Jason Abaluck: Health Economics (Health & Veritas Ep. 9)

On the Health & Veritas podcast, Yale physician-professors Howard Forman and Harlan Krumholz talk about the latest news and ideas in healthcare and seek out the truth amid the noise. In the latest episode, they are joined by Jason Abaluck, a health economist at Yale SOM. They discuss a massive study co-authored by Abaluck that showed that masks reduce COVID infections in a community setting.

Men in Bangladesh wearing a masks provided as part of a study co-authored by Jason Abaluck.

IPA

Jason Abaluck
Professor of Economics
Howard P. Forman
Professor of Radiology and Biomedical Imaging, Economics, Public Health, and Management; Co-founder, Pozen-Commonwealth Fund Fellowship in Health Equity Leadership, MD/MBA Program, and MBA for Executives Program
Harlan M. Krumholz
Harold H. Hines, Jr. Professor of Medicine and Director of the Yale Center for Outcomes Research and Evaluation

November 18, 2021

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TRANSCRIPT

Harlan Krumholz: Welcome to Health & Veritas. I’m Harlan Krumholz.

Howard Forman: And I’m Howie Forman. We are physicians and professors at Yale. And we’re trying to get closer to the truth about health and healthcare.

Harlan Krumholz: Today we’re going to be talking with Jason Abaluck, one of the amazing professors of economics at the Yale School of Management. But first, Howie, what’s on your mind today? What’s got your attention?

Howard Forman: So, a study came out in Health Affairs looking at a large ongoing study in Nigeria, in fact, involved about 37,000 pregnant women. And they just by chance, the study was timed in such a way that the pandemic started in the middle of this study, and it allowed them to evaluate the before effects and the after effects of the pandemic and demonstrated a 20 percent increase in stillbirth and neonatal deaths during the onset of the pandemic. And this occurred despite the fact that by most outside measures, Nigeria has reportedly had one of the mildest outbreaks in in the world. Whether this relates to actual COVID or secondary effects on patients in the healthcare system is not certain, but it is one more data point about the widespread vast negative impact of this pandemic and the challenge of accurately reflecting causes and the actual number of deaths.

Harlan Krumholz: Well, that’s very interesting.

Howard Forman: What about you, Harlan? What’s on your mind?

Harlan Krumholz: Yeah, I thought I would share a study that was presented at the American Heart Association this week, something that listeners might be interested in. So people are always talking about coffee and whether or not coffee causes problems and so forth. And most of the studies have failed to show any sorts of issues around that. And some have even showed health benefits. But these investigations from the University of California San Francisco basically took a bunch of people, average age of about 38, maybe about a hundred people. And they had them equipped with a Fitbit, with a continuous electrocardiographic monitor, a little patch they wore on their chest. They had another patch that tested their glucose. They actually sequenced their DNA, and they let them go out. And then what they did was, they randomized them to, these were all coffee drinkers, and then they randomized them to periodic periods where they went without coffee, and then they wanted to compare what happened.

And this was a study that was really interesting, because it was wholly digital. So they recruited them and they were sort of, by, with an iPhone app, and they had all these sort of wearables on them. And then they did one other thing, which was they had them link to their locations. So they could see, when they were in the period where they weren’t supposed to drink coffee, they made sure they didn’t show up in Starbucks, honest to goodness, as a means to see whether there was compliance with the randomization.

So anyway, just to cut to it, it was really interesting. This was a bunch of cardiologists who did this study, so obviously interested in heart arrhythmias, extra heartbeats. And what they found was interestingly, that there was no evidence of any extra atrial heartbeats, the ones from the top chambers of the heart. There were a few more extra beats that were coming from the lower chamber, but they didn’t think that that was really, clinically, a problem. But what was interesting to me was when people were drinking coffee, they were more active. They took more steps. They also had less sleep. No effect on glucose, by the way. But I thought it was kind of a cool study, and I don’t know, take-home is, if you like coffee, keep drinking it. But, but anyway, kind of interesting.

Howard Forman: No, it is. It’s fascinating. The whole literature on coffee is fascinating because my understanding of it is that there’s probably net benefit, if anything, and certainly overall the evidence is not about net harm with certain small subgroup exceptions. But yeah, it’s great that they did that study.

Harlan Krumholz: Yeah. And what enamored me was the way that they did this study. I think we’ll be able to see more of these kind of digital studies that are really looking at the body’s response to various different behaviors and activities. And we’re going to learn a lot more than what we’ve known because of our ability to now observe things that were really out of our reach before.

Howard Forman: Yeah. So, speaking of interesting design, Harlan, and I don’t know anybody who’s done more studies with more interesting research design than Jason Abaluck. We’re so fortunate to have him here with us today.

Jason Abaluck: Oh, man, this is—

Harlan Krumholz: Oh my gosh, Jason, every time that Howie would talk about you over the last decade, he would only just say like, “Wow, you got to really start doing stuff with Jason. He is just—”

Howard Forman: Oh my god. Yeah, I’m definitely a fanboy. I’m totally a Jason Abaluck fanboy. And while there’s a lot of people that call themselves health economists out there, there’s not a lot who truly have stayed true to being health economists from the moment of their training throughout their careers in an academic setting and making an impact on the field. And when the pandemic hit, whereas many health economists continued doing the good work that they were doing, he really pivoted to COVID immediately, and at very early on, and April in fact, was able to publish a very important case for wearing masks.

Harlan Krumholz: But before we get to the pandemic, Jason, I want to play a little game with you. So what I’m going to do is, read the title of your piece. And here’s the challenge. In one or two sentences, tell a general audience why they should care. Okay. Ready?

Jason Abaluck: Yeah.

Harlan Krumholz: Okay. “[What] Do Consumers Consider Before They Choose? Identification from Asymmetric Demand Responses.” By the way, really great title for an economist: “Asymmetric demand responses.”

Jason Abaluck: Thank you. We worked a lot on that title. So we care when people make choices, whether those choices are informed, especially if we are going to use those choices to determine what makes people better off. So what we’re going to be able to tell is whether, when they chose between different items, whether they were aware of all the possible choices that were available or not. So we developed tools to be able to say, based on the types of choices people make, whether they were actually aware of all the options.

Harlan Krumholz: Oh, that’s great. Actually, a lot of application to medicine. How about this one? “Less is More: Improving Choices by Limiting Choices in Health Insurance Markets.”

Jason Abaluck: Yes. So this is a case where we looked at people choosing health insurance, and we found that in cases where they were given fewer options to choose from, they ended up enrolled in a plan that was actually lower-cost and more suitable for them. And the reason this happened was not because they actually made better choices but because someone else was pruning the choice set for them. So there was a benefits manager in each school district that decided what plans to offer and where the benefits manager said, “Let’s offer three plans” rather than “Let’s offer eleven plans,” what they did was, they chose three plans that are pretty good for everyone. And so it turns out that helped a lot, because when there’s eleven plans, turns out there’s some plans that might be good for a couple people but are really bad for most people, but people choose them anyway because it’s really complicated to decide what health plan you should choose.

Harlan Krumholz: So giving too much choice might actually paradoxically put people in a worse position.

Jason Abaluck: Yeah. It puts them in a worse position, and specifically not for the reason the existing literature thought, exactly. It’s not just that when you have so much choice, you’re like, “Oh, now I can’t choose.” And when you have three plans, “Oh, you can do it.” People choose about equally, whether there’s three or eleven, and “equally well” means not that well, because it’s really, really hard to choose a health plan. But what happens is, the district benefits manager does have some sense of which plans are bad on average and can exclude those plans, and that helps.

Howard Forman: So this is an important point because there’s this vast sort of bounded rationality literature that says that the overwhelming number of choices is what causes people not to make a good choice. And you’ve really confidently disproved it for this case. Do you believe this applies to some of the other bounded rationality situations?

Jason Abaluck: I think it varies from context to context. I see health insurance as being especially distinct in the sense of being nearly impossible. It’s the kind of thing where there are some choices where it’s like, okay, people can do pretty well. It’s like, oh, they’re trying to decide what car to buy. They can’t see everything. They might be misinformed about safety. They might be misinformed about durability, but they can get a sense of how the car drives and how it looks, so they can get a reasonable sense of whether it’s good. For health insurance, I think that’s actually incredibly difficult because the way that the information is presented, it’s not easy without doing hours and hours of research and really having a pretty good conceptual background to evaluate what health insurance is supposed to do in order to decide whether you’re making a good choice.

Harlan Krumholz: Great. Let me try a few more here. “What Would We Eat If We Knew More? The Implications of a Large-Scale Change in Nutrition Labeling.”

Jason Abaluck: All right. So, this is an older working paper I have. It was actually my job market paper. So the basic idea here is, when we try to evaluate policies that lead people to eat healthier foods, people understand the order of how to rank foods in terms of health. They know broccoli is healthy and pizza is not so healthy, right? But they might not understand quantitatively. So it’s like, pizza is less healthy than broccoli, but should they think of it as being a dollar less healthy? Is it $10 less healthy? Is it $1000 less healthy? And so suppose they’re choosing between pizza and sushi, and sushi is $20 more. Should they be like, “Well, I’ll gladly pay $20!” No, that’s crazy, because relative to the health consequences. So that paper was fundamentally about using nutrition labeling laws to figure out how people implicitly value health differences, then compare that to what the medical evidence says and say, is it the case that people are underweighting the goodness of good foods and the badness of bad foods such that it makes sense to subsidize healthy foods and tax unhealthy foods so that we could bring their choices more into line to what they would choose if they were fully informed.

Harlan Krumholz: That’s great. That’s really great. Hey, so, I know that we wanted to talk about the masks, so look, I could get into your research for a long time. It’s fascinating stuff. And thanks for the explanation. Actually, I understand this a lot better now, too.

Howard Forman: So, tell me what it was like to make such a quick pivot. It was a passionate pivot also, and you brought together collaborators from within the economics community but also within the public health community to really tackle a topic that I think you were passionate about, all of us were passionate about, and answered key questions very, very early on in the pandemic.

Jason Abaluck: Well, in some sense, when I think about how I choose research projects, I don’t see it as a pivot at all. And I think what it fundamentally illustrates is, at least in terms of when I think about research, the fundamental importance of cost-benefit analysis and thinking about trade-offs quantitatively. So I think in March of 2020 it became very clear that from a public policy perspective, COVID was going to have first-order impacts on many questions about costs and benefits. At that point we started to see projections that hundreds of millions of people, billions of people might be infected, that there would be millions of deaths around the world, that the economic costs could be in the trillions of dollars. And once you start to see numbers like that, then it makes sense to say, “Okay, the stakes of any policy we consider could be enormous.”

And so, what made me start to look at masks early on was just this idea that, well, it’s a relatively low-cost intervention, although not zero-cost, that could potentially have tremendous benefits. So my initial view in mid-March of 2020 was, I’m really unsure whether this will work. From my initial perusal of the literature, there was somewhat mixed evidence, but the view was basically like, look, if there’s a 5 percent chance that this could even be remotely effective, it could generate hundreds of billions or trillions of dollars of benefits if we had universal mask adoption. So it seemed, just from that “cost-benefit analysis under uncertainty” perspective, a no-brainer that we should be adopting masks as a preventive measure. So then over the next few weeks, I looked more closely at the literature. And so there were two types of existing randomized experiments evaluating masks.

There were device experiments in medical settings, like in hospitals, where basically you could pretty much tell that if you ask people to wear an N95 mask or a surgical mask, that they would do it. Okay. Now, those settings, typically you didn’t test with no mask. You tested, “Oh, is an N95 better than a surgical?” or something like that. And there was some evidence that surgical is better than cloth, N95 is better than surgical. Then there were these community studies where they would do something like, “Oh, take a college campus during the flu season, and let’s try to ask some students to wear masks and see if it reduces their likelihood of getting the flu.” There was a fundamental problem with these studies when I look closer, which is: they did not directly measure who was wearing masks. They asked people in the treatment group to wear a mask, and then they asked them if they did wear a mask.

But one thing we know from behavioral science is, if you ask people whether that person has engaged in an activity that society thinks they ought to do, you get really misleading results. So if you say to people, “Hey, did you give to charity in the last year?,” people vastly overstate the likelihood and the amount that they have given to charity. And we have since seen that this has been vindicated in mask studies. So in our work in Bangladesh and work in Kenya, they asked people, “Did you wear masks in public areas?” Ninety percent said yes. When they surveilled people, 10 percent were wearing masks in public areas. So it’s really hard to get people to wear masks. The bottom line is, these studies have found no effect. I don’t think they were identifying the impact of mask wearing. I think that they just had low compliance rates.

And even then, they would only be identifying whether masks protect, not whether masks prevent you from infecting other people. And that was a key point of the argument early on in COVID. The idea that look, we know there’s asymptomatic transmission. We know that if you have pneumonia, if you have the flu, if you go to the doctor, they’re like, “Hey, when you go outside after this, you should wear this mask so you don’t infect other people.” There was a pretty reasonable medical consensus that masks prevent you from infecting others. The existing studies didn’t test source control at all.

Harlan Krumholz: What led you to look at Bangladesh as a site for doing this study?

Jason Abaluck: Yeah. So I think there were three reasons. One is that there’s this tremendous humanitarian need there. So at the time when we were planning the study, we knew that vaccines hadn’t even been developed yet, but if vaccines were developed, it would be a long time before they were available in Bangladesh. At the time, the alternatives were things like lockdowns, which really were just not feasible in Bangladesh, just because if you’re consuming hand-to-mouth, you can’t suddenly stop working because now you can’t afford food. So you have to work. You can’t have a lockdown. So what other mitigation measures are available? And the answer was, masks seemed like a really important one. So that was the first reason.

The second is that Bangladesh is densely populated. So we thought that COVID risk there was especially high. We were working in rural Bangladesh, but you shouldn’t think of rural Bangladesh as Kansas or something like that. Because these are densely populated villages where they have markets where everyone is breathing on everyone else because they’re standing that close to each other. And the third reason is just that we had.... So my colleague Mushfiq Mobarak and co-author on this study had developed, through many previous studies, this massive infrastructure in Bangladesh, these implementing partners that consisted of IPA, Innovation for Poverty Action, this organization called Green Voice that basically had the capacity to implement an intervention at massive scale.

Howard Forman: And tell us the results of that. So what are the take-home messages for that, which is the largest study of its kind?

Jason Abaluck: So the bottom line is, we took 600 villages. In half of them, we did this massive intervention to try to increase mask wearing. We managed to increase mask wearing from about 1 in 10 people to about 4 in 10 people. So that increase in mask wearing from 1 in 10 people to 4 in 10 people reduced COVID by about 10 percent. Okay. So the question is, what does that imply about if we had broader mask wearing? So, there’s two important facts. One is well, going from 1 in 10 to 4 in 10 leads to a 10 percent reduction. You might think that going from 0 percent to 100 percent leads to a larger reduction. We don’t know exactly what that would be. Naive linear extrapolation might say like a 30 to 35 percent reduction in COVID. Now: then, what we find is that the largest reductions actually occurred among the elderly.

So from our increase in mask wearing from 10 percent to 40 percent, we actually saw a 35 percent drop in COVID among the elderly, who bear the brunt of COVID morbidity and mortality. So what this suggests is, masks can combat COVID. Does this mean that everyone should wear a mask everywhere all the time forever? Not necessarily. We need to do a cost-benefit analysis. So, masks are not completely costless. If I’m sitting in my office, I don’t wear a mask because it’s uncomfortable. I’d rather not wear a mask, and so the question is, are the benefits of mask wearing likely to exceed the costs? I think that to briefly summarize, in places where lots of people are dying of COVID, the answer is almost certainly yes. In places where very few people are dying of COVID and the case rate is low, then the answer is maybe not. It really depends on things that we’re not certain of, like what’s the long-term impact of mask wearing and helping to keep the case rate low as opposed to just reducing the incidents of COVID at that moment.

Harlan Krumholz: By the way, did you go to Bangladesh during this time?

Jason Abaluck: I did not, but many of my co-authors and colleagues were from Bangladesh and in Bangladesh during that—

Harlan Krumholz: No, no, and I don’t say that in any way except to say that this is research in the future, I think, which is that we’re virtual, we’re collaborative, but at that time it was, I think, virtually impossible to go and travel, but yet you were able to conduct a really high-quality study anyway, just—

Jason Abaluck: Yeah. And to be clear, I just don’t go anywhere. I just like to sit in my apartment.

Harlan Krumholz: No, but I think it’s interesting the way that sort of clever workarounds were developed in order for research to move forward, even though—

Jason Abaluck: Absolutely.

Harlan Krumholz: ... we couldn’t see each other face to face outside of Zoom calls or other things.

Jason Abaluck: This brings up my favorite activity, which is complaining about things that referees said in grant applications. In a recent grant application, referees complained about, they were like, “Oh, all the authors are different institutions. How can they work together on this grant?” And it’s like, “Oh, guess what? Almost every project I work on, I just Zoom with everyone anyway even if they’re down the hall!”

Harlan Krumholz: Yeah, I know, so you’re saying like, “Are you from 1950?” I mean, really. That’s just such a ridiculous thing to say. So let me just dig into one thing about this, which is around the rates. So as I could see this, so the control villages report an incident COVID-like illness, so people reported something that could have been COVID at 8.6 percent, and in the intervention it was 7.6 percent. But then you said, “Let’s try to verify this.” And you asked people whether they would give blood. In about a third of them, just over a third, you were able to get blood collection. And then you look at the positivity among the people who are taking that into account, it’s 0.76 percent in the control and 0.68 percent in the intervention. So a couple of things. One, even the 8.6 and 7.6, I get the 10 percent reduction piece, but absolute, it’s small. And then when you go to verify, it gets even smaller. How do you think about that?

Jason Abaluck: So, first of all, yeah, what do those numbers mean? And then we’ll talk about what it means for other settings. So one point is, as you said, about 40 percent of people consented to give blood. So 0.7-something numbers, the 0.6-something numbers aren’t actually estimates of the people with symptomatic COVID in the population because they’re the people with symptomatic COVID and also who consented to give blood.

Harlan Krumholz: And did you have the same percentage of people consenting in intervention and control?

Jason Abaluck: Yeah, exactly. So we do a bunch of balance tests to see that consent is balanced. Not only is the balance between treatment and control, there’s a more subtle bias that can arise if it were differential or different ages and genders, so we show that it’s pretty balanced by all the observables that we see. So it’s pretty close to 40 percent among everyone. So what that implies is basically, what fractions of the population had symptomatic COVID during our intervention period? That is probably between 2 and 3 percent. Okay. Now again, is that big or small? I think when we did the study based on just what we see over the course of the study, I think COVID rates were low for the first.... The study was rolled out from November of 2020 through April of 2021, and I think it was basically in March and April that in fact the majority of the infections that we see occur, so we know that COVID comes in waves and everything. And so I think the bottom line is, over the course of this study, between 2 and 3 percent of the population were infected with COVID. What does that mean? So in each wave of this study, we’re looking for about 10 weeks. So, if 2 or 3 percent of people are being infected every 10 weeks, that’s comparable to what we see in other countries, so I don’t think that that’s especially low.

You’re completely right though, that if we think of the absolute number of COVID infections, symptomatic COVID infections that we’re able to measure or because people consented, then it’s 0.7-something percent. That’s exactly why we did this study in 600 villages, because we knew that was going to happen.

Harlan Krumholz: Well, one of the things that it just makes me think is that just getting back to your research on choice architecture and preferences, it could be that if people really hate to wear masks, that that magnitude of benefit is not worth it, but from society’s point of view, it is really worth it because it has vast implications for opening the economy and so it really almost, I think, illuminates the policy problem, because.... And assume even if it’s twice as big as this, people would say, I’d be willing to take a little risk not to have to wear this darn mask, but on a society level, we’re never really going to get ahead of this unless we can really slow it, and the masks are effective at a societal level. It seems to me that’s at the crux of what maybe what’s happening in this country too, about people making more individual choices and not wanting to yield to a societal benefit as much as what’s best for me in my own freedom, my own choices.

Jason Abaluck: Yeah. So there’s two things to say about that. One is that in the study as we did it, we cannot separate whether masks are being effective because they protect the wearer or because they prevent the wearer from infecting other people. So the effect that we find is a combination of those two effects. We actually try to do a separate individual experiment where we see if masks protect the wearer in indoor markets, and we just weren’t able to get the vendors to be sufficiently likely to wear masks in the treatment group relative to the control group to have any power. So we just abandoned that study before we did any blood collection or anything like that.

But then the question is, so we don’t know if it’s protection or source control. So we can’t really do this calculation to see if it’s individually rational, but it is certainly correct that if we could do that calculation, there is a difference between the individual benefits of mask wearing and the social benefits, as there are with vaccines, so this is what economists call externalities. And I think it has been puzzling, to people trained as economists, some of the public policy discussion around this issue, because the very first thing you learn in Econ 101 is like, “Oh, when we have externalities, individual decisions don’t necessarily align with social benefits.” And so there is a need for some kind of public intervention to fix that, whether it’s a government subsidy or you tax harmful behaviors, you subsidize healthy behaviors. So it’s a no-brainer.

Harlan Krumholz: Or mandates.

Jason Abaluck: Exactly. It’s a no-brainer from an economics perspective that you need subsidies, you need mandates, you need something to internalize the externality, otherwise you’re not realizing what economists call the efficient outcome. What do we mean by that? When there’s externalities, the sense is, everyone can be made better off by forcing them to do this thing that they might not want to do from an individual perspective. From an individual perspective, you might not want to pay taxes, but guess what, if nobody was forced to pay taxes, society might be a lot worse off because there would be no government and there would be anarchy. So there’s lots of things that it actually makes sense to force you to do in order to generate social benefits, and so it’s been a little bit puzzling to see people be like, “Oh, it should just be my individual choice completely whether I do all of these things.”

I see the argument that says maybe, oh, if someone is really adamant about they just don’t want to get a vaccination for any reason or whatever, that maybe we shouldn’t say, look, we’re going to send you to prison. If you don’t do that or we’re going to kill you, if you don’t do that—that seems excessive to me. But does it make sense to say, well, if you don’t get a vaccine, we’re going to make you pay a fine? If you don’t get a vaccine, you can’t work in certain jobs? That seems totally reasonable, because if someone didn’t pay taxes, we would punish them as well. This is how society works.

Howard Forman: It is the fundamental deficiency. We talk about simple biology and simple concepts in math, but this concept of externalities is seemingly a fundamental deficiency in education, because too many people don’t appreciate the fact that it’s not just about community benefit over individual benefit, and many of these cases, everybody benefits because if you have, for instance, a pandemic under control, the economy can actually function much more normally and everybody is made better off, but it requires collective action or collective agreement about what that action is. So, this work is so important in adding to the evidence base for this particular policy measure.

Harlan Krumholz: Yeah, and so thank you so much for being with us.

Howard Forman: We really can’t thank you enough, Jason.

Jason Abaluck: Thank you both. This was great.

Howard Forman: Take care. What’s one thing that either inspires you or keeps you up at night right now, Harlan?

Harlan Krumholz: Well, I hate to be banging the same drum, but I just can’t take my eyes away from what’s going on with the pandemic. New cases are increasing now, maybe 85,000 a day. Hospitalizations are going back up. And I saw some stat out of Washington State, where 30 percent of the new cases are breakthrough cases. I think this is validating the concern about waning immunity. The good news is though, still, if you’re getting infected in a breakthrough case, it seems as if the consequence is less than among the unvaccinated. The vast majority of harm is still accruing in the unvaccinated. We just simply can’t say that enough, but there’s a lot going on.

And then the other thing I’m watching very carefully is what’s going on in Europe, which is giving me some trepidation and that the spikes that we’re seeing in Europe, the increase in the daily confirmed cases. Again, if you’re vaccinated, we’re seeing fewer deaths, it’s still somewhat protective, but the infections are beginning to rage again. And it’s not just in Eastern Europe, it’s not just in the places where there are vast numbers who aren’t vaccinated, but also Austria, Belgium, Netherlands, Ireland, UK. We’re seeing it in places where vaccinations and prior infections, if you add them together, cover most of the population. So I hate to keep talking about the pandemic, but that’s what’s got a lot of my attention this week.

Howard Forman: It’s so important, Harlan. And to that point about eight weeks ago, people were asking me what I thought about boosters. And I felt really confident to say, if you’re elderly or you’re immunocompromised, you got to get the booster now. And that for all other people, I thought it was okay, that if you wanted to get the booster, you should; if you didn’t want to get the booster, it was fine. And I ended that almost every time by saying, come back and ask me again in six weeks. Well, it’s about eight weeks after I was making those comments, and on Monday I got the booster because I just felt compelled at this point, the evidence is so strong that there’s waning immunity. The vaccine does work, but you have to boost it at this point. And we’re heading into a winter where I do believe we’re going to see increasing cases. We are vastly under-boosted among the elderly right now, something like 36 percent of the elderly have gotten a booster shot. We need to get that number up a lot higher.

Harlan Krumholz: Yeah. And I think it is something that for many people, especially younger people, the net benefit might be small if it exists at all, but on the societal level, we’re going to be better off if we can really get ahead of this. How about you, Howie, what’s on your mind this week?

Howard Forman: Well, sadly, there are two trials that are drawing a lot of attention right now. The assailant who killed Ahmaud Arbery in Georgia, and Kyle Rittenhouse, who killed two people and injured another in Wisconsin. And the Rittenhouse trial has drawn more attention and seems to have more firmly entrenched camps on both sides. But I worry a lot about the unrest that could follow either or both, and in fact, by the time we post this podcast, we might even have a verdict in one of them, but it just reminds me of how divided the country is and how much we don’t have faith in our justice system that we believe in with a lot of evidence that our justice system very often favors certain defendants and disfavors others very often on the basis of race.

Harlan Krumholz: Yeah. Yeah, we’ll have to keep our eyes on this, and yeah, the camps have divided along political lines, of course. And I worry about what will happen after the verdicts are leveled as well.

This has been Health & Veritas with Harlan Krumholz and Howie Forman.

Howard Forman: So how did we do? To give us your feedback or to keep the conversation going, you can find us on Twitter.

Harlan Krumholz: I’m at H-M-K-Y-A-L-E hmk@yale.

Howard Forman: And I’m @thehowie. That’s @T-H-E-H-O-W-I-E

Harlan Krumholz: Health & Veritas is produced with the Yale School of Management. Thanks to our researcher, Sherrie Wang, and to our producer, Blake Eskin of Noun & Verb Rodeo. Talk to you soon, Howie.

Howard Forman: Thanks, Harlan. Talk to you soon.

Department: Faculty Viewpoints

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