 All right. So good afternoon, everyone. My name is Vence Bonham. I'm at the National Human Genome Research Institute. I'm pleased to welcome you to this afternoon's lecture. I'd like to welcome you to the 24th lecture in the NIH Genomics and Health Disparities lecture series. The first lecture was held in May of 2015. The series aims to highlight the opportunities of genomics research to address health disparities. In addition to the National Human Genome Research Institute, the series cosponsored by the National Institute of Minority Health and Health Disparities, the National Heart, Lung and Blood Institute, the National Institute of Diabetes and Digestive and Kidney Disease, and the Office of Minority Health and Health Equity at the Food and Drug Administration. Speakers have been chosen by the series cosponsors to present their research on the ability of genomics to improve health for all populations, and often the challenges in making sure that these improvements are accessible and applicable to all populations. The speakers in the series approach this problem from different areas of research, including basic science, population genomics, translational clinical research, social science, and ethical, legal and social implications research. We're pleased to have Dr. Clare Osario join us this afternoon. My colleague, Dr. George Mensa, who is Senior Advisor in the immediate office of the Director at NHLBI and Division Director of the Center for Translation Research and Implementation Science will introduce our speaker, George. Thank you very much, Dr. Bono. Ladies and gentlemen, it's really my great pleasure to welcome Dr. Pilau Osario as our speaker for this 24th session of the NIH Genomics and Health Disparities Lecture Series today. Dr. Osario is Professor of Law and Bioethics at UW, the University of Wisconsin at Madison. Exactly 10 years ago, she became the inaugural ethics scholar in residence at the More Gridge Institute for Research, a wonderful private non-profit research institute that is part of the Wisconsin Institutes of Discovery. She also serves as the co-director of UW's Law and Neuroscience Program. She's a faculty member in the UW Master's in Biotechnology Studies Program, and she's also a program faculty in the Graduate Program in Population and Health, so you see she's very busy. Prior to taking on these roles at UW, she was Director of the Genetic Section of the Institute for Ethics at the AMA, the American Medical Association, and she also taught as adjunct faculty at the University of Chicago Law School. Dr. Osario has a very, very diverse academic background. For example, she received her PhD in Microbiology and Immunology from Stanford University. She went on to complete a post-doc fellowship in Cell Biology at the Yale School of Medicine, and then received her JD from UC Berkeley School of Law. Throughout her career, Dr. Osario has participated in numerous advisory committees and boards that aid governments in setting science policy. She has advised our own agency here at NIH, my own institute, the National Heart, Lung and Blood Institute has benefited from her services. And she's also served at our sister agency, the FDA, as well as Genome Canada and Health Canada. Now, you can see why we're delighted to have her as our speaker today, but before I call on Dr. Osario to speak, let me remind all of you that we would like Dr. Osario to complete her presentation, and then we've reserved time to have a Q&A session. And at that time, my colleague, Dr. Paul Cotton, who's also at the National Heart, Lung and Blood Institute, will take the podium and help moderate the Q&A section right after that. So please, we encourage you to use your Q&A box, submit your comments, your questions during the presentation. And at the end, we would do our best to get all of your questions answered. Now, please join me in welcoming Dr. Osario to give a lecture today entitled, Population Genetics in an Era of Genomic Health. Dr. Osario, the floor is yours. Excellent. Thank you very much. All right. So good afternoon, everybody, and welcome. And I would like to very much thank the NIH, and NHGRI, and NHLBI, and all the other sponsors of this important lecture series. And I just want to get this show on the road as quickly as possible. So I need to start with a couple of disclosures. I either recently have received or will soon receive consulting fees from two pharmaceutical companies. I am not going to talk about any particular drugs today or even pharmaceutical research, but you should know this. All right. So this is a bit of a roadmap for what we're going to talk about today. I'm going to give a background on health disparities. I'm going to focus on racial and ethnic health disparities, although there are a lot of other ways of thinking about health disparities. I'm then going to talk about, particularly about health care in BIPOC communities with an emphasis on Black and Indigenous communities. And I'm going to talk about reasons for including people, diverse groups of people in genomic research, even when they might not have access to good health care. And finally, I'm going to end with just one example of a study that I think is actually a very helpful form of inclusion and diversifying genomic research. So starting with a background on health disparities, this is a definition of health disparities. I think it's one that is relatively uncontestable. And I want to focus for a moment on some aspects of this definition, right, that these are differences or inequalities that are avoidable and comparisons between population groups. So I think lately, a lot of people have perhaps moved from thinking about disparities to inequities. I personally don't love either terminology all that much, but I think the terminology of inequity is maybe intended to focus us on the normative, the ethical aspects of health inequalities. So first thing to think about is that whether we're talking about disparity or inequity, we're talking about comparative concepts, right? We usually we're comparing across human groups, and we're comparing across groups, usually defined by race and ethnicity, socioeconomic status, geography. So geography, we're thinking about county level, census tract level, neighborhood level has become an important way of analyzing health and health disparities, gender or disability. I think disability has until pretty recently been very much understudied in terms of health disparities. But people are starting, I think to do at least some more research in that area. And of course, these categories are not mutually exclusive, they intersect in a variety of important ways. And in fact, socioeconomic status is probably the strongest predictor of health outcomes. Nonetheless, other kinds of disparities remain even accounting for SES. And today I'm going to focus on race and ethnicity, because number one, that's the area of my research. And number two, it's such a salient set of categories for genomic research and a contested set of categories. So if I'm not focusing on your area of interest, apologies. And maybe you can chime in with questions at the end. Next, I just want to say that the concepts of disparities and inequity inevitably include some notion that we want to be focused on inequalities in health that are unjust. And perhaps unjust because they reflect structural racism, for example. And I've heard people talk about this as inequities are undeserved inequalities in health. I personally don't love the language of dessert as a register in which to think about health and health comparisons and access to health care. But I think when people use that language, they are trying to get at some concept of injustice, right? That these are inequalities that we should be doing something about. And finally, as was evident in the definition on the previous slide, health disparities or inequities are avoidable health inequalities, right? So in ethics, we have the concept of ought implies can. And so if these are unjust inequalities, also they are inequalities that we can do something about. And most of us think we should do something about. And on the other hand, if we thought these were inequalities that were so they were somehow intrinsic and unavoidable, they wouldn't necessarily be viewed as injustices or things that we ought to do something about. That's there's a large body of literature on on sort of luck and the way a society ought to respond to luck and when that's a matter of justice and when it's not. But I think the focus on avoidable inequalities is to impart to say, look, these are injustices that we should be responding to and trying to ameliorate or fix. And so I am not going to review an enormous body of literature on racial and ethnic health disparities or any other kind of disparities. That literature is out there. It's widely available. You can find it. What I am going to do with the statement is just summarize the overwhelming consensus from that literature, which is that the root causes of disparities are inequalities in the social determinants of health and not genetic differences among human groups. So it's the way we've structured society to privilege some people and give less privilege to other people. The way we have segregated our neighborhoods and our housing so that some people live in more dangerous housing and more dangerous neighborhoods and neighborhoods with less good education and less good health care. So these are things that people look at and analyze. And there is overwhelming data to indicate that these social determinants of health very substantially explain health disparities. Now, if you are not somebody who wants to spend a lot of time delving into literature, I think we all know from this year of COVID that part of what has happened during this year is that a focus on racial and ethnic health disparities has really come to the fore because racial and ethnic minority groups have been disproportionately affected by COVID. So if you don't want to spend a lot of time reading about the social determinants of health or health disparities, this website by the CDC, which is about the impacts of COVID, actually has a very concise, nice set of explanations about the social determinants of health and how those social determinants of health relate to inequities in morbidity and mortality because of COVID infections. So I also want to say that terminology like inequity and disparity focuses on inequality. And inequality clearly matters in terms of the justice of a situation. So I want to talk a little bit about equality and inequality because it's actually over 1,000 years of scholarship on this concept, which we are not going to summarize here. So I've made a little model here. And if you can just look on the left side of the screen, you can imagine some summary metric of health and state of affairs A in which white people have more of that than Black, Indigenous and people of color. Now here I specifically want to acknowledge that BIPOC collapses an enormous amount of variation within communities and between different communities that are summarized in that acronym. And the worst disparities on almost any measure are between Black and Indigenous on the one hand and white non-Hispanic on the other hand. So just to put that out there, I've just labeled this group here as BIPOC. I'm going to be focusing a lot during the course of this talk on Black and Indigenous health disparities because the gap is most significant for those two groups. And there are more disparities, if you will. So if we were to move from state of affairs A, say that's the state of affairs today in terms of health disparities, racial and ethnic health disparities, and if we were to move to state of affairs B, we might actually feel like state of affairs B is a pretty good state of affairs and that we've improved justice in that state of affairs. In part because even though there's still some residual inequality here, it might be the case that at the level of health that you see for BIPOC people there that it might be that one's experience of health, one's enjoyment of life, wouldn't be substantially changed by an incremental increase in health or by attaining exact equality there. And it might also be that at this level of health, one's access to opportunities in life wouldn't change by incremental increases. So this might be a really good level of health and our concerns about this level of inequality might not be so pressing as they are in state of affairs A that we see right now. So even though there's still some residual inequality there, B would be a pretty good world to be living in. And as an ethical matter, I think under most understandings of justice, that would be a more just world. On the other hand, it should go without saying that in responding to inequities and health disparities, the appropriate response is to improve the health of the people whose health is less good, right? There might be other ways to achieve equality, but that would be, you know, state of affairs B here would probably be considered a less just state of affairs. And at the very least, even though there's equality here, and at the very least, moving from A to B might involve a lot of injustice. So that's something we don't want. Now, I think in the world of genomics, this is the thing that a lot of people are concerned about, right? We're concerned that because of the way we have structured genomic research over the last 20 years or so, or more 30 years, that as we translate the current sort of body of knowledge and research into clinical care that we will advantage the people who are already well off and either fail to help the people who are less well off, or even make things a little worse for Black, Indigenous, and other people of color, right? And so there are a number of ways that that could happen. And I have to say that I've had researchers say to me, well, you know, if I were increasing inequality by making somebody's health better, how could that be bad? And I think the answer that is complicated, right? I think if your research has a realistic possibility of improving somebody's health, that is certainly an ethically good reason to do that research. On the other hand, if you could reconsider and redesign your research so that you have the realistic possibility of improving more people's health, that would be better, right? And at an individual level, when people make these kinds of decisions about how to design their genomic research or other kinds of research, there might be nothing wrong with the decision that they're making. But as an institution of science, if we create policies, processes, organizations, and incentive structures that continually result in individual researchers making choices that are likely to provide primarily advantage to white non-Hispanic people and to leave everybody else behind, then I think we have a structural racism problem, not reflecting malice on anybody's part, but nonetheless, that is a structural problem, right? So this is the situation people are hoping to avoid. But I would say, and so hoping to avoid this usually means some very strong argument for why we need to include black indigenous and people of color in our studies and diversify our studies so that we can diversify our databases and provide the benefits of genomic medicine to everybody and avoid a situation where our genomic algorithms don't work or even work worse for people of color, right? But first of all, I want to say that I think that argument is really overblown. So I might be the only person speaking in this lecture series who is going to say that to you. I think that argument is really overblown. I also think that it's often made in ways that really focus on genetic, essential differences between groups of people as the reason for inclusion. And if you want to learn more about how that kind of thinking perhaps has motivated or a long, decades long push for inclusion, I would recommend this book by Steven Epstein. But almost at the beginning of that push, Otis Brawley from NIH in 1995 and later picked up by Patricia King in two articles talked about the danger of difference, right? The idea that by focusing on, for instance, genetic differences between races that we might, we might achieve incremental improvements in some particular health outcome. But at the same time, we might reinforce racist stereotypes in medicine science and the larger society. And that those racist stereotypes are ultimately extremely harmful to the health of black indigenous and people of color. And so we might create a situation where we have sort of one step forward health wise, but four or five steps back. And so this, this is something that I've written a little bit about other people have written about. People who've heard me give talks on race and genetics before might expect my whole talk to kind of focus on this. It will not. This is the last I'm going to say about it. But there is a lot of good writing in this area. What I want to say instead, and where I want to focus is on the assumption that underlies this kind of the inclusion and difference paradigm, right? The idea that we need to be recruiting people into genomics in particular so that we can get a bunch of different variants that we're not finding because we're primarily including people of Northern European descent in our research. So the assumption, though, is that this inclusion is going to be a benefit medical benefit to BIPOC individuals and communities. And I think that argument assumes that sometime in the near ish future communities of color have realistic chances of receiving healthcare that includes genomic medicine. So that's why I want to talk about healthcare in BIPOC communities. And of course, when we talk about the social determinants of health, some of those determinants are things that occur essentially outside of the healthcare system, they manifest initially outside of the healthcare system. But social determinants of health and structural inequalities that result in poor health also occur inside healthcare systems and through the operation of the healthcare system. And I guess I would include in that differences in access to insurance, as well as, you know, provider patterns of practice, differences in the kind of restrictiveness of health plans that people have access to. I was going to put it in. And well, anyway, there's there's an article that I was going to talk about. And I forgot to put it in that looked at the way health plans assign assign levels of risk and therefore allocate resources to preventive care for people within a healthcare system. And so Dr. Obermeyer and his colleagues published a paper in science about two years ago, in which they showed that these algorithms were these algorithms were basically reflecting or creating health disparities, even. But they were creating them by or instantiating them because they were the algorithms were using amount of money paid per patient in say a year. And it turned out that for the same level of health, measured by looking at aspects of people's health records, a lot less money is spent on black Americans than on white Americans. And so the algorithm had learned that and it was it was considering black Americans with a lot of health problems as having less health risk than they did, because it hadn't learned the inequalities within healthcare systems. Right. So that wasn't confusing. So at any rate, I just want to make the point that particularly black Americans and American Indians and Alaska natives have less access to health care than other groups in our society, and receive on average lower quality of care when they do have access. So this is an oldie but goodie, if you will. It's a report from what was then the Institute of Medicine now the National Academy of Medicine from 2003 that looked at once people do have access to health care. How are they treated? Who gets guideline recommended care? Who gets care known to be effective, etc. And what they were they were able to bring together a lot of different studies showing that BIPOC individuals receive less lower quality of care compared to their white counterparts. And even after controlling for characteristics like class health behaviors and comorbidities or access and insurance, still people of color receive lower quality care. And this is a 2003 report. There have been real efforts to implement policies and practices that diminish those inequalities. And in some cases, I am happy to say those policies and practices have been successful within certain health care institutions. So we know that unequal treatment can be addressed and remediated and improved. Nonetheless, a mountain of studies basically shows that we still have a lot of unequal treatment in our system. And I was actually reading a report written in 2020 for the American Bar Association, summarizing recent data on on heart disease, saying that black patients with heart disease receive older, cheaper and more conservative treatment than their white counterparts. Black patients were less likely to receive coronary bypass operations. And that after surgery, they're they're discharged from the hospital earlier than white patients and at a stage when discharge is inappropriate. And it goes on, right? So this is by way of saying that although we've been able to identify unequal treatment, that is lower quality health care, we still haven't ameliorated a lot of it. And this slide just shows some other examples, right? And I'm not gonna I could spend all day going through examples. I'm not going to do that. Instead, I would just like to for a moment, focus on this paper, just as an example, right, of something that I think is maybe a newer way of studying disparities, but a really important way of understanding social determinants of health and how they affect health. So this is doing basically a geographic comparison. And looking at at racial segregation and inequality in neonatal intensive care units. Now, the United States has become right now we are as residentially segregated as we have been at any time in the 20th century. And that racial that racial residential segregation leads to situations where people live in neighborhoods that have incredibly disparate impacts on their health, right? So some people have access to housing that has lead paint in the walls and lead in the water, and is in neighborhoods with poor educational systems, and under resourced health care institutions. Some people live in neighborhoods where toxic waste is more likely to be in the soil or dangerous factories are more likely to be near your house and polluting your ear. Or highways are more likely to be near your house, right? So these differences in neighborhood really, really matter for racial and ethnic health disparities, because of the intense racial segregation, which particularly affects black people in the US, and indigenous people in the US. So those two groups are the most likely to live in very, very segregated settings. And what this particular study found was that controlling for large levels of geographic variation generally, that still black infants received care at lower quality NICUs. And we also know that black infants and black mothers have much higher rates of death. So this, you know, this study didn't necessarily connect those two things directly, but it's certainly a hypothesis. So at any rate, I, you know, like I said, I could spend a whole hour talking about poor quality health care to which black people in the United States are much more likely to be subjected. Also, American Indian Alaska natives. So, you know, the federal government in its trust relationship with federally recognized tribes is obligated to provide health care for tribes. And it does this under the Indian Health Care Improvement Act, which makes the Indian Health Service responsible for providing primary and preventive health services to American Indian and Alaska natives. And so these numbers, I got this 3700 number from this National Indian Health Board policy agenda, I got the 10,000 number from the World Bank. So these are both numbers from 2018. And I am sure that the National Indian Health Board has access to more information than I do about spending on Indian health. But when I went to look at the IHS website, where they have a lot of budget information, it actually appeared to me that spending in 2018 per capita per patient was even lower than this number. But whatever the spending is, spending on Indian health is way lower than the US national average. And that shows there are a lot of reasons having to do with social determinants of health, that American Indians Alaska natives have worse health. But one aspect of this is that they have much worse access to health care and much lower quality health care, which has been the low quality of health care through IHS hospitals and health care centers has been extensively discussed in congressional testimony, and in Office of Inspector General Reports over three years, or four years, starting in 2016 and going to this 2019 report. So, you know, you know, extensive documentation of hospitals that are incredibly understaffed, have nonworking essential equipment and so forth and so on. And this report was about trying to overcome the challenges for improving health in Indian health service or for improving the quality of health care in Indian health service. And I just do want to say that it is possible after the recent American rescue plan that COVID relief bill that just passed that we will see some improvements because apparently this represents the single largest infusion of resources for health services to indigenous communities, whether on or off tribal lands. And it essentially doubled the IHS budget for this year and perhaps some of the problems with broken and missing and outdated equipment and infrastructure can be fixed with some of this money. So maybe we will see some improvements there. But my point with all of this is that how realistically are we going to implement genomic medicine in medically terribly underserved communities that have high burdens of preventable disease and low resource healthcare institutions, right? And where the high burdens of preventable disease are by and large not caused by, you know, different allele frequencies across different populations, but by very, very different living conditions. And I have to say that the title for this talk really was inspired by a conversation that I had about 17 years ago, when I was talking to some people from an indigenous community in the Southwest about participation in genetics research. And one gentleman looked at me and he said, Look, our band has been participating in diabetes research for nearly 30 years. And diabetes now is worse than it has ever been. And his point was, you know, this was a person who understood research as an altruistic enterprise in which we participate now in the hope that future met will get future medical improvements for the community, right? But his point was that they were not getting future medical improvements for the community. And that whatever knowledge had been generated about diabetes, through their participation, that knowledge was not being integrated back into any healthcare delivery system, or structural changes in their living environments that were improving diabetes on that reservation, right? And this has always stuck with me because I thought this was somebody who was actually, you know, not hostile to research, genuinely interested in research, but also, you know, no longer impressed by researchers promises that if you participate in research, this is going to result in health benefits flowing to you and your community. So, you know, part of my message here is that particularly in medically under resourced communities, we shouldn't overpromise the health benefits of inclusion in research, right? At best, that's a kind of form of magical thinking on our part as researchers, because a lot of things have to happen. Health benefits are contingent on many things that as research institutions, we don't have control over. And they don't just, they don't just happen, right? A lot of work has to go into making it possible for people to benefit from our genomic studies. We're doing translational research now. But I have to say, the vast, vast majority of that is being done in the most highly resourced academic medical centers, as opposed to the kinds of conditions that, in which many people of color receive healthcare. So I think it's really important that we don't go around selling the importance of inclusion by telling people, look, if you don't participate, you're not going to ultimately reap the benefits. Or at least by not overselling that and not overpromising, right? I mean, I think it is absolutely the case that, you know, being included in research creates, it creates the conditions that will make it possible, perhaps, for people in their communities to benefit in the future. But it doesn't mean that they will benefit in the future. And if they don't participate in research now, that doesn't mean that they can never participate in research and could never benefit. So I just think we shouldn't go around over promising with respect to health benefits or overselling with respect to health benefits. You know, at worst, it's disingenuous, if you will. And I also think, you know, we want to be relevant, but we shouldn't exaggerate the role of between group allele frequency variation in causing or having the potential to ameliorate health disparities. And I have seen recent journal articles where I am very sure that people were exaggerating when we put it that way. And I am so sure because actually, in one case, I saw a claim that I thought was so it seems so wildly improbable that I actually and it had no citation. So I, in the end, asked three different statisticians to help me try and analyze this and see if my inclination was correct. And in fact, a statistician, Jay Kaufman, shout out, first brought this to my attention. And nobody could make sense of this, this claim, which I think was just a wildly exaggeration, right? So shouldn't do that. Let me just say. Alright, so now let's get to, well, what are the reasons for inclusion? If those reasons don't have to do with, you know, sort of promises of medical benefit. And again, of course, we can talk about the potential for medical benefit and the fact that this creates conditions that would permit medical benefit to happen. But we just shouldn't oversell that. And so first of all, this is a reason that I'm not sure other people find as compelling as I do. But I think that research participation is an enactment of citizenship. Research is an important activity of social cooperation. It's a form of production, production of knowledge. It's something that biomedical research, the US government has invested tremendous amounts of money into it. It's very meaningful. And when people are not included, when they are actually excluded, not intentionally by any means, but because of the way we've structured our research enterprise, then that signals that, you know, their groups aren't important as citizens, that they're not worthy of respect in this sort of socially important endeavor, and that their contributions don't matter, even though I think, or they don't matter in the right way, let's put it that way. And so I think exclusion is a form of injustice in and of itself, regardless of downstream health impacts. And we need to be thinking of including diverse groups of people and people from all walks of life, even when there are no differences in medically relevant allele frequencies across those groups. Right. So other reasons for inclusion, even if people are not in position where they're likely to benefit soon from genomic medicine. So with the important caveat, if done properly, then I think more inclusive genomic research permits us to better understand the role of social determinants on things like gene expression and penetrance and so forth. But to do that, we have to actually measure those social determinants carefully. And in terms of being done properly, there are a lot of people who can help us to measure social determinants, perhaps better than we do. Secondly, if done properly, partnering with institutions that serve communities of color in healthcare can help to build institutional and individual capacity. And I think this can have real spillovers. And I know that people at NIH are very aware of this. And I think, you know, so I am not a person who believes that research institutions and researchers have strong obligations to provide ancillary healthcare. Research goal of research is to produce generalizable knowledge. NIH doesn't have authority to go around doing things like building new healthcare facilities for people. And it shouldn't it should be focused on doing research. And we have other institutions in our society which should be providing healthcare and are not always doing that. I also think that attempts to provide ancillary healthcare as part of research, they might be minimal kinds of Band-Aids on an otherwise terrible healthcare infrastructure. But they're only minimal kinds of Band-Aids, right? So if you have the roving, you know, the roving healthcare van that goes around providing some health services for people who are in your study, that's going to last during your study. It can create other kinds of inequalities within the community without really building any capacity or infrastructure for the long term. It can compromise people's, you know, voluntariness in terms of their participation in research. Although I don't, that's not my biggest concern. My biggest concern is that, you know, I don't want us to divert research funding into attempts at providing healthcare that is second best. But at the same time, I think we can, we can structure our studies in ways that are legitimately furthering research goals, and also building institutional and individual capacity. So in terms of building institutional capacity, I'm going to give one example, which is that if you know, the powerhouse genomic research institutions partner with, you know, institutions that serve primarily African Americans, or other often under-resourced institutions, to do genomic research, right? In those under-resourced institutions, I think it is a legitimate expense of the research that we would invest in developing the IT infrastructure and the computational capacity in those institutions. That's a completely legitimate research investment, but it's an investment that is likely to have spillover effects and long-term effects on improving healthcare, or at least it could, right? So that's just one example. Finally, I want to say, we should include implementation as part of genomic research in Bicapok communities. So, and this is just my article to remind me that in terms of partnering with communities, there are a lot of people out there who are writing very, who are doing very good scholarship and work, and can explain and help with how to partner properly. But I want to end with my example of implementation research in genomics. And this is an example, I have nothing to do with the study. I didn't know much about the study until relatively recently, but I was so happy to see that the study had been done, right? So this is a study that partners the Mayo and the Mayo ASU and a federally qualified health center called Mountain Park Health Center. So the Mayo and Mountain Park have had already prior to this study created a biorepository that is jointly covered by the Mayo Mountain Park and a community advisory board. And for this study, so this study was part of the phase three of the e-merge. And it's a study on returning genomic research results to participants. So they actually did this implementation in federally qualified health centers. So these are health centers that serve medically underserved communities. They provide comprehensive primary and preventive services. And almost all of their participants and patients are low income. So for this research, the participants were disproportionately experiencing poverty, unstable housing, problems obtaining transportation, fragmented health care interactions, food insecurity, and higher than average exposure to trauma and violence. So this is a real world kind of implementation in a setting where if we are actually going to provide benefit in BIPOC communities, we have to figure out how on earth are we going to implement genomic medicine in these kinds of settings? Or can we? Because if we can't, then, you know, despite having more diverse research, we are not going to be able to provide benefit. And so they had 500 Latinx participants, for 10 of them they had actionable genetic research results of various kinds. And I just want to say a little bit about some of the issues that they found. So one issue completely predictable that because most of the patients were uninsured, so eight of the 10 people who had actionable results were uninsured. So you can do the best quality, you know, recommend medical follow up recommendations. But your participants can't follow those recommendations. Or you can give behavioral recommendations. But if they live in settings where these social determinants of health really weigh against following those behavioral recommendations, it's not likely to happen. So the quote from the paper is that universal adherence to recommended guidelines was challenging. But I think these are things we have to know and figure out how to contend with, right? Another thing, right, that lack of family history. So if you are working in communities where there are lots of immigrants, or where people just haven't been getting health care, then you're not going to have family history. And in this study, it actually made it in a couple of cases really difficult to know what to recommend in terms of medical follow up. But I think, you know, this kind of implementation in communities of color is the kind of thing that we need to do as part of our genomic research. If we are going to make any sort of realistic attempt to actually provide medical benefit for people. So with that, I will thank you and turn it over to moderators for questions. So I will, I will start with the first question up. Can you speak a little bit more about health care disparities experienced by persons who are deaf or disabled and have very real barriers to any health care primary and research clinical trials? You mentioned disabilities, it was on one of your slides, but it liked a bit more. Yeah, I'm, you know, I don't I don't have a lot of statistics at my fingertips. So, you know, my the the person who asked the question probably has more knowledge than I do on the details. What I do know is that people with disabilities actually face a lot of discrimination within the health care system and face a lack of understanding lack of access for a variety of reasons. So I think this is I think the disparities research community has been slow to focus on disability as a kind of a way of grouping people that we should do in order to study disparities. But the body of literature there is growing. I don't have a lot of the statistics at my fingertips. But you know, I, I know, for instance, I mean, just an example of how this works, in terms of people who are hearing impaired at Wisconsin, we have translate a translation program that for people who go in for health care that it relies often on phone interpreters. And many of them are very good. And it works pretty well. But it's not going to work well for you if you're deaf. Paul, I think Dr. Bonham wanted to comment on this. Vince, are you available to comment? No, no, please go forward with other questions. Good. Okay. The next question is, can you talk a little bit about by biopic patients, whether or not they receive better or appropriate care when their health care providers are also biopic? So I think that's a complicated question. We have a lot of data, a number of studies showing that the communication are better when people are sort of race matched with their provider that more time is spent in conversation, that patients feel that they have better understanding of their health condition, and that there is perhaps better compliance with medical recommendations when people of color receive health care from people of color. So we do definitely have some data there. You know, we also have data though that health care systems, when they are, when there's, there are serious programs in place that hold practitioners accountable for following guideline recommended care, then actually, in many cases, the disparities within the health care system will disappear. Not all cases for sure. And I think those those programs can be very hard to implement. So I think we can improve. But it is a serious problem that you can't. We have a dearth of providers of color. Let us put it that way. So we can't depend on, you know, sort of race matching with our providers in order to get better health care. Okay, the next question deals with the normalization of inequities, which further expand inequities due to the algorithms based on historical data. And that's shocking. How can these algorithms be tested to prevent the expansion of these inequities? So first of all, they can be tested, right? And there are I am actively participating in conversations in the in the machine learning community, the machine learning for health care communities. And there are a lot of people who work in data science who are, you know, quite aware of these problems, actually teach a data science ethics class to data sciences. So people are aware of these problems. And the thing is, you, you have to do the studies to find out whether these problems are occurring. And the algorithm that Zayad's group had had studied had been implemented in several large health care systems without anybody testing it to see whether it was actually creating or perpetuating disparities. And it wasn't until, you know, he was able to get a hold of it and really test it. And also, to their credit, you know, he was able to get a hold of the underlying data on which the algorithm was trained and a lot of other data from health care systems to help him do the testing. So part of what we need is what we need better data sharing in order to do this kind of testing in a much more widespread way. I think we also need we also need some standards, even, you know, within the both within the medical community and within the data science community on what, you know, what benchmarks somebody has to meet and what kinds of testing we need to do with algorithms before we turn them loose into the health care system. Some algorithms are regulated as medical devices, but many, including the one that I had tested, are not. And so it's, we need some set of standards there. And there are people working on this, by the way. Okay, the next question, comment. Issue comes up with African Americans and Africans in the diaspora on the continent of Africa. Overall, most geneticists believe that African communities have the most diversity in genetic variations. And then it goes that unfortunately on account of trust and overcommercialization, there's issues with access to the data. How do you believe this would impact our overall knowledge of genomic variations and their overall population health, if there was greater participation? Yeah, so I think there's pretty much no doubt that there's a lot of genetic variation among people in Africa and people of the African diaspora. And more participation would certainly help us learn more about sort of just the variety of human genetic variation. And I think that kind of knowledge is very valuable. I also think that we shouldn't encourage people to participate and help generate that kind of knowledge on the basis of the claim that this is going to lead to better health care for them, or at least not strong claims, not overblown claims in that regard. So yeah, I mean, I think we would understand more about genetic variation if we had more participation from Africa. I think there are a lot of reasons why there is maybe not as much, and again, including infrastructure. So we need more African scientists doing research in Africa, and we need infrastructure to do that. I know NIH has invested in some of that infrastructure with H3 Africa. And there have been some good studies. I think there's a lot more to be learned there. We're getting near the four o'clock hour, and I know your itinerary has another meeting coming up. But I want to ask this final question. What recommendations would you make on translational research to improve outcomes and population most impacted by health disparities? Well, my first and biggest recommendation is that we have to start doing translational research in the kinds of health care settings where more people of color get their health care. And right now, we're not doing that at all. And I mean, I've been struck by the fact that we do so much translational research at, you know, the biggest genome research powerhouses. And it's understandable why that happened. But I mean, things that happen at those institutions where there are big genome centers and so forth, those things couldn't even happen at the University of Wisconsin, where we don't have a big human genome research center, much less at, you know, community health centers or, or, you know, other sites where many people receive their health care. So, you know, really, I was so glad to see that that male study because I've been thinking for a long time that we actually need translational research in communities of color really in our our health serving institutions. Okay, thank you very much. Outstanding presentation. Excellent, robust discussion on topics. And I'd like to again, thank you for this opportunity to provide this information to us and the participants. And would any one of my other panelists like to make a parting comment before we conclude. Dr. Minza, you're you're on mute. Well, for Dr. Minza, I'll just say thank you. We really appreciate your talk today and gave a lot of thought for everyone. And it will be available on genome.gov for others to look at and share. So again, thank you, Dr. Orseri. Excellent. Thank you. You are welcome. My pleasure. And thanks to everyone who joined us. Bye bye. Bye now. Bye.