Now, about ten days ago, the revolt, the ten year revolt for the Nelson lung cancer screening trial came out.
All right. And you're in the midst of me writing an article that's going to go out in the BMJ, hopefully by the end of the week.
So everything I say now, if it sounds good, will go in there. And if you don't understand it, I'll have to rethink it.
For a woman to start with is going back a year and that's what was plain.
I want you to just watch this two minute video. Let's see if we can make this work.
The disease is early. It's a no brainer. It might be regular techniques and standards, but it's not that simple.
The latest screening rollout testing them today comes in the form of lung health checks.
These checks started out in trucks in a supermarket car parks in Manchester, where the incidence of lung cancer is around double the national average.
Your arms away from the chest. NHS England says the Manchester projects on 2541 patients and found 61 lung cancer picking them up at an earlier stage.
Now the trucks will visit ten new areas at a cost of £70 million over four years, targeting people at high risk of lung cancer.
They say it will save hundreds of lives across the country.
But not everyone agrees.
NEWSNIGHT has seen a letter from senior doctors and public health officials concerned even the chief executive of NHS England.
It raises substantial objections to lung health checks.
They say proponents identify the homes and overplay the benefits.
They point to Canadian data which attempt to quantify deaths for every thousand people
scans annually for three years 391 who showed apparent indications of lung cancer.
However, only 40 of those positives are genuine.
Of those, 47 would not have died of the disease, 30 would have died anyway.
And just three lives are saved. Meanwhile, the false positives all suffered major complications as a result of treatment.
And one of those four will die. But.
So that was interesting, wasn't it? We spent £70 million on trucks that now go into supermarkets and case find.
People don't screen their case, find people for lung cancer, inviting people at high risk in high risk areas.
One of them is is North Manchester, where I'm from originally,
and this is a copy of the letter to Simon Stevens and you can see it coming from my email account.
So I'm one of the then senior doctor people who like to make a nuisance of myself by making us think about what we're doing in health care.
Very interesting to me how little people actually think about and go behind what we actually do in the system.
We tend to do things and roll them out. And here's one other point, too.
There is concern that there is no benefit to all cause mortality.
What they would do now come back to the National Lung Screening trial with an American trial.
But in Europe, there's been a trial called the Nelson, and it was published in a conference and it wasn't quite clear what was going on.
We are concerned that enthusiasm for screening feeds a deliberate and independent effort to understand the benefits relative to the cost.
Because this is not going to cost 70 million and this is only in a few areas.
This is going to cost in excess of a billion because the.
NELSON As your theme of annual screening,
in fact it involved four screens within the first two and a half years and it's not even clear what happens beyond that,
whether you should keep screening. So I wrote a blog last year called Understanding Lung Cancer Screening, and in their report,
some really interesting point for the point about what what you've just seen in the programme
that the screening reduce lung cancer mortality by 26% in men and between 39 and 60% in women.
That's what within the come with them. But remember it's this thing called lung cancer mortality.
Now immediately we read I think what happens is we remove these words in our mind and the something comes into our psyche that reduces mortality,
because if you reduce mortality by that much, that would be really impressive.
Wouldn't you be like, what's the on about? Okay.
And what they do is when they do these projects, you get this stage shift of the people who are turning up.
In Manchester, about 18% were diagnosed at stage one and after the scans turned up, more people were diagnosed with stage one, nearly half the people.
So immediately we go, that's a good thing. Surely if you get people earlier, you can intervene.
And that going back to that chart, remember the chart, we said there are some fast and some don't progress.
This makes an assumption that everyone is going to progress. Therefore it's a good thing.
Okay, now here's the analysis. This was this is I love this stuff and I think we need more of this type of stuff because it's really helpful.
This is the Canadian Task Force, which was in the Deborah Cohen article in NEWSNIGHT, and she taken this and broke it down.
And it's very helpful to look at this type of stuff. Okay.
But one of the arguments in the next why it didn't apply in the UK is they had a much more inclusive early diagnosis.
So the size of the nodule matter. So if you take very small nodules, you end up in everybody.
If you take larger modules, you can reduce this number.
And the Nelson said we're going to reduce this number.
So what they've come out with really, Matt, is they're going to say we reduce the false positives and the risks of overdiagnosis.
Okay. So remember, and this is one of the things so when you look at this, this is a thousand people.
Yeah. And I'll come back to some of seven of the 40 diagnosed lung cancers would not have caused illness or death overdiagnosis.
So it's seven per thousand. Yeah. So you identify a thousand people,
you screen them and of them 40 people will have a diagnosis of lung cancer after they've had the but seven of them wouldn't
have progressed whatever that's the overdiagnosis in 33 was that's a sort of estimate of how much overdiagnosis is going on.
Okay. So the Nelson trial comes out.
So this is some of the information I'm now going to provide you and try and walk
you through how I think about trying to tease out the important information.
Now, one of the things about evidence based medicine, which is really interesting, is you really can make a name for yourself if you can be bothered,
because nobody else can be bothered to look at this stuff and try and understand it.
There's only a few people who really can say, I am going to try and understand this and share that information.
So if you want a route to a really impressive career, just having to look at sort of spend an hour of my time and try and understand
it so I can do a talk like this in your own institution and maybe write it up.
So so this is a bit which I wrote the year before.
This is the press release and when it came, but they said we doing the screening and the abstracts.
This was at the conference reported there were 214 lung cancer deaths in the male control lab and 57 deaths in the screen arm.
Yeah. See that thing happening again? The lung cancer disappears.
Yeah. So suddenly. Well, okay. And so actually, it reduces and it comes back again.
Mortality rate ratio for men is 26% reduction in death or 26% reduction in lung cancer death.
Yeah. So and this is how it how it then gets interpreted decrease mortality by 26% in high risk men.
Why are we not getting on with it? Then you see what really matter.
These language matters could get missed out and then this gets interpreted into this and therefore we end up with a screening program.
Now, this is a Nelson trial that came out February six, half the reduce lung cancer mortality volume 50 screening in a randomised controlled trial.
Okay, now how long for mortality was significantly lower among those who underwent volume screening?
Among those who underwent no screening. Okay. All right.
I'm going to try now break that down, because when I read this stuff, I never feel that informed.
I read these abstracts and I try and go, What is this trying to tell me?
And look at the way the data is presented. Things like 4.91 cases per 1000 person years.
What does that mean? What's a difference between 4.90.91 of a person?
Is that important? How are we supposed to interpret that?
Then you flip to relative rate, cumulative rate ratios, and then you come back to another one and then you've got the rate ratio.
So we flip in math all the time. And so even I find this really difficult to understand.
And the only people who don't are the ones who don't read it so soon.
If you read these and you go, This is difficult, you should do. Because it's not clear to me if this is deliberate,
an attempt to make sure nobody quite understand this so you can make this or it's just incompetence or it's deliberate about everything, isn't it?
But it certainly isn't wrote in, in a way, to understand the information.
So let me take you through the information and you can see.
So the first thing is table two screening test results in each screening round for male participants.
So the first thing is a go. Right, here we go. Round one, two, three, four.
Okay. So there's 22,600. Yeah.
And uptake of that I'm happy here is 90%.
That's helpful information. 90%. That's qua what isn't it. In a screening round, this is a pretty compliant population, but that's helpful.
But what I do then is try and break this down into useful information because I don't think these figures help me.
So I break everything down to one thing. If I just take that thousand people a bit like Deborah and showed us what, and if you take a thousand people,
71 people will have a positive test for screening round of which 31 will detect lung cancer.
That's 44%. Yeah. And 40 will be falsely diagnosed.
Yep. Happy with that. And so that's that figure there, the 44% I was round up because I don't believe there's such a thing as a half a person.
Yeah. And, and so it's either 44 or 44, but that doesn't matter.
So you happy? That's my first step. When I interpret the take a thousand people, how many will have a positive test?
Now, if I took that and took that away, you can actually say anything sensible about that could be based on that table.
But once you give a thousand, it becomes really simple. All right.
But 71 people have a positive test and nearly a half of them will have that lung cancer and just over half won't.
And that's that important figure there. Okay. So that's my first bit.
My second bit then is to start to look at this piece of information which tries to tease out some of these issues of overdiagnosis.
So what what this says, if you look at the whole number of cases here.
Yeah. Any lung cancer, if 344 and in the control group it's 404.
Yeah. So the difference between the two is what they're trying to say is the overdiagnosis component.
You've got 40 excess cancers that you've detected. Yeah.
And that's in the group they say an excess of was found among the male participants in the screening
group ten years after randomisation a suggested excess in the in the incidence rate of 20%.
That's what it's got in the paper. But again I can't understand that just doesn't make sense to me.
What am I supposed to use that information. So again, I take.
Okay if it 40 cases overdiagnosis per fictional thousand people screened that's six per 1000.
Very similar to the actual number in the analysis, isn't it? But the same actually.
That's helpful. It backs up and verified. It makes me feel that makes sense.
So I find this information much more useful than that which is on interpret all to me.
I don't know how I'm supposed to use that. Well, and then third, we come to table four, which is a incredibly interesting table.
If a cause of death of deceased male participants at ten years follow all the important information.
So let's just break this down. The first bit of information is the cause of death.
And here's the lung cancer death. Okay. Hundred and 16 screening group versus 210 in the control group where you can see that death.
And that's point 76. That's where you get the 24% reduction.
Everybody happy with that? That's your lung cancer specific.
Mortality is a really important piece of information.
Okay. Which is down here. But again, you see how here we have the numbers and then suddenly we now go two deaths per thousand person year.
Why did we do that? What's going on now?
And in fact, when you do that, what you see here is actually there's no difference.
And in fact, there's 1% more death in the screening group than there is in the non screening group,
which is really interesting and I don't mean it with all this mortality and if you go on the table, you'll see.
Well, I was speaking to Jeff Aronson today. I mean, are there any clinicians in the room?
So on death certificate, I've done that with you.
How many people would say the cause of death is symptom funds and abnormal clinical and laboratory findings?
If you did the death certificate, you would be in front of the GMC in the beginning.
You don't know what you're doing. That's a medical student potentially.
He's got all these elevated tests. So I'm like, These people are supposed to be blindly allocating this,
but somehow you're 86% more likely to die if you've had screening from symptom signs and abnormal clinical and lab finding.
Just somewhat. Not right in my mind.
And not only that, look at this you are twice as likely to die from integrated nutritional and metabolic diseases.
So imagine if I released a paper thing. Screening increases double your risk of death from endocrine and nutritional and metabolic diseases.
You'd be incredibly worried about that. So this part of the graph is extraordinary to me what's going on that actually,
although you're talking about this reduction in lung cancer, we've actually seen some increases in other diseases.
So what I do that is I try and bring everything together into one piece of information that help me understand it.
Okay. And this is what it looks like. This is a flow chart, which I do for and I put the important information about who's in the trial.
And so I take 2000 high risk individuals. A thousand have lung cancer in 2000 don't.
Yeah. So the first thing, if there's 132 that that and hundred and 30 that there's a little bit more.
But you can say it's no difference really easy. But there are some death there are 24 of these are caused by lung cancer as opposed to 32 here.
But extraordinarily, there are 108 or the causes here compared to 99% more likely to die from another cause if you have screening.
Oh, my God. And then under of the Fathom screen you have 71 will have a positive screening test of the 31
will have lung cancer 40 will be both positive and of the 31 effect will be overdiagnosed.
That's that's my one slide to explain to you about lung cancer and the effect of screening.
Now, if I was in a decision making mode, if I explained and showed you people that you can come back to the end of this talk, we might put it up.
It can help you really understand simply the numbers when you do it this way.
But actually it takes a bit of time.
I've done this for years and it still took me a bit of time to tease around the paper, to pull them bit of information.
But every table in the paper is unhelpful to me to get to this point.
Isn't that interesting? And all the information we want is in that way.
So I think this is incredibly interesting when it comes to decision making.
And I want to point this.
This is a systematic review that basically shows of all the studies, you put them all together that actually you come to the same example.
It doesn't include the now for mortality data, but when you add that in, you'll see there's no mortality effect and it gets this pretty similar,
about 20% reduction for GFR screening if you put in lung cancer survival.
Lung cancer survival. Okay, now.
For really interesting epidemiological concepts to think about lead time bias.
I'm not really going to discuss, but that's why five year survival rates are a waste of time.
The more you screen, the more people pick up earlier, the more they're going to live longer.
But you might not affect overall survival. That's a very clear concept that we understand and trying to understand.
But ten years ago, everything came in five year survival.
And the classic one is in America, our five year survival for 80% for prostate cancer in the UK, the 5%.
And that's why we can't have UK national health care.
But actually the mortality rate and the survival is identical.
You die at the same point. Both the US and the UK, you don't affect survival.
But this is competing interests. Competing interests. The key diagnoses bias and slippery linkage bias.
Isn't that interesting bias? If so, the first is competing risks.
Competing ref means that things compete for your for our death simpler.
And one of the problems is again is if you censor people when they've had the event of
interest so if they die of some of the cause means they can't die of lung cancer either.
So you also have a mathematical problem that you take them out of the analysis and therefore actually
overestimate in the screening group the event rates if you don't account for competing interest.
Yeah. So some studies don't take account of competing interests and prostate cancer is one of the best way we know you do that.
People would effectively prefer a more likely outcome now.
But in lung cancer is incredibly important. But there are also mathematical issues in terms of the censoring, which is important.
And this paper is really neat because what it says is your kaplan-meier hazard ratios overestimate the effect
if you don't account for the fact you're taking people out because you sense that them when they died.
The second thing is these two concepts which are really interesting,
we run about a thing called the catalogue of bias and IT Fund, and these are two new additions to the catalogue of bias.
Sticky diagnosis bias occurs when death from other causes in the experimental group,
in the screening group are incorrectly attributed to the disease is a target screening.
So you could do it both ways. You can say, oh, I think we're interested in lung cancer.
We're going to attribute this to lung cancer because that's in our minds, it's a it's a thought heuristic that happens.
But you can do it the other way when you flip it the other way, which biases the favour, the screening group.
So you can do it the other way when you're slippery.
And so there are different biases occurring which particularly can occur if you're really invested in screening.
You might falsely attribute things to biochemical factors when actually they may have had a lung cancer, they may have had something happen.
You've removed the lung cancer, but a year later they died.
You don't go, well, actually, maybe that was attributable to lung cancer, so you split the diagnosis.
So I'm going to finish here with this position statement that the World Health Organisation talked about.
I think it's very important in terms of screening both lead to an improvement in any results defined in terms of mortality, physical,
social and emotional function, pain and satisfaction among those in whom early diagnosis is achieved or in the other members of the community.
Why is that important?
Well, okay, I'm okay if you have a screen detected group that actually there's no overall mortality effect if you affect some of these other issues.
So for instance, if you can remove my lung cancer at stage one and I have a better quality of life because of that, I still die at the same point.
That's a worthwhile intervention. So all of the outcomes have health related quality of life in there,
but nobody has reported the health related quality of life beyond the first six months or a year,
because all they're interested in is the emotional impact of the first six months after screening,
and they say everything's okay by the year you're okay.
But actually we really interested in what is the overall impact of quality of life.
But none of this has been reported and it's really quite an important issue that what we
stuck on is this measuring screening is where it started with this idea of a cancer.
Specific mortality is why we screen. But actually a common phenomenon is we don't improve overall mortality.
And that's exactly what's happening in lung cancer. The question now is,
and this is where the debate is is quite a significant number of people consider we should now roll out c.t screening based on that Neilsen data.
Some people who keep writing to everybody saying this could be a.
Significant error because it will divert resources so that when you need your diagnosis,
there won't be enough radiography there to help you and you need to be treated quickly.
You could invest the money somewhere else. For instance, an amazing smoking reduction.
You've got a belly in pain to play with. You might actually, if you can get people to smoke less.
That would be still the biggest prevention and public health issue you can deal with.
So if you were the person faced with that graph, where would you spend your billion in the next year?
Would you spend it on CT screening or would you spend it on something else?
Thank you very much.