Public Health Insight

Colour-Coded Health Disparities: An Argument for the Collection of Race and Ethnic Data

June 02, 2020
Public Health Insight
Colour-Coded Health Disparities: An Argument for the Collection of Race and Ethnic Data
Show Notes Transcript

During the initial stages of the ongoing coronavirus pandemic, it became abundantly clear that there were no comprehensive surveillance systems in place to systematically collect and/or report race and ethnic data for confirmed COVID-19 cases in Canada. This issue was brought to the forefront when it was reported that Black Americans were more likely than the general American population to be diagnosed with and die from COVID-19. Similar health disparities have also been identified for other people of colour and Indigenous groups in North America. Vivetha Thambinathan joins the Public Health Insight Team to discuss the importance of collecting racial and ethnic demographic data in a culturally appropriate manner, the cautions associated with interpreting data without proper context, as well as some strategies to ensure the data informs public health programming and policies to address health inequities. 

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[00:00:00] Sully: Public health is a population-based field of science focused on preventing disease and promoting health. Every week, we will be engaging in interactive discussions and analyses of the latest public health issues affecting you and your communities all around the world. This is the Public Health Insight podcast.

[00:00:24] Leshawn: My name is Leshawn and I am here with the usual public health panel, Ben, Gordon, Sully, Will, and a special guest who will be introduced later. 

[00:00:34] Ben: Before we move on its important to note that the views expressed in this podcast are our own and do not represent any of the organizations we work for or affiliated with.

[00:00:42] Leshawn: In this episode, we will be discussing an article from the New England Journal of Medicine, titled "Racial Health Disparities and COVID-19 - Caution and Context". The article presented data from Wisconsin and Michigan and showed that Black Americans were more than twice as likely compared to the [00:01:00] overall population to be affected by COVID-19. Similar disparities were shown in other racial minority groups in other areas. Preliminary data released by New York City also showed that the virus is killing Blacks and Latinos at twice the rate, as Whites. As such, we will be discussing the importance of collecting racial and ethnic demographic data, the cautions associated with collecting such data, and some strategies to mitigate the possible unintended consequences of using this data.

[00:01:29] To discuss this important issue, we have invited a special guest. She is an Eelam Tamil activist, a proud figure skating coach, and a health professional education PhD student with a strong commitment to health equity. Her true passion lies within breaking down structural barriers for marginalized populations and ultimately empowering individuals to serve as advocates of their own health. She holds a master of public health degree and has engaged in participatory action research projects in her [00:02:00] roles, working at both the Health Equity Action Research Team and the Center for Addictions and Mental Health. She's also the curator of a community platform on Instagram, named "Diversity in Academia", whose mission is to inspire, support, and promote diversity in higher education.

[00:02:17] She truly believes that all change is rooted in community. We are pleased to welcome someone who would do anything for a good chocolate donut, Vivetha Thambinathan. 

[00:02:27] Vivetha: Hi, happy to be here. 

[00:02:29] Leshawn: Throughout the course of the ongoing pandemic, it was not standard protocol to collect or report detailed demographic data in cities like Toronto for confirmed COVID-19 cases and deaths.

[00:02:42] However, an important argument was made to begin collecting race-based data in order to accurately picture how negative health consequences are unevenly distributed to already vulnerable populations. Recently health officials in Toronto, Canada have acknowledged the need and are now collecting this [00:03:00] data.

[00:03:01] So Vivetha, let's start off by talking about what the collection of race, ethnic, and other social determinants of health data means for health systems and public health, in general. 

[00:03:11] Vivetha: Sure. So to start off, I think we have to talk about what race is, what matters is that there's social meanings attached to race and there are political and economic forces that support these and reinforce and enforce these perceptions. So the term race itself carries with it, histories of stereotyping, exclusion, and other forms of social injustice. 

[00:03:32] Gordon: You know, we were speaking off ear earlier about the distinction between race and ethnicity, because it is an important distinction and especially in the context of collecting higher detailed demographic data.

[00:03:44] So if you can maybe tell us some of the differences between race and ethnicity. 

[00:03:49] Vivetha: So I think that's actually very important to consider because even looking at race-based data in the United States versus race-based data in Canada, we wouldn't [00:04:00] collect it the same way. Um, so there was actually. Um, a survey and some research done around how would we collect race-based data in Toronto?

[00:04:10] And there was actually a mismatch, um, and there was kind of a clumping of all Aboriginal groups as one and Aboriginal health care leaders actually provided feedback on the importance of differentiating between Inuit, Métis, and First Nations when collecting demographic data. 

[00:04:27] Gordon: I don't know, I was watching some videos and Ted Talks and they were saying this process would have to be done systematically in that you'd have to determine, you know, would you get a better picture if you had people self-report their race or ethnicity, or would you have a better picture by just having a drop down field and have the potential people not being able to identify with the options that you give them?

[00:04:47] Vivetha: Actually glad you brought that up because even from the previous Canadian census, when you're looking at the race, ethnicity, categorization, it's actually found to be limited by people and researchers within [00:05:00] the health equity fields, they found it limited and inconsistent, and some people even asked why certain countries weren't included and certain languages were included.

[00:05:09] Will: Right. So going off this topic of race-based data and on health care outcomes, what are some of the benefits for collecting this sort of data? And what are some of the pros and cons that you see in place? 

[00:05:22] Vivetha: Yeah. So the term color coded healthcare comes to mind when I think about this, because it looks at the impact of race and racism on health and the role of how medical sciences should kind of switch and shift from moving beyond Biology and Genetics to looking at social determinants of health and understand the social structures that produce these unequal healthcare experiences for racialized Canadians. And I'm just speaking from the Canadian tone texts. I'm sure we've all heard of Brian St. Claire, an Indigenous man who died while waiting for care in a Winnipeg emergency room, back in 2008. He was literally ignored to death waiting for [00:06:00] 34 hours without being seen, because he was assumed to be drunk and sleeping it off, even though he was just in the early stages of a bladder infection. So I'm just saying that to preface that it's been studied and documented that there is a racial bias in our medical system and not just an access to care, but also how people of color are treated once they're in care.

[00:06:19] So the concept of provider bias, documenting that or pain is undertreated and are complaints are minimized. So, um, that's just to kind of give you context for why there is a need for race-based data. And it usually follows kind of the notion that something's gotta be counted to count. 

[00:06:37] Leshawn: So Vivetha, collecting this race and ethnicity based data, how do you think that actually will help us achieve more equitable care?

[00:06:45] Vivetha: I think it's provides the basis and kind of the foundation for evidence, for which policies can be created. So the intention would be to identify and monitor these racial disparities so we can eliminate systemic racism and in [00:07:00] particular, within the healthcare field. And when it comes to coronavirus, it could be where is the access to testing located, which neighborhoods, because it's not just ethnicity and race, it's also socioeconomic status and other related data as well.

[00:07:14] Gordon: Yeah. In our AI episode, we did a couple of weeks back. We were talking about how data can be used for harmful purposes and beneficial purposes. So how do we ensure, that collecting this data will lead to more positive outcomes for vulnerable populations and not further discriminate against these marginalized groups?

[00:07:35] Because one of the things I immediately think of is early on in, you know, when the COVID-19 pandemic started and when we had, you know, community outbreaks in Canada, if this race or ethnic based data was being collected early, and it was said, Oh there's an outbreak, but it only affects Black people. I'm not sure some of these public health measures would have worked because people would say, oh, that's a disease for people of color.

[00:07:58] Right. So there are, [00:08:00] in my opinion, there are harms in maybe how you collect and or report the data to the general public. I don't know if you can speak to some of the other harms of collecting um race-based data. 

[00:08:11] Vivetha: Well, just going off of what you said. Um, that's actually very important. And within anti-oppressive and anti-racism literature, it's always known that there is a distinction between intent versus impact.

[00:08:23] So although we may have good intentions and collecting race-based data, ultimately impact or the effect of it is greater than the intent. And just what you've said about the media perpetuating negative stereotypes. This has been a thing in Toronto before, back when the Police Department collected race-based data to look at crimes and to look at other situations related. And what came out of that was Black people being criminalized, having negative stereotypes, Tamil gangs, refugees kind of having this reputation of being, um, gangs because they [00:09:00] are Tamil instead of seeing the problem for what it was. And this is also related, but this is actually something that I was looking into before this podcast.

[00:09:09] There was actually a anti-racism act in Ontario and it was passed in 2017 and it was a framework for the Ontario government to identify and eliminate systemic racism. And they actually have established, um, race-based data collection and implemented it in three sectors, justice education, and child welfare.

[00:09:30] But that was just to say that this isn't new to Ontario and. The mirror like exclusion of health within this anti-racism act is the reason that we don't have this mandate for race-based collection as a whole, anyway. I just wanted to bring up something else as well. And this is actually a four year project, um, titled "We ask because we care" and it was from 2009 to 2013, and it involved a group of health equity practitioners from Toronto Public Health, St. Mike's [00:10:00] hospital, Mount Sinai hospital, and CAMH and. There was actually no external funding or any additional resources for the committee members involved. That's just to show that there was a lack of political will here, and they actually demonstrated that this was doable, this socio-demographic data from patients and clients, and they basically honed in on the fact that you have to ensure that patients understand the why behind data collection and be able to articulate that, and be able to have standards in place so that race-based data isn't being used without context. 

[00:10:35] Gordon: That's a great example because I think when people go through the public health system or the healthcare system, and they're asked, you know, ethnic based or race based questions, they immediately raise an eyebrow because it's, what are you going to do with this information?

[00:10:49] So I think what you're saying, making it clear, maybe this is for quality improvement in healthcare and, or, you know, to give public health information on what people need resources [00:11:00] most, and stuff like that. I think it's a good thing to say up front. So that's a great point. 

[00:11:03] Vivetha: And it makes sense why there would be a lack of trust, right?

[00:11:06] Historically researchers haven't been good to these communities, especially when these numbers actually kind of gave oxygen and fuel to racist beliefs and legitimized harmful stereotypes. But I do want to bring up how Indigenous communities have this principle called OCAP. Well, four principles. Um, so it's called OCAP ownership, control, access, and possession.

[00:11:30] And I feel like these are the four things that you have to keep in mind when thinking about race-based data, who's owning it? As in do the communities own this data? Control. Um, as in, are they participating in collecting this data? Who's maintaining the oversight? Access. Do we have access to the information if it's about my community? And do we get to decide how we use it?

[00:11:55] And then possession is more of the physical possession of facilitating the [00:12:00] protection and of, of ownership and control. 

[00:12:02] Gordon: Yeah, that's a great point because when we talk about data, we know they're saying data is more valuable than commodities like gold. So if this gets into the hands of private industries who can then use it to enhance their profits, that's something we want to prevent.

[00:12:17] Leshawn: Yeah, that example you brought up even reminds me of another example with the Tuskegee experiment. That was a highly unethical study that looked at the natural history of untreated syphilis in African American males from 1932 to 1972. So the study started when there was no cure for the disease, but as time kind of went on a cure is found.

[00:12:37] But even though a cure was found, these men were not given the cure. So it kind of goes back to possible hesitancy and some of the implications of people's worry about collecting some of that race-based data because of historical events. 

[00:12:52] Vivetha: But I think we should also acknowledge that this pandemic has shown that a lot can be achieved given enough political and collective [00:13:00] will. Because you know, Ontario's Chief Medical Officer of Health, Dr. David Williams did initially say that the province wouldn't collect this type of data because regardless of race, ethnic, or other backgrounds, they're all equally important to us. That's a direct quote. But then within the matter of two weeks, he came back to say that Ontario will begin collecting this data.

[00:13:20] So I feel that we do also have to give credit to communities and activists that are on the ground doing this kind of work and pushing for race-based data to be collected. I think we do have to make sure we're not being patronizing in a way by saying that we do have to include them into decision making process, of course, and they should be on the front lines of this kind of work. And we shouldn't kind of be making decisions on behalf of them, if that makes sense without their voices being heard, because that's just, you know, contributes to like the paternalistic principle of what medicine was kind of founded upon. And that's what we're eliminating here.

[00:13:55] Gordon: Yeah. And something you alluded to earlier was it's important to have context with the data. So [00:14:00] for example, if you look specifically at race-based data alone and you don't look at the other contextual factors, like the environments people are living in, their income and their education, people who are interpreting this might want to ascribe some kind of unique genetics and biological factors as an explanation to why people are more prone to disease. And I think that was something you were trying to refute when we just started talking. 

[00:14:22] Vivetha: Definitely race is connected to health. And I think when we're talking in the context of COVID 19, we can even look at health, as in communities of people of color are already carry a higher chronic disease burden like diabetes, asthma, high blood pressure. But they've all been tied into racial health disparities linked to structural racism. So if I said the first part of that sentence without the latter part of it, I feel like it could be taken into context with, you know, pointing fingers and blaming on diet and other more individualistic factors.

[00:14:53] But there is research out there that has proven otherwise. And with Coronavirus, it's [00:15:00] already made these communities vulnerable to COVID-19. So we're seeing these communities are at a risk for developing serious complications. But I think everything must be contextualized when we're talking about this. And I think that media has an important role in that.

[00:15:13] Will: One thing that I wanted to mention is that like we touched, discussed earlier with the biases in medicine and what not, it seems like it's something that's very much a systematic, deeply rooted issue that if we want to solve and kind of improve the future access for vulnerable populations and stuff like that, we need to target it at the group.

[00:15:33] And I say this because. So I was listening to one of John Oliver's segments. And honestly, I mean, John Oliver is, you can take that, however you like, and not the most credible source, you know, not like on the Lancet or anything like that. But I think what he talked about was very interesting and definitely an eyeopening for myself because so his segment was called " Bias in Medicine".

[00:15:56] And it was, uh, mainly from a U.S. perspective, and you were saying [00:16:00] how in the U.S. there is a studies shown that in just medical school training in the United States, 25% of medical residents in that study legitimately believed that African-Americans had thicker skin, like physically, they, their skin was thicker, and they had, um, more pain tolerance than your White Americans. And, uh, similar kinds of messaging was seen in various nursing textbooks, which has since been removed, which said that like Hispanics believe that pain is a form of punishment that they must endure if they want it to enter heaven. And so it's like if we have our medical professionals who most people consider as medical professionals to be up there in terms of society and there's lot of respect and trust that people have in these individuals.

[00:16:44] If many of them are kind of brought up and trained in this way that perpetuates this form of racism and systematic prejudice. I think it's less so of targeted individuals or individual education facilities or institutions, but [00:17:00] rather kind of reassembling that the whole system, and re-examining that. 

[00:17:04] Gordon: So, yeah, it goes into the whole way that people are, you're saying well, that these things come from somewhere.

[00:17:10] So they're being educated to interpret things a certain way. And that was, I think that was what I was trying to say earlier with my concerns of race-based data. As we get more information on, you know, genetics and stuff. It's, it's going to be easier to say, okay, because there are some conditions that are linked to underlying genetic makeup, right?

[00:17:30] So things like, um, lactose intolerance has a genetic basis to it and things like that. So what's going to happen is you have clinicians and physicians who are. This is proof of concept for certain things, and they might kind of extrapolate it to the wrong thing. Um, such as other health inequities without looking at the underlying social determinants of health.

[00:17:50] Will: Exactly. It's like how we, we approach all issues. Right? They must be context specific. And if you're just taking racial, ethnic, or demographic data and using [00:18:00] it without considering the implications of it, you can have clinicians, like you said, who can easily take that and be like, oh, here, no, it's the data says this, it shows this, therefore this population, whatever kind of thing.

[00:18:12] Vivetha: Well, I'm happy you were talking about this because my research is in health professional education.

[00:18:21] So I, I think it's true that, uh, there's a lot of work to be done with health care professionals and their education. And I think that starts at mandates and, what do medical schools need to have as mandated as part of their education? So even with the example with genetics of lactose intolerance, if we just look at a different illness or disease or a condition, then I think that now we're starting to shift where we're going and we're not just using the, you know, the basic biomedical model that we've been looking at in like undergrad for years now, we're shifting to one that incorporates a more holistic view.

[00:18:56] So you're looking at, social determinants of health. You are looking [00:19:00] at structural factors as well within the healthcare system. You're also looking at genetics and even, you know, the concept of epigenetics. So I think everything just comes together and it's more linked and there has been a shift in some medical schools and this sort of education.

[00:19:15] And I think there's also been a push for more social sciences and the humanities to be incorporated, part of medicine. Because I think what we're lacking is like a lot of empathy and just critical thinking in terms of a person as a person and not just looking at the condition, isolated from the person.

[00:19:33] Sully: So the problem with this type of data is the interpretation aspects. We don't want it to slide into a biological explanation where people take it the wrong way, and then leads to false decisions, and it can lead to unintended consequences. So how do we solve for that? 

[00:19:51] Vivetha: Well, first of all, who's analyzing it. Right? And who's being consulted to develop the methodologies that go into this race-based collection because [00:20:00] again I'm just reiterating the fact that it doesn't have to be communities versus healthcare professionals or communities versus researchers who are collecting this data. There could be researchers who are part of the community collecting this data, right?

[00:20:14] So we just want to keep that in mind. And you could even think about it another way. Um, in which a way I think about it is this is actually a process where we're giving communities, particularly racialized communities, who've been asking for this, a tool, which will enable them to actually quantify their stories and hold institutions accountable for being complicit in systemic and structural racism.

[00:20:36] So it's truth telling from a quantitative perspective, and that is more sunshine and rainbows from that aspect. But I think it all starts with research methodology who is developing this project. And if a bunch of White people are, you know, developing a project that's about a different community, well, we've all seen research and researchers and needing to kind of reflect on the bias and [00:21:00] figuring out who should be consulted on the team to develop this kind of work, and consult from collection, data analysis, interpretation to even reporting findings. Even in health professional education, we talk a lot about community rooted and community engaged research. That means you go back to the community with your results, ask them if it's something that they resonate with before publishing it, right?

[00:21:24] Because it's not your data. You have to think about the ownership aspect differently. And I think that's more progressive and it depends on who's on board for this project, but I think that's an important thing to think about. 

[00:21:35] Gordon: So I was doing some research and they found that Professors or people in high academia of color were less likely to get funding.

[00:21:44] And if they did get funding, the funding was smaller in size. So that's a partial explanation on, you know, if you are someone of color who wants to do good community work, you're going to be limited by the funding that you can receive. So that's one barrier as well. 

[00:21:59] Vivetha: Academia just [00:22:00] measures, you know, publications, the whole idea of publish or perish, but they don't really account for how many hours goes into consulting with the community.

[00:22:08] And you don't just like go in and consult with the community. That's not how community research really works. It's called, you know, building relationships, building trust. And I think that concept is not, it's still not recognized. So I've done a lot of participatory action research and even doing that, it requires a longer timeline.

[00:22:26] And as a PhD student, I am committed to doing that, but I have to recognize that, wow, this might take longer than my four years and I have to bring my own funding, find my own funding in a way for this. And, you know, people will kind of look down on why you took longer than the usual time to do your work, right.

[00:22:44] And it's not valued higher or the same in any way. It's recognized by other progressive academics, but academia as a whole is still rooted in, you know, colonial ideas. So it's harder to get tenure.

[00:22:56] Ben: In regards to academia, we were often [00:23:00] seen as gatekeepers of information because we form all this research and we have all this knowledge and then we have to give it back to the community.

[00:23:07] But we talked a lot about grabbing data from the community, although we haven't really discussed what if the community's hesitant to give that data in the sense of like, if you ask them like questions about what their race is or their ethnicity, the anxiety that they feel, even answering those questions bring because of a preexisting history of discrimination. In your experience, what has been done to mend that relationship between patients and researchers?

[00:23:32] Vivetha: Well, I'm just going to start it off with, if you're a researcher and you go to a community, um, and you ask them to do research with you and they say, no, then leave. Like it's done. Like they don't want to do it.

[00:23:43] Like you're not, you're not about to force anyone to do this type of research. And I think that it's important that we also have people from those communities doing research themselves. So kind of being an insider within your community and doing research. And I think that also goes with, [00:24:00] you know, supporting people of color to get into academia and go to grad school and do this type of work. 

[00:24:08] Ben: So like you ask people to join in on the research, but because of the very fact that you're asking them this type of data, it's so racially charged that it gives them anxiety due to like prior discrimination. So how do you mend that relationship? Like yes. If they say no, then absolutely. You just walk away.

[00:24:24] But in the future, if you're trying to take this data to make the situation better, how do you break bread or bring out the olive branch?

[00:24:31] Vivetha: Yeah. See, I do have a problem with that because I think that kind of the known way of, um, data collection is self-reporting surveys. So they can voluntarily just not answer that question if that's not something that they want to.

[00:24:45] Um, and I think with, you know, mending that relationship, I, I just, I strongly do believe that if it's a no from the get-go, then it's a No. 

[00:24:56] Gordon: In there in public health, there is a social ecological [00:25:00] model that kind of informs how public health moves forward to address underlying inequities. So there are a few elements, but for the purposes of this conversation, we'll talk about organizational community and public policy.

[00:25:13] So from your work in health equity, what types of policies, you know, when you start your career out there in public health, what types of policies that you hope to influence as it relates to, the responsible use or the collection of data?

[00:25:27] Vivetha: One thing that I took from my MPH was this quote, that was "the science behind public health is epidemiology. And the art behind public health is politics". And I think this is really important in how we see data and its potential to influence policies. So I think that's why I'm really advocating for race-based data to be collected because, is, something's got to be counted to count. And I really think that if we have this data and we have the proof that we need and the evidence, then when the policy window shows [00:26:00] itself, then we can use it to create and implement policies. Especially because we were talking about how there's a lot of bias within the healthcare system and a lot of individuals, whether healthcare professionals having these unconscious biases and, you know, um, within both their education and their practice. I think it's important that we start from the top down as in institutional mandates and institutional policies, but this isn't something that's easy to achieve, especially without the data and the evidence. 

[00:26:29] Gordon: That's in line, kind of with what I've been seeing also, because there's something called, um, I think it's called assumed equity, where essentially in health care systems, health administrators are aware of these health disparities that exist in health inequities, but they're not aware that it's happening in their own system. So in order to know that something is wrong, like you're saying, it's something you have to count. 

[00:26:53] And if you count it, you're able to measure outcomes. Uh, you develop interventions, you're able to measure how those [00:27:00] interventions influence outcomes in a positive way. 

[00:27:02] Vivetha: And the other thing is that with this data collection, it's not something that we could just do within a week, because we don't even have the protocol in place.

[00:27:11] The critical success factors that have been listed in, um, research are the standardization of this data collection and the use of best practices to ensure reliability. So even though there is all this media attention about how we're going to start collecting this kind of data in Ontario, there isn't much to go off of in terms of how's this going to start? And how's it going to look like?

[00:27:32] And I think that's why, um, as a group within this podcast, we're hearing a lot of, you know, caution around it because we're not given any sort of transparency around this kind of protocol. So I think there aren't any best practices in place right now because we haven't had enough work in Ontario, or even within Canada to kind of show what is the best way about going about this.

[00:27:57] And I think the report I mentioned [00:28:00] earlier, the four year project called we asked because we care is kind of the most recent thing that we have, in terms of something to compare to. And that's why I'm saying that this is work that has to be done before there is a policy window. So that when we ask for race-based data collection to be mandated, then we're already ready to go.

[00:28:18] But as of now, I'm not sure how this is really going to work out. And we have seen that there is sometimes a lack of coordination within the different public health units. So the question around, is this standardized? And does it look different per region? are important things to consider as well. 

[00:28:36] Will: Another point I wanted to add is that, like you said earlier, so they've rolled out a plan and you know that they want to start collecting this race-based data.

[00:28:45] But as we've talked about today, there needs to be certain level of buy-in with the community. You need to establish your certain relationship with the individual groups that you're looking to collect data from. And if there's no system in place, I see it as something that's maybe 5, [00:29:00] 10 years down the road, where, you know, as policy makers are starting to be aware of the need for race-based data and ethnicity and demographics and all that important public health stuff that we understand, as that starting to be, I guess, come to surface, hopefully this creates an opportunity for researchers. Whether they're community-based or, um, researchers that we're interested in reaching out to those communities to gain that community buy-in so that this data can actually be eventually collected. 

[00:29:29] Vivetha: And I think it's important to think about how this would be implemented.

[00:29:32] Um, and the logistics around that, because we know that the lack of trust that comes from communities is through, is because data collection has historically been a tool for surveillance and exploitation. And you also have to think about where is this research going to take place? So the project that I mentioned earlier, um, they did it at the sites, at the hospital sites.

[00:29:53] So you have to think about, are these communities going to come in to the hospital? Is everyone going to [00:30:00] be reached? Are you doing a fair job in making sure that everyone has input, because you could still limit access in terms of where you're conducting the survey and collecting data, and then totally miss a whole part of the community that you said you were going to kind of advocate on behalf of.

[00:30:19] Gordon: The sentiment today is that collecting race, ethnicity even language, or other demographic data is very important. However, it's not the end all be also in the United States, they do collect in some jurisdictions, they do collect race-based data, but yet they're those vulnerable populations still disproportionately are affected by negative health outcomes.

[00:30:41] So collecting race-based data doesn't necessarily mean those populations will be better off. A plan has to be in place to then use that data to inform policies and programs to erase some of these health inequities. So on that note, I will say, thanks for coming on Vivetha. And, um, I'll give you the last word for the [00:31:00] audience.

[00:31:00] Vivetha: Oh, I was just gonna say that I totally agree. And that merely collecting this data is not enough. And there has to be activism and action linked to it in terms of tangible policies and recommendations in place to actually alter the healthcare system. 

[00:31:16] Leshawn: As you've heard today, the panel discussed the importance of collecting racial and ethnic demographic data, the cautions associated with collecting such data, and some strategies to mitigate the possible unintended consequences. To better be able to serve the needs of our communities, we must ensure that we are considering the unintended consequences of our actions and not further marginalizing vulnerable populations. Thank you for listening. 

[00:31:41] Sully: Remember public health is a field of inquiry and an arena for action to improve lives, one population at a time. This has been the Public Health Insight podcast.

[00:31:51] If you've enjoyed this episode, please drop us a like, and follow us on Spotify, Apple podcast, Google podcast, or your podcast platform [00:32:00] of choice. You can also send us your questions, comments, and suggestions for discussion topics at: thepublichealthinsight@gmail.com. Thank you for listening, and we'll see you in the next episode.