Welcome to Science with Sanjula, where we talk about anything global health.
My name is Sanjula and I am a researcher at the University of Oxford.
Join me as I speak to world leading scientists who tackle today's biggest challenges in health care.
My first guest is Professor Sir Richard Peto, one of the most prominent epidemiologists of the last century.
Professor Peto spent his impressive career investigating why people die prematurely.
He's an Emeritus Professor of Medical Statistics and Epidemiology at the University of Oxford,
and he even invented the meta-analysis and a theory known as Peto's Paradox.
Professor Peto, first of all, may I call you Richard? Please.
You mentioned that you fell in love with science at school at Southampton.
Oh, yes. And further on, you actually fell out of love with science at the University of Cambridge.
Well, I was still in love with it, but I certainly fell out of doing it well.
When I was at school, it was just so exciting. It was an ordinary state school,
but the teachers were really interesting and trying to get across not the stuff in the O-level and the A-level curriculum,
but what they found interesting and they did, they just put so much effort into the ways they taught.
It was really excellent.
And I did very well at school, got a good scholarship to Cambridge, and then somehow Cambridge was much less exciting to me in comparison with that.
I don't know why. I really don't know why. I can't understand those years. But it was just a letdown.
After the excitement of school, I wanted that same excitement to carry on and it didn't.
And probably the fault was more in me than in Cambridge.
I don't know. I can't really sort it out. But anyway. So I dropped completely out and finished up, you know, really failing at the end of three years.
I mean, that's not not getting a first, second, third or even selected resit examination,
and really failing at the bottom of the whole university.
And they said to me, look, this isn't what should be happening to you.
Come up during the summer holidays and we'll teach you some real mathematics.
And so I did. And that really helped to get me interested again.
And I just sort of bounced back off absolute bottom into being pretty useless.
So at the end of the fourth year, this extra year, I hadn't done terribly well but thought, well, I'd like to stay on and maybe do an MSc at Cambridge.
And they said, well, look, I know it sounds odd to say when you failed, you should stay on for a year and now you've partly succeeded, you should go.
But I think it's time that you left Cambridge.
So I did and just was fortunate enough to stumble into the statistics course that was being taught by David Cox, who really is a remarkable statistician.
In one of your previous interviews, you've said that you had a blue motorcycle and long hair.
Yeah, yeah, yeah. During your time at Imperial College in London.
But as you said, you also managed to graduate. You were 24 years old.
You just graduated from your Masters of Science at Imperial College, and you went on to get an interview with Richard Doll.
And when he asked you why you wanted to work with him, you famously said, I do not know if I want to work with you.
And actually I do not know if I want to work at all. Well, it was silly.
I don't know what sort of world I was living in, what I thought I was going to be living off.
But, you know, the sun was shining all the time. Even when it was raining. I got a big 650 motorbike.
It was London in the 1960s, it was just such a lovely...
The idea of just being somebody that went to the office and worked seemed very dismal in comparison.
But when I started working with him, once you get your first result, you're addicted,
even though in retrospect it's not particularly important, but you start getting results and that's very addictive.
But the first ten or more years I was working with Richard Doll, actually first ten years I was working for Richard Doll,
I got to the point where I could give good lectures, which some of the stuff I'd done was,
was original, but nothing had ever actually been of any real use to anybody.
I mean, nothing I've done had ever saved any lives at all.
I've worked on trials and shown that if you do them properly, then the treatments didn't work.
I've worked on, you know, various epidemiological things which had already been studied by other people.
And I remember saying to Richard in the seventies, that after I'd been working more than ten years that nothing I've done saved any lives.
He said, well, I think it's too soon to tell. You know, maybe some of the things that you've done will turn out to be of value.
And that did turn out to be the case in the 1980s.
We started to get results that really did save a lot of lives and did influence the way things were done.
And of course, that makes life much easier.
Now I would like to invite you to give a mini lecture of about one minute about any scientific topic you are most passionate about.
Whenever you're ready. Please go ahead. Yeah.
Well, death in old age is inevitable. Death before old age is not.
That's the slogan we've got printed up on the wall in the Richard Doll building.
And so we're trying to say, why is it that people die before, I'll just say age 70 arbitrarily?
I'm 78, so I like talking about death before age 70. It means that I'm safe and you're not.
Why do people die before the 70, well, how many deaths are there before age 70?
In the world, about 30 million deaths a year and
the death rates among people of a given age in general are going down, but the population is going up.
And so that number of about 30 million a year,
that'll probably continue to be true through the 2020s because the decrease in death rates are counterbalanced by the increasing population.
And most of these deaths now are from non-communicable diseases, heart disease, chronic lung disease, cancer and the big causes of these are
smoking. Blood pressure, blood lipids. And I don't know quite how to put in diabetes and obesity.
Overweight obesity is a cause of high blood pressure, of dyslipidemia, diabetes, smoking, obesity.
blood pressure, blood cholesterol. But smoking is the big one in general.
In some populations, alcohol becomes very big. I mean, in Russia, particularly in the 1990s.
It was this fast increase in deaths from vodka. And that's subsided, but it's still very high.
I think in every interview I've listened to,
you tend to quote that a moderate reduction in a big cause of death may be more life saving than a big reduction in a small cause of death.
You were actually one of the first ones to, well, I might say, invent a new research technique called a meta-analysis.
It's this business of getting all the trials, not taking just the famous ones, because the famous ones will be the ones with atypical results.
But all of them, really find out all the randomised trials that have ever been done, get results from each of them, get them right and then add them up.
Now this works only if you get really big large scale evidence in the process.
How big should the trial be? Well, it depends on the question.
How many depends on how big the treatment effect is. If you've got very striking treatment effect,
then you hardly don't need to randomise and you didn't need randomisation to show that
cigarette smoking is an important cause of lung cancer because the effect is so big.
But when you've got the you know...
Mostly, we've got treatments that have moderate effects, especially treatments for cancer, treatments for heart disease,
treatments for chronic lung disease treatment and treatments for T.B. If you've got two plausible treatments,
the difference between them is unlikely to be big. If there's uncertainty about it, it's unlikely to be big.
But then moderate differences can really matter.
And if you want to work out how to treat 10 million people, then why not randomise 10,000?
But people just weren't thinking of numbers like that. So trial sizes ought to be for many questions in the tens of thousands.
And then there's another debate about so-called subgroup analysis.
Actually, there's one good example of something you did, because I think at some point you were publishing a paper in The Lancet.
Oh, I'll tell the story of that paper. The most beautiful trial results that we ever got, really, was in the 1980s.
We wanted to test aspirin in the middle of an acute heart attack. You know, you've had a horrible heart attack.
God, you know, ring the ambulance, raced into hospital. Get in the hospital a couple of hours later.
Are they going to die today? Are they going to die tomorrow? Will they be dead this week?
And actually, we wanted to do a trial where you just give the patient half an aspirin, half an aspirin a day.
You know, most doctors thought this was ridiculous, but we persuaded them to do it anyway. So we did it.
And so we got half an aspirin a day or dummy pill that looked like half an aspirin but wasn't.
And we finished up with 1000 deaths in those who got the dummy pill,
800 deaths and those who got the real pill, and you can't get 1000 versus 800 just by chance.
It had to be real. But it's really important because it applies to anybody who's come into hospital with a heart attack all over the world.
I mean, this could apply to millions of people a year, you know, maybe 100 million a decade.
I don't know.
You know, when we sent it to the Lancet it's, you know, absolutely delighted.
Great. You know, we've just finally got a result that really is going to save lives.
And also it costs nothing as a treatment. Half an aspirin a day really isn't that expensive.
And the Lancet reviewer said, no, no. Well, okay, you've got this result, but now you've got to tell us who benefits.
You must actually provide us subgroup analyses,
which we've written again and again that subgroup analyses are a reliable machine for producing false negative results.
So if you've got something that works,
then you can reliably produce a false negative result by doing enough subgroups and picking out something that seems not to work.
And so we say on principle alone. By chance alone, you'll get...
That's right.
If you've got something that works equally for everybody and you do enough subgroup analyses, you're going to get false negatives.
And those false negatives, they become urban myths.
Doctors love something about particular patients. They don't want to know what works on average.
They want to know what works for the individual. That's the phrase personalised medicine, they call it.
And so The Lancet said that they would publish our paper only if we did various subgroup analyses.
So we said no. On scientific principle, no. They said, well, we're not going to publish a paper then.
And so we just compromised our principles and said, well, all right then.
But we also subdivided the patients according to whether they were born under the mediaeval birth signs
of Libra, Gemini or Capricorn, you know completely stupid stuff.
So we took 12 subgroups.
And of course with 12 subgroups it's easy even when you've got a beautiful result like that to find...
And it turned out that if you were born under Libra or Gemini, then it seemed not to work.
If you're born under Capricorn, then it seemed to halve your risk of death.
And of course that's so stupid that people realised that this was nonsense.
So we put that out as the first subgroup analysis of our paper
and The Lancet, when we sent it in they said, well, you know, you've done the subgroups so the paper is acceptable now,
But you've got to delete this stuff about astrology. That's not serious. We said no,
that is the only subgroup analysis there that is serious.
That is the only serious scientific subgroup analysis. And this was true.
Anyway, they just published it. They published it with the birth signs? Yeah, 13 August 1988, page 5, the top thing.
And that struck people and it's been used again and again this astrological
subgroups but still, nearly every trial that's reported finishes up with loads of
subgroup analyses. It's completely meaningless and it often gives false negative results.
Hi. My question for Professor Peto is if we already know that tobacco is bad for people and we know it kills people,
why would we still bother investigating tobacco? Still smoking is a cause of about 20% of all male deaths in middle age and old age in old age,
20% of all female deaths in middle age and in old age, and actually about 25% of all cancer deaths.
So still it's a quarter of all cancer deaths still in this country despite that two thirds reduction.
Smoking is causing more cancer deaths than every other known cause of cancer put together.
But you wouldn't think that if you look at newspaper coverage.
Look at COVID, look at newspaper coverage of COVID over the last two years. Actually in Britain, tobacco over the last two years,
2020, 2021, killed about the same number of people as COVID.
We really do need to know the long term effects of smoking seriously, and we need to know the long term effects of stopping at various ages.
And we've done studies in various other countries, India, China, various other populations.
And you've got to get Chinese evidence on Chinese deaths to get the Chinese government to take it seriously.
And at the moment in China there is about a million deaths a year from smoking, nearly all male rather than female.
And because if you take, say, people born in the 1960s in China,
then the proportion smoking was about 100 times greater in males and females.
So you're saying the numbers are actually still going up in other places and therefore it's still important that we investigate tobacco.
Well, in the 2030s there'll be about 2 million deaths per year from smoking in China.
By mid-century it'll be about 3 million. That's partly population growth,
but there's got to be systems set up that just routinely monitor the extent to which smoking is killing people in different populations.
And this will help bring forward the time when more and more effective controls get put in place.
A few years ago, you were actually diagnosed with stage four cancer. That's right.
And actually you've told me this story before. Would you mind sharing that with us?
When I started working on intestinal cancer, basically everybody with stage four cancer died of it.
There's no stage five right? Right.
And Jeremy, one of my good friends died a year before I was diagnosed with stage four.
He was diagnosed with stage four and died of it a few months earlier. So, you know, he was one of my very close friends.
And so basically I knew I was dying and they said, oh, will you take this chemotherapy?
And I knew that chemotherapy didn't work from my previous days.
And, you know, working on trials of colorectal cancer in days when chemotherapy didn't work, I sort of took it almost to be nice to the doctor..
You know, he's such a nice doctor, it just seemed rude not to take his chemotherapy.
I didn't really believe it was going to do any good.
I mean, I basically knew I was dying and they took a chunk out of my cancer to see if it had got any special vulnerabilities.
No, it didn't. And it had spread to my lungs.
That's what makes it stage four. And so and actually before my Festschrift, about a week before my Festschrift, I'd been told,
well, the metastases in your lungs are still growing despite all the chemotherapy.
So I'm afraid that means that even if we remove them, there's going to be other disease elsewhere.
And this is incurable. So that was what I was told a week before my Festschrift meeting when it came to Studying The Bleeding Obvious.
But then I finished up getting my lung surgery, you know,
to take out the bottom of the lung with where they were, these some things that had been cancer.
And when they looked at it down a microscope, they found it actually wasn't cancer.
So the CT scan had been misinterpreted.
And what it was was fibrotic consolidation of where cancer had been but had been destroyed by the chemotherapy.
So it was just scarring where the cancer had been.
And we were all going to go off on a family holiday to Greece, it was going to be our last family holiday to get in a goodbye to life.
And instead, I got this news three days before we went and so we went and it was just amazing.
I woke up this morning in Greece.
It was a gentle breeze, Greek sunshine there and me lying in bed next to partner and you know, with about ten other family members within 50 metres,
you know, it's just I felt like Orpheus coming back up out of the underworld when he sees that first beam of light.
It was wonderful. Well, and then as a final question on this podcast, we ask all the professors, what advice would you give?
I think I'd say whatever you do, you need to study something
That's a serious question. And what do you mean, what is a serious question?
Well, I've defined serious as being substantially relevant to mortality, you know, being potentially an important cause of death.
I've defined it as that. But there's things other than death that matter, those aspects of the quality of life.
And you could study... if somebody could really understand musculoskeletal, you know, the impact, pain or I mean, more things like depression or,
you know, mental health where I mean, apart from suicide, they may not be terribly relevant to mortality.
But of course, mental patients are much more like to smoke and other people and they finish up with higher lung cancer rates.
I think just study...
Study things that are serious and/or study something is beautiful, you know, just because beauty is sort of.
Scientific quality control and results from studies have beautiful questions.
They may not be immediately relevant, but they might well be relevant in the longer term.
And people who just study things because of the delight of the subject and.
Often. You know, they often do find things which certainly change our scientific understanding and may in the long term be of benefit.
Well, thank you very much, Richard. That was a very, very interesting interview.
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