Session 5: Same Disease, Different Outcomes: Early Evidence on Population Variation in COVID-19 Prevalence and Severity
// Session Transcript
Kristen Cribbs (00:00):
This important discussion. I'm Dr. Kristin Cribbs. I'm a senior consultant at vital statistics consulting, and I'm going to be moderating our session today, which is titled same disease, different outcomes, early evidence on population variation in COVID-19 prevalence and severity. I did want to mention that we've, we've done our very best to present information that is factual and to the best of our knowledge and ability. Um, but the, the material presented here is not a substitute for a consultation from medical professionals.
Kristen Cribbs (00:45):
So it is my pleasure to introduce our presenters for today. Drs. Miriam Ryvicker and Desmond Banks. Dr. Ryvicker is a sociologist with extensive experience in health services, research and social gerontology. She has led numerous federally funded investigations of health, service delivery, health disparities, and social determinants of health. Her data analytics expertise extends to evaluation projects, which have leveraged administrative data from Medicare and Medicaid billing systems, electronic health records and geospatial environments prior to joining vital statistics. Dr. Ryvicker was a senior research scientist at the center for home care policy and research at the visiting nurse service of New York.
Kristen Cribbs (01:37):
Uh, Dr. Desmond banks is a senior consultant at health management associates, which is a national health policy firm where he helps States evaluate their Medicaid programs with the goal of improving beneficiary health. He approaches health behavior and outcomes through the lens of the social determinants of health and seek solutions to racial, ethnic, and economic health disparities. Dr. Banks earned his PhD in public health policy from the Johns Hopkins Bloomberg school of public health, where he also earned a certificate in health disparities. So we are very fortunate to have, uh, such knowledgeable speakers with us today. I did also want to mention that we will have some time for a Q and a at the end of the presentation. So feel free to send to any questions that you might have through the Q and a function, which you can find in your menu bar. And we will take those at the end of the presentation. So with that, I will turn it over to Dr. Ryvicker
Dr. Ryvicker (02:38):
Thank you so much, Kristin, Dr. Cribbs for the introduction. Um, I, I'm so pleased to have you all attending this session on this really important and complex topic. If you're attending this session, you may be here because you're wondering about how to interpret some of what we're seeing in the news reporting on different impacts of COVID-19 by race and ethnicity. There have been numerous reports from States and cities indicating that COVID may be more prevalent and possibly more lethal among racial and ethnic minorities and these early findings or early data points raise a lot of questions about what's happening the data, and how do we explain it? So in this talk, we'll walk through some examples of the emerging evidence on possible disparities in COVID-19 prevalence and outcomes. And we'll give a really high level overview of some of the things we already know, um, about health disparities in the us as some context, and we'll discuss some, some possible early, um, and, and somewhat speculative explanations of the disparities that we're seeing in COVID-19 so far and what all this means for public health policy.
Dr. Ryvicker (04:03):
Um, before I start to get into any detail, I should stop to, um, offer some kind of definition of what health disparities are, um, center for disease control, um, presents the definition that a health disparities is a health difference that is closely linked with social economic or environmental disadvantage. Um, and, and this is sort of just one rough and working definition that we can think of have in mind throughout this presentation, although, um, this is a very complex topic and there may be many other ways of defining it and other overlapping concepts like health equity. Um, one point I'd really like to emphasize is that in this talk, we're primarily talking about racial and ethnic disparities, but this really just scratches the surface. There are disparities of many other areas, including socioeconomic factors, language barriers, um, rural and other geographic factors, LGBTQ health, gender inequities. And so, um, um, so this is just mainly focusing on this particular area, given the reporting that, um, the state departments of health and local, um, local County departments of health are reporting on race and ethnicity in the context of COVID.
Dr. Ryvicker (05:31):
So what are some of the emerging and early evidence on disparities in COVID-19? So you've probably been seeing a lot of media reports on racial and ethnic disparities, and COVID at local and regional levels. Most of them focus on fatalities and really we're in the business here of trying to make sense of what is currently limited data. Um, one report by the APM research lab indicates that, uh, COVID 19 mortality rates is, um, at this moment in time, 2.3 times higher among blacks than it is, uh, for Asians and Latinos. And that, um, the mortality rate among blacks is also 2.6 times higher than for whites. And this is just based on, um, uh, conglomerate of data points, um, that are currently available. Um, knowing that that the data is still quite limited and spotty, um, you may have heard a number of different media reports, um, Chicago, um, NPR reported a few weeks back that 70% of people who died in COVID-19 and Chicago as of April 5th were black.
Dr. Ryvicker (06:50):
And there were similar statistics cited in the context of new Orleans and New York city and so forth. And so you may find that you're, um, processing a lot of information and trying to make sense of what you're seeing in the news. So it's important to know that all of these, um, reports are coming from data points from States and from cities and counties, there isn't yet a single, comprehensive data repository at the national level that has complete data by race and ethnicity on COVID cases and outcomes. So we're talking about early observations and statistics, um, that are cited on differences in fatality rates and prevalence as well. Um, these aren't based on controlled statistical analysis at this point. So what we're seeing may be suggestive of disparities, but not necessarily, uh, coming from conclusive analyses that have gone through rigorous statistical modeling.
Speaker 3 (07:55):
Yeah.
Dr. Ryvicker (07:57):
Um, and one thing, other thing I'm sorry, I missed one point that I wanted to make about that previous side. If he could just go back this graphic that we're looking at, um, comes from a, um, a data tracking project that comes to it. It's a composite of, um, a variety of data sources. And it's just reporting the level of completeness by state in the extent to which States are reporting on race and ethnicity among COVID-19 fatalities. And so, um, from the yellow and, um, data not complete at all, all the way till the purple and where you have a hundred percent reporting on race and ethnicity among COVID fatalities. And then, um, it was kind of notable that New York state, which has gotten quite a bit of attention for some of these statistics is somewhere in the middle in terms of data completeness.
Speaker 3 (08:47):
Thank you.
Dr. Ryvicker (08:50):
Um, so States have different ways of collecting and reporting data and it makes it somewhat difficult to compare across geographies. Um, the Johns Hopkins coronavirus resource center has been taking an inventory of which States are reporting by race and ethnicity and for which measures. So, um, this is sort of a summary of what they've been collecting, that when it comes to testing, only three States so far are reporting is numbers by race and ethnicity. Um, when it comes to confirmed cases, 42 States are reporting race and ethnicity data and fatalities 38 States are reporting race and ethnicity data. Um, and so we have varied, um, amounts of information on different measures. Um, and this is not all the measures that we might, um, be wondering about when it comes to possible, um, differences in, in, uh, health status and outcomes. And COVID, um, there may also be different reporting practices by States in reported symptoms in hospitalizations. Um, and something that I noticed we hear less about is, um, variation and attempts to access testing, whether or not someone was able to access a test. This may be important, um, possibly for tracking on disparities, in access to care, um, given some, um, prior research on disparities and access, which we'll get to, um, in a few minutes,
Speaker 4 (10:31):
Please,
Dr. Ryvicker (10:37):
Can we, well go to the next slide? So we might be having a bit of a technical issue. Uh, okay. Thank you. Okay. Maybe a little lag internet lag. Thank you. Um, so we're going to turn to a couple of case studies. Um, I'll, uh, focus a little bit here on New York and this data here comes from the New York state department of health, and it suggests that Hispanics and blacks are disproportionately represented among fatalities in New York and this, um, hones in on New York city in the left hand column. And then the right hand column is New York state excluding New York city, which is really important. Um, uh, an important comparison to make an important, to be able to break this out because New York city being the epicenter in the us, um, and, and, uh, you know, high concentrations of, uh, COVID fatalities. Um, what we see in the New York city context is that 34% of the fatalities are among Hispanic patients. So they Hispanics represent only 29% of the population. So there's a discrepancy in terms of, um, the representativeness among fatalities. Um, there's a similar pattern for blacks. 29% of the fatalities in New York city are among blacks. So, um, they represent 22% of the population. And it's really important to note, as I was mentioning before we have kind of spotty data that we're working with, that this is based on 63% of the face totality data, having complete information on race.
Speaker 3 (12:41):
Next slide, please. Thank you.
Dr. Ryvicker (12:50):
So this graphic focuses on New York city in particular. Um, this is from the New York city department of health, the previous side focused primarily on fatalities. And this is more a combination of both some prevalence, um, data points, as well as severity. Um, these bars are showing the age adjusted rate per hundred thousand people in the population, um, age adjusted, meaning that it accounts for differences in the age distribution of these populations for non hospitalized confirmed lab confirmed COVID-19 cases. Um, the blue bar up on the top is showing hunt, uh, 806 cases per hundred thousand population that are black, or African-American 667 are Hispanic or Latino, 618 that are white and 303 that are Asian, so that you see that the pattern in these bars, um, does not reflect the general population in terms of the, um, racial and ethnic composition of the general population in New York city.
Dr. Ryvicker (14:07):
Um, uh, and just as a note, um, you know, we would, we would expect, um, the, the black and Latino bars to be somewhat smaller. Um, then, then Y if, if this were actually reflective of the composition of the general population in New York city, um, you see similar pattern, although that it, it starts to compress a little bit among nonfatal, uh, hospitalized cases, and then as well among confirms and probable cause of deaths in New York city. Um, it's important to note that New York city is now reporting on both confirmed and probable deaths, uh, or those deaths that are suspected to be related to COVID even if they weren't, um, confirmed. And this is just another point about some of the variation that we see in the different ways that States and localities are reporting on the data. So I'd like to turn it over to Dr. Banks, to focus on a case study from Illinois.
Dr. Banks (15:14):
Thank you very much. Uh, Dr. Ryvicker, there was an excellent, uh, analysis and really getting us started. And I'm going to continue on with the case study from Illinois as one of the earlier slides indicated there are only three States currently reporting, uh, COVID-19 testing data by race, ethnicity, Illinois being one of those States and Kansas and Delaware being the other two. One of the reasons why we focused on the state of Illinois is that we all know that I need to have a representative sample. And the state of Illinois on various metrics is much more representative of the overall us populations in terms of race and ethnicity. Also other socioeconomic factors that are based on census data. So fairly representative sample that represents the, um, so you can extrapolate findings from the state to the country at large, and a similar patterns here. As we saw within, uh, New York state and the city, I've got to direct your attention to that second road there, where all the blacks just make a 15% of the state population advocates indicates, uh, among, um, fatalities, uh, due to the COVID 19, they represent 33% of the deaths.
Dr. Banks (16:19):
Uh, these three pie charts here are taken directly from the Illinois department of public health website. And they're accurate as of two days ago, May 10th. You can go to the URL right there and see the same figures. Um, and again, that far left column, um, only three States reporting, uh, testing database, as you see that large green slice of pie there that even among this state, we have an absence of data, uh, on race ethnicity by over half this population, 52% in all of us who work in, uh, you know, health data. I mean, that's usually what I'll be more readily available, uh, pieces of information, um, at the demographic level. So if we're not even, you know, having that, those are, it really makes, and I'll just go back to Dr. Ryvicker's previous point. Um, these evidence, um, you know, indicates health disparities, but, you know, as we know that without complete testing data, it's really hard to say what's really going on because we look at this, uh, the pattern here, you know, at the top second row, blacks, 15% of the population, but testing, uh, if you look that orange slice, um, like 10% of, of that, or it's, but what's at the other half, all right, is all this other 51%?
Dr. Banks (17:31):
Are these all blacks? Is it white? So if we go over to the cases, you know, the middle, middle pie chart, we see Blacks are 19% of cases, but again, we still missing a quarter of the data, and then we just go straight to this. So a lot of the data that you're seeing now, we don't have, we only have testing for three States. So if we just look at cases and deaths, yes. You know, blacks are having 33% of the deaths, but if we don't, if we can extract that back to the level of testing. So for instance, and use an example of blacks represent 75% of the tests, but just 33% of deaths, even though there's still a disparity there. Um, but there's a difference that we really can't say that that's a health disparity. So, um, you know, the data's definitely leaning towards one way, but in order to make those sort of sophisticated analysis is all just preliminary, descriptive data, right?
Dr. Banks (18:20):
With non-representative samples and, and complete data. We really just have to be cautious when making those assessments, uh, next slide please, and is striking, is some of the, um, conceived, you know, or just indicate a health disparities are among blacks and, and other populations. Here's an analysis of, of zip code. Okay. This is the incident rate of COVID-19. This is against from the Illinois department of public health website, that your idol is right there. And I've just, you know, extrapolated some of the lowest incidents rates, five of the lowest incidence rates and five of the highest incidents, right? This is by zip code. Unfortunately they didn't have, um, COVID deaths or race, ethnicity broken down. So there's level, but as we can see here, I mean, this is the spirit, right? And as Dr. Ryvicker indicated earlier, you know, race, is it, you know, race drive in place or place drive and race, you know, we really need to have a better idea of what's going on here.
Dr. Banks (19:17):
Why is it in zip code six, two, six, five, six, we have a 2.3% incident rate versus, you know, area code, uh, zip code, I'm sorry, six zero six, four, over half of people being tested or tested positive for COVID-19. I mean, th this, this is, this is striking. So before we, you know, it's very, very tempting to look at everything that we know about social determinants of health and health disparities, and point, right. To, you know, race and ethnicity, but a key point here that we really must play close attention to that virtually everything that we know about health disparity, concerns, chronic disease, chronic disease, and health disparities. This is infectious disease. So this is a totally different ball game. But again, I'm not saying that they health disparities don't exist, um, within, you know, COVID-19, but before we just really need to take all of this early evidence is just that early evidence that we really need to troll further before we make any definitive conclusions. Next slide.
Dr. Ryvicker (20:19):
Thank you. Dr. Banks is a really important point and I think is a really good segue into, um, taking some lessons of what we have learned, um, from previous research on health disparities in the us, but also being cautious, not to impose what we know or, um, what we think we know into the context of infectious disease, but there are some places where it intersects and that's one of the things that, um, I'm going to highlight here. Um, it would be really impossible to attempt to summarize, um, the best body of work that's been done and health disparities over several decades, really. Um, but I'm just going to emphasize a couple of high-level points. Um, that may be important in this context. Um, there is a large body of work, um, showing evidence that racial and ethnic minorities in the U S have historically had poor access to health care, including factors such as insurance coverage and availability of providers, timeliness and appointments, um, and other kinds of quality measures, um, in the facilities, um, where, where they receive care.
Dr. Ryvicker (21:39):
Um, and, and this has been shown time. And again, it may or may not be playing out in the context of COVID. We really, um, can't speak to that yet. These studies haven't been done yet in the context of this pandemic, but one thing that is important to have at least in the back of our minds is that, um, patterns observed in access to and quality of care may have important implications for the risk factors that have been noted as, um, having important implications for COVID complications. So, so what I'm referring to here is, um, management of chronic conditions such as diabetes and hypertension, heart disease, and asthma. Um, these are all conditions that can kind of referring to the, the prior research on disparities in the context of chronic disease, which we have to sort of disentangle these things. But, um, one thing that we have seen historically, um, is that racial and ethnic minorities in the U S um, have, have had, um, uh, in general worse, um, quality of care in terms of the facilities that they have access to, and a variety of complex factors that may, um, be interplaying, um, with the risk factors for COVID complications.
Dr. Ryvicker (23:11):
In addition to that, um, there's a disproportionate burden of chronic disease among racial and ethnic minorities, including the chronic diseases that I mentioned just now that have been noted, or at least, um, suggested as, uh, so far as risk factors for severe illness with COVID
Dr. Ryvicker (23:35):
Slide, please. So how do we understand these observed disparities in health care, quality access and in health status? Well, it's probably not surprising. There's no single causal explanation for the disparities that have been observed in any one disease. Um, we really generally take a multilayered perspective that a person's health evolves over the life course, and it's influenced by a variety of factors, including personal behavior, family systems, social support, and a variety of aspects of the environment. And that means the economic conditions that somebody lives in and works in, um, neighborhood factors, such as safety, transportation, access to parks, um, air quality, and many others, and the healthcare environment that they're interacting with over time, including health care, access to quality patient provider relationships and trust in healthcare providers,
Speaker 4 (24:44):
Please.
Dr. Ryvicker (24:47):
So with that all in mind, what are some of the possible explanations of the disparities that we're observing? Um, well, I'd like to emphasize that over will probably be a long time before we have complete enough data for really comprehensive research and statistical analysis that parses out the causes of the disparities that we're observing or that we, um, we think we're observing something that appears to, what's like, um, be a pattern in disparities and COVID prevalence and outcomes. Um, and what's been proposed so far is really, um, sort of suggestive, uh, or these are hypotheses that are combining some of what we already know from prior research, um, with case studies and sort of anecdotal observation. So at the current stage, we're really, um, posing questions more than we are, um, generating answers. Um, so as, as I've mentioned, this is a complex and evolving story.
Dr. Ryvicker (25:52):
Um, we can speculate as to some of the possible explanations or partial explanations in separating out both prevalence and severity. So, um, there, there have been some, um, uh, sort of reports or synthesis, um, and, and interviews with public health experts and disparities, uh, experts in the field who have posited that some factors maybe, um, in terms of prevalence, um, housing conditions differ, um, you know, that more, um, crowded housing conditions may be a barrier to social distancing and not maybe, um, a problem for, um, prevention, um, that racial and ethnic minorities may be disproportionately represented among workers. And especially among those that may have less protection and also who's who, who need to rely on childcare, um, um, and daycare and so forth. And that, that may be another risk factor for exposure, um, and also greater Alliance on public transportation to get to these essential, um, jobs, um, and to take care of essential household needs.
Dr. Ryvicker (27:06):
These are all just sort of, um, hypotheses really, um, around what some of the reasons might be underlying disparate prevalence, um, when it comes to severity, uh, some of what has been posited, um, really focuses on the higher concentration of risk factors for COVID complications, um, namely those chronic conditions I was referring to before diabetes, hypertension, heart disease, asthma, um, as well as, um, obesity as a risk factor. And there's also, uh, a study that came out of Harvard just recently suggesting possible evidence that, um, that those who experience greater complications in severity, um, from COVID, um, may be more concentrated in areas with higher levels of air pollution. So this is also again early suggestive, nothing yet conclusive. So we're just keeping all of that in
Speaker 4 (28:15):
Please.
Dr. Ryvicker (28:20):
So what does all of this mean for public health policy? In this context of this crisis? There have been some local strategies that are designed to address some of the barriers to prevention and to social distancing that may be disproportionately affecting a minority and low income communities in the context of New York. Going back to our case study earlier, um, there have been efforts to put in place temporary lodging accommodations for people who need to quarantine, um, because they've tested positive, but their housing conditions are not conducive to quarantining. Um, and also there have been efforts to place testing sites and also mask distribution sites and low income areas. Um, so these strategies, I should just note that, um, I mentioning strategies here that speak more to prevention to address some of, um, the observed differences in prevalence. Um, but when it comes to that, the possible X planetary mechanisms around COVID complications and severity, those really speak to more longer term public health issues that, um, that these all take to public health issues that are not unique to COVID per se, um, in terms of a variety of that, uh, you know, possibly environmental factors and healthcare environment factors and all these kinds of things, but when it comes to chronic disease management, um, these are very, um, broad public health concerns that, um, you know, are sort of happening on an ongoing basis.
Dr. Ryvicker (30:04):
And so we're, we're looking at what are the strategies that are being addressed that are being implemented in the, in the short term, um, in this crisis situation to address, um, barriers to prevention. So I'll turn it back over to Dr. Banks, um, to look at some examples from Illinois and some possible strategies.
Dr. Banks (30:27):
Thank you very much, Dr. Ryvicker and I'm turning back to Illinois as, as we saw, um, as opposed to one of the important things, um, to do right now, I think that there is always a, a rush to do something right. Let's let's, you know, we have COVID what's going on, but it's important to be cautious because of the limitations in the data, right. I mean, we saw earlier that we only were missing data on, on ha on testing alone, um, 50%, half of racer as a race by race and ethnicity. And so we also saw in that second slide that like the, you know, despite the perceived or, you know, indicated disparities by race, ethnicity, the disparity by place by location could be even farther even far greater. So it's just, you know, with this early day, Hey, let's go do more surveillance or, you know, treatment of young Hispanics, well, perhaps it's, you know, better to do it, or the data indicates that, you know, by, by, uh, by zip code or by location.
Dr. Banks (31:22):
So we definitely need more data by across all fields, but definitely by race ethnicity before we indicate anything on disparities or what to do with, uh, already limited healthcare dollars. And, uh, you know, there's other things, you know, we need more data on where the testing is taking place. You know, there is no uniform system in place right now are these, you know, just, you know, clinics who, you know, individuals, you know, just suspect that they may have been exposed or just, you know, general surveillance or assisted testing being done, you know, any emergency rooms or in the hospitals where people are already been hospitalized or an inpatient treatment, you know, and they're suspected of, uh, being, um, you know, uh, of having COVID-19. So that's, that's really got to skew your sample. You know, we're not doing a randomized controlled trial here, so it, you know, we really need to have better data to have a uniform and comparable and where the, uh, data is taking place, um, far to missing geography, but also just testing what type of test, I mean, we're, so you saw the slide that just says cases, you know, but as we know now that there are tests that can show whether you're currently, uh, active with COVID or if you put in previously, uh, previously, um, exposed, but you don't, you're not currently active.
Dr. Banks (32:31):
We're not currently disintegrated by even what the case means, you know, in a data. So, uh, before we do anything, I know that there's a brush and we have to do something, but in order to make sure that we don't do that wrong thing, a race waste, uh, time and other valuable resources, we really need to be cautious and wait for data to come in before we start taking a drastic approaches. Next slide.
Dr. Ryvicker (32:56):
Okay. Thank you. So that sort of brings us to some of our takeaways and really this drives home, the point that we really do need complete and standard data collection for COVID-19 metrics, by race and ethnicity and other kinds of demographics, and to Dr. Bank's point, um, geography is, um, uh, you know, in other public health research, we do see that, um, health status and a lot of different factors kind of intersect with geography and we, we need, um, a comprehensive data collection, um, and repositories to really be able to parse this out and assess the, um, whether and to what degree and where, um, these disparities are playing out so that policy makers can actually make smart decisions, um, based on, based on the evidence. And there really is a dilemma. Um, and Dr. Banks was referring to this a minute ago that, you know, we, it takes time to develop these data repositories to, for, for researchers to really do the analysis, to make it systematic, to adjust for all these factors, um, for, for robust, um, research and, and a robust evidence-based.
Dr. Ryvicker (34:19):
Um, and yet we have policy makers who are in a crisis situation and meeting to make decisions on an urgent basis. Um, and so this really is a dilemma there. This is, um, sort of something that I've, um, personally seen on a smaller scale when it comes to sort of policy evaluation or other kinds of improvement efforts at an effort to address, um, either a problem in healthcare or a public health problem that sometimes, um, you know, you're, you're in a situation where decision-makers need to act yesterday, and yet it takes time to really, um, collect and analyze the data in a robust way, such that you're actually confident with your findings. Um, and that is meaningful, and that is actually, um, helping to make informed decisions on how to allocate public health dollars. So that's really what, um, one of the most important, um, reasons why we need, um, more standard and complete data collection on, on these demographics. Um, so those are the main takeaways I'll leave with. And I think, um, uh, Dr. Krebs, I'll turn it back to you in case we have questions. Absolutely. Well, thank you both so much
Kristen Cribbs (35:38):
For such a thoughtful and insightful discussion and, you know, beginning to tease apart, all of these complexities, we clearly have a lot more to learn, but I think, uh, you know, really important that we begin having these conversations and start to try and toss out these hypotheses and begin to make sense of, of what we're seeing, um, to be able to inform, you know, more effective responses. So with that, we'd like to open it up for Q and a, if anybody has questions or comments that you'd like to share, uh, we encourage you to do so by submitting them through the Q and a function in your, uh, menu bar.
Speaker 3 (36:23):
Okay. So
Kristen Cribbs (36:26):
I have, uh, one question that just came in asking whether there are standardized efforts to collect D aggregated race, ethnicity, COVID data, uh, for example, this, this attendee heard that COVID is heading South Asian. For example, Bangladeshi community is in New York city, particularly hard, which might be missed when grouping all Asians together in datasets.
Dr. Ryvicker (36:56):
Um, I'll take a stab at this question. I don't know if I have, uh, the most satisfying answer here, but, um, um, to my knowledge, I don't know the any standardized efforts to dis-aggregate, um, within these broad categories. I think it's probably, um, it, it may be too early and, and what it really requires is anytime you think about, um, are there efforts to collect the data? You have to think about the process through which that data is being collected. So if I'm going to show up to a testing site and I'm entering my information, whether I let's say I go to Sydney MD, which is an urgent care chain here around New York city, which is doing more widely, why am I doing testing more widely? And there are many, many sites unless they have in their electronic health record or in their process. When I walk in the door a system and asked me those questions on that more granular level, that then gets reported to the state, if they are not doing that, then the data won't be there.
Dr. Ryvicker (38:12):
So the data is only good as, as good as the systems that are built to collect it through the process of administering the tests and the treatment or whatever else, however you're navigating through the healthcare system. So, um, so, so that is where the data comes from. And because, um, typically, uh, those, those electronic health record systems are not collecting that data on such a granular level. Um, it's, it's unlikely to be available outside the context of a concerted effort or a research study. Um, that's trying to drill down more specifically, and dis-aggregate, it's a really important question. Um, um, and it's, it's easy for us to, I think in, in, in the world we live in, which is so data-driven and swimming and data, and it seems like, you know, you open up your computer and all these platforms know things about you, and it's not so much the case in healthcare and healthcare processes.
Dr. Ryvicker (39:19):
Um, uh, you know, Amazon will know more about you perhaps then, you know, then your own demographics in, in your electronic healthcare records. So, so that's just some things to keep in mind. Um, I, uh, this is, it's an, a really important question. Um, I don't know, off the top of my head, whether there are any efforts right now going on to collect that data and dis-aggregate, um, uh, you know, um, particular ethnicities or nationalities within some of these larger groupings and these groupings, by the way, we haven't mentioned yet are problematic in and of themselves because we know that these aren't always mutually exclusive, um, categories and, you know, there's a lot of variation within them. Um, so, so as crude as they are, um, they're, they're important and yet there's still a lot more, um, complexity that it won't uncover.
Dr. Banks (40:17):
I think that's an excellent response. I think that's highly satisfactory. That's the best response to that could be given, uh, right now. And just, um, you know, with that question kind of speaks to just preexisting limitation. They are mainly categorizations, like African-American, you know, most, um, you know, the population they're talking about most black Americans have never been to Africa, and there's a very, very distinct difference between, um, a person who, you know, comes culturally and socially, economically African families who kept having to be like true African-American families, versus what, you know, commonly is called, you know, African-Americans among blacks, you know, we're native, uh, native to the United States. So that's just one example of that, but it's a great question. I would definitely agree with Dr. Robert Kerr on one, it's going to vary by jurisdiction by state of public health department. Um, you know, but I would say, uh, you know, the answer, you know, not yet, you know, I think a lot of things that before this, it was like, not yet, but we're going to start to now I think that this is going to be changing, uh, data collection across the, um, across the spectrum.
Dr. Ryvicker (41:24):
Thank you. So another attendee asked how we can better support older African-Americans through this COVID-19 crisis
Kristen Cribbs (41:34):
When many have chronic illnesses, which I think gets at a really important point, which is the intersection of, of these disparities. So we talked predominantly today about racial, ethnic disparity is there are obviously many other, uh, purported disparities we're seeing across, you know, chronic illness, age, uh, numerous other categories in vulnerable populations. So would be curious if either you, either of you have thoughts on that.
Speaker 3 (42:07):
Yes.
Dr. Banks (42:08):
Right. Uh, well, one of the things I think that, uh, it's thing in public health, like once, um, as researchers, we see a, a health problem and we get laser focused on it and we try to stop it. Right. I think this is a prime example of that. So we have, you know, COVID, Oh, you know, social distancing, let's, let's, let's, let's shut things down. You know, we gotta shut down the schools, we've got shut down in gyms. Uh, but everything that we know about public health data, the association between, um, education and public health and income that, you know, then public Elsa was shutting down jobs and, you know, we're attacking one public health problem, which is an important problem. Don't get me wrong. But, um, you know, uh, education is also important. We have years of data, which show that income is also important.
Dr. Banks (42:55):
So, you know, people losing their job, you know, versus that, um, once a gym is also important, but you know, not just obesity, you know, you know, mental health, you know, basically all of these things. And so that's address the question, how can we support all the Africans, you know, socialization among all populations, but particularly among older adults, um, there's this, the social bonding and that connectedness, I mean, we're, we're shutting down churches. You talk about an older African Americans, they're in church, you know, especially in the South now, you can't go there. We were trying to adjust one public health problem, but we're shutting down one of the primary resources and sources of social connectedness, mental health, physical health, spirituality, all of those things. So I think that, um, we, we just have to be smart and, uh, and cautious and not just, you know, jump to what I like to, you know, knee jerk reactions.
Dr. Banks (43:45):
Let's just close out everything, but, you know, let's wait, you know, some of these things are beneficial. Um, and so just, you know, during this time for all the, African-Americans just making sure that they don't feel connected. I know you've seen, I don't know if, how many of those, you know, watch live PD or it's been just on the news where a person have been happened to be African American. They were trying to speak to their spouse through the window of a nursing home because you can't get in and that's not helpful. Uh, you know, as far as, you know, prevention of COVID. Yeah. That's helpful as far as that, but, um, I think we just have to make smart decisions and making sure that, you know, the solution isn't worse than the problem.
Speaker 3 (44:26):
That was a great answer. I don't know if I have anything to add to that. Um, yeah. Um,
Dr. Ryvicker (44:32):
It's, it's a really complex, um, challenging problem, a challenging question. Um, and yeah, I, I, I really appreciate the chance to kind of think this through and discuss it. Um, I think there's, uh, one other question I'm seeing here, but Dr. Cribs is preferably keeping track.
Kristen Cribbs (44:58):
Yes. So another attendee asks whether unequal access to telehealth might be a possible mechanism for differences in some of the outcomes that we're seeing.
Dr. Ryvicker (45:13):
Um, this is a really good question. Um, we, we do know that there are, there are signs that there, there are inequities in access to telehealth. Um, so it's, it's a really important thing to think about, um, whether it's, uh, uh, a mechanism in differences in outcomes. Um, uh, we might want to think about whether it's a mechanism in, in differences in prevalence, and if you, um, have an urgent healthcare need, um, and you, that that is not necessarily COVID related and you don't have access to your provider via tele-health. Um, then you may risk exposure by going into the doctor's office or an urgent care center, or, um, we also know that when issue, and especially in, uh, those who are in more under-resourced, um, healthcare, um, environments or neighborhoods, um, is that very often some, um, non-emergent medical needs get addressed in the ER.
Dr. Ryvicker (46:29):
And so then you're walking into a situation that is at a higher risk for exposure. And so, um, if you think about like a lack of access to telehealth, further upstream, um, just to address more everyday or calming illnesses, then, then you may be risking exposure by going into, on onsite, um, a healthcare provider, an office, or a facility where maybe it could have been addressed by telehealth. So that may be, um, a mechanism that, you know, going forward, some of the, um, research could investigate. Um, uh, but one of the things that, um, sort of secondary to COVID outcomes in particular is all the other health ramifications of having, uh, less access to your physician during this time to address other medical needs. Um, and, and so if you know your provider, isn't taking, um, office appointments for anything other than, you know, more urgent needs, you know, some of your routine medical needs, um, management of chronic conditions may fall on the back burner without access to telehealth, um, mental health care.
Dr. Ryvicker (47:51):
Um, this is a really important area where, you know, if, if there is, um, if somebody doesn't have access to their mental health providers through tele-health, then they may be more at risk, um, uh, for, um, mental health emergencies. And so, especially for those with sort of severe persistent mental illness. Um, so this is a, it's a really important, um, I mean, tele-health, um, is being rolled out, um, to some extent, um, for providers that were not already doing tele-health, there's a lot of patchwork going on, um, to address these needs and to maintain continuity of care. Um, so while we don't know yet whether, whether it's, um, a mechanism in differences in COVID 19 outcomes, per se, um, it may very well, um, as we see this play out, be and mechanism in other, um, kind of secondary outcomes that are happening, um, during this pandemic. And we also know that, um, you know, there, there is evidence, stormy of disparities in access to, um, to an internet access and, and technology that would, um, enable somebody to, uh, to connect with a provider in, in sort of an interim, um, on an interim basis, if they're not in, in a routine kind of telehealth, um, situation.
Dr. Banks (49:20):
Absolutely.
Dr. Ryvicker (49:21):
Thank you. So one of our attendee is had a comment around your response and noting that continuity of care is already a problem amongst vulnerable groups. And this person is wondering whether you think that the COVID crisis could exacerbate this problem, or whether there may be opportunities for potential improvements in this area as a result of the pandemic.
Dr. Banks (49:49):
Um, on another great question. I, I, you know, just from a real life personal example, I'm usually always overdue to go to the dentist, but, but now, you know, I'm really long overdue and, you know, a lot of, you know, you just can't sell it, you know, something like that. And that is, um, you know, like I said, the non acute non COVID related conditions, uh, dental health is extremely important, extremely important. It's associated with a number of other, you know, chronic and acute conditions and his actor, Dr. [inaudible] indicated earlier, uh, the digital divide, you know, there are parts that, you know, many of the health disparities remember those of mirror, those of, uh, ex internet access. And, um, also just, you know, imagine you have a household with, you know, two adults and five children, right. And now many of the schools are closed nationwide, and we have to, you know, education has been moved to online, so you need seven computers, but you got one or two computers in the home.
Dr. Banks (50:45):
So just trying to access, you know, that just for the, you know, the children, you know, the education or adults, you know, doing distance learning, everything's moving online now. And so I think that, um, just with that, and then you, you've got job loss and just add into that. So I think that a lot of those things, which, um, you know, which unfortunately get pushed to the back burner, a lot of those, you know, chronic conditions, you know, ailing things, if it's, if it's not acute, if it's not, you know, critical to my, you know, current survival, I mean, a lot of people are in crisis mode as a nation prices, survival mode. So a lot of those things that will affect and impact health, um, you know, down the road or not, or I would say that they are at risk of not being given the attention prioritized now, understandably, because there are so many things that, you know, are at the forefront of our minds right now.
Dr. Ryvicker (51:36):
I just like to add, um, add a bit on the, the question about, um, access to that continuity of care. And it kind of piggybacks on the question about tele-health. Um, I do think that with greater rollout, um, and more systematic applications of telehealth in response to this pandemic, that it could be an opportunity to improve access, to care for, um, vulnerable groups that, um, you know, especially, um, potentially at rural populations, um, you know, telehealth has been used in a more limited capacity historically. And you know, now with this crisis, um, it's being rolled out on a larger scale for, um, conditions and for, uh, health care interactions that it wasn't really being used before. And maybe that is one way, um, to support, um, continuity of care for vulnerable groups in the, during the pandemic, but also to, um, implement and, and create systems that make tele-health more mainstream and more widespread such that it may support more in the longterm and some continuity of care improvements for some vulnerable groups that may be more isolated or may have a harder time getting to their doctors, um, because of a lack of social support, or maybe they live alone or they're home bound and all kinds of other, um, considerations.
Dr. Ryvicker (53:14):
Okay. So I think we have time for one more question. And other attendees asked, has the federal government set aside any funds for tele-health in rural or isolated populations? Um, to my knowledge, yes. Um, I don't, um, I, I can't speak in great detail about whether specifically for rural or otherwise isolated populations, but my understanding is that there is a federal push, um, to roll out tele-health, um, to community-based, um, health centers, clinics, um, mental health facilities, um, that may be providing outpatient and also to some extent, some inpatient services where, um, there needs to be some distancing, um, uh, strategies for providers, um, who circulate among different facilities. Um, so I, I believe I do know that there, the federal government is setting aside funds for tele-health. I don't know how specific it gets to rural, but I, to my knowledge, it doesn't exclude rural. Um, but whether it's, uh, specifically focusing on rural populations as well, um, that's something I could look into as a follow-up if, if you'd like to reach out happy to look into that some more.
Kristen Cribbs (54:35):
Great. Well, thank you so much, and thanks to all of our, our attendees for asking such thoughtful questions, um, we hope that you will continue the conversation, certainly reach out to us. Um, these are very important issues that, um, you know, we'll certainly, uh, hopefully be learning lots more about in the coming months, um, but definitely important issues to be thinking about and, and talking about as well. So with that, I think we will conclude this session, thanks to everyone so much for taking the time to do attend, to participate. We hope that you found the presentation helpful and informative. Um, we have a great, uh, lineup of presentations and speakers tomorrow. We hope that you will join us and, and take a look at the agenda and, and tune into some of those presentations. And thanks again to our speakers.
Dr. Ryvicker (55:32):
Thank you, Dr. Chris for keeping us on track and moderating. Appreciate it. My pleasure. Thank you.