So thanks for coming. I am in family and community medicine. So I have the joy of getting to do ICU trials as well as community trials. So I'm going to kind of talk a little bit about bias and stigma and be maybe a slight wet blanket, but with the caveat that I think this is really important work. It's really essential work. We just have to be kind of careful and thoughtful about it. The mouse may or may not work. So with some disclosures, I have research support with NIH and CDC. I also am a consultant for the Eureka Institute of Translational Medicine. There's going to be a lot of, okay, there'll be a lot of clicking. So some objectives. I'm going to try to be a little bit practical. I want, the goal is to sort of think about what bias might be in these real-world data because there's bias in clinical research, in research, in the world. And I'm not going to buy a lottery ticket today. I feel like maybe today is not my day. And then also really just try to think through how to understand and maximize the potential of real-world data in diverse communities. I'm in Tucson, Arizona. I'm also an equity researcher. So these are things I think about a lot. Next slide. I guess I'll just have to tell, can I just go like this? Maybe you can click. Okay. Sorry. So bias and stigma in clinical research. Can you go ahead and click? Clinical care and clinical research exist in the real world. There's bias and stigma, oppression in the real world. We don't get to ignore that. It doesn't stop at the doors of our institutions, right? It's sort of pervasive throughout. And I think we just have to realize that, acknowledge that, and just sort of accept that as the world that we're living in when we think about research. Next. So big picture bias. I'm not really talking about statistical bias here. I'm talking about sort of like the larger bias, right? This idea of lack of objectivity. I have bias. I'm a social scientist at heart that lives in a health world. So it's hard for me. I see things as a social scientist, right? I'm an equity researcher. It's really hard for me to not see things with an equity lens. That's my bias. It's not bad. It just is what it is, and it's useful to acknowledge that it exists. Next. Stigma. Stigma, people are devalued because of an attribute, right? And so when we think about stigma, we often think about people feeling shame or discredited. This is something that we see in clinical world. In the clinical world, we see it in clinical care. We also see it in clinical research. Next slide. So what it can look like in clinical research. Lack of diversity in screening. A lot of times we think about lack of diversity in our actual enrolled participants, but then if we go back and we go upstream and we look at our screening pool, that pool often looks very similar, right? So it can be more diverse than the people that we enroll, which tells us that our inclusion criteria are consent processes, right? We're not developing trust. We're leaving people out. But sometimes we're also just not going to where people are and where we want them. Next. There's biased inclusion criteria. Sometimes we have this inclusion criteria that sort of like an administrative or like the PI says, they may not be a good fit, and that can be a place where we see a lot of bias and stigma, and we're losing people kind of along the way there. We also make some very practical and appropriate and financially supported language and legal authorized representative decisions that exclude people from our trials. Next. And then sometimes we have selective retention. Time and capacity are finite. We can't follow up on everybody. Sometimes we unintentionally follow up on or try to retain people that look like us, that look like our patients, and maybe we're just kind of creating a more standardized and less diverse sort of patient population, which increases our bias. Next. And then I see this a lot. We have some maybe like scientifically unnecessary follow-up visits, which overly burden participants. We don't need them to come in. We could do a phone call, but we want them to come in, and people with childcare and transportation issues aren't going to be in that study anymore because they can't feasibly see themselves doing that. Next. And then just in general, right, a lack of diversity in our enrolled participants. We all sort of say and we're expected to say that we're going to have an ethnically and gender and age distribution that matches our population. We so rarely don't because that's really complicated. It's very expensive. Next slide. And it limits generalizability. Go ahead. Click. So we have biased research, and we've talked about biased healthcare data. Go ahead. Or biased healthcare. Those lead to biased data. Next. Which leads to biased rural data. So we can't really like stats our way out of this bias, right? If we're just kind of living in this biased world, we have to just acknowledge that that's going to continue to exist, and it doesn't mean that it's not the right thing to do. It just means we have to understand the limitations and be cautious about what we're doing. Next. So I want to give an example. This is from our Sari Prep, which is a discovery project funded by the CDC Foundation. So it's a multi-site observational trial that utilizes EHR collected demographic data. EHR data is great. The demographic data is suspect, right? So we have age, sex at birth, race, ethnicity, insurance status, and zip code. So can we answer this question? Go ahead. Are there racial ethnic differences in COVID-19 ICU length of stay? Okay. Next. What about what's driving racial ethnic differences in COVID-19 ICU length of stay? I work with a lot of residents, and this is their question. And they want to use EHR data to answer this question. Can we? Go ahead. Next. Hopefully, you know, but sometimes we don't think through the details here. So go ahead. So what's missing when we think about this EHR data that we're collecting? We're missing folks that have a non-traditional or non-standardized by the EMR sex at birth gender. We're missing gender. We're missing transgender. We're missing individuals on home run therapy. If our research question is related to hormones, we're not going to be able to answer that question. We're missing things like intersectionality. I'm a social scientist. Danielle can use GitHub. I can use intersectionality. So we're missing the complexity of multiple social structures and influences that compound. So it's not just race, ethnicity, and or gender. It's intersectionality. It's this multiplicity and sort of complexity of these things working together. We're missing social determinants of health. We're missing structural, financial, and access to preventative primary care, continuity care. We're missing all of those kinds of things. We're missing social vulnerability. There's these really brilliant and great and so useful social vulnerability indexes that we cannot use with zip code. And we're also missing all these environmental impacts, which we know impacts health. We don't have that if we're just looking at this. Next slide. So we really have to think about who's also missing from the data. So we understand maybe the difficulties with using the data, the biases within the data. But we need to know who's missing. Go ahead. So we know that there are structural, political, and financial barriers to accessing care. People can't come to see us. And they can't come to see us when they really, really want to, or sometimes until they really need to, and sometimes even when they do really, really need to, right? Maybe there isn't an ICU in their area. Or they don't have access. They're undocumented, right? All of these kinds of barriers. We also know that people that are not represented by standard demographic questionnaires are missing from our data. And sometimes our questionnaires are so overly specific because they're trying to capture everybody that we have to remove groups because they're too small and we can't power on them. So we have kind of this, like, we're not asking or we're asking too many. And there really isn't a perfect answer. But we kind of are missing them on both sides. Go ahead. So I want to just share some preliminary findings from SariPrep. There's also a talk on Tuesday afternoon that Dr. Savransky is going to do a little bit about this. But go ahead. So using this data, are there racial, ethnic differences in COVID-19 ICU length of stay? And can we answer that question? Go ahead. So, yes. Go ahead. We can. We know that from our data, we identified that our Hispanic participants, and we're really lucky we have a fairly demographically representative cohort, that our Hispanic patients had a longer length of stay than non-Hispanic white, non-Hispanic black, non-Hispanic Asian, and then, unfortunately, other, which is just a bunch of other people, a large amount of people in the cohort, and also very different from a clinical characteristics perspective. But that's sort of where we had to end up, right? Next slide. Now, if we look at the second question, go ahead, what's driving racial, ethnic differences, can we answer that question? Go ahead. It depends. It's my favorite answer. I'm a social scientist. It's my absolute favorite answer, because it really does depend. Go ahead. If we're sort of thinking, again, social scientists, what's our conceptual framework? What's our causal pathway? If it's driven by treatment, like, go ahead, probably, we can probably answer that question. Maybe not necessarily, but we can probably answer it, as long as we were consistent and thoughtful in how we were collecting that treatment data, and we were keeping up with the COVID treatments. Go ahead. What if it's driven by clinical risk factors? Go ahead. Maybe. Let me show you an example of this. It depends on what we think is driving this, but we might be able to do this. Go ahead. My favorite question, is it driven by social determinants of health? Go ahead. Unlikely. I can't really say much about this. I know my hospital. I know who we recruited. We had conversations with them. I know some of those patients, but I still can't maybe use the data. Very, very likely, and I'd be very skeptical of somebody using exclusively EHR data, unless they're really sophisticated. My healthcare system is not. Go ahead. So this is preliminary findings. So this is kind of our looking at, are there risk factors that might be driving? Go ahead. You'll see some orange boxes here. We can see some differences here in comorbid conditions in this cohort that could potentially be driving those racial ethnic differences, right? So I might be able to make a strong case here if I look really closely, but again, maybe. Go ahead. So what can we do? This is where I'm going to get really practical, and this may be very obvious, but I work with a lot of clinicians and a lot of community-based trialists where we have this conversation repeatedly. So I'm just going to use this opportunity to share this. You can just kind of click through, because these are meant for me to click, and it'll be really annoying. So we need to reduce bias and stigma in healthcare and clinical research. Done. We can oversample and target groups traditionally underrepresented in research. This is hard. This is where the funder and the time and the contract delays and the IRB delays, and we're like, we just got to get 200 people in the door, and we're just going to take the first 200 people, and those first 200 people usually look like us. They're usually, you know, I'm going to be real honest, my dad, he loves to sign up for clinical trials. He's retired. He's got free time. He's got friends. He does it, right? Do we need more white men in their 70s? Probably not, but he's going to be there right there, right? But if I want to get somebody that looks like my patient population, looks like my community, that takes time. It takes energy. We can't rush that, right? And so there's this kind of constant tension. Go ahead. We have to create inclusive recruitment materials. There's really good guidance on this. It's not that hard, but we do a really bad job of it, right? So we want somebody in their, very classic example, we want somebody in their 70s, so we put a photo of somebody in their 70s on our flyer. That's going to recruit somebody in their 90s. We want somebody in their 70s, we put somebody in their 50s playing tennis, right? It's how do we think about ourselves versus how other people see us. There's really good evidence on this, but this is a really, really important piece. Go ahead. We engage communities to increase trust in research and healthcare. The first time we go into a community isn't because we have a trial and we have one month to get people in, right? We really need to be a part of that. The easiest way to do this is to collaborate with people in public health, social work, family community sciences, all of these other places, they're there already. Partner with them, have them invite you in as a trusted speaker and build that, work on that, expanding that relationship instead of going in for this one trial and then bailing because you're then making it harder for the next person that goes in, right? Next piece. And then taking a really thoughtful and critical approach to the data that we're using. Making sure that we're not doing the work to go into the community, doing the work to pull these folks in so that they show up in our science and then when we send them the newsletter with the results, we've edited them out of the analysis because there weren't enough of them, right? So they don't exist anymore. We've edited them out. Next. So this is, just click all the way through this really quick. These are just some questions to think through. These are very common questions. I think they're common sense, but I like to make sure that we're thinking about this. What does the data include? What's missing? Who's missing? What was its original purpose? Am I trying to use some data that was collected for something completely different? Who owns it? Can it be repurposed? And can it really answer the question that I'm asking, right? So again, just trying to think about how can we reduce that bias and stigma by taking a little bit more of a holistic kind of approach. And that's it. Thank you.

bias

stigma

inclusive recruitment

community engagement

research diversity