Hi, everyone. While we're waiting for a few more people, I'm going to type in a question in the chat box. And if you could respond, that'd be great. So I just typed in a question to see how many of you may be thinking about doing a capstone. I will just wait a little bit for some more people to come in. For those of you who just joined in, welcome. I just typed in a question in the chat box you can respond to or you can respond to the poll. We're just trying to have a bit of a sense of how many of you may be interested in doing a capstone instead of a thesis. So far, oh, no. Oh, no. I'm going to type in another question, a real cake, and just see where you all are. And if you can respond to that, what time zone are you in? OK, so as you are typing in about whether you're thinking about capstone or what time zone you are in, we'll get started. And I will get started with the screen share in just a minute, and you can keep typing. And it's really good to see you all. Wow, we have quite a bit of Eastern Standard Time. Nice. Korea. PST, Pacific. Wow, we have a lot of Eastern Standard Time. UTC plus A. CST, OK. Great. Can you all see my slides? Yes, we can. Great. All right. Some are in plus three in Kuwait. OK. Since we don't have anybody who's doing a capstone so far, I won't feel too bad on focusing on thesis in this session a little bit more. But you can always change your mind, like we indicated last time. And the session is recorded, especially the front part. And then we'll also spend some time working together. So probably the first 30 minutes will be me talking only. But feel free to stop me or punching a question in the chat box if something is not clear to you right away. And then we'll spend at least 30 minutes, maybe a little more, work on a couple of steps of the research process. So you can go back and then work on your own and before you fill out the form next week by next week, right? So I just want to encourage you to fill out that form definitely by August 17th, because the faculty will meet a few days later to match your choices with our expertise areas. So you want to make sure you fill that form by August 17th as we indicated. OK. OK, so just want to recap last time some of you may not have attended or maybe this been a while. And so what's the thesis? What's a capstone? In this case, I'm going to blend them together. So you need to structure an effective argument and make a compelling and logical case, especially for thesis, right? And gathering and analyze evidence in a systematic way and it's propositional, right? It is we're not offering a crystal ball into the future, not focus on the future, especially for thesis. And then for capstone, you are applying knowledge and skills to address important planning issue or issues. Now, that's not to say thesis, you're not doing that. You're not, you're doing that too, but it's not required to you do that, right? So if you're doing a thesis that's fairly theoretical or fairly empirical in a sense of collecting data on past trends, that is acceptable, OK? So that's just the recap from what we talked about last time. So you notice all the things you need to do above on the slide is a process. That's the process that we're going to be talking about or sketch out today. And you will be doing in the next six months or so, right? Really, if you count the real time, it's more like six months instead of two semesters, right? It's a process that will lead to a product towards the end that you either have a thesis or a capstone report. And let me just borrow what Lance Freeman, our colleague, you know, your professor, Freeman, he used to say the process matters more than the product. And I would totally agree because you will see six months time goes by really fast. And some of you may have really ambitious topic and you realize you're running out of time. What do I do if my thesis is not perfect? If my capstone is like two steps away? Well, get it done. This whole six months is meant for you to get started of doing a research or doing a large project on your own. The product may be great, the product may be OK. As long as it's satisfactory, sometimes the product will have to be what it is because of time. And so the process, learning how to do the research process and to create a product that you are at least happy with or proud of is really all it matters. So you'll see this towards February and March next year that you will have some time constraints and you will have to let go some parts of your research. You'll have to let go some interviews that you've planned online and so on. And that is OK. So what is this process that you will have to go through? Even if you're doing a capstone, you are going through a very similar process. So the very first step of a research process is to choose a research problem. So I'm going to lay out this somewhat abstractly without giving an example. And with the next slide, we will have two examples. So bear with me if it seems all very abstract at this point. And I also hope you've read that short piece of reading that I distributed called the research process. So the first step, you really want to think about a research problem or frame it in a way as a research question. And then perhaps in your mind, think about a potential answer to that question or even potential answers multiple to that question. And one your potential answer is a descriptive statement such as the crime rate in the city of New York has increased during COVID. That is a descriptive statement is what we call a proposition. And so I can say, oh, maybe the research question in that case is how has crime rate changed during COVID? So that's a research question. But if I revise my research question to the following, I'm breaking my own words. I'm giving you an example. It's hard to talk abstractly. So what factors have influenced the changes in crime rates in New York City during COVID? That's also a research question. But it's a little bit different. That research question is a little more complex. It's not descriptive. It's not asking for the trend. It's asking for the trend plus what factors influence the trend. And in order for you to tentatively answer that question, you will have to have a hypothesis. It's a statement that includes at least two things. One is the crime rate. One is the factors. And basically, you are trying to look at the relationship between the two things. And that's called a hypothesis. And a hypothesis is a statement that predicts a relationship between two or more concepts. OK, so you're saying, how do I come up with a research question? I'm just interested in crime rates, or I'm just interested in COVID. Well, let's wait a little bit. And in a couple slides, we'll talk about how do you develop a research question. And we'll have exercises today to do that. Anyway, so that's the first step. You really have to come down to a research question, or at least a research problem, and think about the potential answers. The next step is, OK, if my question is, what is the trend of crime rate in during the COVID time in New York City? I will actually have to collect some data, right? So how do I collect the data? I'm going to go with the easy research question. I'm just going to go with the trend question. So you have to formulate how you collect the information. That step is called formulating the research design. What data are you going to need? Where are you going to get it? What kind of indicators you are going to use to measure crime rates? Crime rate is not a concrete concept. It could be different kinds of crimes and their rates. And so you have to think about how you measure that concept of crime. So that's research design. More details for next slides. Then you go out and actually gather the data from whatever source you find possible. Then you either code, or maybe the data are in really good shape. You don't need to code. You just analyze the data. And then you interpret the results and testing the hypothesis or just simply showing the data, the results, and write it all up. Now, this doesn't mean you only write up to the last stage. You really should be writing all along all these five different steps. Some scholars will say there are four steps and maybe six steps. But generally, these elements are in there. But it is not a linear process. So I had a student last year who wanted to look at what New York's, to what effect, New York's Safe to Travel program or road improvement projects have had on traffic for accident rates. She did lots of work in trying to look at different data. Some data are not available. Some data are available. So in the end, she actually had to come back and reformulate her research question. And so all I'm saying is these five steps are not linear. You don't go from one to five and never look back. You look back a lot. Hopefully not until your fifth step, then you're looking back to reformulate your research question. But sometimes you do. Data availability, the availability of the people you want to talk to, the availability of the communities you want to visit, all may affect your research question. And you may have to go back and reformulate your question. Then you might have to change your research design and collect some other kinds of data. It's all possible. So during the fall semester, every breakout group under each advisor, the job of the advisor is to work with you through this process to come up with a feasible and viable research plan or research proposal that includes at least the first and second step of this process. So you have a good research question. You know how to gather your data. And some people move fast. They have already gathered some data during the process. And that's all basically what we're looking for in the fall semester. And then you move on in the spring semester to actually collect the data through the analy analysis and the interpretation. By data here, I do not just mean numbers. I also mean text information. I also mean qualitative information. And I also mean visual information, all sorts of information that constitute data. So it's by no means just numbers. So let's take a look at this table. I hope you can see well enough. And this is basically a recap of the reading that you did that I distributed early on. And the whole point of this recap has to do with how you design your research is really important. It might very well affect your results, your interpretations, and any implications you want to draw from those results that you say that are relevant for planning. So design your research properly is really important. But it also means that there isn't just one way to design a research. Meaning there may be multiple answers to the same research question. And that is totally fine. And we also know that when we do research, we generally have the goal of saying, well, if we're only focusing on New York City, does this mean that it's representative of another city? You have to also think about that. So when I summarize this reading, it really speaks about the issue of rep, oh, I cannot pronounce the word, replicability. Meaning if you use this research design in one setting, you should be able to get a set of results that can be replicated in a similar setting somewhere else. And that really is a good test of how what we call robust your research design is. But for a lot of master's thesis, I think these research design questions can be a little bit less rigid, which means we want you to go through the process at least once. But if you are interested in doing doctoral studies or become a researcher, research design would require a lot more your attention. And you might see that some research design needs more real work. So let's just look at this particular set of approaches in the same talk. So the research question is essentially, what is the social effect of density on humans? You know, as planners, we worry about density a lot. And so as you can see, there are two teams, at least summarized in the reading. One team is behavioral scientists, meaning psychologists mostly. The other team are primarily planners and urban scholars who are community-based or even city-based researchers. You can see the first team essentially has a hypothesis that it is the same exactly as the second team. Density causes harmful effects on humans. Now you're saying what's harmful, right? And that's subject to our interpretation. You could also reframe the hypothesis in a different way. You could say, as density goes up, the negative impact on humans goes up. That's also a hypothesis. That's essentially the same as this one. So there are also different ways of formulating your hypothesis. But I want to draw your attention to how they measured the two concepts in the hypothesis. One is density. One is social effect, right? So you notice, for both teams, they use exactly the same variables, social effect and density. But move on to the second box, right? Vertically, you notice the behavioral scientists use individuals as the unit of analysis. So a unit of analysis is essentially the subject that you are studying for your research. So if you're studying people, so each individual is a unit of analysis. If you're studying a neighborhood, the neighborhood is a unit of analysis. But you say, can I study people within the neighborhood? Yes, you can. But this is about unit of analysis that corresponds to your research. So we'll talk about the complexity when we get there. And then the population is essentially the totality of all your units. So the first team used 121 students. The second team actually used the unit of analysis very differently. It's a neighborhood, it's a community area, and then 75 areas in Chicago. Totally cool, right? Totally valid. And then data sources are also very different across two teams. The first one is primary. Meaning whenever we say primary data, at least in research design, we mean that you as the researcher went out and collected the data on your own. You talk to people, you interview people, you email the people, or you put the people in an experimental setting. In this case, it was experimental. They put students in a room and then they surveyed them. The second team used secondary sources. That means data that are already collected by other people. So anytime you use census data, Pluto data, Pluto data are New York City data, right? Or you used statistical yearbooks in different countries. Those are data that are already collected by other people. You're simply using them to do additional analysis. That's called secondary data in general. And so here focused on measure for density is totally different. For the first team is how many people are in one room. And second team, because they focus on neighborhoods, they focus on residents per acre. That's really kind of commonly, right? We measure density by how many people in that area. And then measurement for negative effects. The first team used attitude towards a hypothetical stranger. That say, if we add another person to the 121 student class, do you feel annoyed? Do you feel angry? Or do you feel just fine? That's basically the question that the researchers ask the students. So the measure is your attitude towards yet another person into this already large room. And then for the community researchers, they use delinquency rate, essentially crime rates in different ways.  And then the data collection, as I already alluded to for the first teams to interviews of number of students who were put in that room and being, you know, or having experienced the whole process. And then the second team used fact book, essentially secondary data sources, right? Now you read the chapter, so I'm not going to ask you, but you've read the chapter. Essentially the first team find, basically density has negative effects. We've said, you put more people in the room, people are gonna start feeling more negative towards the potential new strangers. So, and then researchers tried it on different classes and in different settings, they got the same results. The second team, however, in their original research design, did not find a definitive relationship between density and negative social effects, meaning you have community areas that have higher population density per acre, and those with lower, and the rates of delinquency and admission to mental hospitals vary all over the place. There's no relationship. Well, you say, okay, well, I just reject the hypothesis, right? Yes, you can do that. If you're doing the research and doing a thesis, that's your result, you have to report that. Maybe it's a research or design problem, but maybe it's just, there is no relationship between the two concepts. But the researchers obviously are aware of other research that's going on that indicates a negative effect. So they went about revising their research design and you also read about that, you read that chapter, the way that they, one way they modify the research is to change the measure for density. So they changed it to measuring density by how many people are in the individual housing unit. So that's a big change, right? In terms of measurement, and actually a little bit closer to the first team. And so basically, and then they also say, how many residents are in each bedroom or room, that's even the more refined density measure, meaning we find more micro in scale. So more closer to individuals rather than aggregated in a neighborhood. You know what they find? They find similar results as the first team. That is the more people who are living in the one room, there will be more issues related to delinquency rates and admission to mental hospital. There you go, you say, oh my God, really is very different, the results. So that example, oh, actually that's like two examples, really kind of give you a good sense, I hope you should read that chapter if you haven't, that research can be done in many different ways through different designs, how you choose the measures, how you choose the data sources and are all very, very important. Of course, how you collect data, there are lots and lots of different issues. And I'm just going through the basic skeleton of things today. And as you walk through the fall semester with your advisor in the breakout group, you will work on your own, right? So the first thing today that we wanna do a little exercise in about five minutes is that I want you to work with each other in small groups to look at choosing a research problem or a research question. Then you say, okay, all I know is I'm interested in looking at transportation planning. How do I really narrow it down? It's not so easy, but I wanna encourage you to start thinking about what aspects of transportation planning you might be interested. So that will be our exercise, right? So in the first step of research design process, you want to develop this overarching umbrella question that addresses your topic. So in the example we used is the effect of density, a social effect of density on humans. And then you want, so once, so it will take you quite, sometimes it may take you a couple of weeks to come down to a research question, and that's okay, all right? And, but once you have that, you want to identify the key terms in that research question, or you can go the other way around. Maybe you identify the key terms that help you formulate research question. That's okay too. So in this case, we notice density in the effect of humans, right? Humans can help you understand what your unit of analysis might be. But you notice from the comparison of the two projects, it could be individual, it could be residents in the total neighborhood. So humans can be measured in different ways. Density and effect, all can, right? We already know that. So generally a research question is a question that you do not know the answer to, but it's kind of interesting.  Yeah, you can potentially answer, but that's just a guessing. And your final research could either prove that potential answer or disapprove. We call nullify, right? You've done that in planning methods. It's called either prove the hypothesis or nullify the hypothesis or disapprove. So a good research question is important in the real world. So especially for planners, we're not looking at just theoretical questions and most of our research is applied research. And you also want to think about your research question as one that can be investigated systematically and answered empirically, okay? So what does that mean answered empirically? Meaning you can have some evidence to show whether the question should be answered the one way or another. So we're not doing, some of you may have had philosophy background or may have had even philosophy major taken philosophy courses. Sometimes we use logic a lot in philosophy. And it's about, you know, coming up with the logical steps to design certain things or to design a thinking process. Generally in social sciences, that's not a approach unless you're doing a theoretical thesis. And if you do, if you do have that, let us know and we'll also help you with that. A theoretical thesis requires a lot more argumentation, requires a lot more logics and that's usually not common. I want to reiterate that our thesis requirement as well as have some requirement requires all of you to not to base your thesis or capstone entirely on literature review, meaning entirely on other people's work. We require you to have new empirical or other kinds of evidence or new analytical framework or your own analysis, right? So just purely literature review kinds of thesis is not acceptable in the program. Okay, so next step is to practice asking questions about a topic as a pre-research. So Laila, our moderator is going to... Oh, sorry, I forgot to introduce Laila. Laila is the programming director at GSEP and she helped us with the first session and she's helping us with the second session and she'll organize these breakout rooms for a group of three of you to get into your little exercise. So rise and urban or planning problem you really care for and then write one or two questions you have about the problem. The way we're gonna do it is I have a set of Google Slides, which I will share with you just now. And then I know there are three of you may all have thought about your research question. You all want to share the three, that's great too. Just read out more than one slide. That's totally cool. Or you're still thinking about it. Maybe one of you would be willing to write down your research question. All right, so I'm going to stop sharing. We are going to go to this Google slide that I will share the link with you in just a minute in the chat.


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 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.


Everybody for us here with Donnie. We're up in 2023 inception baseball hobby 16 box case number four pick your team style One sec still alphabetizing the names here Which way not Shopify guys. Oh, there goes. There goes as fast today Alec v. Tigers Andreas with the J's Anthony P Giants Ben B rays Brent C with the Royals Brian M Orioles Christopher C. White Sox Dave S Dodgers Excuse me David why Mets Doug are Guardians Edward P Phillies Jordan and Rockies Joshua B Padres Justin K pirates Kurt S Yankees Matthew B Reds Michael P Nationals Michael Y Angels Nick B twins Peter and Astros Ryan S Cardinals Ryan S Different Ryan S's Ryan SU Cardinals Ryan SK with the Cubs Ryan B Red Sox Ryan D Brewers Steven H Diamondback Steven G Rangers Tim T Mariners Vaughn with Miami Wenchu with Oakland and Wilson with Atlanta as always best of luck. Thank you all for joining Like we're sold out daytime on inception. We are so the next one is sold out We have four one-on-one cases with break spots available everybody Here is the random card style case number five the first random card style one You are guaranteed something in that break There's one auto Per box two cards per box the other is either a base base parallel or insert the inserts will be downtown in timeless moments The inserts are basically the chase in the product That in Thomas moments in big rookie autos essentially so should be super fun The upside the upside at that entry point is is pretty massive It's high risk high reward though. You'd hit a nice palo a nice Chet Two monster two monster rookies from that class You'd hit a LeBron James downtown Had a luke a downtown Palo downtown. Oh my god hit the palo sneaker spotlight auto the other night Yeah, thanks that he went to the Yankees Yeah, I'm not like super upset that he's gone. He's a good player and like we could use him But I feel like there's they're gonna pivot to something else or Hope Durand stays healthy or something. So Yeah, yeah, that might make sense Glad costus is gonna play full-time first base though. That's awesome. That's that's that's what I wanted to hear Green Kershaw It's a 150 rookie Coloss White Sox Volpe base Yanks first auto of case number four is a Brian Dealer cruise from Miami to 99 Bonnie's guy Yeah, hey, sometimes it happens. Sometimes you buy part of playing like Are you hit a card and player in a break? You're like, all right, let me get some more. I like I like what I see. I think Miami was like the first team I got like invested in on baseball And then I started going like time back. So it was Reyna stone Garrett Prato rookie green for Kansas City Yeah, I would imagine that too bomb like he's solid like he's okay, but they need to upgrade the defense Green Machado Spencer steer rookie for the Reds Trey Turner And rookie auto Kyle Stowers at 299 for Baltimore Hey, now not a book, but that thing is dummy thick. It's like four four five or six C's in that thick All right, get ready y'all I'm just gonna move that to the side. We'll get that later. I just make everybody sweat green Yuri Perez rookie Miami Red blue day rookie for the marlins of 75 Matt Mervis Got soto. Oh man Mets Mets rookie Mets rookie Oh my god red baby four of five team logo rookie auto. Let's go. Oh my goodness The inception autograph jumbo hat patch right there That is clean, dude. Whoo. That's cool. New York Mets. You kidding me? David why? That's pretty incredible That is freaking awesome I would try to get this with like a filler and like a 360 point out how player warren hat relic as well.  They're all the five. There's new era. There's team logo and there's mlb logo They're all the five For the for the hat patch autos anyway There are team logo auto books and stuff like that one-of-ones or they're just different They're only to five no high or no lower on the the fives That is freaking crazy What is this now? What is that now? What is going on? You got green vato You've heard that on there, right? Oh green buchette.  I mean he has had a sky rocket to the top of like draft rankings everywhere He has gone from like solid prospect to like Getting close to like marcello meyer status like that high that hype.  So it's all about getting the young guys up and seeing what we got What do we got here? Padres? Six of six Buttnorellic autograph Fernando tateesh jr Joshua there he is. There's your tateesh beautiful tateesh On the inception auto patch button parallel Back-to-back boxes with monsters. That's what you love to see Send me with that card How do I get this on a car? Good thing these cartons are adjustable like a glove Frank thomas. Is this a new genics commercial? That's that's a good point Is this a garden hose commercial? Well, it doesn't have um Oh, there he is dug fluty he showed up I called it. Yeah, we're like, oh, where is he? I guess it's not. Oh, there's fluty got rookie green harris for the braves We found him It's 150 langoliers rookie for the a's like a weird wall though and the Edward julian commercial All right Morale there there's the 75 nico horn or autograph for the cubs There we go cubbies. All right guys. So inception. It's all that daytime. Let's get one in one moving up here First two cases are sold for tiered. We got two more tiered cases and two random card style cases Extra card in this one too Yeah, there it is. What's going on? Good case We got procto Chateau green outman rookie for the Dodgers cards for outman Gunner Henderson rookie pink out of 99 baltimore. I expected more for you for us And patrick bailey rookie for the Giants Yeah, that's available. I'll get it in second here in anerson auto Atlanta 25 little braves And marino rookie greens we had an extra Heralel now when I think they were rookie green. I thought that was an extra hit. It's an extra card d-backs marino Uh, they just basically made him a rookie so like Adley rutchman is also a rookie in this set too Sometimes if a rookie debuts a little later Tops will insert them into the following year so that there's more products of them essentially That happens a lot So like in baseball cards next year in 2024 all year. L. E. Dela Cruz will be a rookie Uh, it's there. It's their traditional rookies. There are no like It is the rookie year cards of them basically green yordle now varez reds It goes from like bowman like a like prospect cards to rookie cards and that yeah, so harris's first rookie card was in Early 2023 essentially green alvarez pink wit to 99 Pink wit me. Oh Miguel amaya Yeah, of course. Yeah happens. It's it's pretty common It's like the only sport that they really do that in because with football and basketball It's like if they're drafted they just get rookie cards So like even though chet holgren is a rookie this year in the mba because they didn't play last year He will not have rookie cards in 23-24 But baseball they do it a little differently because it's not like a Guys get called up all over the place. So and if it's like a big name Like a big big name that people are going to really look out for Tops will kind of like if they if they're not for the start of the season to get rookie cards They'll kind of move them to the next year so that they can have a full release Like calendar of that player as a rookie rookie auto out of 50 for the Giants david vr He comes to that door donnie do not let him near me. I'm just kidding I'll be like this. Look at this. Look at this Look at look at look at look We're like, oh, yeah, we pulled 17 logo patch autos in one box green bonalea rookie guardians green carol rookie d-backs Outman angels what we got to 99 Shohei otani patch auto Oh man, we got him y'all big show with the angels here Michael y Showtime, baby Showtime Player worn patch auto 38 of 99 Holy cow really good case here Love it, man. Okay. So this product is hands down better than it has been in the past hands down It's better than it has been in the past Every time I break during the day. I look over to the tv and michael russo is yelling at us Anyone else have that issue?  He should be a full-time mlb player Full-time starter usually Yeah, none of those teams are gonna be good von. I'm not worried I'm worried about the red sox developing the talent that they have and putting winning pieces around Still more worried about if the Orioles actually decide to spend money In their window Green urie pares rookie miami Raleigh green tigers rookie to 150 netto occasionally, yeah, kyle knows And stone garret rookie autograph for the diamond backs here to 75 I'm a back stone garret To be fair, he's right about this. I'm so tired of the vision winners being like 500 and getting a home playoff game Like change the structure, dude What are we fine? We can change everything add another team do all this stuff and they still have The bucks last year hosting a playoff game Miguel amaya rookie green for the cubs green michael harris braves rookie Hunter brown. We got two hits I think rookie auto quass for the for the royals here Oh, what is this? Alec thomas patch auto d backs to 149 Nice card zona Yeah, nba has it right the nhl also needs to get rid of their current format. Okay, here's a question Can the falcons still make the playoffs winning their division or are you mad about that or like it just needs to be Like what they did what they made the playoffs, but then I think that'd be fine I'd be fine with that if you would do division like like we got to balance it out a bit because then like we don't In an ideal world, they'd be able to play more games. You have a bigger sample size, but you can't do that in the nfl So I think they should get a playoff game, but then then them hosting is crazy Langleers capel urie perez rookie Hoskins They have a pretty weird old pirates rookie autograph out of 299 They should just do what the nca football does and they just get to choose who they like Like uh, well the giants have three wins, but they have a bigger market. We're gonna put them in 299 rookie for the pirates on the auto How is the NBA do it? by conference and then the top eight in each conference And then there's well right now they have a play in but when you get to the playoffs itself There's eight seeds in each conference the eight C plays the ones you the seven C plays a two seed So just the top seed the top that yeah the top the top seed has has home court. Why don't we just do that? We're just go by conference on division Yeah, that's how it should be get rid of divisions is the way to do it Then you want to get stuck with like the nfc south like Or like when the nfc west was bad like the seahawks made the playoffs at like seven and nine that one year Yeah, they won their game. That's not the point green vato. They were also at home Which might have helped them winning rookie purple to 150 steer for the reds Seattle's a hard place to play Exactly the Saints are at home that game probably goes kind of different Maybe not we'll never know we surrised and auto to ten Padres mani machado Josh we're killing it here with the Padres like I said Just had to get a couple Machado Padres auto that one ten will mag that one Yeah, yeah for sure Scott Walker. I like the NBA in season tournament. I am a fan of it It seems like the players care too like they are the games have been really good It's gonna be weird though It's gonna be weird having like January and February kind of boring after the in season tournament I was him kidding, but yeah Nothing wrong with the nfc south. Yeah, except they all stink Kyle got green bueller buchette the 50 for the jays Oh 100 von. It's like the pitch clock thing Gray rod rookie for baltimore. It's it's it's exactly that Corbin carol silver signings What a hit for the diamond backs now Stephen H Yep 82 of 99 love the silver very clean auto for the silver ink here Congrats, Stephen again has had a few very nice diamond backs hits today Oh nice drive You got the expose randid johnson donagrainus. That's pretty cool. Are those on card? Guys random styles of this available tonight with sam We'll have personals 6 p.m. Eastern with kyle There's the lane right now with pyts as well green rookie of gray rod for baltimore green wit and an amaya And we do not stop.  Oh, there's a printing plate Adley's cut long going in top loader So I about that o's There's a printing play. I guess we'll save that one for towards the end here I think it's a base printing plate Michael harris julian All right, let's see what we got here Got rookie auto live on soto angels of 299 green buchette and Printing plate is for the jays. It's vlady. That's pretty sweet one-of-one printing plate of vlady. It looks like the yellow plate That's what's up. Take we take one-of-ones for sure nice one toronto Yeah Draft this kind of is relatively simple to figure out the chase is usually what I do at first at first As you look at the drafts like the first and second rounds kind of and you look at like batters And then you see if they're in draft or if they're going to be in 2024 bowman's like dylan cruz should be in 2024 bowman they usually roll over like the second bat taken and then some other Some other first round bat or otherwise first round players overall um, yeah langford And uh clark are your big chases in that tom brady is a big chase But it's gonna be a little tougher to hit a tom brady auto or like a base So this is your chases. What a case. What a case. We got five up next guys Mama mia All right, so we had that printing plate of vlad for the jays Other numbered cards. We had a steer rookie rally green Langliers colos yori pares bobby witt goner henderson bow buchette jj bladay autos we had patch auto alec thomas That's sweet for the dbecks levonso toluber piguero jose quass kyle stowers stone garret niko horner brindale our cruise david vr von grissom and ian anderson Nice to 25 for the braves are in both of those Uh, we had that adly rutchman rookie inception auto patch to 99 Yeah, skeens as well. Sorry. I miss skeens. Skeens is probably the biggest picture chase we've seen in recent memory Um, a chateau auto to 10 padres patch auto otani out of out of 99 city connect number patch there Silver signings of carol to 99 dbecks. This is a crazy case tauti's button relic auto six of six padres and a rookie A team logo hat patch auto number to five a bread baby for the mets That does number four number five coming up very shortly