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0:03
- Hello, everyone. It's very exciting to start out this science lecture series. Sandro Galea was an emergency physician and epidemiologist. He's the Robert A. Knox professor and Dean of the Boston University School of Public Health. He is the former chair of epidemiology of the Columbia University Mailman School of Public Health. Prior to his academic career in public health, Dr. Galea practiced emergency medicine in Canada and served in Somalia with Doctors Without Borders.
0:31
He was named one of Time magazine's epidemiology innovators in 2006, and Thomas Reuters listed him as one of the world's most influential scientific minds for the social sciences in 2015. Dr. Galea is a past president of the Society for Epidemiologic Research and elected member of the American Epidemiological Society. He was elected to the National Academy of Medicine in 2012 and shared two of IOM's most recent reports on mental health in the military.
0:59
Dr. Galea serves frequently on advisory groups of national and international organizations. He formerly served as Chair of the New York City Department of Health and Mental Hygiene's community services board and as a member of its health board. We are thrilled to have Dean Galea here, and please join me in welcoming him. Thank you. [APPLAUSE]
1:25
- Good evening. Thank you very much. Thank you, everybody, for inviting me to come and present here. It's really a pleasure to be here. So I'm delighted to open this series, although I'm not entirely sure that I am going to do justice to epidemics. Because my title is about epidemiology counts, but I want to talk less about the academic application of epidemiology, which those of you who are not so inclined may be relieved, and more about the core meaning of epidemiology.
1:54
And by the core meaning of epidemiology, I mean that, since epidemiology became a discipline roughly 150 years ago, the core definition has been that this epidemiology is about understanding the causes of health so that we may do something about them. So understanding the causes of health so that we may do something about them. And I actually think that that meaning-- so in my title, I said epidemiology counts. I think that meaning counts.
2:18
I think that it is important to understand the causes of health so that we may do something about it. And I'm going to make an argument that how we think about those causes has clear consequences for whether we succeed in promoting healthy populations or not. So that's the arc of my talk. And I'm going to start off by trying to lay out a challenge, and say, this is what we'd like to get to. Point out some problems and then offer some solutions. And then, of course, I would be delighted to have
2:46
some questions. So let me plunge right in. So let me start by arguing that we want a healthier world. And when we say you want a healthier world, it seems to me reasonable that, if this is the world, we are interested not simply in a few people with disease, schematically. We're actually interested in understanding the whole world and understanding a universe of people who may have disease. Right? That our interests are not particular to just a small group.
3:20
We're interested in a healthier world for all. It seems to me like it's a tough argument to say that we want something else. And ideally, we want to understand the health for all, and assuming that the people in the dark blue are disease people, so that we actually make them better, right? So ultimately, what we want to do is we want to understand the world, understand about the disease, and make some people better. That's ultimately our aspiration. Now, I start with this-- and this
3:46
is a very, very simple concept. And you're going to say, OK, OK, I know that. That's OK. But this has implications for how we think about the cause of health and the cause of disease and what we do about it, as I'm going to get to. Now, let me start with good news. I'm going to start with-- there's abundant good news. One of the challenges of being an epidemiologist or a public health person is I always come to audiences and I talk about people dying and being sick; it's all bad.
4:10
So I want to start with good news. All great stuff. This is the best slide I know that says good news all around, which is this. So this is life expectancy from the 1500s to the present, and what I want you to see is the longest records of life expectancy are in the UK, and life expectancy really grumbled along around 40 all the way till about 150 years ago-- really in the mid 40s till about 100 years ago. So life expectancy grumbled along, like, 40 to 45. And I would ask everybody in this room,
4:48
how would your life decisions have been different had you expected to die at 45? And I mean, we all know people do die at 45, but let's be honest. We expect to live much longer than that. So, not so long ago-- 100 years ago-- we could expect to live till 45. Now look what's happened in the past 100 years. It's been this extraordinary zooming upward. And these are just selected countries. The United States is over-- I can't remember which one of these it is. And it's one of these.
5:14
But we can see all countries zooming up in the past 100 years. So that is extraordinary. Like, this is great good news. We haven't gained, in the past hundred years, about a 40% increase in life expectancy after having not budged life expectancy for hundreds of years. That is great good news. And you can see this good news manifest in all sorts of other ways that we measure health. So I'll give you another example. This is global child mortality between 1800 to the present,
5:45
and the red is the share of children who died before age five, and the blue are the ones who live past age five. So look at 1800. That's not that long ago. I understand in our short lifespans, 200 years seems like a long time, but it's actually not that long ago in human lifespan. Nearly half of all children used to die before age five, and look at today. We're down to under five percent. Right? So this is all good news. So we've done some things right. So I want to start with the good news.
6:14
Now, if it's all a good news story, you probably don't need to invite me to say, well, there are things that we should do better. So let me talk a little bit now about how we fall short. And I'm going to start with a domestic lens, and then I'll-- for briefly, I'll expand to a global lens. But let me start domestically. So this is a sophisticated audience, so everybody is going to know the first slide I'm going to show you. So the first slide I'm going to show you, domestically, is this.
6:39
But this is non-communicable disease mortality. So is death from, essentially, non-infectious diseases. The line between the two blurs, but that's fine. And all I want you to see here is that this is the US comparing ourselves to any number of other high income countries. You know, we're worse even than Greece, and goodness knows they can't get anything right, but we still kill more people than they do. You know, Italy, Japan, Spain etc. I always like putting in Greece in there,
7:05
because whenever I compare-- like if I compare to Sweden, everybody says, ach! But the Scandinavians, they're just good at everything. But you know, hard to argue the same for Greeks. So we have worse life expectancy-- I speak above people from where I come from, so it's OK.
7:22
But everybody knows this. You all know that life expectancy, or the measure on any number of health outcomes in this country is worse than our peer countries. Everybody walked into this room knowing this. Now, what I suspect you do not know is that it wasn't always like this. And in fact, it wasn't like this not so long ago. So let me show you another graph. So the way to read this is, this is 1980 to the present. These are the OECD countries-- high income countries. There are 17 of them.
7:55
The gray dots are all these countries, the red dot is the US. So in 1980, we were in the top half. Everybody see the life expectancy? And over the course of the past 35 years, we have steadily drifted to the bottom. Now, there's an upward slope here, so our life expectancy has been going up, but at a much slower rate than everybody else. So we now have the worst life expectancy of any of our high income peers. The bad news is that we have wrought this upon ourselves in 35 years.
8:26
The good news is, well, if we've done it in 35 years, we should be able to reverse it over the next 35 years, right? That's the good news. This is clearly not intractable. Because I feel like the public discussion-- the informed public discussion about US health-- often stops at the slide and says, oh, it's terrible. It's terrible. We're worse than everybody else. Yes, we are. But we weren't always that bad. We actually-- not so long ago, we were not that bad. Now how bad are we now?
8:50
I just want to deepen the misery for a second. So I'm going to show you two slides about mortality in the US compared to median mortality in high income countries. And I'm just going to divide them by age, just to make the point. So let me start with young people. And by young, here, I take the liberal definition of under 75. So young people under 75 75 is the new 55. We all know that. So here are people under 75. So here's the way to read this-- the blue is mortality in the US, the red
9:26
is median mortality in all the other high income countries. So what you see; under one, one to 14. So first of all, you see this U-shaped mortality curve. That's very standard. We have more mortality at the youngest stages, more mortality as you older, right? Nothing surprising there. But then look at this. We kill more newborns than anybody else, you know, and that's not-- the safest age to be is always one to 14. That's like when you live, and then you start killing people again.
9:52
But look at this-- the blue bar is always higher than the red bar. So we are consistent that we kill more people at all age groups than all the other high income countries, which at least would give us for consistency. So that's slide A, but now let me show you the second slide. If I may take the liberty to say over 75 is the older age group-- not old, just older. So now let's flip to over 75. So these are over 75. And now you see, again, US and other countries. So this is 75 to 79.
10:20
Roughly even. Over 80 we're less, 85 we're less, 95-99, 100 plus were less. So we actually are doing great in mortality over age 75. So this is an amazing country to live in as long as you live until age 75, because then you live forever. Because then, nobody dies. If you go to, like, 150, our mortality goes really way down. So when you compare how we're doing to these other high income countries and just rank us. So you rank us, one through 17. There are 17 OECD countries. One through 17.
10:54
What's our rank? So here's a graph of that. The way to [INAUDIBLE] these ranks, [INAUDIBLE] 17, so 17 is bad. There are two lines here, men and women. They behave the same. Pretend they're one line. So we are 17th. You see it? 17th, roughly, at all age groups all the way to age 75, all of a sudden, poof, we jump to number one. So we are great. Now, you say, OK, OK, OK, but you're complaining that things are not as good as they could be. But we just don't-- you know, you start off by saying
11:20
we care about health of populations, but maybe we don't, really. What we really care about is shiny iPhones and having nuclear weapons that we can drop on Korea. I mean, that's what we care about. We don't really care about health. So we're not spending that much money on it, so it's OK. This is OK. This is what we get. Well, that's not true, because this is national spending. All these colored lines are all the other rational high income countries, and the purple line is the US.
11:47
So are we spend nearly 40% of our GDP on health, and our rate of increase is steeper than anybody else, and our change in the rate of increase, which means second derivative, is also steeper than anybody else. So we spend a lot of money on health. And perhaps the best graph to capture that-- if you want to have a bar fight with someone about this, this is the best graph, because it maps life expectancy and spending. So you see all these countries? You know, more spending, more life expectancy.
12:14
And you see us, where you have more spending and life expectancy sort of trundles down the non-trodden path. So there are clearly problems, right? And it seemed to me, when I was given the challenge of opening up this magnificent lecture series you're having, that if I was going to talk about the causes of health, like, I felt like you couldn't start this lecture series without understanding these basic facts. Now this is the US. I just want to, just for a second,
12:39
make a quick global comment, because ultimately this has to be a global lens. So let me just go back to a slide like the one I showed you earlier. This is maternal mortality. This is death of mothers in childbirth. And what you see here, you see the US, again, is higher than all these other countries I showed you before, right? So nothing surprising there. But all I want to do here is, I'm going to add one more country. I'm going to add to this graph Ethiopia.
13:06
So maternal mortality, I add Ethiopia. That's Ethiopia. That's the same graph. Let me go back. You see these countries here? Just look at the axis. 0 to 16. Now the axis is in the 0 to 400 scale, because that's the US and that's Ethiopia. So I just did want to, for a second, take us out a lens to say that, while I was talking about the domestic lens, there is absolutely no reason why we don't take a global lens, and from point of view of health equity,
13:31
there is absolutely no reason why this happens. This is just as unacceptable as is that. OK? So I just want to label that, that these issues are-- one can take a national lens as well as a global lens. Now, let me get to the crux of my argument. So where do we go wrong? And I want to make a three part argument, and then I will counter it with a sort of three part solution just for the sake of symmetry. And why is this the case? Now, there's plenty of blame to go around,
13:59
and I approach this very much from the perspective of someone who has spent my professional adult life, really, within the Academy. So I tend to tend to try to figure out where I've gone wrong, where we collectively have gone wrong, rather than trying to point fingers at broader, other forces. But with that in mind, I think there are three places we have gone wrong, which I think gets at this notion I was trying to make that, if we understand the causes of health, that has real consequences
14:29
and allows us to act properly. So three ways we've gone wrong. Number one. Number one, we lose sight of what causes health. Now, you all say, well, I know what causes health. Well, you know, heart disease and stroke and cancer, et cetera. So, that's not quite right. And I want to illustrate what causes health with a story. So This is a blues man. His name was Blind Willie Johnson. So this is the only known photograph of Willie Johnson.
15:00
Any of you who like the blues will know Blind Willie Johnson. He was born at the turn of the 19th century in Texas. And he was born in Texas, he was poor. The story is that when he was three, his mother threw lye in his eyes in an act of domestic violence, and he became blind. He lived fairly poor. He learned how to play the guitar, he made money playing the blues, and he busked. Essentially, that's the way he made money. He was married, he lived in a house, the house burned down,
15:31
he had no money so he continued living in the burned down house. And when he was 44, he developed malaria. So blind Willie Johnson, who was blind, poor, and black in Texas, and this is now in the 40s-- 1940s-- his wife took him to the hospital, where he was turned away. Unclear whether he was turned away because he was blind, because he was black, because he was poor; and went home and died. So the question is, what killed Blind Willie Johnson? Well, obviously the malaria killed him, right?
15:59
But it wasn't just malaria that killed him. Right? It was his malaria, it was racism, it was domestic violence, it was poverty. All of that killed Blind Willie if malaria didn't get him, something else was going to get him any day, right? Now, why am I telling you this obvious, obvious story? I'm telling this obvious story because unless we ground a conversation in the foundational causes of health-- unless we understand that, if we fix malaria,
16:25
and we don't deal with the racism, with the poverty, with the domestic violence, we are not going to fix health for all. We actually go down a totally different path. So I'm starting with an obvious story. Now, I would argue that while this may be obvious, this is in no way where our national conversation is at. And that's bad news. But here's good news, perhaps-- that this kind of battle about what causes health, we have been having in the world for hundreds of years. Here's a classic example.
16:57
These two generously mustachioed gentlemen are two of the founding fathers of public health. The gentleman on the left is Edwin Chadwick, the gentleman on the right, William Farr. And William Farr is credited with being the father of statistics. He started life table counting in the UK. Edwin Chadwick is the architect of the British Health Act of 1848, which was one of the sentinel health acts that created health in cities. But they had a big argument, and the argument
17:30
was as follows-- that William Farr, who was counting deaths, said that the babies of poor mothers were dying more than babies of rich mothers. So he said, poverty is killing these babies. Edwin Chadwick was a politician, and he said-- and there's a quote which I'm not going to get exactly right, but-- babies do not die of poverty in civilized London. He says, it's not poverty. So what was it? Well, it was nutritional deficiency, said Chadwick, that was killing the babies, not poverty.
18:03
And the argument went back and forth, because Chadwick was a politician; Chadwick won, and nutritional deficiency was recorded. But the reason I'm showing this is because this gets at the heart of how we think about what causes health. Now, how about today? What is the implication for today? In Epidemiology 101, Public Health 101, any health 101 class anywhere in the country, you will have a discussion of a table like this, which says the causes of death in the United States
18:27
are heart disease, stroke, et cetera; or tobacco, poor diet, alcohol consumption, bugs, toxic agents, etc. And these are the [INAUDIBLE] those people. But is that what causes health? Or is this what causes health? Low education, low social support, racial segregation, income inequality, poverty. Look at the number of deaths from this. Low education, 190,000. Acute heart attacks, roughly the same. Low social support, stroke, roughly the same. Racial segregation, lung cancer, the same.
18:53
Obviously, all of these are causes of health. I'm in no way saying it's this, not that. But what I'm saying is that, unless we keep our eye on the multiplicity of causes of health, we will not invest in these other causes. To go back to the Willy Johnson example, we'll invest only in malaria and forget about everything else. Now you say to me, oh, we're better than that. We're better than that. We understand this. We understand this. There there. Why don't you sit down. We get this.
19:23
We know that these things matter for health. Of course we invest in them when it comes to health. But do we? Now, we're in Massachusetts. We have the privilege of being probably the most progressive state in the country. Do we understand this in Massachusetts? Does our money follow where our ideas are? Let's look at where health spending is in Massachusetts. Massachusetts health spending. These are causes of health. It's a real schematic. You know, access to care, most people
19:53
think it's about five to 10%. Genetics, socioeconomic physical environments, healthy behaviors, interaction about everything. Here's our spending. 90% of our spending is on medical services, but less than 10% is on healthy behaviors. Most estimates are that we spend 3% to 5% on things that aim at prevention. How has this changed? You're like, OK, well, maybe this was bad over time, but it's changing. We're getting better. Let's look at change over time.
20:18
Past 15 years, Massachusetts spending 2001-2014. We have increased our spending in medical care 81%. Kept the same or decreased, primary and secondary education, law and public safety, mental health, public health, higher education, early child education, and environmental education. So you tell me that we actually understand the Blind Willie story. Because surely if we understood it, we would not be doing this, right? And this, of course, directly maps on to the slides I showed you earlier
20:49
that talk about the fact that the US spends far more than anybody else and has far worse health indicators. Because of course we are spending more than everybody else, but we're spending it on just one aspect of what causes health. In some respects, if the audience actually understands that point, my work here is done. Because the core point is that if one thinks about the causes of health-- remember, that's how I started-- and one understands that there's a full range of causes.
21:17
If we're spending on only one of those causes, we are inevitably going to be underperforming. I want to show you a metaphor, and then move onto the second thing I think we're doing wrong. So this is the best soccer team in the world. This is the US Women's National Soccer Team. And I know that soccer is not the most popular sport here, although in Massachusetts, it's probably better. But for those who don't know how the game is played, there are 11 people on one side, 11 people in the other,
21:45
and they get the ball to the other side and try to score. They can only use their feet. That's why it's called football, it's the real football.
21:54
Except for the goalie. The goalie is this woman right here. They wear a different uniform. Her name is Hope Solo, she's a terrific goalie. Yeah, anyway, there's just-- good goalie. Leaving that aside. The goalie can catch the ball with her hands. Now for those who don't know soccer, football, the misconception is that the way to win is to have a great goalie so that any time the ball's coming at you, you can save it, right? But anybody who knows football knows
22:23
that when you look at a good goalie, she is spending all her time yelling at her teammates. And what she's telling her teammates is, get the ball away from me! Because every goalie knows, doesn't matter how good she is, you are going to score on her if the ball comes close enough. So the metaphor is this-- the goalie is medicine, right? We want a good doctor. We want good medicine. I want to be very clear on this. As Sean said in my introduction, I started my life in medicine.
22:50
When I'm sick I want a great I want a great doctor, I want a great goalie. But I would rather the ball never comes near the net. And how do you do that? Well, that's was for the other 10 players are about. And those other 10 players are those other dimensions that I mentioned and blind Willie's story. They are the racism, they are the violence, they are the homelessness, they are the poverty. They are what we're under-investing in in Massachusetts.
23:13
Early childhood education, public parks, public safety. That's what those other 10 players are. And that is what ultimately is the core combination of causes of health, and that's what we misunderstand. So, issue one. We misunderstand the cause of health. Issue two. This leads us to focus on the urgent rather than the important. So let me just talk a little bit about a couple of things that are potentially important. And actually, this echoes one of the
23:41
lectures you're going to be having in the series later obesity. Let's talk about obesity for a second. You have a lecture, I think, called the obesity epidemic. I just want to show you one slide that-- three slides, actually. I've captured the obesity epidemic. I'm sure you'll see this in that lecture. This is obesity in the US in 1990, and the darker the color is, the more the obesity. All I want you to see here is, in 1990, you had several states that had about 10 to 15%
24:05
of their residents who were obese. There was 1990. Jump to 2000. Now you have a lot of states-- actually, only one state is even in that range, 10% to 15%. 15 to 19% in all these states, and these states are all 20% to 25%. And you jump to 2010 where 20 to 25%, 25-19, more than 30%. That's over 20-- what did I say? 1990 to 2010. 20 years, right? 20 years. Just to be clear, 20 years is a blink of an eye in human time span right? Like, for all of us, our life's been short.
24:40
Twenty years is a long time. But it's actually not. Life-- 20 years is [INAUDIBLE]. So this is a problem, right? This is a problem. Over 20 years. Now, let me ask this question. What has not changed about humans in the past 20 years? One thing that has not changed? What do you think has not changed? I know you can think of all many things that have changed. I mean, something's changed, right? I mean, something's changed. I mean, we're all fatter, so something's changed.
25:10
If there's a cause, something's changed. Tell me something that has not changed.
25:16
Genetics right. So the obvious. You got there right away! So therefore, given this epidemic, of course it makes sense that we're not going to invest more and more in genetics, because clearly genetics can't explain this, right? So, you know, we launched a Precision Medicine Initiative, also called personalized medicine, and this is our funding in genetics over the past 10 years. Now you say, OK, OK, OK. Well that's-- you're making a strawman argument,
25:41
because of course we are aware of this epidemic and realize it's bad, so our funding and everything else has also increased right? Because you know, for example, that things like this have happened. Look at this food, 20 years, the number of calories, today; a bagel has gone from 140 to 250 calories. Cheeseburger, my favorite, has almost doubled the number of calories. Pasta-- don't even go pasta. it's 500s. So those are things that have changed. So surely, understanding this, what we have done
26:11
is, we have spent more on these things, right? More on research in these areas? And the answer is, not really. So we have focused on areas that, I think, are urgent and not important. I think it's discovery science. I want to be very clear-- discovery science, the Precision Medicine Initiative, has tremendous potential. And I want to be very careful in my comments, that in no way am I saying we should not be doing that. I'm simply saying that we are investing in the wrong areas
26:43
if one comes from a place of understanding the core causes of health states, which is why this is ultimately framed around the core cause of health states. So is the second problem. And the third problem is this. This has resulted in a loss of confidence in our science. And this is a very simple cartoon which I quite like, which is the newscaster saying, according to a report released today coffee can cause depression in twins, but equally well, he could have said
27:15
day care causes a feeling of well-being in men 25 to 40.
27:23
This matters! This matters because I often ask our faculty and students this question. Whose job it is but ours to communicate the causes of the health of populations? It is ultimately the fundamental job of those in the Academy. So if we are, A, misunderstanding the causes; B, focusing on urgent but not important; C, that inevitably is leading to loss of confidence in us, which leads to poor communication of these causes, which means that society will not be acting appropriately.
28:03
So those are the three problems. Now, I promised I would actually say, OK, what should we be doing instead? Let me start my three part argument. Number one, we should be focusing on what matters most. And when I was reviewing my slides, which I created a couple of weeks ago-- and I was reviewing them yesterday and [INAUDIBLE] today, I realized that the example I chose in what matters most as an example is even more poignant today than when I created my slides, because it's one about firearms.
28:34
I had created the slides before the Las Vegas horror happened. So we all realize that there is a firearm injury problem in this country. And I'm using choosing my words carefully. It's a nonpartisan comment, it's simply an epidemiologic comment, OK? And just to show one simple example, I mean, this is our rate of gun deaths in children and teens. I'm choosing children and teens, assuming that by and large they are not complicit in their own gun death
29:03
and this is the US versus all other countries. So when gun deaths happen, there is a great collective gnashing of teeth and disagreement on anything except for one thing, and one thing alone-- and that one thing that we generally agree on is this-- is that actually, what we should do is make sure that guns don't make it into the hands of people with mental illness. Right? There is broad bipartisan agreement on that, and everybody says yes, that is the problem.
29:32
Let's make sure that no crazy people get guns. So we'll deal with our gun problem. So you might say, OK, well, does that really matter? I mean, when you look at the psychiatric evidence, people with psychiatric disorders-- more psychiatric disorders-- are, indeed, more likely to be violent than people without psychiatric disorders. There is also, by the way, people with psychiatric disorders are much more likely to become victims of violence than those without,
29:53
but just to label it, this is epidemiologically correct. So as a result, you get an avalanche of us saying, OK, let's make sure people with mental illness do not get access to guns. So national instant check system is, essentially, checking to make sure that people are not eligible for guns, and this has increased dramatically, a lot of that because of mental health check. So the question is this-- is that likely to matter much? Well, you know, we have a handy little experiment here, the US.
30:19
But we compare ourselves to Canada. I don't know if any of you have been to Canada, but for Americans who have been to Canada, Canada is just a lot like the US. It's only people like hockey more. So you say, OK, what's the prevalence of psychiatric disorders? What's mental illness like in Canada? So this is the US, this is Canada. Bipolar disorder and major depression and anxiety disorder roughly the same. It's a little bit higher in the US, but that's not unreasonable, seeing
30:45
as you can't go to a concert without getting killed. So broadly, we're the same. And this is our firearm homicide rate. Right? So what causes the difference? So what's causing the difference? Well, here is the proportion of households owning firearms. That's the US versus Canada. So-- and this is-- I'm reducing a complex argument, making it very simple. But if you want a complex argument, there are papers that make the complex argument. It's very, very clear that what matters
31:10
most in the context of firearm and the firearm epidemic in the US is firearm availability. Now, we can have a charged political argument about what to do about that, but let us not allow ourselves to get distracted by issues that actually do not matter. Because what matters most is firearm availability. Period. Solution one, focus on what matters most. Solution two, embrace approaches to deal with complexity. Now, this is very unpopular.
31:39
This is the most unpopular part of my talk, I realize that. Because you came here-- astonishingly, even though epidemiology was in the title-- and epidemiology gives this impression like, going to find a cause. We're going to find one cause. And I'm here to tell you-- I hope you're seeing that was the theme of my talk-- there is no "a cause." There are multiple causes. There are architectures of causes. It's all really complicated, and not just complicated, it's actually complex.
32:05
And that's why I show you one example. So let us for a second as I presume, if I may, that, since you all decided to spend an hour plus of your time on a lovely Monday afternoon at the Radcliffes Institute, you're all pretty smart. OK? Actually, when I put it that way, it sounds-- but anyway, you you know what I mean. So I'm going to ask you one question, OK? A very simple question, which is, how much of your cognitive ability-- that's a polite word for brains, smarts--
32:37
is determined by your genes. So, put another way, everybody here is sophisticated. You all understand complex causes, et cetera et cetera, right? I settled that. So here's my question. What percent of your IQ-- I'm just reducing it-- do you think is due to your genes? Now, I would like you all to be honest with yourselves and write it down or come up with an answer, because then I'm going to back to it, OK? You need to be honest about what your answer is, so I'm going to give you a second.
33:03
What percent of your IQ is due to your genes? I don't know, it's-- I'm not taking questions now, sorry. Now it's your time to come up with an answer. I'll come back afterwards. Everybody has an answer? Yes So let me just show you a schematic. Here's my schematic. I'm going to answer this question. These are people, again, as I showed before. And I'm going to assume that two things matter for your IQ, your genes and your environment, right? I'm reducing things, but it seems plausible, right?
33:34
That your IQ is your genes and your environment, OK? So I'm going to illustrate the smart gene as the characters turn black and the smartogenic environment as the characters being in green. OK? So smart gene is black, smart environment is green, and I'm going to assume that, to be smart, you need smart gene and smart environment. Now, there are exceptions to this. We all know people who like, you know, grew up in horrible environments and they are still smart,
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you don't understand how that happened. So I'm going to throw in a few random, like, super smart people, just like that. These reds. So CA plus is cognitive ability plus, which means they're smart, and these people are smart. But everybody else, apart from these random smart people, needs to have the gene and the environment to be smart. OK? That's it. So then you do a simulation. So let's do a couple of simulations. Scenario one-- here's scenario one, where the environment
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is nearly universally good. See this? All green. These are the red smart people. These are random smarts, but then the black-- this is the same as the genes I showed before. I'm showing them as dots here just so you can see it across the green, OK? So this is where the smart genes are. But the smart gene, smart environment, that also becomes red, right? So what do you see in this scenario? Well, what you see is-- I just want to point out for a second-- everybody who
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had the smart gene became smart, right? Because everybody who was black is also in green, and they became red. So everybody who had the gene became smart. So when you do the math-- I'm not going to bore with the epidemiologic math-- the relative risk of brains given the gene is about four, and the fraction of brains attributed to genes is 100%. Why is that? Just intuitively, because everybody who had the gene became smart. Now you're beginning to smell a rat, I can see it in your eyes.
35:15
Because now I'm going to show you a different scenario. Here's the same black gene pattern, right? See, the same-- this turning black, here's the random smart people. Only these two now actually overlap with the smartogenic environment. So those two become red. Now I have a situation where there's a lot of people with the smart gene, and they're not smart. So when you do the math, relative risk of 1.7, the proportion is about 40%. So the answer to the question, what percent of your IQ
35:50
is due to your genes-- the only correct answer to that is, it depends on what else also causes smarts. Now, I want an honest show of hands. How many people- hold yourself accountable-- said to yourselves that was the answer. How many of you said that was the answer? Yeah. It's usually-- in an audience like this, it's usually 10% to 20%. I would like to point out that I didn't trick you, right? I didn't-- I did say-- I told you exactly what I want you to say
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and you were entirely within your domain to say it depends. But most of us-- by the way, I do this all the time, and every single time I give myself a present, because it's so easy to think that way. The only point I'm making is that you simply cannot say how much of your brains is due to your genes unless you know other things. I didn't change the genes here at all. All I changed was the environment. By the way, is this all theoretical? Is this all epidemiologic flim flammery? Or No, it's not.
36:50
There's actually abundant data to show this. This is actually plotting IQ variance on SES due to genes. So actually, the richer people are, the more likely it is that, the math shows, that genes matter. Because of course it's the same thing, that you have actually a positive environment. Let me go back to the other topic, actually, before. This is obesity. This is a fat David. What does complexity mean? Do genes matter for obesity? Of course genes matter for obesity.
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But do food environments matter for obesity? Of course they do. There's a map of New York City that looks at poor food environments. These are zip codes in New York City where there is only unhealthy food available-- South Bronx, northern Manhattan, central Brooklyn. By the way, they happen to be the areas that also have most obesity. There's been a lot written about the food environments lately. But how about things like schools? Where are the bad schools, by the way?
37:47
Where are the bad schools in New York City? Well, they're in the South Bronx, northern Manhattan, central Brooklyn. Same area. So questions. What's the greatest return investment to lower obesity, better food or better schools? And these are the kind of complex questions that I actually think that we should be tackling if we want to understand the cost of health and disease. And this is a-- sort of one analysis that's actually-- the answer to that question is, if you're
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going invest in something you should invest in schools. That's the second solution. So the first solution is focus on what matters, second solution is accept the fact that complexity is inevitable and somehow deal with it, and third solution, which is-- now I'm really veering from-- remember in the beginning, I said this is not an academic epidemiology talk. This is not about what happens in academic epidemiology departments and it's not a critique of academic epidemiology departments.
38:30
it is simply taking epidemiology at face value as being about understanding what causes health. And ultimately, what causes health is inextricably tied with our values. It's inextricably tied with our values, and there is this conflation of our values with the mathematics of the causes of health, and the two are inseparable. Because ultimately, if we are not allowing ourselves to go back to the Blind Willy story, and say, we want to deal with the homelessness,
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want to deal with the poverty, want to deal with the racism, because without them we're not going to improve health. If we say we actually don't care about that-- all we care about is fixing malaria-- and if Blind Willie happens to be black, poor, and homeless, that's his problem. If that's what we're willing to say, then it changes the entire conversation. And that this is a schema which, actually, I published recently, which talks about the challenge that we have in thinking about our values.
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And all I want to just show here is, there's sort of a spectrum of, we don't have enough evidence and we know what to do; and over here is, we actually think this is an acceptable way to operate; and this, we don't know what's acceptable. So just give an example, polio eradication is an example. We broadly know what to do eradicate polio, and we all accept that's a good thing to do. Zika is a good example. I heard you say somebody is coming to talk about Zika.
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We don't really have all the evidence, although we all agree that we should do something about Zika. Now we're getting into messier territory. Injection drug use. We know that syringe exchange reduces the harms of injection drug use, and yet, we are squeamish about expanding syringe exchange, resulting in worsening of many infections diseases epidemics, like the HIV epidemic. And then, I put it here, where we don't actually know enough about what we should do,
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and we actually don't know what we value or not. And of course, the reason we don't know enough of what we'll do is because of the fact that funding in the area has been hampered because of very particular interests. OK, so, now I'm going to sort of start winding down. So, I outlined three problems, [INAUDIBLE] solution. You say, OK, but how will this then change our approach? So supposing you're right, which is a big supposition, how would this approach change what we do?
40:35
Well, we know a lot of things that, actually, we can do to improve health. We know a lot of causes of health. I go back to definition of epidemiology, understanding the causes of health so we may do something about it. We actually do know things we can do. Just to give you a couple examples. We have evidence that shows that if we expand the transit development in a city of a million residents by about 40%, we end up saving about $200 million in health.
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And by the way, I have citations for all of these, and I specifically took these examples from examples the CDC has on their website, just so that you don't think I'm actually digging into some journal [? Academia Obscura. ?] This is actually-- this is all on the CDC website. OK? Number two. We do know that financial support to create better housing doubles the proportion of people with excellent health. We do know that multi component work site
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obesity prevention programs, result in about a five percent weight loss in workers resulting in $90 average per person saving a year. We do know that early childhood education has a five-to-one benefit to cost ratio, and we do know that every time we increase the Earned Income Tax Credit by 10%, we drop infant mortality by 23 per 100,000. These are data that we have.
41:51
So why don't we do this? I mean, my argument is very simple. I go back to the beginning, which is, we have not been clear about what the causes of health are, we have not focused on what's important-- we're focused only on the urgent, rather than what's important-- we have lost public confidence. But if we focused on what matters, if we actually understood that the causes of health are complex, and we make sure that our values were aligned with what we know, we would do this.
42:20
There's no reason why we would not do this. Now, just to be honest, one of the reasons we don't do it-- and let's just be honest about this-- is that somewhere in the back of our minds, there is this notion that, yeah, yeah, yeah, OK, that's good, but I'm smart enough and well-off enough that I don't need any of this, because I can insulate myself from this badness. So just briefly, can you really? Can you really ? Unless you can wrap yourself in bubble wrap, i don't think you can.
42:51
Just give a couple of examples. Up to 50,000 people have died from flu since 2010. This is largely vaccine preventable, and in no small part driven by herd immunity around you. I'm not even going to get into children dying of a vaccine preventable disease because people around them are not getting vaccinated. How about children dying from car accidents? 10% of kids killed by motor vehicles are actually killed by completely unrelated alcohol impaired drivers. This is a slide which I always enjoy.
43:15
Your risk of cavities increases by 15% if you don't drink fluoridated water. One-third of the country, by the way, does not have sufficiently fluoridated water. Do you know if your water is sufficiently fluoridated? How many of you have checked that recently? I will not comment about the Republic of Cambridge. And, by the way, we also know that you know your risk of becoming obese is influenced quite a bit by your friends. So just a very simple point that,
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if you think you can insulate yourself from all this, you really cannot. So let me conclude. So, at the end of the day, you came here to hear about the causes of health and why understanding those causes has consequences for us, and I just want to end with a schema that shows that it is impossible to improve health without improving the fullness of the causes around us, including the causes that are not really about us, but around the world around us.
44:11
So let me just go back to my schema of sort of small people here. And I'm going to show you an example with blue and red, and these are the people in the population. And supposing supposing for a second that we accept-- suppose you accept everything I said. And you say, OK, OK, that's great. I get everything you said, so we are just going to tackle racism. We are just going to deal with racism. OK? So you say, well, some people experience more racism and some people experience less.
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Note I'm not saying all or none, I'm just saying more and less. So red, they're experiencing more racism. And you say, look, that is associated with disease. So here there are Xs, right, where there's disease. And a bunch of people with the red have Xs in them. You see them? They experience disease, and some part [INAUDIBLE] racism, but some of the people in the blue also have Xs, because it's not just all or none; it's just more racism and less racism. So that's the way the world looks.
45:05
Now let me just change it to a simple curve like this. So this is a simple bell curve. And this is racism experience. So let's say that modally-- this is the number of people. OK? So this is a lot of racism, this is a little bit racism. Let's say that most people experience somewhere in the middle. Some people experience a lot, some people experience less. And you say, I get it. I get what you're saying. So I'm really-- I just think all we should focus on is racism.
45:31
So let us actually deal with the people who experience a lot of racism. Let's draw a line and focus on that group, and if we really work at it, we are actually going to fix that, and nobody else is going experience so much racism. Right? So in some respects, that could be the conclusion of what I said. What's the problem with that? The problem with that is that that strategy, going back to my world I showed you before, sees only this. Because it's only dealing with the people
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with more racism, which are the red people, right? Some of whom have disease as a result of racism. But it's missing all of those people, because you just ignored them altogether. The alternate strategy is to say, that's racism. And ultimately, it comes as a complex with all sorts of other things. Yes, access to care. Yes homelessness. Yes poverty. Yes, not having adequate food. All of these things come together, and what we really should be doing
46:25
is shifting the entire curve, and that everybody experiences a little bit less racism. Because if we did that in the population health strategy, we actually are looking at the whole population, and any of the days, what we're doing is, we're reducing racism experienced by people from all over the population. And if you'll remember, that's where I started. That's the slide I started with, where I said we care about the whole population, and we care about reducing experiences
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that can produce poor health in the whole population. And that gives us a healthier population. So I'm going to end with my last metaphor, and this is my pet goldfish. So, as you heard in the introduction, for the past few years, I've had the privilege of having a dean job. And the problem with a dean job is it's very busy, particularly combining that with one's own scholarship. So I don't have much time for a pet, but I do have a goldfish. And I love my goldfish.
47:24
And I want my goldfish to be healthy. So I say to my goldfish, we're going to genotype you to figure out that we understand your genome, so that when you are sick, we can give you the perfect personalized medicine for your particular little fishy disease. I say to my goldfish, you need to exercise. So I want you to swim 10 times clockwise, 10 times counterclockwise in your little bowl every day. And when I feed you little flaky food, don't eat too much, don't get fat.
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And when you have a goldfish companion, make sure you have safe goldfish sex. I say all these things to my goldfish. And I'm like, OK, I got it. I nailed this. And one day, I walk into my living room, my goldfish is dead. How could she be dead? I'm so distraught! And there it is. Ah! I forgot to change its water. Doesn't matter everything else you do-- if you don't change its water, the goldfish is going to die. So the point is-- what's the point?
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The point is, if we care about the core definition of epidemiology, understanding the causes of health, the causes of health are complex. The causes of health ultimately do involve the water, as well as the fish's behavior, as well as the fish's genome. Going to to Blind Willie Johnson, we need to care about the malaria, we need to care about homelessness, care about racism, we need to care about violence. And unfortunately, unfortunately, I am here to tell you that there is no magic bullet.
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And the reason why American health indicators have been as bad as they have been is because we have focused on only one aspect of this. That if we are serious about improving health, we need to take epidemiology to its core definition, understand the full range of complex causes of health, and work on all of them at the same time. If anybody actually is still interested in what I have to say, this is my latest book that just came out, which actually has a lot of these ideas. And that's it.
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I'll stop here. Thank you very much. [APPLAUSE] [MUSIC PLAYING]
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