Welcome, and thank you for joining me at this year's Diversity Workshop. Today I will be speaking about the effects of social context, culture, and minority status in health. A special thanks to Dr. Hashemi and Dr. Gutierrez for organizing and co-chairing this workshop and series of presentations. A bit about myself. Like many young professionals today in the healthcare fields, my career was shaped by the arrival of a global pandemic, the HIV pandemic. Although trained as a clinical psychologist, I became a behavioral scientist, a community intervention specialist, and a health policy influencer. I come to you with a long history of seeking a better understanding of how culture and social context interact with our health status and with our ability to prevent acquiring diseases. My hope is that what is shared with you today can increase your own participation in ensuring that our research methodologies, our clinical assessments, and our healthcare systems are void of structural inequalities and instead provide avenues for reducing health disparities and achieving health equity to improve the health and well-being for all. I have no financial relationship with a commercial interest to disclose. Well, what a year we've been having. A confluence of natural disasters, a global pandemic, social unrest in response to racial injustices, and living in quarantine, struggling to live our lives in unnatural ways. As scientists, health providers, and health promoters, we are confronted with a time where science is questioned, where slowing the pandemic eludes us, where public health officials are villainized, and where social context and cultural beliefs are driving the current health status of our nation. Yet this moment has also been the clearest example of how social context and culture deeply influence how disparities emerge and highlight how social inequities fuel those health inequities. The silver lining is the fact that racial and ethnic disparities in the acquisition and treatment of COVID-19 have been identified early and in some regions have created equity policies to address them. I'll say more about that later. Why am I discussing the COVID-19 pandemic at the liver meeting? Because there are many lessons applicable to all areas of health, and I hope it is sparking a new insight on how we study and intervene to reduce health disparities, reduce health inequities, and strive to achieve health literacy and health equity among all people we serve and hope to help. Let me start by making sure we are understanding all the terms and definitions. The term health equity has evolved over the years. In 2006, when I established the Health Equity Institute, it was a rare term, and many people said to me, oh, you mean health disparities, right? You're going to study those? And my response politely was no. We know about health disparities. We've already learned about those, but health equity is about solutions. We're going to focus on finding solutions to disparities. Recently, I had the opportunity to serve on the Secretary of Health's Advisory Committee for Healthy People 2030, where we set national health goals for the nation for the next decade. Health equity is at the core, and we agreed with the general definition as equal opportunities for all people to be healthy and to seek the highest level of health and well-being possible. However, the Robert Wood Johnson Foundation expanded this definition, stating health equity means everyone has a fair and just opportunity, not just opportunity, but a fair and just opportunity to be as healthy as possible, which they explain requires removing obstacles to health, such as poverty, discrimination, and their consequences, including powerlessness and lack of access to good jobs with fair pay, quality education and housing, safe environments, and health care. The emphasis on fair and just is also a way to emphasize that we are not talking about equality, but about equity. I'm sure you've seen many different versions of this comparison. The important distinction is that those with worse health and fewer resources need more efforts expended to improve their health. In this picture, giving everyone bicycles is giving them equal opportunity to cycle, but ignores the fact that they may have very different needs to be able to equitably cycle. And so to distinguish other related terms, you see here that disparities simply means we see differences in health or health outcomes among specific groups of people, but with no specific cause. Health inequalities are ways in which groups may differ in health status by vulture of age or social strata or other things that are simply factual, whereas health inequities are differences in health and well-being and outcomes that are avoidable, unfair, and unjust and affected specifically by social, economic, and environmental conditions. This is a simplistic assertion, and forgive me to those who might be plant or animal experts, but isn't it interesting that when we think of health of our plants or our pets at home, we often start with environmental conditions. In human disease, we go directly to the individual and rarely explore the social and environmental conditions and context. In my own experience as a person diagnosed with diffuse scleroderma, a rare autoimmune disease with unknown etiology, I was struck by the lack of interest about what was happening in my life when the symptoms began. I thought surely if they don't know the etiology, they would want to know about things beyond my blood tests and organ functions, but no, I was never asked. Now, it was reminiscent of what we went through with the HIV pandemic, a similar phenomena, just find the cure, and of course, we wanted that because people were dying and we wanted to find the cure, but it meant dealing with questions of preventions or the context of risk would be delayed, and ultimately, it slowed our ability to develop more effective interventions, and I suggest we not continue to do this over and over again. Let's consider this larger framework for health equity. On the right-hand side, we see what is the traditional medical model. We start really by looking at individual risk behaviors, risk factors as contributors to disease, and of course, these are influenced by other things such as the role of genetics or the individual's health knowledge and their access to healthcare, but we do believe in the medical model is that this is where we're starting, right? We look at these behaviors that may be causing disease and injury and ultimately influence life expectancy and mortality, and this is really what we have typically discussed as factors in understanding health disparities. If we expand to include the socio-ecological factors happening upstream, if you will, that includes environmental conditions, institutional structures that impact opportunities, and experiences of social oppression based on one's demographics, then we see where the health inequities begin to shape our health vulnerability before even any behavior or risk factor might come into play, or it may even cause such behaviors or risk factors that we have in the traditional model seen as an individual choice or an individual weakness. So let's go back to the COVID story. This virus, mainly transmitted through aerosol drops of saliva exiting our mouths when we talk, sing, cough, and sneeze, could not really blame individuals for bad behaviors. It begins as a less stigmatizing disease than HIV, and we work hard to understand how it functions. And within the first few months, we also see racial ethnic disparities. Blacks, Latins, American Indians were all overrepresented. It becomes evident that these health disparities are reflecting the role of social determinants of health and health inequities. This figure from the Healthy People 2030 website, and I advise you to all go and see what we've done in planning the next decade of health goals, provides a quick graphic of the different social determinants of health that play a vital role in health and well-being. These include access to quality education and healthcare, economic stability, the neighborhood and broader built environment, and the social and community context. Let's ask what might be behind some of these health disparities. Looking at an analysis by the Kaiser Family Foundation, they studied patients in the Epic Health Record System. This represented 53 health systems, 300 hospitals across 21 states. The analysis was restricted to active patients with known race and ethnicity, which was about 83% of the total population, but it resulted in the analysis of roughly 50 million active patients that were included. So how might we explain the finding that people of color are at significantly increased risk for infection from COVID-19 compared to their white counterparts? Well, as you've probably heard and has been discussed lately, more than likely, many people of color are working in low income and are essential jobs that cannot be done from home. And this means they have more potential to become exposed. Many are living in larger households where a lot of people are under one roof, in densely populated areas, major cities, and again, viral availability may be much higher. Many have to utilize public or shared modes of transportation if you do not own a car, and again, makes you much more vulnerable to exposure. So in this case, we see that determinants of health, such as economic stability, having to work no matter what, the built environment, the type of housing that people may have to have, or the cities in which they need to live to make a living, and also other social conditions of not being able to find a way to get to their work other than putting themselves at risk, are all at play here for increasing their risk for infection compared to others. I provide a link here so that you can see the study in its entirety, although I will still going over a few more points from the study in particular. And what about testing rates, positivity rates, and the need for a higher level of care at time of a positive test? Well, here we see and can say there are potentially barriers to testing that contribute to these delays, and therefore, people come in in a more serious condition compared to white patients. Their wait times or travel times to access testing might be a factor, and more limited access in general to testing within certain neighborhoods. They may also be more likely to be uninsured, it costs quite a bit to get tested on your own, and other barriers to health care, which again, may contribute to delays in obtaining testing or treatment. And of course, this is a study of people within the health care system, so you can only imagine that it's probably exponentially worse among individuals who are not connected to health systems and face other barriers to testing and care. So again, here in the social determinants of health model, we see access to health care, economic stability, and social conditions, again, at play. The long-term impact of social inequities on health cannot be understated. If getting from point A to point B requires overcoming enormous barriers that are created by unfair and unjust inequities, how do we change that? Again, in this picture, we see that if you have a lot of money and privilege and connections, then you're probably on the people-mover, on the inside track of going from point A to point B. And the farther left you move, in terms of having less resources, perhaps poorer education, you're experiencing the unconscious bias of others, you're asked to compete using standardized tests that were not really matched to the education that you've received. Things like the old-boy networks that are not available, underemployment, racial profiling. Again, if you see that, and the person on the farthest left, a woman of color, for us to get from point A to point B, we actually have a lot more obstacles that we have to overcome that have come before us. And that's really one way to understand why we have to address these inequities in order to allow for equal opportunity that is fair and just. So earlier, I said I would talk about an example of an equity policy that requires actions to help the most in need. You may have heard that in California, the larger counties will not be permitted to reopen their economies further unless they reduce coronavirus infections in the hardest-hit places where the poor, Black, Latino, Pacific Islanders live. Under a new state requirement for reopening during the pandemic, counties with more than 160,000 residents must bring infections down in these specific places and invest heavily there in testing, contact tracing, outreach, and providing means for infected people to isolate. So again, we're not using an average here like they are in many other places. No, what we really need is this kind of equitable policy that won't let our economies reopen until we have helped the most in need in our geographic areas. That is an example of the type of ways in which we need to attend and create an equitable situation. Absent from the health equity framework is the additional role of culture on health. This is a standalone consideration, and unfortunately, it would take a lot more time and perhaps even a whole course to do it justice. But let me share a few considerations in thinking about contributions to health and health outcomes. The beliefs, the cultural beliefs that people hold are critically important. What are their attitudes about health, about death, about destiny? Are there traditional remedies that they are using but perhaps haven't been asked about that might be influencing what you're seeing in their treatment outcomes? The food they eat, do you understand what that is and, again, how some foods may be interacting or causing particular issues? Who's making the decision about health and health care? Are there gender norms involved from a cultural perspective? And, of course, language for those who aren't native English speakers. Are they truly understanding? And are you, again, like we talked before in general, making sure that your patients are understanding what it is you're trying to convey? Many people flee their countries because of cultural and religious discrimination. Let's make sure that they're not afraid that that will happen in their interactions with the health care systems. It's important to try to understand and to encourage the sharing of cultural and religious practices so that, again, you can put this context into understanding the health and health outcomes of the people you're serving. And acculturation, at what level is the cultural influence happening? As we get more and more distant from our cultures of origin, some of that influence gets less and less. So understanding, are you first, third, fifth generation? Where do you fall? In this picture of my family, there are four generations here, but the youngest represent the fifth generation of our family. And everyone in this picture was born in the United States. But we do have the last name Gomez. And again, we are assumed often to be immigrants when, for the most part, at least the younger people here rarely even identify with their cultural origin. So keep that in mind. So what role can you play? What does this mean for you and what you do. Let's look at this basic explanation of the causes and risk factors in liver disease. This is what the general public might learn. You might notice that this stays pretty true to the medical model that we discussed earlier. Almost all risk factors, and I put them in red, are about individual behaviors. If I was worried, I might have liver disease, I can immediately see that I will likely be blamed for it. What about my social and environmental conditions? As a Latina, would you have a particular bias about why I might have this disease? Would it be important to know something about these other realities in my life? Would there be any reason for you to want to know if I have access to healthy foods, if my drinking practices or those of my family might be because the only stores in the neighborhood are liquor stores, or that I've had no quality education, or that I live in neighborhoods that have had historical discriminatory practices? These are all things to be considered. I know you will be hearing a lot more about research methodologies from other speakers in the workshop. But if you are in the lab or developing a research study to look at racial and ethnic disparities, here are some aspects I've learned in conducting studies with many different groups and communities, and just wanted to cover them briefly. First of all, is there equal access into your study? Do you want to have an over-representation of people of color? There are things that you can do that really would increase trust and encourage people to participate. One thing to look at is your study staff. Do they represent the different communities that you're trying to engage? Remember that the first contact a person might have could be a receptionist. Are they trained properly to be non-judgmental, to be welcoming? That will make a huge difference in developing that trust from that first encounter. What about recruitment strategies? Are you engaging community advisory boards? Are you doing community preparedness if you're going to go out into a particular community? Are you asking those local leaders the best way to recruit? Again, this builds trust in places that really deserve that trust to be built, because of ways in which we've treated many communities in the past. What about the protocols? Are you making people come in for three hours? Is it necessary, really? Are there places where you could cut the time? Are you offering childcare? Are you offering food? Are you offering transportation? All of these things represent that you are thinking about the whole person, that you're taking all things under consideration. What about the data collection? Are you collecting relevant data that would help you understand disparities beyond the body, the biological, the physiological? Again, learning more about your patients will really help better inform us about the causes of disparities and where our interventions might be most effective. Now, on the clinical side, I'm sure many of you already do this well, but you would be surprised at how hard it is to take that moment to learn more about your patient, to really assess if they are understanding your jargon, and to demonstrate respect and cultural humility. When I see a new provider, the assumption sometimes made is that, one, I don't speak English, two, that I'm not very educated, and three, I will probably just do as I'm told, no explanations needed. Well, imagine their surprise when they realize that they are dealing with a somewhat educated Harvard grad, et cetera, et cetera. It changes the tone of the interaction, and that makes me sad. That should not be the case. Subtleties matter, and they affect the trust in the relationship. Think about things. Do you know the social context of your patient? Do you know the cultural influences of your patient? Demonstrating cultural humility really means that you don't assume, oh, I was trained on how to treat Latinos, so I know. No, that's not cultural humility. Cultural humility is knowing that you have plenty to learn from your patient, and you help them educate you so that you're sure that they are understanding what you're trying to explain. And have you assessed their health literacy? Again, even as a highly educated person, sometimes when I'm in the doctor's office, things kind of just pass me by because I'm worried or anxious. So make sure that folks are walking out knowing what you want them to know. And make sure, are you bringing any implicit bias into the room? It's very, very easy to have that bias, so don't feel badly about it, but awareness of it will just make that interaction so much better. Being a minority status person has many ripple effects in our health-seeking behaviors, and you're part of the fabric of social cohesion of your patients and research participants. Our country desperately needs more social cohesion if we are going to achieve health equity for all. And again, social cohesion, a group or population that works towards the well-being of all of its members and fights exclusion and marginalization, creating that sense of belonging and promoting trust. It's really what we need to continue to improve the health of our patients. Finally, I'd like to give you just a few takeaways to consider from today. Apply an equity lens to assess how patients' social, built, and natural environments influence their health in both research and practice. Attend to groups that have experienced major obstacles to health associated with socioeconomic disadvantages and historical and contemporary injustices. Eliminate any prejudice and discrimination. This is fueled constantly around us. And think about how you're making sure to eliminate this in your research protocols, in your clinical practice. And share what you find. For some reason, researchers often only share to their peers in medical journals. But don't you think your patients or your participants, your research subjects, deserve to learn? Even if you think they won't understand. We made it a standard practice to create a community research outcome two-page form that we sent to all of our research participants. And you'd be surprised at how helpful and how grateful people were for that information. And you're contributing to their health literacy. So consider doing that as well. And finally, promote social cohesion within your own circles of family and friends, your co-workers, your patients, your neighborhood, your town, your state, your country, and obviously the globe. We all need to be in this together and to help each other reach our full potential in health and well-being. Thank you for joining me. Good afternoon, everyone. My name is Lauren Effue. I am an assistant professor of medicine in the division of gastroenterology and hepatology at the Indiana University School of Medicine. I am truly honored to have this opportunity to be a part of this important session, where we will talk about designing prospective cohort studies in disparities in liver disease and liver transplantation. Much of the work that has been done in disparities in liver disease and liver transplantation has been retrospective and hypothesis generating. However, as we work towards the goal of eliminating health disparities, many of our observations and interventions will need to be explored prospectively. So I'll start off by giving an outline. I'd like to start by refreshing our epidemiology and talking about the nuts and bolts of prospective cohort studies. We'll talk about the assumptions, the advantages, and disadvantages so that we can think about how they can be leveraged for disparities research. Certainly, a large barrier to prospective cohort studies and disparities can be recruitment. So we will spend some time reviewing some of the data around minority participation in clinical research, as well as strategies for recruiting. Finally, I will review a famous disparities prospective cohort study as a model of what I think can be done to explore disparities in cirrhosis and end-stage liver disease, and that's the Southern Community Cohort Study. And I'd also like to encourage the junior investigators in a group like myself that all prospective cohort studies may not be as large and expansive as the Southern Community Cohort Study, but could still be informative. And so I will share some of the work that I'm doing in Indianapolis to examine hepatocellular carcinoma in a prospective cohort. So we'll begin by talking about prospective cohort studies nuts and bolts. A prospective cohort study follows patients in time forward from an exposure to an outcome. One of the valuable things about a prospective cohort study is that the exposure can be identified after the study has begun. For example, you may have a defined population of patients who live in, let's say, Framingham, Massachusetts, to take an example from possibly the most famous prospective cohort study. You follow that cohort forward to look for multiple exposures that may develop over time, obesity, hypertension, diabetes, with the ultimate goal of determining the proportion of patients with exposure of interest that develop the disease of interest. In the Framingham study, that would have been cardiovascular disease. Now, the key difference between a prospective cohort study and a clinical trial is that there is no randomization to these exposures. They are happening naturally in the environment. Because of that lack of randomization, when the hierarchy of research study designs are discussed, prospective cohort data is often described as lower quality data than a randomized controlled trial. Due to that non-experimental design for the prospective cohort study, you're never really able to prove causation. However, you are able to show an association between, for example, obesity and cardiovascular disease. Many of the questions, however, that need to be answered in disparities in liver disease and liver transplantation do not lend themselves well to randomization. You're not likely able to randomize someone to Medicaid, for example, to explore the impact of insurance on outcomes. However, it has been argued and demonstrated that a well-done prospective cohort study can provide a point estimate very similar to that from a randomized controlled trial. Now, this is a study published in 2000 in the New England Journal of Medicine. It's been cited almost 2,000 times. You, too, may have cited it in a grant. The author set out to explore how close the point estimates are for observational studies and randomized controlled trials on the same subject. The open circles in the figure represent the observational studies and the filled circles represent the randomized controlled trials. And they show that the point estimates are similar for the two. They also showed that the confidence interval was tighter around the observational studies compared to the randomized controlled trials that where the confidence intervals were a bit wider. And I present this data to show that while some disparities questions may not lend themselves well to randomized controlled trial, there still can be very scientifically valid information gleaned from quality prospective cohort work. So some of the advantages of prospective cohort studies are that the exposure clearly happens before the outcome. And so you are able to, therefore, calculate incidence rates, hazard ratios, and attribute overall risk and really provide some rich data about your cohort. Another advantage is that multiple outcomes as well as multiple exposures can be explored. For example, you may have a cohort of Black women with autoimmune hepatitis that you follow for multiple exposures, insurance status, referral, or opioid use. And you may follow them for multiple outcomes, including hospitalization, readmission to the hospital, cirrhosis, or liver transplantation. You can study rare exposures with prospective cohort studies. For example, you may want to look at the group of women in that cohort who were exposed to gudezini. It also allows you to capture accurate information about things that may not be gleaned well from retrospective studies, like immunosuppression history, exercise and diet, or socioeconomic status. Some of the disadvantages of prospective cohort studies are selection bias and generalizability. If all of the patients you enroll in your autoimmune hepatitis study are enrolled at your suburban clinic because the research staff there is robust, then this may not be generalizable to all the women with autoimmune hepatitis in the city. Other disadvantages that we talked about is that this is a non-experimental study design. Exposures are just happening. So you're not able to establish causation, just association. And finally, the biggest barrier to prospective cohort study work is that it's very expensive at times to follow large cohorts forward for many years in order for outcomes to occur. And so it can be, cost can be prohibitive. I'd like to move now to talk a bit about enrollment of racial and ethnic minorities into clinical research now that we've refreshed our minds about the epidemiology of prospective cohort studies. So in 2012, African-Americans made up approximately 12% of the United States, and Hispanics made up approximately 16% of the US population. And despite the increasing diversity in the United States, patients of color were underrepresented in clinical trials, with just 1% of Hispanic participation and about 5% of African-American participation. Here's some more recent data that suggests these numbers may be improving. However, they reach nowhere near the numbers that are represented in the US population. This figure is involved, but my goal here is just to highlight the reasons for disparity in clinical research participation for racial and ethnic minorities. It's often cited to be related to distrust and rooted in the historical context of the Tuskegee experiment. And while certainly patients of color approach the medical community with some caution given this history, there are a multitude of reasons from access to bias that contribute to this story. It's also a bit more nuanced of a story than just Tuskegee. There's evidence that when patients of color are asked to participate in research, they do. This is a table taken from a study looking at consent rates from multiple different types of clinical trials. I chose to present the surgical interventional trial table because those trials have been described as being particularly difficult to recruit. And here you can see if you look at the summary statistic that the odds ratio here crosses one for the difference in consent between patients of color and non-Hispanic white patients. So there was no significant difference in consent rates when all of these trials were taken together. What's more interesting and remarkable is that only 5% of people offered enrollment into the clinical trials were from minority groups. And so this is including Black, Hispanic, and Asian patients. Only made up 5% of this cohort of people offer enrollment of 7,000. And so this really highlights that there is really a lack of opportunity, perhaps, for patients of color to participate in clinical research. But when they are offered the opportunity, they are interested in due consent. There's also further data that shows that patients of color often participate in phase one clinical trials. This is interesting work that was published in the American Journal of Public Health exploring minority participation in phase one clinical trials. The authors look at one large clinical trial site in the Northeast and one in the Southwest, and they find that Black patients made up 42.3% of the cohort in the Northeast. And Hispanic patients made up 54.9% of the cohort in the Southwest. The authors discuss several potential reasons for high rates of minority participation in phase one clinical trials, including that they are often run by private sectors with limited physician involvement, and they use trained recruiters who are used to recruiting a wide variety of patients. The facilities are often in urban areas, and that really facilitates access for different types of patients. And that there are also financial incentives to participate. And so these are things that we need to think about when we're thinking about enrollment in some of our non-phase one research studies. So some of the solutions that I gleaned from the data to potentially increase minority enrollment is that the most important step is really convincing physicians and investigators and collaborators that patients of color are interested in participating in clinical research, and when they're offered the opportunity, they do participate. We also need to remember that these phase one sites are located in locations where patients of color live, and so we must involve community health organizations, safety net hospitals, methadone clinics if you're interested in viral hepatitis, so that we may recruit more diverse populations. We need to remember that time is a luxury and a privilege, and patients of color may not have that luxury, and so we need to be able to reimburse people for their time with money and financial incentives if we can. For younger populations, we might consider technology and virtual video visits to try to lower the burden of follow-up in our clinical studies. We also want to think about eligibility criteria and make sure that we aren't inadvertently eliminating patients of color. Finally, there's some data that suggests that if you allow patients to have more control of their health by encouraging them to ask their doctor about clinical research studies, so just posting a sign that may say, ask your doctor about clinical research may encourage some patients to ask their doctor, and then that may encourage providers to speak with them when they may have not done that previously. So now that we've talked a bit about increasing minority enrollment in prospective cohort studies, I'd like to talk about two prospective cohort study examples. The first, as I mentioned, is not a liver disease-specific cohort, but I think it's a great illustration of what is possible and the methods that could be leveraged to answer important questions about disparities in chronic liver disease. So the Southern Community Cohort Study is a prospective cohort study that recruited 74,000 patients from 2002 to 2009 from 12 states in the southeast. Sixty-five percent of this cohort was Black. These patients were not recruited from academic health centers. Eighty-five percent were recruited from community health centers. The interest questionnaire for this study was only 45 to 60 minutes, so it was not terribly onerous. It was administered by an interviewer and not just kind of handed to the patient on a tablet or a handout. Blood and urine samples were also based on each participant. The questionnaires really included some rich data that could not be captured from a retrospective study. So there was diet history, physical activity, social support, income, education, transportation. And then there was one additional questionnaire that was mailed at five years. So there was not multiple, multiple points of follow-up, but one point of follow-up to try to make this study feasible. This cohort has yielded over 100 publications, and even though it ended over 10 years ago, there's been over 20 publications just in 2020. This data has been linked to the National Death Index and the National Cancer Registry for outcomes to Medicare and Medicare to look at cost, excuse me, and data on encounters. So the studies that can be done from a cohort like this are very rich, and there's been studies that have looked at risk factors for NAFLD. There's been translational studies. There's been genome-wide association studies looking at breast cancer. And I believe that this is the type of large-scale, multi-center cohort that's really going to be needed to explore disparities in cirrhosis care and cirrhosis outcomes. On a much smaller scale, I would like to tell you about our cohort study. As more junior investigators like myself prepare ourselves to tackle the large cohort studies that are needed to be done, I believe we can begin with some smaller cohort studies and questions. So mortality from hepatocellular carcinoma is nearly double that in Blacks than it is in Whites in Indianapolis. And I know that that's true in many places in the country. The mortality rate is 4.2 per 100,000, and Whites is 7.6 per 100,000 than Blacks in Indiana. And much of Indiana's rural and the bulk of the population lives in Indianapolis and in Gary, Indiana. 53% of Black patients with hepatocellular carcinoma actually live in the Indianapolis metropolitan area. So when I was thinking about my study sample, generalizability in my sample, I knew that I could capture a good majority of patients right in the Indianapolis metropolitan area. So before setting out to do a prospective cohort study, I wanted to see if I could understand this disparity in mortality with a retrospective cohort study. We looked at over 1,000 patients in our health system over the past decade with hepatocellular carcinoma. We found that there was a significant disparity in liver transplantation from hepatocellular carcinoma. This has been seen in multiple SEER database studies as well. We hypothesized, though, that we would be able to explain that disparity with a very granular chart review, that if we understood differences in disease etiology, in liver disease severity, tumor characteristics, and comorbidity, that we'd really be able to explain this disparity in transplantation. However, when we added multiple variables to our model, you can see that our odds ratio didn't attenuate, and there still remained a significant difference in transplantation between Black and white patients with hepatocellular carcinoma, even within the line criteria. And so this really suggested to us that there were unmeasured confounders here that needed to be explored. Our study collected data on a multitude of clinical factors, and even some variables about medical care. However, we collected really no data on structural or social determinants of health other than insurance status, and certainly no data on health behaviors. These are very difficult to capture even on a granular retrospective chart review. And we know the health outcomes are largely driven by these social and structural determinants of health and health behaviors. And so this really raised the question that, could we explain the disparity in transplantation, and therefore the disparity in mortality in Indiana by delving deeper into some of these social and structural determinants of health? So this is how we began our prospective cohort study. Really, it was born from that retrospective study. So we sought to begin to enroll a cohort where we could ask some of these more granular questions. So our primary outcome is to look at one-year survival, as well as the receipt of curative therapies, re-recruiting from the Indianapolis metropolitan area health system hospitals. So in addition to Indiana University, where we found really no disparity in mortality, which was curious given the disparity seen in the state, we thought it'd be important to expand our enrollment to our safety net hospital. So we're collecting data on demographics and liver disease severity, comorbidities, the things that you would, the clinical variables that you would expect, but also the value of collecting data prospectively is that we can collect data on functional status at the time of diagnosis, literacy, financial adequacy, transportation, education, income, and factors that might help us to further explain this disparity in liver transplantation and mortality. Hopefully we can find something that can be mitigated. So this question really lent itself to a kind of smaller cohort, prospective cohort study design because of the short latent period. So the outcomes, liver transplantation and death, unfortunately occur relatively shortly after diagnosis. And so we don't need to follow people for 10 and 20 years to wait for them to develop the outcome. This design is also nice because it allows us to explore a multiple different exposures like insurance status, functional status, and in addition to race and gender and how those things impact our outcomes. And finally, there's a knowledge gap. There's really little data that's been collected prospectively on the social and structural determinants of health and how those impact HCC outcomes. So to recruit this cohort, we've tried to implement some of the strategies that I discussed earlier. So we really worked to engage our community. So we are enrolling at our safety net hospital in Indianapolis where we had a previous research footprint. So this wasn't their first introduction to our investigators. We also really wanted to get buy-in from our referring providers. Next, we really wanted to capitalize on having a diverse research team. And so our interviews are done by a research coordinator who is Black and Latina. And our study brochure was written by a research coordinator who is Black and Latina. And so we really wanted to capitalize on having a diverse research team. And so our interviews are done by a research coordinator who is Black and Latina. And our study brochure reflects our diverse study personnel. And my research coordinator tells me anecdotally that when she added our pictures to the brochure, that it really encouraged patients of color. And she got a lot of feedback about their excitement about seeing people that look like them as study investigators. Incentives, as we discussed earlier, time is certainly a precious resource that many patients of color may not have readily able to give away. They may need to work or help with parking and childcare. And so we were able to seek some grant funding to pay for cash gift cards to compensate for time. Some feedback we got from patients was that they'd rather have cash gift cards as opposed to store-specific gift cards so that they can use it more widely for some of their needs. Finally, again, time is certainly very valuable. And so the interviews that we do can be done over phone or in person. We limited them to 25 minutes. And while there were certainly a multitude of variables that we wanted to collect, we tried to really narrow down and hone in on just those that were really critical to push some of our hypotheses forward so that we could really be judicious about the use of time. So in conclusion, prospective cohort studies cannot identify causation. But they can identify association. And the point estimates from a well-done prospective cohort study can closely approximate randomization. And so the work that can be done in disparities can be very scientifically valid using this method. Next, I hope that we can remember that racial minorities want and do participate in clinical research. And this has been shown in phase 1 participation as well as in interventional studies. We just must properly strategize to increase enrollment. And we must ask patients to participate. Finally, large and smaller scale prospective cohort studies have the potential to answer a multitude of questions and inform disparities research. I'd just like to take a moment to thank my mentors and my research coordinator, my family, as well as the Indiana NCTSI for the funding to do this project. And I'll be looking forward to answering questions in the chat. Thank you. Good afternoon. My name is Patricia Jones. I'm a hepatologist at the University of Miami Miller School of Medicine. My research program focuses on racial and ethnic disparities in hepatocellular carcinoma risk and outcomes. I'd like to take this opportunity to thank the ASLD, specifically the Inclusion and Diversity Committee, as well as the chairs of this workshop, Drs. Hashemi and Gutierrez, for the opportunity to present on this critically important topic, how to build a successful health disparities research program. I have no financial relationships or commercial interests to disclose. In this presentation, I'll characterize health disparities overall and build on Dr. Nephew's presentation to discuss health disparities in the context of common chronic liver diseases and liver cancer. I will discuss challenges to the successful conduct of disparities research and draw on historical and contemporary examples. While previous workshops have characterized disparities in liver disease, this workshop and this presentation will expose attendees to appropriate research methods and tools to address and improve ongoing health disparities. We will do so by talking about key research questions specific to health disparities in liver disease, research frameworks by which to examine disparities, and best practices in disparities research that will increase the success of your disparities research program. We will start by talking about the Healthy People Program. Healthy People is sponsored by the U.S. Department of Health and Human Services and provides science-based 10-year national objectives for improving the health of all Americans. For three decades, Healthy People has established benchmarks and monitored progress over time. One of HP 2020's four overarching goals is to achieve health equity, eliminate disparities, and improve the health of all groups. Health equity is defined by HP 2020 as the attainment of the highest level of health for all people. I find this definition somewhat vague. According to the World Health Organization, health equity, or equity in health, implies that ideally everyone should have a fair opportunity to attain their full health potential and that no one should be disadvantaged from achieving this potential. HP 2020 defines health disparities as a particular type of health difference that is closely linked with social, economic, and or environmental disadvantage. Health disparities adversely affect groups of people who have systematically experienced greater obstacles to health based on race, ethnicity, gender, religion, socioeconomic status, age, sexual orientation or gender identity, mental health, disability, geographic location, along with other characteristics that have historically been linked to discrimination or exclusion. Dr. Gomez discussed how culture, minority, or social status can affect health. On this slide, I've included examples of disparities that can impact outcomes in all disease states. These are disparities in access and education. Leading health indicators are critical health issues that, if addressed appropriately, will dramatically reduce leading causes of death and preventable illnesses, and I've included three examples of leading health indicators on this figure. In the top figure, you'll note a bar graph with the percent of persons with health insurance under the age of 65, and there are significant disparities in insurance coverage. While 93% of Asians and 92% of non-Hispanic whites are insured, only 88% of Blacks and 80% of Hispanics are insured. The lowest insurance rates are actually in Native Americans and Alaskan Natives at 71%. Educational attainment is another huge source of disparities, and in the middle figure, you have the percent of students graduating from high school within four years of starting ninth grade. 89% of non-Hispanic whites graduate within four years, compared to only 80% of Hispanics and 78% of non-Hispanic Blacks. Insurance is critical, we know that, but engagement with the health system is another key metric. While nearly 80% of non-Hispanic whites have a PCP, only 72% of Blacks and 70% of Asians have a PCP, according to AHRQ. Health care accounts for only a small proportion of an individual's overall health state. However, genetics, the external and social environment, socioeconomic status, education and literacy, cultural norms and personal behavior all contribute powerfully to an individual's health, according to McGinnis and colleagues. The existence of racial and ethnic disparities in chronic liver disease have been very well described. It would be impossible for me to exhaustively review previous disparity studies, so I'm just going to mention some key findings from a few studies, and we're going to focus on viral hepatitis. Hepatitis B has disparities in risk, vaccination, screening and treatment. Hep B prevalence is highest in the WHO Western Pacific and African regions. In the United States, an estimated 1 to 2 million people have HPV. The largest disease burden is in immigrant and racial minority populations. The CDC launched the Racial and Ethnic Approaches to Community Health Across the U.S., or the REACH US study in 2007, to address efforts to close health gaps among racial and ethnic minorities in the U.S. A total of four communities were selected and competitively funded. Of the nearly 54,000 minority respondents, 43% of Asians had been screened for Hep B, followed by 40% of African-Americans and only 35% of Hispanics. And there were also disparities with respect to antiviral therapy. Of the 1,235 people who reported a history of chronic Hep B, only one third had seen a physician. Roughly half of Blacks and Asians with chronic Hep B had ever received antivirals, and this was significantly lower than the treatment rate in Hispanics, which was 67%. Similar to Hepatitis B, Hepatitis C has disparities in risk, screening and treatment, and it predominates in regions where racial and ethnic minorities originate. Based on an NHANES study of 2013-2016 data, approximately 2.4 million persons in the United States are HCV RNA positive. Blacks and Hispanics are disproportionately affected by Hep C. Minorities are less likely to be referred to care and to receive HCV treatment compared to other racial ethnic groups. With the widespread use of DAAs starting in 2014, racial differences in treatment response are much less pronounced than were in the interferon era. However, there are significant disparities in the HCV continuum of care. And while there were large absolute increases in the percentage of Blacks and Hispanics who were treated for Hep C at VA facilities, Black patients still had significantly lower odds of receiving DAAs when compared to Whites. And this disparity was significant even after adjusting for baseline differences in demographic, clinical, and virologic factors. These findings are echoed in other studies, including analyses of Medicare, Medicaid, the Kaiser Permanente system, as well as the Truven market scan database. There are well-characterized disparities in HCC risk, stage of diagnosis, treatment, and survival. HCC incidence is highest in racial and ethnic minorities. Globally, the highest rates of HCC are in regions that are endemic for hepatitis B, such as Asia and Sub-Saharan Africa. In the United States, age-adjusted incidence rates are highest in Hispanics, followed by Asians, Blacks, Native Americans, Alaskan Natives, and finally, non-Hispanic whites. There are significant disparities in stage at diagnosis as well as treatment. In a serounos analysis, compared to non-Hispanic whites, Blacks had significantly more advanced HCC at diagnosis, whereas Asians were actually less likely to have advanced disease. Among patients with HCC meeting the Milan criteria, Hispanics and Blacks were significantly less likely to receive curative therapy, defined as resection or liver transplant, whereas Asians were more likely to receive curative therapy compared with non-Hispanic whites. For survival, we know that Blacks with HCC have the lowest survival compared to non-Hispanic whites, and this has been shown in analyses of several administrative databases, such as SEER, but also in large, multi-centered, and single-centered retrospective studies. In Hispanics, the data is less clear. Some studies have noted decreased survival in Hispanics relative to non-Hispanic whites. However, these differences reversed after adjusting for the type of insurance, liver function, stage at diagnosis, and receipt of treatment. In our single-centered study, we didn't note any significant differences between Hispanics and non-Hispanic whites in regards to survival, but we actually found when we looked at the state that Hispanics in Florida actually have increased survival compared to others. So while there is a wealth of information describing the existence of disparities, very little is known about the specific determinants contributing to health disparities in chronic liver disease, besides the obvious differences in socioeconomic status, insurance status, and access to care. This gap in knowledge exists because few studies have been designed to gather the requisite data that would help us to navigate this complex labyrinth. And there are many challenges that might hinder our ability to understand and attenuate racial disparities. Lack of funding from both a research standpoint and a societal standpoint is a major challenge. So research that proposes fundamental or mechanistic investigations is funded at a higher level than research at the community and population level. I would argue that the greatest challenge is the lack of diverse cohorts and individual data regarding how genetic, behavioral, and education factors interact with social determinants of health and clinical factors to drive disparities. In order to overcome these challenges, research programs focus specifically on understanding health disparities and well-designed prospective studies, which include diverse participants, are critically needed. Well, so who cares, right? We all should. In 2011, African-Americans and Hispanics comprised 12% and 16% of the U.S. population, respectively, but only 5% and 1% of trial participants were African-Americans and Hispanics, respectively. Failure to enroll a representative study sample compromises external validity. The scarcity of existing diverse cohorts and the lack of minority representation in existing clinical studies stem from a myriad of causes. And while it is not necessary for all researchers to develop a disparities research program, I do think that it's possible and perhaps even imperative for all researchers to look at their program through a disparities lens. Whether we consider ourselves disparities researchers or not, we must all question whether our science is contributing to either the alleviation or the exacerbation of existing disparities. And then we can consider what tweaks we might make to our science, our methods, so that we are doing the former. Challenge to the conduct of successful disparities research or research that recognizes the need to include diverse participants can be conceptualized broadly as challenges in outreach. So minorities are contacted less frequently about participating in clinical research and are generally less aware. Some researchers may perceive that minority patients may not trust them or that they may not understand the study or that there may be some social issues that would prevent them from adhering to study procedures. In addition, researchers often fail to consider the unique needs of participants, specifically those needs that are unique to minorities in recruitment approaches and the development of study designs. There is limited dissemination of and knowledge about specific techniques to conduct disparities research. So researchers tend to work in silos, but workshops like this are a step in the right direction. There are also challenges in engagement. We must also consider and acknowledge the historical context which might make some members of vulnerable communities or populations reluctant to participate in clinical research. So this quote is taken from a participant interviewed by Sprague-Martinez and colleagues as they were investigating the perceptions of cancer care and clinical trials in the black community. We know that people are getting paid to do these clinical trials and do all of this research and none of that money never gets into the community. Information that's obtained never gets into the community, never benefit in most ways, shape or form from the research. To successfully recruit minorities to participate in clinical research studies, we must consider the historical context. While these topics are briefly introduced in the curricula of most contemporary medical schools, research teams are diverse with regards to training and experience. And it is key that principal investigators ensure that all team members understand and appreciate the history which has led to mistrust. We can start in the 1600s because that's when slavery began in the United States. And many enslaved persons were exploited in early medical research. In 1810, Sarah Bartman, an illiterate South African woman signed a contract and was brought to Europe by a British doctor where she was put on display for her body habitus in large buttocks. She died five years later. However, her brain, skeleton and sexual organs remained on display in a Paris museum until 1974. Her remains weren't repatriated and buried until 2002. In 1909, the California eugenics law was passed. Anyone committed to a state institution could be sterilized. Many who were committed were sent there by court order. And from the 1920s to the 1950s, thousands were sterilized with obvious racial differences. Latino men were 23% more likely to be sterilized than non-Latino men. And Latinas were sterilized at 59% higher rates than non-Latinas. Most have learned something about the Tuskegee study of untreated syphilis in the Negro male, which began in 1932 and was conducted without informed consent. And 399 participants with syphilis and 201 without. This study was only supposed to last six months, but it lasted much longer. Penicillin became the treatment of choice in 1945, but the study still didn't end. It wasn't until 1968 that black medical students raised ethical concerns and the study finally ended 40 years later in 1972. And typically when people discuss Tuskegee, oh, it happened, it's over, it's a terrible stain, but I think it's important to realize that this is actually part of the current consciousness. The last study participant didn't die until 2004, which was only 16 years ago. Because history likes to repeat itself, I've included some recent headlines. And on the top, I have the story of ICE allegedly coordinating hysterectomies and detainees who did not provide informed consent. And then we're all aware of the need to develop an effective vaccine for SARS-CoV-2. And it's also clear that persons from underrepresented communities, such as blacks, Hispanics, and Native Americans are at highest risk. So while we all understand how important it is to engage these communities in vaccine research, the messaging is critical and it's been somewhat concerning and it may have actually increased mistrust. So what are we going to do differently? How do we define success? And this is clearly different for each and every researcher. But like any other research program, a successful disparities research program should start with clearly defined vision, mission, and goals. To inform strategy and implementation, we will need to seek out information and learn from prior experience because there really is no sense in reinventing the wheel. So I'm going to now share with you some of the resources that I have found to be helpful. The majority of current research has focused on documenting health disparities or outlining their underlying causes. However, a little work has focused on identifying the key methodological issues. I really like this conceptual framework, which was published by Kilbourn and colleagues. And I think it is very useful for developing a research agenda and it breaks activities down into three phases, which they see as sequential. Based on my experience, I think the process is actually more cyclical and iterative. In phase one, we're detecting. So we're defining health disparities, we're defining vulnerable populations, we're measuring disparities in vulnerable populations, and we're considering selection effects and confounding factors. In general, there's a wealth of foundational data identifying vulnerable populations from administrative databases. So very much has already been done in phase one for chronic liver disease and liver cancer. Building on this foundation, we've been moving into phase two, which is understanding. So we're identifying the determinants of health disparities at the patient, individual, provider, clinical encounter and healthcare system level. And ultimately the goal is to get to phase three, where we are reducing disparities. We're developing interventions and testing those interventions. We're translating and disseminating our findings. And ultimately our goal is to change policy. The National Institute on Minority Health and Health Disparities or NIMHD research framework is a tool for conceptualizing and depicting the wide array of determinants that promote or worsen minority health or cause, sustain or reduce health disparities. These determinants may reflect ideological factors related to health outcomes, as well as intervention targets to improve minority health or reduce disparities. So on the Y-axis, we have domains of influence, biological, behavioral, physical, sociocultural environment and healthcare system. On the X-axis, we have levels of influence, individual, interpersonal, community and societal. The combination of these dimensions produces 20 cells that each reflect a unique set of determinants that may be relevant for any particular minority health outcome or health disparity. So as an example, racial disparities in liver cancer mortality may be driven by genetic risk, which would be in the individual biological combination, or it could be driven by insurance coverage, which would be in the individual level but healthcare system domain, could be driven by engagement in cancer screening, which is likely in that same box or in decreased availability of services, which would be in the community level healthcare system box. We probably all agree that a successful disparities research program is one that's funded. The NIMHD developed this framework in 2015 as a vehicle to convey their philosophy and priorities for minority health and health disparities research. Conducting research entirely within one cellular framework, such as the biological or behavioral, actually constitutes the bulk of traditional health research, but it may result in knowledge that's incomplete because it does not address the cumulative or interactive effects of multiple determinants. To conduct disparities research that goes beyond mere description, it is necessary to engage various stakeholders and form partnerships. This includes patients, families, community members, and community organizations. And this is important for several reasons. Number one, community participation can enrich the quality and the relevance of research questions that we ask. Number two, given the past and current climate of mistrust for research, engaging the community decreases the risk or perceived risk of exploitation and ensures that proposed recruitment methods and study procedures are culturally appropriate. Boyer and colleagues published this continuum of community or stakeholder engagement in research in 2018. And this provides recommendations on how to develop a meaningful patient-centered and patient-engaged research program. This approach allows ongoing input from highly engaged stakeholders while leveraging focused input from larger, more diverse groups to enhance the patient-centeredness of research and increase relevance to broader audiences. So on the Y-axis, we have the extent of engagement, and on the X-axis, we have the number of stakeholders. So you see at the base, we have the knowledge users and experiencers, and we may do things like surveys, online polling, listening sessions, going up to reviewers, interviewees, and consultants. And these tend to be the participants in focus groups or semi-structured interviews. Generally, this is short-term involvement, and that's different from the above, which is more of a sustained, ongoing involvement. So it can be as much as community member as a PI or co-PI, or community organizations as research partners or team members, and I often use the advisory groups. So stakeholders serve on boards, councils, and committees that provide oversight and guidance. I mentioned the silos earlier. It's very common for researchers to operate within a narrow window informed by their expertise. That is the safe thing to do. However, successful health disparities research requires that we examine how multiple levels of influence interact. For example, how do individual risk behaviors and biology interact? Therefore, a transdisciplinary team-based approach is necessary. I tried to find an existing figure that would illustrate the various research disciplines involved in disparities research, but I couldn't find one. And this is likely because the disciplines involved are actually going to vary from researcher to researcher and from program to program. On this slide, I've included the various research disciplines that I've collaborated with when conducting my disparities research over the past five years. Some things make sense. For example, it makes sense that health equity research or community-based engaged research methods are involved or integrate well with health disparities research. In addition, when we're thinking about the individual in context of his community or environment, we might need to start thinking about using geospatial research methods or environmental research methods. Of late, I've become very interested in the interaction between precision medicine and health disparities, specifically genomics. How do we integrate genomics when the data that we have derived is mostly derived from homogeneous non-minority populations? So there's some institutions that have strengths in health disparities and also the complementary disciplines, and there are other institutions that do not. And success is definitely going to require looking outside of your division or department and likely outside of your institution for collaborators. In addition to engaging researchers from other disciplines, the decisions you make in regards to building your team of research support personnel will influence your ability to recruit diverse participants and successfully conduct disparities research. All institutions require research personnel to complete human subjects research training, but this does not delve deeply into the complex history of mistrust or the systematic barriers and structural racism that have created and continue to perpetuate health disparities. When hiring team members, it is reasonable and advisable to inquire about baseline knowledge pertaining to medical mistrust and engaging patients and populations that have historically been difficult to reach. If team members don't see this as an issue, it will be reflected in their behaviors and recruitment methods, and we know that the one-size-fits-all method doesn't work. Diversity in regards to the composition of the team is key. Communication skills and linguistic flexibility are of utmost importance, especially considering that our target populations may have limited English proficiency and or limited health literacy. So research personnel that are bilingual or trilingual and have the ability to modify their message based on the understanding of prospective participants is a skillset that is often overlooked. While racial concordance between the provider and the patient may affect health outcomes, there's no evidence that racial concordance will increase minority participation in clinical research. Trust is far more important. Like any other research discipline to excel in health disparities research, we must do the work. We must seek out the perspectives of patients and communities and learn from them. We must seek out and respect the expertise of collaborators and complementary disciplines. Oftentimes they are disregarded and their science is discounted as soft. We must seek out training to fill gaps in our knowledge, and I've listed some useful resources. The most widely publicized is the NIMHD Health Disparities Research Institute. This occurs about every other year and features lectures on minority health and health disparities research, small group discussions, mock grant review, and seminars, and institute participants have the opportunity to meet with NIH scientific staff who are engaged in related health disparities research across the various NIH institutes and centers. Also, there are numerous certificate courses in health disparities, and I just listed two that I found. In addition, there are focused disparities conferences. As my focus is on cancer, I've benefited from attending the AACR, Science of Cancer Health Disparities in Racial Ethnic Minorities and the Medically Underserved. It's a great opportunity to network, to learn about the science of disparity research as applied in other disease sites or at other institutions. NIDDK has a network of minority research investigators and there's an annual conference. This is open to anyone who's interested in minority health research, including individuals from traditionally underserved communities who conduct research in the fields of diabetes, endocrinology, metabolism, digestive diseases, et cetera, all of the diseases that fall under NIDDK's umbrella. In addition to educating ourselves, we must provide ongoing educational opportunities for our research staff. So to summarize, we must learn from history. There's a long legacy of mistrust in medicine and in clinical research that's contributed to difficulty studying health disparities in minority health. It is important to understand this history on a national or international level, but most importantly, we must understand the local context, which has influenced the relationships between the research community and our local community. We should be building relationships with the community as well as collaborating from other disciplines. And we really need to make a good faith effort in establishing meaningful partnerships. We should use and or modify existing conceptual frameworks to design our research program and or research project. And there's well-established disparity science. It's really important that we educate ourselves in order to conduct high quality research and achieve success. It's important that we build a diverse, culturally competent team, and that we commit to continuing education and ongoing discussions, providing resources for staff and other colleagues. I really wanna thank you all for listening this afternoon. I hope you found this informative. I can be reached by email. Please find the list of references that I used to build this talk today. I'll stay on each slide for a little. Again, if you have questions, you can always email me. Thank you.

Lauren Efue

behavioral scientist

community intervention specialist

social determinants

culture

minority status

health outcomes

health disparities

health equity

research methodologies

COVID-19 pandemic

prospective cohort studies

community involvement

hepatologist