Steven LEVITT: My guest today, Bapu Jena, is a medical doctor, he’s a Ph.D. economist, and he’s unbelievably creative. What happens when you mix those three things together? You get someone who’s finding ways to answer important questions that no one else can. And the good news is, if you like what you hear today, he’s got a brand new podcast on the Freakonomics Radio Network called Freakonomics M.D.
Welcome to People I (Mostly) Admire, with Steve Levitt.
LEVITT: I first met Bapu when he took one of my classes as a second year Ph.D. student at the University of Chicago. It was obvious to me that Bapu had immense talent. One of the most special students I’ve taught. But I also thought he must be a little bit crazy. Who does an economics Ph.D. and an M.D. simultaneously? But I’ve always liked people who are a little crazy, so I did what I could to help him out while he was a student and I’ve kept an eye on his progress since then. And wow, even I’ve been amazed at what he’s done more or less single handedly bringing Freakonomics-style approaches into the mainstream of medicine.
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LEVITT: A couple of months ago, I read a story in The New York Times about how some researchers had used the timing of birthdays to learn about the transmission of Covid and I thought, wow, what a brilliant idea. And then I read a few paragraphs further and there was a quote from you, Bapu. And I said, “Of course, it had to be Bapu. No one else in medicine thinks this way.” So that’s a pretty high compliment.
Bapu JENA: I appreciate it. Thank you.
LEVITT: So partly why you think differently is that you are not just a doctor, but you also got a Ph.D. in economics from the University of Chicago. And I think back in the day you even took one of my classes, didn’t you?
JENA: Yeah. You may not remember but you were on my doctoral committee. It was a long time ago.
LEVITT: I was on your committee but it was fake. I didn’t really advise you. They just needed a third person to sign. I would love to take credit for some of your success but you and I will both agree, I probably didn’t help you very much when you were a grad student.
JENA: It’s the thought that counts.
LEVITT: So, before we get into all the interesting stuff about you and how you think about the world, I think it’s really important that we describe the standard way of thinking in medicine. And in medicine, which is true really in all the scientific disciplines, randomized experiments are seen as the gold standard, right?
JENA: Yeah, that’s exactly right. Medicine is one of these areas where you start with this biological hypothesis about what drug might work and how it might work. But ultimately, when you move from the bench to the bedside, there’s so many things that can go wrong. These are high stake decisions. And what you think might work may not actually end up working. And that’s why we do these randomized trials — to give doctors a good sense of what works and what doesn’t work.
LEVITT: Okay, and that makes perfect sense because in medicine what we care about is causality and we want to know if you take this drug, will it make you feel better? And randomized experiments are the gold standard of causality because you’re able to hold everything else constant, vary one thing — do I give someone a placebo or a drug, and then see whether the people who got the drug do better. But randomized trials aren’t the only research strategy used in medicine. There’s also this thing called epidemiology.
JENA: Yeah. Why don’t we have randomized trials for every single decision we make? They’re costly to do. It takes time to recruit patients. Not all patients want to participate in an experimental drug. It sometimes takes years to get this sort of information. So that’s why as a field, as a discipline, medicine has had to rely on other approaches to trying to understand causal questions like, “Does drug A do a better job at treating disease than drug B?” And so there’s this field of — I would call it clinical epidemiology because epidemiology does a lot of different things. It studies the spread of disease. But this field of clinical epidemiology is really designed around trying to use real world, typically historical, or observational data to understand whether one treatment works better than another. And in that data, you have information on sometimes tens of thousands or millions of patients who receive one drug versus another, and then a doctor or an epidemiologist, or maybe both, will work with that data to try to figure out whether that drug is better than some other drug.
LEVITT: Okay. And just to be clear, what differs between this and a randomized experiment is that nobody’s randomized who’s getting the drug. Some people are getting one drug. Some people are getting another drug and that’s choices that they’ve made, or the doctors have made — not a randomization.
JENA: Yeah. Like, let’s suppose you have a population of patients with cancer and you want to know whether or not a brand new, fancy, expensive oncology drug results in improved survival. One way you could do that is you could do a randomized trial where you randomized some patients to receiving that drug and other patients to receiving either placebo or standard of care, and then measure the outcomes, in this case survival. The other way you could do that is look at historical data on patients who receive that drug and compare their outcomes to patients who didn’t receive that drug. Now, what happens if you find that patients who receive that fancy new drug have worse survival? They’re more likely to be dead in the next year. If you look at that data, you might conclude that the drug actually harms patients. But what if the patients who are offered that drug are the ones who have worse cancer, who have passed through all other therapies up to that point and are really left with one option, which is this new experimental drug? In that case you would reach the wrong conclusion. It might be the case that the drug actually works, but because you’re not comparing like to like, you’re comparing patients who are not similar in the treatment arm, which is this drug and the control arm, which is a different drug. You’re going to come to the wrong conclusion.
LEVITT: Okay, exactly. So what you’re saying is in clinical epidemiology, what you’re looking at are correlations between did you take the good drug and did you die? But if all other things aren’t held constant, like they are in randomized experiment, then you can come to the wrong conclusions. And of course, epidemiologists aren’t stupid and they understand this. And they try to do the best they can to control for other factors, but they’re also limited by what data they have available or what data they think to collect. But you’re always left, at least, I’m always left, a little bit uncertain, maybe a little bit — well, I’m often left very skeptical at the end of the day, when, as a consumer of the newspaper, I read about these epidemiological studies and I’m told to do one thing or another.
JENA: Wait, are you telling me that eating peanuts at age six doesn’t cause dementia at age 66? You don’t believe that?
JENA: I got to retract my new paper then. Okay.
LEVITT: Okay. Economics and medicine have something really important in common, namely that both disciplines care about the answers to many questions where it’s hard to do randomized experiments. So I’m not allowed to induce massive unemployment in some cities for my research to see what the effect of unemployment is. Or even in my own studies, I’m really interested in the effect of prisons on crime. And there’s no way in the world I’m ever going to convince a random set of states to let 20 percent of the prisoners out in that state and maybe another state will lock up another 20 percent of prisoners. It’s just not going to happen. Totally impossible. And so, what economists have put in an enormous amount of effort and thought into is developing models that use this non-experimental data, everyday data, but that might plausibly have a causal interpretation because we look for special settings that mimic a true randomization and we call it a natural experiment. I actually prefer the name “accidental experiment” because I think it’s a better description of what happens. And that’s really what you do, but you do it in medicine.
JENA: You know, correlation is not the same thing as causation. That statement it’s endemic in the way medical students are taught and that’s good and bad. It’s good because I think it introduces a healthy dose of skepticism in anybody who’s training to be a doctor about how to interpret a new study that shows there’s a link between red wine and whatever outcome. Now, the problem with that though is that it goes so far as to make doctors think, or at least the doctors I know think, that really the only way that you can get at questions of causation is a randomized trial.
JENA: And it goes so far as if you’re writing a article for a medical journal in which you’re using a natural experiment, or you know, to borrow your words, an accidental experiment, any language that reflects causation, “This study shows that X caused Y” or “the effect of X on Y was this” — any language like that is typically removed from the manuscript because there’s this belief that you can’t use observational data to reach causal conclusions. And I think that’s a challenge that our field has to overcome.
LEVITT: Let me just try to explain how I explain natural experiments to people. A randomized experiment — a real randomized trial — has two key features. The first one is that the treatment group gets treated different than the control group. Okay. We all know that. The second key feature is that except for the treatment, we would have expected the treatment group and the control group to have the same outcomes on average. Okay? So you start from that and then you say, so a good natural experiment just tries to mimic those exact features. We go out in the real world and we try to find settings where otherwise identical people, essentially by chance, get treated very differently. So, if we can do that we’ve more or less mimicked a randomized trial without actually running a formal experiment. You’ve had a lot more success convincing your colleagues that natural experiments have merit. So, what words do you use when you try to describe what a natural experiment is?
JENA: Steve, I don’t know that “success” is the word I’d use. You haven’t seen all the studies that I’ve tried to get published. You’ve only seen the successful ones. That’s a different bias. You know, the way I describe it it’s very similar. But when you see a paper in a clinical journal that presents the results of a randomized trial, most often the first table, the first exhibit in that study, shows the characteristics of patients who received a treatment and a control. And you can look at that table and you can see that the characteristics almost always are nearly identical between the two groups. And that gives doctors, I think, a lot of faith that these two groups are balanced and we expect the outcomes in those two groups to be otherwise similar, were it not for the fact that one group is going to receive a treatment and another group is going to receive a control. And therefore, any difference that we end up observing between those two groups in the outcomes is attributable to the receipt of that treatment, as opposed to underlying differences and characteristics between those two groups. What I’ve tried to do in most of the natural experiments studies that I publish in medical journals is try to, you know, essentially replicate that table one. So, if I’m looking at what happens to patients who are hospitalized during the dates of a national cardiology conference, when all the cardiologists are out of town, and I want to know the answer to whether or not care is different and whether that difference in care leads to differences in outcomes — let’s say mortality at one year later. The first question someone should rightly ask is, “Well, Bapu, how do you know that the patients who go to the hospital when cardiologists are out of town just aren’t different from the patients who go to the hospital when cardiologists are in town?” And I start by saying, “Well, do you have a reason for why they would be different? Do people choose to have heart attacks?” You’ve got to fight that criticism. And so the way I fight is just to show it in table one. Look, the patients who are hospitalized with the heart attack during the dates of the American Heart Association meeting are identical to patients who were hospitalized with heart attacks during the surrounding weeks of the year.
LEVITT: And so what happens to those folks who do show up at the cardiologist office only to find out the cardiologists are all at the national convention?
JENA: Oh, what do you think happens? They actually do better. That was a shocking thing of that study. I remember being in the cardiac intensive care unit around the time of one of these meetings and the composition of senior doctors was different. And I just thought to myself, “I wonder what happens when patients get hospitalized with these acute medical conditions when these meetings are happening?” I thought they would do worse because either staffing was lower or the most experienced doctors would be away at these meetings. And what we found was the opposite. We actually found that they did better. And if we’re interested in making people’s lives better, helping them live longer, what the hell is happening that’s different in the hospital during those two periods, that’s generating an improvement in survival that is larger than statins and aspirin and beta blockers — everything else that cardiologists do? We know that’s probably a causal effect of something that’s happening during that meeting date compared to other dates. What we don’t know and what our randomized trial can’t always tell us is what’s the mechanism of that effect? And one thing that we found was that rates of a particular intensive procedure were lower. And so that led me to hypothesize that maybe what’s going on is during the dates of these meetings, it’s just that less intensive care is performed and on the margin, the people who would have got that more intensive care aren’t getting it. And the risk-benefit profile for them would have been such that they’d have been benefited by not getting the care.
LEVITT: So, has anybody followed up to see whether in fact it is hurting people on the margin?
JENA: No, they haven’t. I’m sure you’ve seen this problem in economics, which is, that was a controversial study. It had this very unexpected finding and I thought to myself, “Wow, let me replicate it because there’d be a lot of interest in showing that this finding could be replicated.” We had the hardest time getting that study published. And you know what the journals would say to me? They said, “You already published this finding. So there’s nothing novel here.” And that actually has made me think a lot about this replication crisis. There’s no incentive to replicate findings, at least for these types of findings. And so now I write a study and I leave it to the world to replicate it.
LEVITT: Yeah, it’s so troubling. I’m sure it’s true in medicine. It’s been extremely true in psychology and in economics that so much of what is done could not be replicated, randomized trial or otherwise, but the incentives within these professions and at the journals are totally screwed up when it comes to trying to sort out the truth. You think that all these journals, that all these researchers should be after the truth, but really it’s a very different game that’s being played, a game of how do I get published and how do I get citations for my journal? Look, I don’t have the answer, but I think if outsiders knew how academic publishing worked, they would be discouraged by it.
JENA: Steven, if I knew how academic publishing worked, I would be encouraged by it.
LEVITT: I once made a foray into trying to do natural experiments in medicine and I got burned so badly that I’ve never gone back. I started, I think, in 2004 when you were still a grad student, and I think it’s quite possible that you might’ve been my inspiration. And I’ll give you credit for what’s going to turn out to be maybe the most unsuccessful project I ever worked on in my life.
JENA: I’ll take credit for the lost year of your life.
LEVITT: Okay. So the idea is really simple. When a patient shows up at the emergency room, he or she has no idea what doctors are going to be working at that time. And if some doctors are better than others, then as a patient, I can get lucky or unlucky depending on which doctors are working. So, I didn’t assign any patient at random to any doctor, but the set of patients who show up are essentially random, and so I can compare outcomes across the time period, say, a Tuesday, July 29 — let’s say you’re working that shift. And let’s say that July 22, Tuesday, I’m working that shift. Then if the patients who come in on July 22, have better outcomes than on July 29, I’m a better doctor than you. That would be the kind of inference one would draw from these data. Okay. Does that set up make sense to you?
JENA: Yeah, absolutely. Yeah.
LEVITT: Okay, So I was working with a hospital. They had an E.R. The E.R. was staffed by three or four doctors at a time. So I was able to get years worth of data and outcomes on the patients. And I was able to find some patterns in those data. And I was super excited and I went to present at what’s called the grand rounds, where the doctors sit around and they listen to someone who’s supposed to know something explain a new concept to them. And it was unreal. The reactions I got were confusion, anger, a consensus in the audience that I had no idea what I was talking about. So then I showed the results to my dad, who’s an eminent medical researcher. Probably has 500 publications. He’s got lifetime achievement awards in medicine and he’s a super smart guy. And I explained my method, and he listens to the whole thing and he says, “Wait. So you’re telling me that to figure out whether I’m a good doctor, you’re going to look at the outcomes of all the patients who come on my shift, not just the ones that I take care of?” Okay. And that’s exactly right. You understand. It was this moment of triumph. I had finally explained it. And my dad says, “That’s the dumbest thing I’ve ever heard of in my entire life. If I didn’t treat the patient, it’s not my fault if he has a bad outcome. That doesn’t even make sense.”
JENA: Yeah. yeah.
LEVITT: So that was the last natural experiment research paper that I ever tried to do in medicine. I never got it published. So hats off to you that you’ve managed to rattle off a string of these papers. And I’m not sure we’ve really made it clear but is it fair to say that when you started doing these natural experiments in medicine, literally were you not the only person publishing studies like that?
JENA: I would say this is what I’m known for. There is a Canadian physician named Donald Redelmeier, who I would think as the pioneer in this area. He did a lot of work with Amos Tversky and Daniel Kahneman years ago on behavioral economics. But besides that, there’s not a ton of people who really specialize in natural experiments.
LEVITT: How many medical researchers are there published in journals would you guess? Oh — 10,000? 20,000? 30,000?
JENA: Probably more than that? Yeah —.
LEVITT: Okay. 50,000. Okay. Okay. Let’s say there are tens of thousands of researchers. And there are, I think, by what you just said — let’s be generous. Maybe there’s a dozen people like you who are looking for natural experiments. If you look at economics, I would say something like, a third or a fourth of the papers that are published in top journals are taking advantage of natural experiments. It sure seems to me like medicine’s making a huge mistake by not focusing more on natural experiments.
JENA: Yeah. So there’s a ton of research that’s what you might call health policy. So what’s the effect of Medicaid expansion in one state on some outcomes. There’s much, much less work that uses natural experiments to try to answer questions that might be of clinical importance to a doctor — what therapy to provide or not provide. To your point, it’s the minority of observational studies in a medical journal that use these sorts of methods. Whereas in economics, you’d be taken into the coals if you didn’t have a good natural experiment. Now, I think the challenge though — and this is what I face — is, it’s hard to find these natural experiments. I’ve gotten good at it and this is the way my mind thinks nowadays, and I’m sure that’s the way you think about the world. But it’s hard to find natural experiments for every question that you might want to answer. And the view of a lot of medicine has been, well, let’s just try to answer it anyway. Take for example, red meat. So tons of studies that look at the relationship between red meat consumption and any number of outcomes, mostly cardiovascular — hopelessly non-causal, right? There’s no way you can look at those studies and think that they’re causal and I, for years, tried to think of situations where I could find a natural experiment around beef consumption and like looking at whether or not, for example, when mad cow disease outbreak in England — was like 10 or 15 years ago. I looked to see whether or not there was changes in cattle output and consumption in parts of the world. And maybe that could then be used to show changes in cardiovascular outcomes. That was hopeless. I didn’t find anything there, but it’s hard to answer a question that people would want to know the answer to is, should I eat more or less red meat?
LEVITT: Yeah. Look, there’s a million questions I’d love to answer that I haven’t been able to answer with a natural experiment either because there wasn’t one out there or more likely, I just wasn’t clever enough to find it. But it sure seems to me that the medical profession is making a systematic error in the sense that by putting so much effort into randomized experiments and so much faith in this epidemiology, but missing out on the middle ground just seems like an obvious mistake to me. And one that somebody, somehow — the profession should change and you’re the guy to do it.
JENA: I think that’s a good name for a podcast, “I’m the guy to do it.” Yeah.
You’re listening to People I (Mostly) Admire with Steve Levitt, and his conversation with economist and physician Bapu Jena. After this break they’ll return to talk about some of Bapu’s research on Covid..
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LEVITT: Morgan, what do we have on the table today?
Morgan LEVEY: Steve, I want to check back in on something. In one of our episodes with Sendhil Mullainathan you said:
LEVITT: I’m making a promise to myself — at least for one week, inspired by Sendhil, I’m going to make play a priority.
LEVEY: How have you been doing with this promise to yourself?
LEVITT: Oh, God. Terrible. So, I really did try and I found it to be painful on so many different dimensions. First, I tried to play harder with my toddlers and God do I hate toddler play. Barbies and princesses and dragons. I mean, that is just not my thing. But I thought, why don’t I play with my teenagers? And every minute that I spent trying to play with my teenagers felt like torture. Not because it wasn’t fun in and of itself, but because I just have so deeply built into my mindset, this idea that I shouldn’t be playing, I should be doing. I have gotten so allergic to wasting time that at least in a week I couldn’t overcome it.
LEVEY: I think you’re going to just have to reframe play in your mind from “wasting time” to actually doing something that makes you feel good.
LEVITT: It’s so true. And I tried to do that but maybe it’s one of those things where you just have to build up a little more tolerance. I also discovered something interesting about myself that I should have already known, but I just can’t be okay at things. I don’t mind being terrible at things — really, really bad. But anything I put effort into, I need to be really good. People who know me would say, “Wait, what do you mean you don’t play? You play golf all the time. You study trivia all the time. Isn’t that play?” And the funny thing is for me, those are not play. I’m deathly serious about golf and I’m deathly serious about trivia. And, Sendhil’s probably right. Probably what I’m doing — is it a terrible idea to only focus on things where I’m really going to excel. But at least that first week’s experiment — F. I failed. Failed completely.
LEVITT: Actually in my entire existence there’s one thing I can think of that I do. And I do not very well, but I enjoy it. And that’s ping pong. I play ping pong with my son Nick probably three times a week. And it is roughly the only thing I do where my goal isn’t to get better. So maybe the lesson for me is I got to think about what it is about ping pong, where I feel liberated from that tension, and try to find a little more of that.
LEVEY: Great. Well, I can’t wait for the next project you have that’s been inspired by your ping pong play. If you have a question for us, please write in. The email address is firstname.lastname@example.org. Steve and I both read every email that’s sent.
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LEVITT: So the research of yours that I read about in the newspaper — the one about Covid and birthdays — it became the subject of the first episode of your new podcast, Freakonomics M.D., where you talk about it in some detail. Can you just sketch out for us that study?
JENA: Yeah. There was, and has been, a lot of concern about understanding how the disease spreads. We know that it spreads from person to person. That’s obviously well known. But what we didn’t know as well is whether or not small social gatherings, the type you might have with friends or family, with people that you trust, would be a place where the virus that causes Covid-19, would be likely to spread. And so that was the question that I wanted to try to get at. But of course, like how do you study that?
LEVITT: You could do a randomized trial, but how in the world are you gonna do a randomized trial, where you either force people to hang out with their friends and family or prohibit them from hanging out with their friends and family? Never going to get that done in a million years.
JENA: That’s right. There’s no randomized trial where you could randomize someone to spend time with their in-laws cause everybody would be — I don’t know what, the treatment or control, but that’s not to happen. I have good relationship with my in-laws. I’ll just put that out there publicly. I have a great relationship with my in-laws.
LEVITT: Okay. So what you’re saying is we have a problem we want to know the answer to. So, Bapu to the rescue, what happens next?
JENA: I realized that the data that I often work with, which is called insurance claims data. Any time you go to the doctor, if you have insurance, your doctor bills your insurer for that care. And there’s detailed information about the diagnoses. And the other thing the insurance company happens to know is what is your date of birth? And so I said, “All right, well, why couldn’t we look at insurance data where we know information about an individual’s birthday and look at whether or not Covid-19 diagnoses increase after a person’s birthday?” And you just follow these households out two weeks later and see if households in which a member had a birthday have a higher rate of Covid-19 diagnoses. And what we basically found is that the rate is about 30-percent higher in a household that has a birthday.
LEVITT: Yeah. The obvious mechanism is that people celebrate the birthday. They’re hanging out with their friends and family. The friends and family, despite seeming trustworthy, are spreading the virus.
JENA: Exactly. I think the cleverness here is that we don’t see people celebrating.
LEVITT: Yeah. You don’t see any parties. You just use the birthday as an indicator of the party. Now I presume that — at least in my family, we take kids’ birthdays a lot more seriously than we take adults’ birthdays.
JENA: Just to be clear, I would take your birthday seriously no matter what. But yeah, so there’s an effect in both groups. But that effect is much larger if a kid has a birthday. And we couldn’t look at this in the data, but I could just speculate as a parent — two things are possible. One is that parents are just more likely to get together around a kid’s birthday than an adult’s birthday. And the second is that if you have a kid’s birthday, you might expect that the nature of interaction would be different. A lot of blowing candles, like heavy breathing, masks down, running around, touching all sorts of different surfaces, getting close. It’s different than like having a dinner party with some adult friends.
LEVITT: Yeah. That’s true. Now this is a weird thing about Covid. Now Covid has obviously been one of the most important events of our lifetimes. And it’s disrupted life. It’s taken many lives. Are you at all surprised that this far into it, we still know so little about transmission?
JENA: Yeah. This is an area where I think economics, physicians, epidemiologists could have gotten together and figured out how to answer these questions in creative ways, outside of a randomized trial. I remember getting boxes from Amazon and my wife saying, “Just keep it in the garage for three days.” And she’s a doctor. And I was like, “You know what? Let’s keep it in there for four days, because I don’t know what’s growing on this box.” Like, if you had detailed data, identifiable data — you know where people live. You know whether or not Amazon packages are delivered to them. You could look and I’m sure construct a good natural experiment to understand whether or not that sort of packing — touching boxes that other people, many other people have touched, are associated with higher rates of Covid-19. I’m sure there’s a good, clever way to design a natural experiment to answer that question. But the data was always there. It’s just hard to put together and get people behind it.
LEVITT: Yeah, I think that’s true. So that reminds me of another topic closely related that I want to get your opinion on, and it relates to medical ethics — because I have found that to be an area where economic thinking and medical thinking lead to very different conclusions. And so, I’m interested in hearing what someone who’s been trained in both areas thinks. So, let me take a very specific case, which is Covid vaccines. So before Covid vaccines were approved for widespread use, the manufacturers ran randomized clinical trials into which volunteers were randomly assigned to either be vaccinated or not. And these needed to be big trials to get enough data, maybe 30,000 people. And it took a long time to enroll the people. And then we just had to wait it out to see which of those 30,000 people were going to get Covid and what the outcomes were going to be of those who got Covid. Those trials took about four months, but we could have cut that four months if we had done what’s called a human challenge trial, and that’s where you vaccinate people and then you expose them to Covid intentionally to see how they do. And the value of a challenge trial is that you need a much smaller number of volunteers and you don’t have to wait around for people to get exposed to Covid in their everyday lives. But medical ethicists say it would be immoral because these trials expose volunteers in the study to risk. But as an economist, that drives me so crazy. So as an economist and a doctor, where do you come down on that kind of issue?
JENA: I usually keep my mouth shut. No. Where I come down — it is three words, “Willingness to pay.” I thought you were going to say, if you offered these volunteers in these human challenge trials, a hundred thousand dollars to participate.
LEVITT: Oh, a million dollars —
JENA: $10 million to participate.
LEVITT: Yeah. Absolutely.
JENA: You could throw up tens of millions of dollars and it would have been worth it. It would have been a bargain at that price. I would go even one step further. I mean, think about the architecture for clinical trials. Like why does it take so long for clinical trials to get done? One of the reasons why is because it takes so long to recruit patients. As medicine gets better and better, guess what? It gets harder and harder to recruit patients because nobody wants to be in the treatment arm because the control arm, the standard of care, gets better and better. We’ve seen this in HIV, I’m sure it’s present in other areas. And in that case, you think about what’s the value of information that is generated by a randomized trial. It allows people across the world to be treated differently. The number of life years, the value of that life in the economic sense is staggering. People have talked at length about whether or not we should pay clinical trial participants to participate. As an economist, I would say, “Why not?” I certainly understand the ethical challenges that are involved, but I’ve got to think to myself, there’s gotta be a way to balance those ethical challenges. It can’t be this kind of binary decision where compensation in any form or an assessment of trade-offs between the person and the public is off the table. We’re making those sorts of trade-offs now, as we’re thinking about public mandates for vaccines. So it’s not like society doesn’t make those sorts of trade-offs and decisions in other aspects of our health. It just doesn’t happen in clinical trials.
LEVITT: It seems to me totally obvious that with Covid we should be doing these challenge trials. and like you said, we should pay the volunteers a million dollars, $10 million. We should have lines out the door of people saying, “Please put me in this trial, give me the vaccine, and then expose me to Covid. I’m willing to do that.” So I had Doctor Slaoui on my show. He’s the doctor who led Operation Warp Speed. And I asked him — I thought he would agree with me. I said, “Why not do human challenge trials?” And his answer was “Well, they wouldn’t be any good because you can only include people in those trials who aren’t really at risk of being hurt for Covid.” I said, “No, I want to do it on the sickest people.” I’m sure there must be 80-year-old people who, maybe they got cancer, maybe they’re high at risk. They want to get $10 million so their family can live well after they’re gone. They’d love to be in that trial, even if there is a real chance that they’ll die from it. He could not have disagreed with me more. And I was really surprised. I’m always surprised at how pervasive and how deep the ideas in medical ethics are that just collide completely with an economic way of thinking.
JENA: I agree with you completely. Take it to an extreme, an individual who is in the ICU, who is ventilated, meaning a machine is breathing for them, who is so sedated that they’re unlikely to be able to recover from that, like highly unlikely. You could imagine doing trials in that setting. Now, of course there’s obvious ethical issues around autonomy. That person wouldn’t be able to make that decision. And I certainly don’t want to dismiss those, but there’s gotta be some gray area where that sort of thinking would make sense. It turns out we actually think like that in a lot of other ways. So for example, we are not likely to transplant organs, which are a scarce resource, into people who have very limited life expectancy after organ transplant. So clearly we’re rationing a health product. We clearly think very carefully about intensive care or aggressive measures for people who are at the end of life. Why is that? Because we’re making a trade off. There is a chance that something would work, but we’re balancing societal needs, costs of care with the likelihood that they would benefit. So, it’s not like these sort of trade-offs don’t happen all over the place in clinical medicine. But for some reason in this area they’re walled off and I think it’s a limitation.
LEVITT: I think the reason is it comes back to the idea of doing no harm and the idea of actively going out and hurting a volunteer intentionally, even though it’s a volunteer, and even though it’s one person to save a hundred thousand lives, I think that flies in the face of what the medical profession feels like it’s their job to do.
JENA: Yeah. I know this historian and he told me the story about Hippocrates. So apparently before Hippocrates said, “First do no harm.” He said, “Maximize social welfare.” And that didn’t go over so well. He had to go to the second best.
LEVITT: So you’re starting a new podcast. Now, seriously, does the world really need more podcasts? I think we had exactly the right number of podcasts the day before I started this podcast. My podcast pushed us over the edge. So what do we need your podcast for?
JENA: Wow. So the name actually — it’s highly creative. It’s Freakonomics, comma M.D. Why fix something that’s not broken? But, you know, I think people just need to hear more about this stuff — the people who listen to this show are hopefully not going to be mostly doctors and people who work with data, but just people who are living their lives and have a curiosity about economics and medicine. I hope that there’s some element of empowerment and education that comes out of it. So, for example, in the cardiology-meeting study, one of the principles that I think we showed or that I tried to show was that the intensity of care that is provided to a patient, it matters. And there could be settings where too much is done for patients. In medicine, we call that less is more. But patients, when they go to a doctor, they may not think that. What they’re going to be offered by a doctor may not be the right thing for them. And so I think if that episode is listened to by people and when they go talk with their cardiac surgeon about a procedure, or they talk to their cardiologist, they say, “You know, how likely do you think I am to benefit from this? You know, is it possible that I’m going to be better off with just medical therapy for this condition?” And having a doctor be put in a situation where they have to explain the risks and benefits I think is always a good thing. And so that’s the value that’s going to be added to people who are listening to this, above and beyond the fact that it’s going to be fun and creative and I hope to tell a lot of good jokes — not all of them scripted, of course, but you know.
LEVITT: I do think you’re completely right about the need for people interacting with doctors, to be able to challenge doctors. I’ll give you an example from my own life. So my last daughter, my wife had gotten a positive value on a toxoplasmosis test while she was pregnant. It’s some kind of horrible thing that you get from cat poop. It does awful things to babies, potentially. So my daughter was born and the experts in the area came into our hospital room. This is hours after my daughter’s born and they wanted to use anesthesia on her and do all these things. And I just said, “Well, why?” And they said, “We want to understand whether she has toxoplasmosis.” And I said, “Well, what are you going to do about it, if she does?” They said, “Then you’ll know that she’s probably gonna be mentally challenged and all these other things.” And I said, “I’m going to know that anyway in the course of time. Sedating her when she’s two hours old doesn’t seem necessary.” So I ruled out the first two. Then they a had a third thing they wanted to do. And I said, “What do you think the chances are that if you did that, it would lead to a treatment that you would apply now that would actually make a difference?” And much to my surprise, the doctor said, “I think the odds are about one in 10,000.”
LEVITT: To which I said, “I want to pass on that one, too.” It turns out it was a false positive.
LEVITT: They tested my wife again for toxoplasmosis a few days later. And she came back negative. And I really do think it’s important for patients to be empowered, to feel like they can ask those questions. I feel like I can ask those questions because I have a Ph.D. in economics. And when I hear that my wife has toxoplasmosis, I go read about it. I understand just enough about the problem — at least to ask smart questions. So, that’s a good reason for people to listen to this podcast, for sure.
JENA: The type of thing I’d like to do in this podcast is just introduce people to a different side of medicine. The part of economics that’s always been most fascinating to me — and I suspect to you — is the ability to answer questions in this really creative but also rigorous way. Now, there’s a lot of economic studies that are really creative. But when you think about what the implications are of that idea, it’s hard to stretch out an implication that is really going to matter for someone’s life. The beauty of, I think, Freakonomics M.D. , the podcast, is it takes the elements of Freakonomics that I’ve always liked and marries it with something that is going to matter for people, which is their health and their wellbeing.
LEVITT: Up until now, there’s only been one project where Stephen Dubner and I allowed the use of the name Freakonomics without having day-to-day control of the operations, and that was the Freakonomics movie. And let me just say it didn’t turn out exactly as we hoped it would. So we’ve been cautious since then. And the fact that we’re allowing Bapu to use the Freakonomics name in his new podcast shows you just how much faith we have in him. My own personal favorite episode of Bapu’s podcast is called “Do As Doctors Say, Not As Doctors Do.” We love your emails and questions, keep them coming. The address is email@example.com. Thanks for listening.
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People I (Mostly) Admire is part of the Freakonomics Radio Network, which also includes Freakonomics Radio, No Stupid Questions, and Freakonomics M.D. This show is produced by Freakonomics Radio and Stitcher. Morgan Levey is our producer and Jasmin Klinger is our engineer. Our staff also includes Stephen Dubner, Alison Craiglow, Greg Rippin, Joel Meyer, Tricia Bobeda, Emma Tyrrell, Lyric Bowditch, and Jacob Clemente. Theme music composed by Luis Guerra. To listen ad-free, subscribe to Stitcher Premium. Thanks for listening.
JENA: Can I just make a suggestion? It should be Freakonomics M.D., Ph.D. I gotta, I drop the mic with that, right there.