Metrics, Enumeration, and the Politics of Knowledge in Estimating Racial Health Disparities in the COVID-19 Pandemic: A Dialogue with Alexis Madrigal, Co-Founder of The Atlantic’s COVID Tracking Project
This dialogue is the second installment in a series on COVID-19 and Racism produced by the Science and Justice Research Center’s Theorizing Race After Race (TRAR) Collective at the University of California, Santa Cruz. It follows the first in the series entitled “Black Geographies of Quarantine.” In this dialogue, Jaimie Morse, Dorothy Santos, and Aitanna Parker (TRAR Collective) are in dialogue with Alexis Madrigal, journalist and co-founder of The Atlantic’s COVID Tracking Project that operated from April 2020 to March 2021. The Atlantic is a major media outlet that produced alternative statistics on COVID infections, hospitalizations, and deaths during the first year of the pandemic, acting as a watchdog on the federal government’s data and reporting. The Atlantic was among the first media outlets to report racial health disparities through its COVID Racial Data Tracker before the CDC released data by race. In this dialogue with Alexis Madrigal, we explore the politics of knowledge production and how data can advance racial justice. What follows is an edited, condensed transcript of the dialogue.
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Aitanna Parker: Alexis, would you like to start us off with an introduction as our esteemed guest?
Alexis Madrigal: I am a staff writer with The Atlantic magazine. At the beginning of the pandemic in the United States I co-founded an organization called The COVID Tracking Project, which compiled data from states into what resembles a national dataset on testing, cases, deaths, hospitalizations, long-term care facilities, and race and ethnicity data as it is available and in the condition it’s available in from the states. It became a much relied upon source by broadcasters, big and small newspapers, and magazines. It has been cited by the Trump administration and now by the Biden administration in their coronavirus strategies. We used volunteers. We’ve had 400 people contribute to the dataset. The project is part journalism, part public health, part information science, and part data science. It’s kind of a pop-up institution that just kind of came together to be what was needed data-wise for the pandemic.
AP: Could you tell us more about your work with the COVID racial data tracker project?
AM: We were already collecting data every day and wanted to collect racial data using the same methodology, which is to say, going to each state and compiling what they have. We realized quickly that few states were actually providing complete race and ethnicity data. Through time, all of the states collected this data. I think we played a small role in the multi-causal kind of reasons for why they ended up publishing this data. We also have seen wild variability between the states. If you look at the CDC data that’s available, the national data reflects race and ethnicity for about 50% of the cases, which is quite low if you think about it. Along the way, from someone getting a positive COVID test to patient information getting to the federal government, you’re losing race and ethnicity data. We don’t actually know if what’s there is representative of what’s not there. It’s difficult to know who is not being counted.
AP: In 2020 the initial lockdown was followed by the George Floyd and Breonna Taylor murders and national protests. There was a spotlight on COVID race and data. Once data on racial disparities and the pandemic became the subject of popular discourse, what controversies emerged about how to explain these disparities?
AM: People try to attribute health disparities to behavioral deficiencies of the impacted populations. I was on a forum with Kimberlé Crenshaw for a radio show in the Bay Area. A person called. (To add, I’m Mexican.) And they said, “the real problem is that all these Mexican people are just still gathering” — and not that Latins in the U.S. have a disproportionate number of essential worker jobs and jobs which they can’t do from home. They have a risk profile that’s quite different. Also, I think people underestimated the effect of the old timey metrics around overcrowding in housing. Old school pandemic science basically states that the more people per room is something that’s counted in early public health. In progressive literature, the number of people per room was a measure of overcrowding. If you look at Los Angeles, there’s a very high number of people per room because housing prices are very high. You’ve seen a version of this and every possible controversy over disparities and statistics on race and ethnicity. I would say that I’m a pretty strong structuralist and believe it’s largely, if not totally, systemic.
Dorothy Santos: One of my interests in this project had to do with the way that QTBIPOC (Queer, Trans, Black, Indigenous, and People of Color) data was collected because this affects how we understand the pandemic and any pandemic hereafter. One of the controversies is our lack of preparedness. Filipino nurses are being affected by the pandemic disproportionately, which I think is not often as discussed. I think in light of the protest that happened in 2020, it was just almost the perfect storm to talk about the disparities in health care resourcing and accessibility. The Black Geographies in Quarantine dialogue brought up how people had this belief that there would be spikes in COVID cases after the BLM protests. That actually wasn’t the case. But you did see spikes in cases after anti-mask protests for obvious reasons. I felt that that was one of challenging things to see in the media. Alexis, I was very curious about the relationship between what is shown in the media versus what is shown at an insular level. In terms of the collection of data, what is a life cycle of a data point? And what does that look like from being hospitalized all the way out to how it has been included in big data and then projected out for the public to understand?
AM: The COVID data is extremely messy. All data has quirks. There’s no such thing as raw data. I think that’s been especially true in this case. For each data point that we track, there’s a different pipeline. Because we track on a per state level, there’s 50 plus pipelines for every data point. That’s extremely complicated because these things don’t really have a rhyme or reason to them in a lot of cases. What can our system output? And at what point do we send this data file off to the CDC versus when do we publish it on our dashboard, which is what our governor cares about? Our governor is our boss, ultimately. A really interesting example is testing numbers. One would imagine that the federal government should have the most complete data. The federal government can ask for a standard set of things, but the states themselves have to execute those orders because federalism is a system that we have stuck with and we have no real national healthcare. I need to make sure that that’s up to date for their own dashboards. We see those things fall out of concordance and then they’ll catch up and then it’ll fall out of concordance again. You get these variations in the data that come down to labor. With both states and the federal government data dashboards, the extraction and presentation steps are done by a few people. We’re talking a handful even at the federal government level. One of the reasons why The COVID Tracking Project has been effective in getting attention within the federal government is that we have more people than they do. At the end of the day, this is a volunteer driven project that has more hands-on deck to do things like find out how states are reporting. Both during the Trump administration and now during the Biden Administration, they need us. Because there isn’t the kind of attention paid to a lot of these things that people who are really deep inside the data would pay attention to. The thing that haunts a lot of us on the project is what experiences aren’t represented there. Like COVID long haulers, where are they? They might or might not be in a hospital. Particularly if they became infected in spring 2020, they might not have been able to ever get a confirmatory test. We know white doctors are less likely to believe people of color having all kinds of problems, and a whole range of experiences aren’t represented in that data.
Jaimie Morse: What’s made visible and what is rendered invisible due to the lack of data has been at the core of my concerns around the tracking that exists. I am also concerned about the lack of infrastructure to track more variables like occupation to prove that essential workers are hardest hit. Studies from social epidemiologists say that we have these racial disparities, but how do we explain them? It’s hard to change the narrative around victim blaming and that individuals are responsible for their health outcomes when we lack the data to prove that there are structural inequalities that are at the heart of it, like occupational stratification by race. Alexis, from your perspective, are there ways that the studies that have more complex analyses can enter the media discourse more prominently?
AM: It’s hard because the basics are still not widely known. In our project, people would just recreate our graphics in local television graphics packages. Everybody wanted the simplest version of things. That’s mostly the national or state top headlines. How many cases do we have? How many hospitalizations? How many deaths? Many of the more moderately complex things have proven to be very difficult. Because the COVID data is so messy, it’s hard to gain any kind of precision. For example, death data — you would think it would be quite solid, but it’s not. One of the things we’re seeing is that the death data pipelines may vary between states. The process of declaring why someone died turns out to be very personal. There’s a medical examiner with a set of beliefs, let’s do X or Y, and they have different parts of the death certificate to fill out. There are two parts, and both are subject to interpretation, as you might have guessed.
I think this is something we really need to talk about because I would call it a kind of white, petite bourgeoisie driven, anti-lockdown thing for small business owners. These community members drove the discussion around numbers and introduced a steady stream of misinformation content. It was an evolution of some of the things that have been done around climate change or tobacco related to the idea of Merchants of Doubt. You don’t actually have to have an alternate theory that works about what’s happening. All someone has to do is flood the zone with misinformation. You can’t even get people to agree on the foundational stuff, because some people are still saying that people aren’t dying of COVID. They claim people are faking those hospitalizations. The cases are because PCR tests somehow don’t work, even though that’s clearly the foundation of modern medicine. Now you want to stack up these other more complex things that are going to have their own confidence intervals. There has been a breakdown in the ability to sort information.
AP: We have a multidisciplinary group here. Our respective disciplines might influence our relationships to data. Data is a tool that we use differently. For each of you, what is your way of encountering or grappling with these numbers? What is your relationship to big data?
DS: One of my areas of interest is death data. I’ve been reading through Jacqueline Wernimont’s book, Numbered Lives. She’s looked at the bubonic plague and the different types of individuals who were responsible for capturing that data or information for documentation purposes. They were called searchers. The contemporary term for them would be disease investigators or contact tracers. My relationship to data is looking at big data and how, historically, it was recorded and what has changed.
I’m also interested in vocalizations, such as assistive technology. How are our smartphones allowing for different types of developments in surveillance or bio-surveillance tracking? Not only exposure notifications, but also what it means for engineers and scientists working on vocal banking. For example, could you tell someone is sick and has a certain type of flu through their voice? My relationship to data has more to do with something we really can’t see, which is our sonic experience of it. I say that because more recently there have been patents that have been created for tracking vocal aberrations. My relationship comes from the historical contextualization of how a pandemic is captured and documented and how that data is used. In studying the census, what does enumeration actually mean? Especially at a time when a pandemic has affected the world, not just the U.S. I’m fascinated by the global reach of U.S.-centric data. I’ve looked at The COVID Tracking Project, the John Hopkins dashboard, and The New York Times to better understanding the relationship to the rest of the world. I’m also interested in transnational data capture. What would that even begin to look like? I’m more interested in the thornier parts. The parts that are untapped and maybe obscure. I rely on individuals such as Alexis and Jaimie to help inform how I look at those types of experiences that people wouldn’t otherwise see as data.
AM: Searchers is a much better term than contact tracers. I like that. I came up in the STS (Science and Technology Studies) tradition. Porter says quantification is a way of making decisions without making decisions. Generally speaking, I agree with that sort of sentiment that data is not a reflection of reality. That’s why it’s kind of hilarious I ended up running this project that actually produces all this data. But I think it’s because the fundamental, productive tension of the project, particularly relative to other trackers or any other source of information, is that we have constantly criticized it. Most trackers [the creators of these trackers] claim their data is the best. We thought, “No, this data is terrible. Everybody should know how bad this data is!” Not just ours, but everybody’s data, because this is the nature of this thing. I think that’s been a very important orientation throughout this whole thing, because it has always been easy to get something that looks right, then stop there and claim it is good enough. One invisible problem we discovered is that the Feds were initially including antibody tests alongside PCR tests. We know that 10 or 15 states lumped an antigen test, a different kind of test with different characteristics, with their PCR tests. This is how we’ve discovered all these problems with death data. It’s how we realized that hospitalization data is quite good because we kept looking for all the ways where it could go wrong.
In general, my relationship to big data specifically is that it oftentimes tells you very little more than basic data. In the case of the pandemic, we didn’t need any of this to tell us the basics that needed to happen. What big data is good for is optimization of production systems, like within companies. They’re interested in it because they’re trying to save 2% on whatever thing. I’m a bit of a skeptic that on an insights level that there’s much to be gained there.
My big complaint about the U.S. response is not that the data wasn’t good enough, although that clearly was a problem and the preparation for good data was minimal. You’d go look at every pandemic planning document you can find. Good data is assumed. I don’t think anyone should ever assume that for any pandemic effort in the future we will know the exact number of cases. Some people would say we need to build a perfect case surveillance system. We need to know precisely about hospitalizations and reform the death certificate process. I think that’s probably true, but I don’t believe that we are going to do that. I think the biggest problem is that we have a white paper class that’s good at putting out detailed ideas about how things should work. But for a variety of reasons, we don’t execute it like that. We plan like Singapore, but we don’t execute like Singapore. There are things we can learn from countries that don’t have the resources the United States has because they know they’re not going to have dependable data. When you look at Vietnam, they know they’re not going to have complete data. They don’t necessarily need it though. But they know the things they need to do. They built a system that is resilient to dirty data. We totally could have done that too.
JM: I had a career in public health before I decided to get a Ph.D. in sociology. I was trained in social epidemiology and community health. One of the most important, animating assumptions of that field is the presumption that, without the data, we won’t know there’s a problem. If we don’t know there’s a problem, then we can’t do adequate resource allocation and address health inequity. I decided to pivot to sociology because I was interested in the questions that were behind the research itself: what questions are being asked? what questions aren’t being asked? It’s wonderful, Alexis, to hear you talk about how the data is being tabulated and all of the pitfalls. Even some of the biggest datasets that estimate racial disparities in infection and death rates are only covering maybe 50% of the population. It’s striking to me that these debates are still playing out on this terrain, which is based on this assumption that if we don’t have a record, then we can’t prove it. If we can’t prove it, then we can’t act or we can’t act skillfully.
AM: That’s exactly right. It was such a disaster to proceed on that assumption that we would know. I think it’s what froze a lot of the public health leadership. It’s not just that it was Trump. It was that they saw low case numbers. Even though they knew that testing had broken down, I don’t think that people could believe what was happening. They were looking for influenza-like infections. They were expecting to see all these spikes and these kinds of things, but they weren’t. It turned out those surveillance systems don’t work in this kind of a situation. To reemphasize the point, that’s exactly right. They were expecting to see data to know there was a problem when every qualitative sign was, “oh, we’re in trouble here.” Yet they were frozen because they didn’t have the data.
JM: This linking of the data, the surveillance, and then the resource allocation also raised the problem of the systematic disinvestment in state infrastructures. I don’t just mean data collection, but also healthcare. As you mentioned at the very beginning, without universal healthcare coverage, there’s a lot of gaps in who’s actually getting a test and who’s actually getting treatment. The fact that other social safety nets have been eroded over the last 50 years also became very clear, such as the absence of universal paid sick leave, inadequate unemployment insurance, and the lack of expansion of Medicaid in some states. The eroding social safety net really came to the surface at the time that preexisting inequalities were being amplified and put on full display because of COVID’s disparate impacts.
For me, one of the things that I’ve been thinking about is the role that these models and numbers were playing in framing this new crisis. It was on a global scale; it was clear; it was immediate; it was urgent. Taking a step back and thinking through questions about the work that models and metrics do: how are numbers in these models shaping and bringing into being this new social phenomenon? I found really useful parallels to climate change modeling, for example asking: how did scientists render global climate change visible? As part of the TRAR Collective at UC Santa Cruz, Dorothy, Aitanna, and I wanted to understand the work that these estimates and models are doing, both in terms of how they shape our understanding of the pandemic and how we can identify who’s being left out of the counts and who needs more assistance. That was part of the reason we wanted to think through this with you from, as you mentioned, an STS perspective. Paul Edwards uses this term “knowledge infrastructure” to describe all of the disciplines and measurement tools and histories of data collection that made it possible to model climate change, but specifically to construct a global temperature that could be shown to vary over time. It is interesting to think through the question: what is the knowledge infrastructure that we’re using to know the pandemic? Not just what is the quality of the data, but exactly what are we focusing on and how are these kinds of models training our attention in certain areas and perhaps not in others? And circling back to Dorothy’s point, how would we think about this beyond the United States? In what ways are these models affecting the way that we conceptualize the pandemic and the ways that we’re responding?
In terms of how we approach big data and our relationship to it, I often see this tension between, on the one hand, the fact that we do need this documentation in order to do resource allocation better, and, on the other hand, this constant query of: what exactly are we building here? What are we actually bringing into being? And how are people able to interact with these numbers or not?
AM: What’s your worry there? I have my own, but what’s your worry here?
JM: For me, it’s the question of what’s counted and what’s not counted. Certain groups are systematically more difficult to capture in the data. For example, in my review of the racial disparities estimates, early on in the pandemic, certain groups were presented and others weren’t. It got better over time. But for example, counting Native American deaths is very challenging, and it’s a statistic that often would get dropped out of media coverage of racial disparities in COVID infection and death rates. Also, something that Aitanna brought up in our discussions and that I’ve seen in my own work is this tension surrounding the degree to which we rely on metrics to make problems visible. As you were saying, Alexis, do we need big data to show that a problem exists? And are we inadvertently undermining subjective experience by, in a way, privileging the documentation as the evidence? Aitanna also brought this up in our conversations, asking: why do we need data to prove that Black and Brown people are dying at higher rates? It raises the question: how do we know what we know? And is data displacing lived experience or a subjective kind of interpretation in ways that are important, especially given that the data is inadequate and that there are a lot of gaps and it’s not a perfect measure?
AP: It gets frustrating that I need data to back up my lived experience when I tell you something’s wrong, especially if I am telling the government that I’m paying money to “protect me.”
AM: Particularly in the early going of the pandemic, I think it was a huge problem. I think the hard thing is you had a lot of people in this country who were saying, “Well, in my lived experience, I don’t know anyone with COVID, so why am I doing anything?” And the lived experience of a lot of people was: “I’m losing my business right now” or “I lost my job.” I think what’s interesting — and where climate models seem quite similar — is that you need to build this collective action, even if you haven’t seen it or you haven’t experienced it yet. So the models act as this sort of charismatic object that people can gather around, like the IHME (Institute for Health Metrics and Evaluation) model. For some people in the early days, I think they saw a curve and could understand what that curve means: to flatten that curve would be better. STS scholars are familiar with the book, Seeing Like a State, which is a great critique of the way that states try to make themselves visible to themselves. Most of that book is a critique of states doing that. But I have to tell you, watching a state be unable to produce national statistics to understand what’s happening has made me a believer that states should be able to do that, to have that capacity. Whatever violence is done in the process of creating those numbers, there’s other violence that’s done when you don’t produce those things. In the U.S., the key problem was not that we had too much state capacity to know thyself; it’s that we had too little for understanding what was happening.
There was this desire from a lot of leftists to make the government smaller, to make the government have less capacity, because it was so obvious that governments could do damage, particularly to vulnerable or marginalized communities. Yet when you see a state that is as rich and powerful as the United States being unable to understand the basics of what’s happening in the country, all governance breaks down. For me, a big question that this all raises is: how do you increase state capacity and not get rid of it? For example, why didn’t we manufacture N95 masks in huge numbers from the very beginning? Why didn’t the state pay everybody to stay home? We could have done that. Maybe it’s not more state capacity, but it’s better state capacity. State capacity just feels necessary; it probably does have some negative consequences, like the balance between statistical summary and subjective individual experience.
DS: One of the things that I was thinking about as I was listening to Alexis and Jaimie answer the question about the relationship to big data was data as a cultural artifact. I think many people don’t realize that it is a cultural artifact until years later. How a pandemic or how an issue or problem has been managed within the culture or society is always based on the media that are produced and is emblematic of that time period and related to advancing racial justice. It’s tough because I think a lot of information can be skewed to tell a story. I mean, at the end of the day, that’s what statistics are. It’s to craft a specific type of narrative around not just numbers, but people. I mean, we saw this in the earlier answers that Alexis and Jaimie provided. Even if we think of, as Aitanna has brought up, subjective experience or lived experience. What does it mean to look at death data when an autopsy may not always be the thing that’s available to tell us what’s happening to a body? It’s also the right to consent to offer up this data to the public or even to one’s kin or next of kin. For example, how do you educate or make a public knowledgeable about how their data or lived experience could actually inform other people within their community? Alexis, you made a good point about some people’s lived realities. They don’t know anyone with COVID, so why would they need to worry about this airborne virus? This is in contrast to my family: half of my family are essential workers. How do we get individuals to understand why they would need to stand six feet apart from someone? A lot of people rely on the media to tell us the truth. But if you tie that to racial justice, you’re not exactly getting safer. As I brought up earlier, you’re not getting the Filipino nurses talking about this with the media, for instance. How does anecdotal information push social and cultural change and racial justice along when people can watch a story, read a story, and can share it? How do we get to a solution that can help trans Black, Indigenous people of color? The four of us, we care about media, we care about the science, but we’re talking about over 140 million Americans who won’t necessarily have the conversations that we have around this.
Also, what if your data diminishes? That’s another reason why data is unreliable: people move, people die, people are displaced. In addition to data becoming a cultural artifact, what does it mean to start to create research methodologies that become the solutions for better data and knowledge production, or creating a stronger knowledge infrastructure in the future?
AP: In the Black Geographies of Quarantine dialogue, the first in our series on COVID-19 and racism, the participants asked: what kind of data can make Black lives matter? Essentially, what can data really do? What are the promises and pitfalls of data collection as a technique for advancing racial justice?
AM: I have two different answers to this. One thing is working on co-creation of data with communities, making sure that people whose communities are affected by the problems are involved in the data collection. With COVID, there’s COVID Black, which is a series of scholars and researchers in universities primarily, but who are working on these issues. There’s Data for Black Lives, another group that was trying to foreground Black experiences with Black data scientists. We interacted with both groups quite a bit.
I would also want to see more anti-racist policy in place. By that, I mean at an economic structures level, to reduce wealth inequalities. I think without that, it’s hard to make data do anything. You can do work in the media, for example, The 1619 Project and The Inheritance Project. But as long as the answer for most white people is that “I should change my heart,” then there isn’t a direct connection to changing policy that is adding to the wealth gap. One that really opened my mind to a lot of housing policy problems in the case for reparations is the specific problems of housing policy in the United States. Those things can be redressed. You’re never going to get exactly a period of time, like 1945 to 1980, with housing appreciation and then inflation, which takes away lots of people’s debts. That was a perfect storm for building white wealth in the suburbs. It might not seem related directly to COVID, but it is, given what I said about overcrowding and the way that housing conditions increase infection rates. Also, why are so many people of color so-called “essential workers”? It’s because they have less capital. So if I were to say, where is the most interesting use of data? It would be quantifying the effects of racist policy, with the intent of saying, “all right, you’re newly into anti-racism, well, let’s get these things changed.” That feels like a good goal.
JM: This question came up in the Black Geographies of Quarantine dialogue: what data can make Black lives matter? The context for the question in that dialogue was the fact that there’s been documentation of racial health disparities since the early 20th century. For example, W.E.B. Du Bois’s work is canonical in the social sciences, including his data visualizations. We’ve been documenting this for so many years, and we continue to see stark racial health disparities. Social epidemiologists have been documenting the social determinants of health. With Nancy Krieger’s work in particular, I’ve been inspired by her attempt to look at how historical injustices become literally embodied, such that there are measurable health impacts of Jim Crow and trying to prove that through data. I deeply admire her work. There’s also a part of me that is asking the question: why do we need more studies to prove that intergenerational trauma, historical injustices, and Jim Crow policies still matter and structure people’s health outcomes? There is a tension. I am concerned about potentially undermining individual lived experience or subjective experience because of this cultural investment in numbers and objectivity and proving that the social determinants of health are, in fact, the reason we see these disparities and not individual bad behavior. Getting caught in that metrics-based paradigm means that we have to keep turning out studies to prove these things. What are the politics of that? And what are the ways that our investment in metrics becomes its own project? I would like to see us learn from this huge literature on social determinants of health as the root causes of racial and ethnic health disparities and say: what are the structural interventions that we can pursue? We need to address occupational stratification, workplace issues, and access to healthcare. That’s part of why we, as part of the TRAR Collective, wanted to facilitate a dialogue that looked at and returns us to the politics of these numbers: what work are the data and models doing? what work can they not do? Metrics and models can’t speak for themselves. How do we reckon with that as we try to move towards racial justice?
AM: Racism matters in the health system. One of the things I have been wondering is why people haven’t solidified around – as a political goal — is just to say, let’s close the life expectancy gap. For me, I think it would probably be specifically between Black people and white people. It just strikes me as completely unassailable that people in one part of town should live as long as people in the other part of town. The things that you would have to restructure in order to do that are enormous. That’s where the data would be useful. The key goal of American policy ought to be that people should live for the same amount of time, and then you can start working down the list.
AP: Community organizations don’t need data to have a call and response for actions that are tangible to people’s lives. Recently, there have been calls for volunteers to escort elderly people around because of the increase in hate crimes. It didn’t require a study. It didn’t require a researcher, yet this is happening and this is keeping people safe.
DS: Aitanna, you bring up such a great point about mutual aid. And how this is common in so many cultures. Even if they don’t call it mutual aid, it is a holistic way of caring for one another at an insular level that I hope will ripple out.
AP: You don’t need to read theory to learn how to care for people. How can we think about creating accountability, community safety, and preparedness for the future? What does that look like within our own fields? What can we do in our respective disciplines to address racial health disparities in the pandemic and beyond?
AM: My field is media. And it is not reflective of the diversity of the country. And that affects particularly science, technology, and health media, which is very white and Asian, generally speaking. It leaves out a lot of Latins; it leaves out a lot of Black folks; it leaves out all kinds of people. Not everywhere, not in every case, and not in every city. But by and large, if you look at a lot of the big newsrooms, they are under-representative of everybody, but white men.
There’s a deeper need within the media to really understand what it is that we’re doing. In journalism specifically, there’s such a dumb version of objectivity that is at play. Journalism kind of painted itself into a very tiny box in which only a few things can be reported on and said; there can’t be community centered goals for the coverage. So people will write a huge exposé on high Black maternal death rates. But the point ultimately is never: how do we get rid of this? That part never gets done because it’s about highlighting the problem, not going down to the next steps. Because that would be seen as advocacy. I get why in the context of Republicans and Democrats, people don’t want to take sides in that way, but 99% of our field is structured by that, even though 90% of it is not that. I think the field needs a total reconstitution, more or less, to understand the systems that are being reported on and not just the interest groups that are being quoted, if that makes sense.
JM: Sociology as a field is oriented towards structural inequality and interpretations of disparities that link to social and political determinants of health. There’s so much work in sociology about systematic inequality, but the question is how to translate that.
I’ve been thinking a lot about our role in unpacking the systems that are more difficult to take on. For example, with vaccine access, one of the most important features of that is the legal system and intellectual property rights. As Amy Kapczynski has argued, one way to get widespread vaccine access would be to have some kind of public manufacturing of the vaccines, which would require cooperation from the major vaccine producers. It’s not impossible; it’s being discussed. But to scale up at that level would require concessions. There would have to be agreements around affordable pricing and global access. That’s been a debate in the law and health field since the access to HIV/AIDS treatment campaigns in the early 2000s. Right now, COVAX, the global vaccine sharing initiative, hopes to make vaccines available for approximately 20% of people in 92 low- and medium-income countries by the end of the year, but is behind its target. As Gregg Gonsalves has explained, these are exactly the conditions under which a virus can evolve and variants can emerge that could be resistant to the current vaccines. But what would it take? It would take a huge investment in rapid production. But these are the tensions that I think are critical for folks who study health and law. It is essential to unpack expert systems and legal regimes that are determining who has access to key medical interventions. A big part of what I think is needed is to leverage these structural analyses, but towards actionable ends. As Alexis pointed out, the data often is messy, and it’s not going to point you in exactly the right direction. But there are certain things that we can do and policy platforms that we can support with what we know right now.
DS: I love this idea that one of my really dear friends An Xiao Mina and I have discussed, which is the idea of slow journalism. As someone who is within the arts division, who makes digital media and looks at creative coding, as a media for making artwork and disseminating stories out to the public, it’s hard because I think in a lot of ways, we’re never going to get to a place where everyone sees themselves in data or perfectly represented in media.
I always feel people think of the arts last. I remember when I had an artist residency at Stochastic Labs in Berkeley in 2019. My work had to do with how people understand clinical documentation and data, the stuff that nobody wants to pay attention to. Within this dialogue, I’m most concerned with: how does a patient understand what a clinician is telling them? How do they know if what they have is COVID? How do they start to see themselves embodied in the things that they’re seeing in media? Those are the things I feel could create that accountability and help us plan for the future to do something and make or create actual solutions to what Alexis, Jaimie, and Aitanna have brought up.
AM: We’ve covered a lot of ground. One thing that I would want to add is that there was a huge amount of public interest in blaming the Trump administration exclusively for what has happened. But the thing is, we can’t go back to the status quo. The things that were happening in public health departments and at the CDC before the Trump administration were not great. I think if you were to look at the leadership and how the CDC, for example, worked, there was not a lot of diversity. It is an extremely old boys kind of place. The former director has a sexual harassment suit. There’s a lot of need to inject new perspectives into the public health infrastructure, and the money should flow down to the local level.
The other big thing that I would mention is the federal system that we have held onto for a lot of reasons, but a lot of them have to do with being a slaveholding republic that built itself around those structures. It doesn’t work! One of the major learnings from COVID should be that the federalized public health system — the way that it works with all these different levels and the idea of local control — is not effective. Also, institutions like the Senate, the electoral college, and the way that we allow for gerrymandering add up to minority rule in the U.S. Those things need to change so that we can get an effective governing system. The majority of Americans isn’t really who decides what happens in this country. For me, voting rights and structural changes, and making sure we don’t find ourselves in something that slides back into apartheid, will lead to good public health outcomes. I think being up close and personal with a lot of the government interfaces has made me believe that you can have people who worship the letter of the constitution and who do so because it is their defense line on protecting the compromises of the slaveholding republic that protect minority rule. So we need reform. We need a modern system that works, in which governments can pass bills and obstructions are not rewarded.
AP: Thank you so much for speaking with us today, Alexis. We appreciate your time, your efforts, and your work.