465. Introducing a New “Freakonomics of Medicine” Podcast

[0] Hey, Bapu.

[1] How's it going?

[2] I like your podcast.

[3] Thank you.

[4] So why don't you just say your name and what you do?

[5] My name is Bapu, Jenna.

[6] I'm an economist and a physician at Harvard.

[7] I teach healthcare policy and health economics.

[8] I see patients at Massachusetts General Hospital, and I'm a professor at Harvard Medical School.

[9] As if you need another job, you are getting ready to host a new podcast for the Freakonomics Radio Network.

[10] We are about to play now for our listeners, a pilot episode of your news show, which I am incredibly excited about.

[11] But first, let me just ask, Bapu is not the name on your birth certificate, is it?

[12] Is this being recorded for legal purposes?

[13] My first name is Anupam.

[14] Bapu is my middle name.

[15] It's really more of a nickname.

[16] It's probably on every legal document except for my birth certificate.

[17] So Bapu means father in a variety of different Indian languages.

[18] It's what they used to actually call Mahapagandhi.

[19] Not that I'm trying to draw any, you know.

[20] So there's some important distinctions to be made.

[21] Bapu, how many people are there in the world like you that have both an MD and a PhD in economics?

[22] Oh, in the world, I'd say, I don't know, maybe 10 to 20.

[23] So I guess you could look at that two ways.

[24] One is you are a very, very, very, very rare bird, and that's awesome.

[25] Or you could look at it from the demand side and say, if there's so little demand for that, it must be a waste of time.

[26] I know, exactly.

[27] When you see so few people going into something, you have to wonder why, unless there's some market power or something like that's the only thing unifying explanation maybe that's what you're after then yeah exactly i'm on a quest for market power you have a bit of history with freakonomics radio can you recall what you've told our audience in the past so i've been on a couple times the first time was about a study that i had with some others looking at what happens to patients who are hospitalized during the dates of national cardiology conferences when cardiologists are way.

[28] They're out of town at these meetings.

[29] We found that patients actually do better.

[30] Their mortality rates fall during the dates of those meetings.

[31] So that was the first time I was on the show.

[32] And then you did a really nice series on bad medicine, which I was on a couple of times.

[33] And I presume you didn't ask me to join because I'm reflective of bad medicine.

[34] No, we didn't.

[35] So, Babu, as you know, we love economists around here.

[36] And we also love doctors.

[37] They each seem to have their own intellectual superpowers.

[38] So is it fair to say that you are both Superman and Batman?

[39] Yeah.

[40] Although Batman doesn't actually have superpowers, does he?

[41] Other than common decency, which does seem like a superpower these days.

[42] The way that you ended up marrying the tools or blending, I should say, the tools of medicine and economics in your research, where did those curiosities come from?

[43] A lot of the work that I do is sort of like Freakonomics meets medicine.

[44] It's questions that relate to medicine and health care.

[45] That's criteria.

[46] One.

[47] Criteria two is that it often requires large amounts of data like economists are very familiar with.

[48] Criteria three is that there has to be an approach that is causally valid.

[49] Like, I'm not interested in associations for the most part of my research.

[50] I really want to know whether X causes Y. And a lot of those tools that economists use, I implement in my work.

[51] Some of your research highlights the fallibility of doctors, or at least the fact that they are as human as the rest of us.

[52] I'm really curious to know how that's received in the field when you write a paper that shows that older doctors, for instance, that their skill set seems to deteriorate.

[53] It's certainly the case that when that study came out, I got emails from doctors who had tons of clinical experience who said, you know, this just absolutely can't be true.

[54] To which I responded, I said, it may not be true for you.

[55] But that's not really the question I'm trying to answer.

[56] What I'm trying to answer is if we took 1 ,000 doctors above the age of 65 and 1 ,000 doctors between 35 and 40, and we randomized patients to those two groups of doctors, where would we expect to see better outcomes?

[57] And we'd expect to see better outcomes in the doctors who are younger.

[58] Now, there's certainly going to be doctors who are older, who have a lot of experience, who maintain high volumes and their clinical practices that would have superior outcomes.

[59] And maybe the doctors who email me would fit into that category.

[60] But it's certainly possible that some of the people who email me didn't.

[61] And maybe that's why they had time to email me. Why'd you want to make this podcast other than the fact that, you know, I called you and begged you to do it?

[62] Yeah, I don't have anything else to do it.

[63] You know, I think that there's a real interest in the intersection between economics, human behavior, and medicine.

[64] And that is exactly my sweet spot.

[65] So the idea here is that every episode you will dive into a single medical study.

[66] including some studies that were done by you and your colleagues and others done by other researchers.

[67] Why is that the right way to talk through an issue?

[68] I think the structure of a study, it follows a structure of a podcast.

[69] It starts with a question and then, okay, what would you need to answer that question?

[70] What would the data need to be to answer that question?

[71] All right, now you have the data.

[72] How would you answer it?

[73] What approach would you take?

[74] All right, you have an approach, you have an answer that's tentative.

[75] Now, how do you know that that's the right answer?

[76] what sorts of ways you have to interrogate your approach to make sure that you've gotten to the right conclusion.

[77] That's exactly what researchers try to do in every study that they published.

[78] And then the last thing is, so what?

[79] You know, what do we do about this?

[80] And that's the most interesting part coming up with those implications.

[81] Now that you are a medical doctor and a Ph .D. economist and a podcast host, how much harder would you say it is to be a podcast host than to be just the doctor or an economist.

[82] Oh, I mean, I think being a podcast, this is the most incredibly difficult thing in the world.

[83] It requires immense scale, intelligence, and really defining good looks.

[84] Is that correct?

[85] That's pretty much the answer I was going for.

[86] Yep.

[87] And humility.

[88] Yeah.

[89] Glad we're on the same page there.

[90] Now, to our podcast listeners, we want your feedback on the episode you're about to hear.

[91] We're at Radio at Freakonomics .com.

[92] Bapu, Jenna, thank you so much for coming to play in our sandbox.

[93] Let me shut up now and ask you to handle what we podcast hosts call.

[94] A tease?

[95] The show will start right after these messages.

[96] How was that?

[97] There is apparently nothing you are not good at, nicely done.

[98] I'm going to call today's episode, on your mark, get set, croak.

[99] In Boston, where I live, there's a big annual event that I love to watch every spring.

[100] The Boston Marathon.

[101] It's the oldest annual marathon in the world, and they've run it every year, since 1897, except last year because of the pandemic.

[102] This year, it's been delayed until October, but it still feels like marathon season here in Boston.

[103] Every year, the marathon draws about a half a million spectators along its 26 -mile route that starts in the suburbs, and it moves its way all the way into downtown Boston.

[104] Now, I've never run a marathon.

[105] In fact, the closest I've probably come to it is watching a Harry Potter marathon on TV, which I recognize is not quite the same.

[106] But my wife runs, and although she's never run a marathon about five years ago, she ran a 5K charity race here in Boston.

[107] The route for that race went right by Massachusetts General Hospital, which is the big teaching hospital where I work.

[108] I've got a parking spot there, so on the day of the race, I decided to drive into the city and park at Mass General, so I'd have a good spot close to the route.

[109] It was my wife's first time doing a race like this, and it was important to both of us that I'd be there to cheer her on.

[110] But as I drove to the hospital, I hit a snag.

[111] It turns out that one of the main roads was blocked off because of the race.

[112] The hospital was just a few blocks away, but there was no way for me to detour around that roadblock.

[113] I was cut off from the hospital and from my perfect spot because of the race.

[114] So I was cursing my decision not to take public transportation, and I turned around and headed back home.

[115] Hours later, my wife got home after finishing the race, and understandably, she was a little bit surprised that I hadn't shown up, and I felt horrible that I'd missed the race.

[116] But when I explained to her what happened, and she's a radiologist, by the way, she thought about it for a minute.

[117] And then she said, I wonder what happened to all the other.

[118] people who needed to get to Mass General this morning.

[119] And I thought, what did happen to all those people who needed to get to the hospital?

[120] Especially the patients.

[121] What if someone had an emergency?

[122] I'm a doctor, but I'm also an economist.

[123] And nothing gives me more of a thrill than a question that really gets both sides of my brain going.

[124] And that doesn't happen every day.

[125] Here, one of those questions had fallen into my lap.

[126] And I only had to miss my wife's race to get it.

[127] There are thousands of marathons held every year around the world.

[128] And anyone who's lived in a city hosting a marathon knows how disruptive they can be.

[129] Like my wife's 5K, but in the case of marathons, it's obviously much worse.

[130] Block roads for miles.

[131] Impossible to get around.

[132] Traffic grinding to a halt on the morning of a race.

[133] For patients who are sick and need to get to the hospital quickly, those sorts of disruptions could be a recipe for disaster.

[134] So I wonder, would it be possible to measure whether marathons cause delays in treatment?

[135] Because if I could measure that, I'd have a way to measure something else.

[136] How much of a difference does that delay make in the survival of those patients?

[137] So I put together a small team of researchers at Harvard to analyze the data.

[138] We made a list of the 11 biggest marathons in the United States.

[139] We included cities like New York, L .A., Honolulu, and Chicago.

[140] And we mapped their routes.

[141] Then we looked at more than a decade of Medicare data to find people who lived in zip codes along those race routes, who happened to have either a heart attack or a cardiac arrest on the day a marathon was held in their city.

[142] We look specifically at data for Medicare patients because that automatically narrows it down to the over 65 crowd.

[143] And the over 65 crowd, simply because they are older, are the group that's most likely to have heart attacks and cardiac arrests.

[144] Now, why do we focus on heart attack and cardiac arrest as opposed to any other sort of medical care?

[145] Well, for one, they are emergencies.

[146] And that means they're random.

[147] People don't choose when they happen.

[148] The second reason we looked at heart attacks and cardiac arrests is that these are serious business.

[149] A heart attack is when a blood vessel that supplies your heart is blocked off.

[150] There's a clot that forms and blood can't get to the heart and your heart muscle dies.

[151] So it's a big deal.

[152] A cardiac arrest is an even bigger deal.

[153] It's when your heart stops pumping blood to the body.

[154] It's the closest you can come to being dead.

[155] Sometimes we're lucky to bring people back to life from a cardiac arrest.

[156] So these are both really serious conditions and the treatment is really time sensitive.

[157] Every minute counts.

[158] Just like me trying to get to my parking spot to watch my wife's race, patients who live near a marathon could get cut off from the nearest hospital by block roads and detours.

[159] So here was our goal.

[160] We wanted to see if living near the marathon route made it more likely that you would die if you had a heart attack or a cardiac arrest on the day of a race.

[161] We looked at data for marathon days, and because heart attack mortality can actually vary day by day, we compared mortality on marathon days to data from the same day of the week as the marathon, but in the five weeks before and the five weeks after the race.

[162] the non -race days basically served as a control group.

[163] So if the race fell on a Sunday, we looked at the five Sundays before and five Sundays after the marathon.

[164] Now, it is possible, though really unlikely, that heart attack mortality could be higher on race days than non -race days for reasons that are unrelated to marathons and ambulance delays.

[165] So to address this possibility, we have to be higher.

[166] had a second control group as well.

[167] We looked at similar patients on marathon and non -marathon days who lived in zip codes just outside areas affected by the race routes.

[168] These are patients who should not have been affected by any marathon -related delays.

[169] All in all, we looked at 1 ,145 patients who are hospitalized for a heart attack or a cardiac arrest in the cities that we studied.

[170] And we compare them to just over 11 ,000 hospitalizations for patients on non -race days.

[171] What do we find?

[172] For patients in areas near marathon routes, the percentage who died within 30 days of being hospitalized, the 30 -day mortality rate, was 13 % higher.

[173] If they had a heart attack or cardiac arrest on race day, then if they had either one of those conditions on a non -race day.

[174] We didn't find any increase in mortality on marathon days in those people who lived in nearby zip codes that were unaffected by the race route, which makes a lot of sense.

[175] Their trip to the hospital should not have been affected by block roads.

[176] You might be thinking correlation isn't causation.

[177] When we do a study like this, we have to be sure to eliminate all the other possible reasons for the effect that we're seeing.

[178] So what are some of those other possible explanations?

[179] What if the people having heart attacks were actually running in the race?

[180] Well, we studied patients aged 65 years and older.

[181] And, okay, there are a lot of runners who are over 65.

[182] So we looked at people with multiple medical conditions, people who are chronically ill and therefore were really unlikely to be running a marathon?

[183] Or what if for some reason the patients having heart attacks on marathon days were just different from patients on any other day?

[184] It doesn't seem plausible, at least not to me, but to double check, we compared patient characteristics, like their age or other cardiac problems.

[185] And we found that those characteristics were about the same on race days versus non -race days.

[186] What if hospitals are short -staffed on Marathon Day?

[187] That didn't explain it either, because hospitals were performing all of their typical cardiac procedures on marathon days, which suggests that there were plenty of people on hand to care for heart attack patients.

[188] It occurs to me that those folks must have gotten an early start on their commute so they didn't get stuck in traffic.

[189] What if ambulances took patients to hospitals that were further away to avoid roadblocks.

[190] We found that the hospitals that patients were taken to were actually the exact same on marathon days and non -marathon days.

[191] It just took patients longer to get there.

[192] All we were left with to explain the difference in mortality was a delay in treatment.

[193] So what kind of delays are we talking about here?

[194] On a typical non -marathon day, the average travel time in an ambulance for patients with a heart attack or cardiac arrest was 13 .7 minutes.

[195] On a race day, that went up to 18 .1 minutes.

[196] That means on average, it took about four and a half minutes longer, 32 % longer, for patients to get treatment on the day of a marathon.

[197] That may not seem like a long delay for your average commuter, but even small delays in care can lead to six.

[198] significant heart damage, which, by the way, is why we say in medicine that when it comes to heart attacks, time is tissue.

[199] It's also worth noting that we show that ambulances were delayed.

[200] But in our data, about 50 % of people who had heart attacks didn't arrive by ambulance.

[201] Someone drove them.

[202] Those people likely faced even larger delays because private cars can't do the same things ambulances can do, like going through red lights or breaking the speed limit.

[203] Just to recap, what we found was that even small delays in care lead to 13 % higher mortality in these patients.

[204] And that finding relates to one of the fundamental questions we ask ourselves in medicine.

[205] How fast do we need to act when someone is sick?

[206] Do we have days, hours, or just minutes, the gold standard for experiments in medicine is the randomized control trial.

[207] And that kind of trial, we identify a big group of similar patients and some of them get one treatment while others get a different treatment or in some cases even placebo.

[208] The patients don't know which group they're in and neither do the doctors.

[209] Now, we could never have conducted a randomized trial where some heart attack patients were told, hey, hang on a couple of minutes, and others were given treatment immediately.

[210] And then we saw who did better.

[211] That wouldn't be ethical because we already know that acting fast can save lives.

[212] Like I said, time is tissue when the heart is under this kind of stress.

[213] But we just don't know exactly how fast we really need to be.

[214] This study actually gave us what economists call a natural experiment to help us answer that question.

[215] In a natural experiment, conditions out in the world that we have no control over randomized patients for us, if only by accident.

[216] In our study, the combination of randomly timed emergency events and highly time -sensitive medical conditions allowed us to answer the question of just how time -sensitive this care is.

[217] So if there's one thing I want you to take from this study, it's this.

[218] If you're experiencing chest pain, don't hesitate.

[219] Call an ambulance.

[220] For me, this study was right in the sweet spot where economics and medicine converge.

[221] And it was all prompted by my wife's observation.

[222] Sometimes it just takes a random thought or a random traffic snag to get the mind going on a bigger question.

[223] And it's questions like these that we're going to explore in this podcast.

[224] Like, what can supermarket pricing tactics teach us about which patients get cardiac surgery?

[225] And what does that tell us about the problem of overuse in medical care?

[226] Questions like why children with summer birthdays are more likely to get the first.

[227] flu, and what that finding shows us about the importance of making health care more convenient.

[228] I'm hoping that this podcast will give you some of the analytical tools that will help you look under the hood of the latest medical headlines, distinguish correlation from causation, and weight costs and benefits in your own health care decisions.

[229] I'll end this pilot with a public service message.

[230] if there's a race happening in your city and someone you love happens to be running in it and is really counting on you to be on the sideline even though it's only a 5K and it's over in about half an hour, don't count on your normal parking spot.

[231] Go really early or take public transportation, okay?

[232] That was Bapu Jena, doctor and economist, with the first of what we hope will be many episodes of his new podcast.

[233] What do you think?

[234] All feedback is, welcome.

[235] Also, we're looking for a title for BAPU's new show, so let us know your thoughts.

[236] Our address is Radio at Freakonomics .com.

[237] This episode was produced by Matt Frasica.

[238] The Freakonomics Radio Network staff also includes Alison Craiglo, Greg Rippin, Joel Meyer, Tricia Bobita, Mark McCluskey, Zach Lipinski, Mary to Duke, Rebecca Lee Douglas, Morgan Levy, Brent Katz, Emma Terrell, Lyric Boutich, Jasmine Klinger, and Jacob Clemente.

[239] You can get all the Freakonomics Radio Network shows on any podcast app if you'd like to read a transcript or show notes.

[240] That's at Freakonomics .com.

[241] As always, thanks for listening.

[242] And remember, let us know what you think of this new Freakonomics of Medicine show.

[243] Our address is Radio at Freakonomics .com.

[244] We'll be back next week with a new episode of Freakonomics Radio.

[245] Until then, take care of yourself.

[246] And if you can, someone else, too.

[247] So deep down, would you say that you're more of a doctor who practices economics?

[248] on the side or an economist to practices medicine or maybe some mythical hybrid like a unicorn or a dragon.

[249] I noticed you didn't pose the option of first -rate doctor, first -rate economist.

[250] That's not on your differential, I guess.

[251] The Freakonomics Radio Network, Stitcher.