicate with us. Thank you very much for your attention. Thank you so much, Eliseo. That was great. I invite all of the speakers now to join us back. Put your cameras back on for our Q&A session. That was really a remarkable set of presentations. I guess we'll still have Eliseo in audio only, although still delighted to have him. There's a couple of the first questions that came in very much went back to this question about social determinants of health. And I think a lot of questions about place in particular and trying to understand, for example, very practical questions. For example, how many digits of zip code are needed to really get a good enough assessment of what's happening in terms of place? How do we really think about how you can determine SDOH from EHR data, even beyond zip codes? How do we really build the systems that allow us to have some of these key elements built into our clinical data systems going forward? Anybody can start. I think maybe starting with you, Kirsten, if you have any thoughts there. Yeah. So, I do think you should go beyond zip code, zip code that you can take the real address that people have, and actually fairly, you need people who know how to do this, but to basically create geolocalized latitude longitude. That's the best way because then you can link to the lowest sort of unit, the census tract, where we collect a lot of national data that shows a lot of factors by census tract. We've done some analysis, I wish I could show you the slide about the challenge of just going to zip code, that the census tract is the one where if you're really trying to use and understand what's happening, that the lower unit, the census tract also has the value of being, it essentially is about the same number of people in each census tract. So, it has, it's another way of comparison. What Eliseo just described at the end, you can use imputation from based on the census tract demographics to actually tell a lot about your population distribution. So, that would be my recommendation. If you have to use zip code, it's better than nothing, but I would sort of urge to try to go for something with more definition. Yeah, and it's interesting that you were able to use even ethnicity within census tract to be able to see the unique differences among the Latinx population in San Francisco. That was really fascinating. Yeah, it's really, there's such a wealth of data out there, I would, and that once you get over the hurdle of measuring it, but you know, the programs to do this are actually fairly well defined. There are a lot of people who have GIS skills now, and you have to make some decisions about missing data, and we don't collect all of our data perfectly, but a lot of people have these skills, and the ability to link to other things is really quite well defined now. So, this I'll say, I have lobbied with Mike Lauer to require that all investigators report on socioeconomic status on the human participants, like we do now for gender and race ethnicity, and this would be the out. Now, you know, a unique address is, as Kirsten outlined, is optimal, but some people don't like to say where they live. They're afraid of being watched by the government, so a zip code is maybe less threatening, and it's better than nothing. Yeah. But Helen, going forward, think about the things that we typically don't require in clinical medicine. We continue to capture race, which is a very crude marker of something, when what we really want to understand is what is the life and living circumstance, and what is the burden of SDOH, and in today's data sets, it's not that difficult. Whether you use the deprivation index or the vulnerability index, or you have a more sophisticated way of capturing this information, it's not that much more difficult to have a better descriptor of the life and living circumstances, which is what race is probably inferring now. Certainly, it's not inferring biology, and so I think we should think about moving in that direction. That's a great point. I would just, one more point I just want to make is that, you know, we've set up our clinical systems now that they all have these wonderful, like, social determinative health screeners, and a lot of their electronic health records. A lot of that is actually, there is a lot of value to that, so I'm in favor of that. A lot of this can be done on the back end, and so it doesn't take asking patients, asking trial participants, or whatever, a lot of this information, so it's another way to enrich the types of data that are associated with at least where people live, just as Della was saying. Yeah, and actually, one of our prior presentations that we had, Andrew Ramirez from Vanderbilt, who works with the All of Us Research Registry at NIH, really described the ability to collect those data and then have them and link them across multiple data sets. I think this whole sort of vision of what cloud-based research allows you to do and the linkages between them, I think, connects very nicely with what we could potentially do by having, I think, back to Eliseo's point about if we actually had standardized measurements and definitions for demographics and social determinants, that it would be easier to then build it into our clinical registries, our research, our data systems. So many of the groups who've joined us in the past have been large health systems like UCSF, who have really done this work. The question is, how do we build it out across others who don't have this just remarkably rich data system that places like UCSF and others have? Bill, any thoughts there from your perspective of sort of building the data hub? I don't have too much to add beyond what's just been discussed, and I appreciate these perspectives. This is an area that we're looking at right now, and I can't say that we have our final approach set in stone. That having been said, I do think that one of the nice elements of, again, a meta-society approach is that we have the ability to tap into pockets of expertise around the country, and we are very, very interested in learning from places where this has been done quite well, and I think we've already heard examples of that today. I'm always eager to welcome collaborations. As I said, not exactly an answer to your question, but it is a call for participation and collaboration, and we would be delighted to get additional insights. We're doing this in the best way possible. Great. I know a lot of our registries are struggling with the exact same question. I want to go back to something, Clyde, you said on the very first presentation around this idea of COVID-19 as the burning platform, which I think is so powerful, the bellwether, as you called it in your paper. What does it take to turn what has been sort of crisis, pulling data together to understand what's happening in COVID? How do we build disparities into the fabric of everything we do in terms of data in a way that isn't always what I think it's tended to be over the years, as sort of the afterthought after you finished your research, let's go back and think about whether there are disparities, maybe just responses from each of you, maybe starting with you, Clyde. It's a COVID-19. You think about how much discussion we're having right now about health disparities. You think about how many medical societies, I'm in cardiology, Bill is in hematology. I know no medical society that's not having active conversations and wondering what can we do as an organization, as an entity, as an enterprise to address this. The light has been shining. The question becomes, what's our bandwidth on this interest? Is it going to continue? Are we going to stand up strategies? Today, we're talking about data acquisition. Will we build into EHR as a way that we can protect some of this disparate behavior? Will we create in clinical trials a way that we can consistently capture place as we're evaluating the impact of new therapies? But I think we've already had the moment and the attention is happening. What we have to do is really make certain it's focused and that this is not just energy spent, but actually enterprises that are built. If we can make that the outcome, then I think we'll see something different emerge. We can't be a utopian here. I mean, so much of this is baked into society, but we can certainly say to the extent that these egregious differences have emerged, we should be able to continue with that going forward. That's great. Thank you. Kristen Eliseo, same sort of thoughts about how to build on the COVID burning platform here to make this stick? Yeah, I do think that the pandemic exposes the things that were already preexisting in terms of disparities, but there is a lot of urgency around this and it makes people think, I think, in different ways. Clyde mentioned, we have the subspecialists here thinking about this. I know even across our public health department, we're thinking across the housing sector and the food sector in ways because we have a large portion of the population that can't do the things we want them to do and are faced with a greater economic burden at this time. If we as a city are going to get the pandemic under control, we have to think about how to address those needs and to think across our silos, health system, public health, housing, whatever. My hope is that... I already see that people are thinking creatively, they're collaborating in ways that I haven't seen the type of collaboration before. I think the key is the sustainability. I think we have the opportunity to... We don't seem to be getting out of this pandemic tomorrow. We have the opportunity to think of what are the strategies about doing this in a more sustainable way going forward. I think that's going to be important for the pandemic and it's going to be important, frankly, for all of the other health conditions that we all care about because those are also more in the communities that we're talking about in general. And so I think the same strategies when I think through how we're going to manage hypertension or diabetes is the same strategies that will require us thinking across sectors as well. It's so interesting that there's this intersectionality between the common conditions, obesity, hypertension, diabetes, and the vulnerability to coronavirus infection, and then the burden of COVID-19 complications. We just can't disaggregate this. We have to take a very concentrated public health approach. I know that Kirsten is like saying, what did he just say? Public health approach or cardiologist? But yeah, I think that's what we have to do. Yeah, that's absolutely right though. I think if we don't do that, we're just going to lose out. I mean, it's the person's lived experience. I think the structural racism issues, I'll say are brought up, are going to be the same issues across diabetes, heart disease, COVID. And if we don't understand the underlying issues that resulted in these disparities, we'll be back to the same place of many, many more papers demonstrating disparities without a lot of effort of how to actually have actionable steps to address them. Eliseo, any thoughts on this? Just to reemphasize the standardized collection of these social determinants demographics is critical. I can't say enough how many times I've talked to peers at NIH who are astounded by how important these factors are. And these are clinician, investigators, fellows who are in subspecialty areas at NIH in the clinical center, and they just never thought about it. It's not routinely done. Hopefully now, thanks to leadership from all of you and many others, people are beginning to do this. Okay, we got to get these measures done, just like we take weight and blood pressure and heart rate, just exact same value in thinking about this. And then the second emphasis is what Kirsten brought out. I think the novel place for us in science is how are we going to work with other sectors if we're really going to make a difference? Because you know, it's great to describe, it's great to get science to understand pathways, but how can we make a difference? How can we make everything better? And it's going to require innovative interventions. It's going to require different thinking. And if we're going to improve a community, you're going to have to work with the transportation sector, the housing sector, the criminal justice system, you know, urban planners, and that's going to be new territory for us. And I'm saying NIH, not just you guys. So I am optimistic. I'm always kind of an optimist, and I do think that this horrible pandemic has created the conditions where we can really see some major change in perspectives. And added to all of this, of course, is this whole issue of structural racism, which is woven into the fabric here. And that's why these other sectors are critical. So. That's a great point. Another question that has come up in the chat is also this question of how do we actually engage people from these communities? So they're part of the research efforts. I know this is something Ash has done a lot with, for example, their community groups around sickle cell, but how do we really engage patients in the work of these clinical registries, in the work of clinical research? And, you know, thinking about, for example, some of the significant concerns we may have about patients who won't trust how these data are used, particularly for patient populations for whom these data have, in fact, been misused or, you know, may not necessarily be used in a way that they think will improve their care. Any thoughts about that? Maybe starting with you, Bill, because I know this is something that's been pretty prominent in the work you guys have been doing at Ash. No, I appreciate that question. I think it's a very important one, and it makes me think of a couple of different issues. One is that this issue of trust, it's a long-term project. That's why it's a bit challenging to establish trust in areas of mistrust and kind of the spur of the moment when COVID-19 comes. And so I think that the more we can be attuned to how we place these structures in place now, we can do what we can under the context of the current pandemic. We can also be prepared for additional threats to public health. So when we think about what does community engagement mean, how are we talking about data with participants? How are we being very transparent and clear in how this data can be used and what we intend to do, and how can we communicate on an ongoing basis with participants? And I think that brings me to the second issue as well, and that's something I think a lot about from a medical specialty perspective, which is that, you know, from those of us who are practicing in tertiary care centers, we see who we see, and many times we're not seeing individuals who are affected or who are at risk of being affected, particularly during pandemics. We saw that a lot in cancer care where, and this has been a story that's been played out across the specialties as well. So I think that the issue of how to reach people where they are, how do we bring both research and care home, is something that we all need to do, I think, a little bit more thinking about. And I know it's, this is a concept that affects, you know, research to implementation everywhere, but illustrated again in the setting of COVID is that we've seen disproportionate impact of COVID in specific geographic locations around the country, and specific examples of success in different locations, and how do you actually translate some of those, you know, local innovations to the kinds of practices that we can actually start experimenting with to implement in other areas around the country, I think is something that we got to have to continue to work on across different boundaries. Yeah, great point. Krista, did you want to say something? No, just nodding in agreement. I think when we did our session, our last webinar, specifically on strategies for patient engagement and prioritizing patient-generated data, I think there was also some real issues around trust, for example, for some of the long haulers who don't feel like some of their concerns are being taken seriously, and really embedding them, not just sort of advisory role on research, but actually being part of developing the surveys, part of, you know, identifying what are the ongoing symptoms, I think is also maybe another shining light that COVID has shined on in terms of what we then need to do differently in terms of clinical research and registries going forward. Clyde, I think you talked about this trust issue quite a bit in your editorial as well. Any thoughts there? Yeah, it's a huge barrier, and it is a responsibility that we need to own in medicine. We have to recognize that this didn't just start, and this is decades of experience here where things have not always been done in an ideal manner. We have to understand that the Henrietta Lack story tells us about how we've misappropriated human tissue. The Tuskegee experience is real. There are multiple cases of that. I respect the fact that Bill is here, but sickle cell anemia has had a really difficult journey in our awareness of the condition, our treatment of the condition, our management of the patient population, and so I think those in the community can look at these events and say, I'm not certain that you've earned my trust, or how can we re-earn their trust, or how can we avoid any more affronts? So I think it's a big hurdle, but as we've all outlined, the right thing to do is not to wait for the metaphor for the patients that come to downtown Chicago. We have to go to where the patients are. We have to go to their life and living circumstances. We have to set up activities there. We're in a concerted effort to recalibrate our medical center, and we've had a series of town hall meetings with the community. It's a remarkable moment, and what they've told us, and something that will resonate with me for a long time, we're not interested in community engagement. We're interested in community investment, and I think that really captures what it will take to earn their trust. Don't just come talk to us and then disappear back downtown. Make an investment of your money, of your time, of your presence, and let us know that our life and living circumstances are important to you, but that really was a moment of clarity for me. Not the engagement. Make it the investment. That's a really great point. Thank you for that. I think given the fact that we're at 359, I think that will be the last word. I want to thank all, and a great one to end on, certainly Dr. Yancy. I can't think of a better way to end it. Just been an extraordinary time with all of you. I'm sorry we can't see LSAO's smiling face, but his words resonated regardless. Thanks again for all of you for joining us. Thanks to the Moore Foundation for supporting this webinar series. We've learned a lot, and I think we're going to try to bring this back to our societies and continue to push on how our clinical registries can really make a difference, help work with academia, work with our patients, work with our communities to make a difference, and try to work on reducing disparities here. Again, the recording of this webinar will be available pretty soon, within the next week or so. We look forward to a short evaluation. If you could complete it for us, we can continue to improve on the work we're doing. Any questions or for more information, feel free to contact me or just info at CMSS.org. Then I think I just saw our last slide here. We are delighted to announce that our – that was a good timing there – our annual meeting for our 45 specialty societies and CMSS will be October 28th to 30th. We're going to continue a bit on this theme, specifically COVID-19 and beyond, the digital transformation of healthcare, research, and education. So, really understanding how, for example, the growth in telehealth, remote patient monitoring, this new paradigm around research and patient engagement, and then also the amazing pivot all of our societies have had to do around virtual learning, what works, what doesn't work. It presents a huge opportunity. So, we look forward to you joining us, and if you have any ideas or thoughts about other webinars or things we can provide for the broader community, we'd be delighted. So, just final thanks to all of our presenters today. Thanks to our staff for putting this together, and we look forward to continuing to improve going forward, and hopefully COVID really is the burning platform for significantly, finally, making an impact on disparities. Thank you all. Bye-bye. Thank you, Helen. Thanks, everybody. Thanks very much. Thank you.

CMSS

clinical registries

COVID-19 disparities

health disparities

social determinants

systemic racism

geocoding

socioeconomic disparities

ASH Data Hub

standardized measurements

community engagement

interdisciplinary collaboration