So if you'd just like to start by saying your name and your title and affiliation.
So I'm Professor Julia Cox and Professor of Clinical Epidemiology and General Practice
in the Department of Primary Care Health Sciences at the University of Oxford.
Lovely. Thank you. And without telling me your entire life story, because I didn't have enough time for that,
could you just give me a little outline of how you got from your first interest in medicine or medical science to to where you are now?
Okay. So I trained as a doctor and in Sheffield University I did an academic training alongside training to be a general practitioners.
I work as an NHS GP in Oxfordshire. And then I love to do research.
And basically I've spent over 20 years doing research initially in the University of Nottingham.
And then in the last three years moved to the University of Oxford, where I'm not based.
Okay. That's lovely. So if you had to define a sort of single big question that gets you off to bed in the morning, what would you say it was?
Um, so how how can we better inform the decisions that doctors and patients need
to make about their health care to take take account of their preferences,
but also that different risks and benefits so that we can enable people to make decisions about the health care.
Okay. So and what was your main area of research focus before COVID came along?
Okay, so I had been working for some years in electronic health records, so I've developed a few very large electronic record databases,
one called the Key Research Database, which takes general practitioner information or anonymized data about patients.
I was actually when your patient records and it's linked to other healthcare data so hospital admissions mortality
council registry and I was using that data to answer questions about how common different diseases were,
what the outcome is with patients, what they got those diseases, what treatments did they get,
but particularly who's at risk from different conditions and different diseases and who might therefore benefit from specific interventions.
So that was one theme. It's about predicting risk, modelling risk.
And then the second broadly was actually looking at safety of medicines and looking at when a new medicine is being introduced.
And in medical practice, we normally go with it.
It's effective because it's been tested in randomised controlled trials,
but there's generally limited information about safety until the medicines are actually used in large populations.
So actually looking at safety of medicines as well as their effectiveness has been a key interest.
And both of those two things, both the risk modelling and also safety of medicines,
have become major pieces of work that themes in the COVID pandemic that I've been working on in the last couple of years.
So let's just look a bit more closely about the whole question of health, these health databases.
I mean, I remember I forgot which year it was, but a decade or so ago,
there was a lot of fuss in the media about a national scheme called Care dot data that was eventually dropped.
But nevertheless, there are these big databases. Can you explain a little bit more about how they work?
Yes. Okay. So so we set the Q research database up sort of about 20 years ago.
And it was the first time that an individual patient level data had been extracted from the EMA system,
which is the biggest and the most commonly used computer system in general practice.
And all of the data that is extracted then now is anonymized data.
So we can't tell who individual patients are. Some don't have names and addresses, and we can't work out why people are.
But we can see a lot of information about what illnesses they've got are what say,
what blood tests they've had and what treatments that they're taking.
And this database was sort of set up as part of a sort of philanthropic venture, really, with the company that makes the computer systems,
because it's a unique and incredibly valuable source of information about health care and
the treatments that people get as people need and then how they use and how safe they are.
And that was been in place for some years.
And I think the kinds of data could see that the people running that could see the value of these databases for research,
but could also see a wider value for health delivery of health care.
And they were, I think,
did become quite controversial and subsequently did as well because of the devices
that we've got in universities that we're only using data for research purposes.
And a university like the University of Nottingham, now the University of Oxford is a very trusted research environment as we pretend taking such
using these data sources and we're not able because it's none of my business was able to use
the data provided purposes and some of the concerns I think related to that to the use of
white purposes which weren't clear at the time exactly what the scope of those focuses were.
But by focusing really specifically on research, questions of medical research, we managed to continue and expand the work that we do.
But we have got now quite a broad patient representation as well throughout all the stages of our in that endeavour so that we make
sure that we take the public with us in terms of the data is being used appropriately to answer questions of the benefit to patients.
So what you may have mentioned a number of a figure earlier, and if you have forgotten, but what sort of size are we talking?
How many patients are we talking about? So we've actually got such 5 million patient records on the database.
Now, those patient records go back to 1990.
So some years ago now tracking over 30 years, and it includes people who've left their GP practice,
some people who sadly died and also new patients that have come along.
So as a snapshot, it becomes about a quarter of the current population because the sad distress that we don't know the names of just those people,
we just have anonymous records of those of those patients.
But it is one of the largest, if not the largest such dataset in the UK.
And it's it's been widely used for research over the last 20 years, an assortment actually of football moments now.
And you actually set up your own company glitter can risk.
Is that right? So so back in 2008, we developed some prediction software.
The first the first type of prediction software called the key risk score, which basically takes information about a patient,
their height, their weight, their cholesterol values, the family history and their medical conditions.
And it built a predictive model which identifies the risk of somebody getting a heart attack or stroke in the next ten years.
And in order to implement that software, we needed to set up a company to produce the software to make sure there was insurance for it.
And it could be used as a medical device on it, and it could be embedded in in the NHS systems such that it could be use point of care.
And so the company Psychiatrist Ltd was set up as a Start-Up Company really
arising out of the University of Nottingham to implement the results so that you
could translate the the research findings into something which is tangible and
beneficial for patients and that is used across the NHS and still use our NHS.
And yes, so that just kind of emphasising that point so that, that, that software has actually been adopted.
Has it through throughout the NHS. Yes. So, so the, the steps where we developed initially, the risk, risk score,
we published the paper in the British Medical Journal Oxford University is in fact researchers
validated that risk to check that it worked correctly identify the high risk people.
It was then recommended by the Nice National Institute complex and school as part of that guideline,
and it was also adopted by the Department of Health for the NHS Health Checks programme,
which is offered to everybody between the ages of 45 and 75 on a sort of rolling basis.
And then that then led to the development of other tools.
Rather than just looking at heart disease. We developed similar risk tools for Cardiff, for diabetes,
for identifying people at high risk of having osteoporotic fractures, high risk of having cancers,
with the idea that all of these tools could be used to actually identify people in the risk category where we could target something to them.
So target an intervention like a medication or a prevention or a referral to to make sure they haven't got cuts of example.
And then these risk tools have been built into clinical systems such as the EMA system that I mentioned,
so that the GP's who provide data to us actually get some benefit back in terms of uteruses that exist in the clinical system and
therefore a through that patients can actually get some direct benefit back from that from the utilities that we've developed as well.
So it's been a a really interesting sort of cycle to do it and to be able to take a research idea really, and then change it into such a question,
then deliver some results in an academic setting, but then translate that into something which is tangible,
which people can then use to help with clinical care on a day to day basis.
That's been a really satisfying thing to do as an academic because sometimes it can be quite far removed from actual,
such tangible outputs because of the nature of the work that you do.
So really privileged to be in that position. So doctors can essentially call people up and say,
I think we ought to investigate you a little bit further rather than waiting for people to fall ill and and come and see them.
Absolutely. And so with cardiovascular disease, you know,
we've got interventions now that we know works and we know that statens again receptor Myles is in the
University of Oxford did that looking at effectiveness of statins to reduce risk of cardiovascular disease.
So we know that that works.
And what the risk score does is actually helps quantify what that person's risk is so that we can then give that information to patients.
And then if you were to then take a step down, then this would be a benefit to you and helping you to reduce your risk.
So it's an interesting thing to do, and I think it's opened up a whole raft of similar sort of endeavours by people.
I think it opens up the sort of it was the first time anybody had actually done predictive risk modelling
using deep electronic health care records and produced a tool which could then be used in clinical practice.
So it served as a sort of example really as to how you could you could do such a thing in the future.
And are you personally interested in the geeky side of it, the coding and so on, or do you get other people to do that?
Geeks Yes. So before I went to university, actually, I had a year I was supposed to be going to two.
Another university to read physics.
But I changed my mind and I spent a year programming computers for a large company, and I absolutely loved writing computer codes at that point.
But I then went into medicine and then I come out.
I'm still working in in medicine, but actually found that the computer skills as I got the mathematical skills could
be combined with medical skills as I've got all wrapped up in some epidemiology,
really in the way of being able to sit to implement things.
So. So I was one of those doctors,
doctors who code a central group of people who if a lot of people really who liked the side of things and this quite was quite
a few of the survivors actually them who liked to be able to take things and then put them into software and make them useful.
So let's finally get round to cope with it.
So can you remember where you were or how you came to hear that there was something going on in China and that it might actually become quite serious?
So I can remember back to hearing about the emergence of the virus.
So back in 2005, actually, we went to such a scheme called the Queue Surveillance Infectious Diseases Surveillance System,
and that has arisen because of concerns about bird flu.
And at the time there was there was a real concern that it was going to be a pandemic and that the government would need
very rapid access to information at the points that it's about to alert to a pandemic and then to be able to manage it.
And so they wrote back that we sort of set up a surveillance system covering an even bigger group of patients,
actually nearly 40 million patients, and collecting information basically to alert to things like flu outbreaks, infectious disease outbreaks.
And that was run and run for many years,
basically as a sort of a surveillance system and have been part of like endeavours to look at pandemic planning and things like that in the past.
So as my awareness of pandemics was already, if you like, quite heightened.
And so when I heard about Wuhan virus and heard about the cells, you know,
I was really quite concerned about that because by the time that you think about trying to contain this, it's probably already already spread.
So I can remember that quite, quite clearly. And I can remember being being asked by the government really quite early on to look at the surveillance
systems that we've got in place and to see what we could potentially do to help enhance those.
And then my direct involvement happened, I think it was in early May 2020.
So so Chris Whitty, Chief Medical Officer, identified a list of patients who we thought might be at high risk of getting severe outcomes from COVID,
who might need to have special interventions.
And the way that that was done was basically to look at what we knew about flu and the people who were susceptible to getting flu each year.
And again, because of the work I've been doing on surveillance.
So for many years we've been tracking flu outbreaks and flu virus vaccine uptake and things like that.
And they produced this list and it was the best we could do in the complete disaster information and the lack of that data at that particular time.
But it became very apparent, particularly to Chris Whitty, but also Jenny Harries, now Dame Jenny Harries,
that the people who were coming through the doors on the intensive care units were not necessarily the people that were in this high risk category.
It didn't look the same. So there was a different ethnic mix and some of the conditions that we thought were
high risk did seem to be and then other things which we didn't think did seem to be.
And there was obviously a lot of public concern at the time.
And so we were asked to sort of start to look at building on the work that they knew that we were able to do,
which is basically develop prediction models for an outcome to then see whether we could actually make that outcome.
So that kind of it's a poor outcome.
But so basically somebody being admitted to hospital in Tesco or somebody dying type it.
And so we then started off on some of the quickest research that we've ever done,
which was only really possible because we've done this quite a lot of times before.
So the conditions over the previous ten years as I was able to assemble new data as it was coming through.
So the information, I think we were one of the first recipients of the the dataset from the Public Health England,
as it was called, the Infectious Diseases Source Data Files.
It's coming through. We're able to combine that with data we already had on hospital admissions.
We get new and faster data on hospital admissions that was just becoming available.
And because we already had the construction infrastructure,
we were able to basically tag these extra things on and we were able to look at the expertise already in terms of modelling to be able
to run and develop really very quickly some sort of new predictive models to look at who was at high risk of getting struck with COVID.
We did a really extensive patient engagement at that point, so there were lots of it quite, quite understandably and predictably.
There were lots of patient groups who were very concerned about that particular condition.
People, for example, who were not actually recognised on the original shielded patient list.
So people with severe kidney transplant, severe renal failure always had kidney transplant, for example.
People with severe. Disabilities. So we were able to sort of work with those through the Department of Health,
but work with a large groups of charity type organisations and patient groups,
professional groups, ready to assimilate what the long list of conditions might be that we should look at.
And we were able to then cycle through and look at those and develop the the research underpinning research,
which we then published in the British Medical Journal.
And we developed a model which we were then able to test and validate, not just on a sort of the dataset that we had,
but we were also able to work with colleagues up in Scotland and Wales, Northern Ireland and and the Office of National Statistics.
So we developed the model and then we got it to be tested to make sure that it worked in those different settings
up in Scotland and Wales and also across the whole of the national database in the Office of National Statistics.
So this was coming, I suppose, with the intention that we would be able to identify quickly people who needed interventions so
that we could target those most at risk to get treatments that they needed and to get them first.
And so one of the the prime use cases of this time last year were just I think
that very last year we took the algorithm that we developed and we applied it.
I say we we as a sort of a corporate way, if you like. So Oxford University Innovations did all the medical device regulation certification.
Oxford Computing Consulting that works very closely with the university developed
some sort of industrial scale software which was then sent to NHS Digital.
So that's the sort of national organisation that organises and hosts a lot of the nation's health data.
And then they then applied the algorithm to the national database to sort of stratify, basically put into risk categories, everybody on the database.
And what that did is they added overnight, whereas overnight they took a month or two to get the software working,
but once it was then run, we were able to end it,
just as you were able to write to one and half million patients nationally
to identify the fact that they were at higher risk of severe COVID outcomes,
that they hadn't already be on to the original list. And then for those patients that they were given like a priority letter,
and then 800,000 of those patients were able to come and get a vaccine earlier in the program.
And they would have done just by virtue of their age, although the current arrangements are in place.
So that was a very tangible thing that we were able to do and those patients came forward and I think there was a broad understanding at the time.
I think most people just looking back,
I think I think society sort of feels that the most vulnerable people that we have should be getting interventions.
And people were pleased to see that grannies coming forward or people with serious health conditions sort of being prioritised.
And I think one of the reasons that we could do that from the ethical perspective was because we knew that everybody would get a vaccine eventually.
And what we were doing is just actually prioritising people according to the level of need,
but doing it in a way that didn't make a decision, if you like.
It didn't actually make people have a vaccine. It just basically identified the people who were at risk so they could be prioritised first.
And that probably just just reminds me to mention that the enormous team effort this was really because of that there 70 dimensions
and one of the dimensions was actually the ethics and the moral brought us into contact with the Moral Ethics Advisory Committee,
which I was not aware of before, but which is a government led organisation which is was currently,
which was chaired by John Montgomery, also from Oxford,
which basically was able to look at the different approaches that we could have taken to stratification because there needs to be,
there needs to be some sort of approach about how do you get the vaccines to everybody in the most appropriate way.
And really to help us sort of think through what the issues were and to make
sure it's compliant with what it what the general public would find acceptable,
but also would be compliant with the legislation.
And that protects lots of things like that as well.
So there was a lot of team working on that and there was also a lot of team working to actually ensure that the information
that was made available to patients is understandable as well and that the people could and to health care professionals.
So so that was the the sort of the main outputs from that initial piece of work and then.
The second output was that we developed a social sciences text by email saying, I'm just getting so close.
So the second aspect was that we developed a clinical calculator which you can find at WW WW dot org.
So the risk assessment tool was called COVID. And that's partly because the database which we've developed is called the Pew Research Database,
and we've put generally a queue at the beginning of things too as a bit of a brand.
NHS Digital also produced a risk assessment tool which allows an individual or a clinician,
a doctor or patient to date to be able to put information out about their characteristics and their age,
the sex and ethnic group and information about them.
And then it would calculator a risk for them and it would compare that risk to other, other risks, for example,
like the risk of somebody of the same age and sex as some who didn't have maybe severe health conditions.
And that's possibly they go back to the beginning of that sort of thing,
trying to provide to people better information about what the risks are that we're trying to avoid and therefore
what benefits are by actually having the treatments that are needed and why they might be prioritised.
So that's was the second thing that was produced.
So that at the end of 2015 inches s a 52,021, we were asked to update the risk model that we've done because we have now got a vaccinated population.
So one of the challenges has been the fact that this is not a static thing.
So unlike cardiovascular risk, which is changing slowly over a decade, this is a situation which was changing month by month really,
and that we we could go from unvaccinated population to a vaccinated population or a largely vaccinated population.
And that the chief medical officer wanted us to find out what who was still vulnerable despite having a vaccination.
So we had to be models again,
this time linking in the vaccine data so that we could look at people who were still vulnerable at that point despite having been vaccinated.
And then that was called UK Bits three, so that they could give it to is actually the model that we did because we have a new wave.
I think it was the alpha wave looking back there were massed in the UK,
but three was actually the the model that we did take account of the vaccinated population and we're just now being asked to look at COVID four,
which is to take account of the on the second wave. And so, again, it really keeps you on your toes, I think.
And I think one of the real interests for me about this is not just producing a tool that can be used,
but actually when you've got a complex system so you've got new treatments becoming available, you've got a virus that doesn't seem to obey any rules.
You know, it's just mutating without asking. And we need to keep the models up to date.
So what's the best way to keep the models up to date? How do we do that?
And so we're working quite broadly with other experts in the field to look at dynamic update models and how best to keep these models up to date.
And then there's more recently even still. So in this particular subset to 2020 to some of the work that we've been doing has been trying
to prioritise people for new therapeutics apart from vaccines that are become available.
So drugs that are called monoclonal antibodies or maps are sometimes called.
These are particularly useful for patients who can't make their own antibodies
or who don't as a response to vaccines might be very sick for other reasons.
And so the work that we've been doing has been helping prioritise patients for those particular treatments.
Great. Thank you. Certainly good talking.
And so I was just going to ask you. Going right back to the beginning, the first paper that you did,
what were the main groups that you uncovered that hadn't been picked up in the initial shielding lists?
Yeah. So the main that so people who got significant neurological problems,
those people with much different substances to graft and multiple sclerosis, that was one category of people, people with Down syndrome.
Again, that was that that was a a brand new finding that they seemed to have particularly high risks.
And that finding is said to be replicated in other and other datasets.
And, in fact, that people with Down syndrome were prioritised for interventions.
And then, as I mentioned, the people with severe kidney disease and had transplants, they they were included.
But the actual model is it isn't so much that it's a group of patients.
It's about a cumulative thing.
So it takes account of somebody's age, ethnicity and their medical conditions and indeed things like their level of obesity.
So some of the research we did was used to basically perform some of the obesity planning,
which is because as your body mass index goes up, as you become more obese and your risk of having certain outcomes also goes up.
So and that is one of the few modifiable risk factors actually, where people can make a difference themselves through that, but that they're doing.
The other piece of work probably to mention, is coming back to the theme of vaccine safety.
So once again, this has been the same sort of success in my work over the years,
which is once you've identified a group of patients who might need an intervention,
you then also need to make sure that that interventions go to the right people, but that intervention is safe for those people.
So over the years where we started with cardiovascular disease and we identify
people high risk contact Cox's you might then have had started prescription.
Our next theme of research was actually to go and look at the risks and benefits of statins to make sure there weren't any unexpected side effects.
And so what we've done over the over Christmas, really in the run up to Christmas this year and in the middle of last year,
is to look at the safety of the COVID vaccine. So we were very, again, very privileged to get access to the national datasets for COVID vaccination,
which meant that we could look at 46 million people,
all anonymized data, but we could compare who's had the Oxford vaccine, for example, who's had the Pfizer one use up the maternal one.
And we were able to look at the side effects that were being uncovered at the time, and there was a concern to move to that quite quickly.
So looking back to Easter last year, there was some concerns around some of the side effects, the thrombosis risk associated with the Oxford vaccine.
So we had some funding from a priority government funding source to look at that quickly.
We were able to we were able to confirm that there was a signal to do with the vaccine was first on basis risk.
However, we were able to put that in context with the risk of the same outcome if you were to get infected with COVID.
And in fact, actually, although there was an increased risk with that with the vaccine,
it was nowhere near as big as the increased risk associated with the virus.
And so therefore, whilst ideally not any sort of adverse events,
when we know in the real world that there are, but the important thing is to be able to quantify that.
So it was still able to be a positive message, which is that it's it's better to be vaccinated than it is to be to actually contract the virus.
We were then able to follow that work up as concerns about myocarditis, which is inflammation of the hearts of such too much back.
So but the other vaccines and again we were able to quantify those risks and
identify the people most at risk within within some of those groups as well,
published in papers in Nature Medicine just around Christmas time,
which have been I think because the UK used three vaccines and it used them quickly and its vaccine programme was very successful at getting coverage.
It also left us, I think, with a responsibility to to make sure that we use that data wisely and informative so that we could
tell other people who might be in the programme in their own countries that were less advanced,
sort of what, what the direct comparisons are between those different risks.
And so that's been a. Important stream of work that we were just finishing there.
And the message I'm just just to confirm that the message is that the risk is actually extremely small of the vaccines.
It's not not nothing, but as you say, compared with actually catching the virus, it's it's much more that.
That's right. So, for example, we were able to to present information.
It may not be that for every million patients that have been vaccinated,
then there might be one or two extra people who end up with one of these side effects that might not have otherwise occurred,
but had the virus, then there might be ten more or 20 more, etc.
So it's that and we've worked closely with the Edinburgh Institute,
the National Institute at the University of Edinburgh to basically produce nice graphical images,
basically which which allow you to contextualise that information and present it to
people in a a readily accessible format so that anybody basically can understand it.
And we've certainly found that that has been a valuable way of being able to communicate science in sort of a pictorial form,
in simple, easy to use sort of graphics really, which can communicate complex.
So it is complex doing these analysis, but the results are quite simple, you know,
that you could summarise in the picture quite often, but there are maybe some subgroups that this country has changed its policy on,
on the because of the effects of some of the vaccines in particular age groups like for example,
the Oxford vaccine is now not used in younger people because of that,
because of the it's such a risk that there are, but there are other vaccines which can which can be used instead.
So I think it comes back to really the whole driving force for me that all my research is around trying to better quantify the risks and benefits of,
of, of the people face because they might get, but also then the decisions that they need to make about what treatments or what what to do next.
Because there's always a trade off really that you need to do.
And I think to get consent from people as a doctor say,
you need to both provide people with reliable information so they can make a decision about what they want, what they want to do.
So is the ability to use these huge databases is clearly speeded up and make it much more accurate to the kind of risks,
the risk stratification and also the spotting of side effects.
How how was that done previously? So it's been done, I think.
So I think with the drug safety surveillance, as we might call it, it's been done at a sort of smaller scale and not necessarily in a systematic way.
And so that there's a thing called the yellow card system, which is run by the Medicines House Regulatory Agency, MHRA,
and that's encouraged for as long as I've been qualified, which is which is some years now, but is encouraged with the new drug.
If you think that somebody might have had the it up from too much, you fill out a card and you send it in.
And and that certainly has got a value.
But what it doesn't tell you is just does it tell you how many people have had the drugs and not had the side effects?
You don't have that sort of denominator. So it's good for generating sort of concern.
And that's partly how some of the concerns around that about thrombosis you talk about were raised.
But it's not so good for actually quantifying it because you don't know what the
denominator is and you can't work it out in the way in the way that you've just described.
So I think one of the things with the future, which I think is really important,
that we take the lessons that we've learnt from the pandemic and we take some of the new infrastructures that we built,
the new data linkages that we've been able to do,
and that we we harness that because some that some of the approaches that we've used statistically, for example,
can be programmed into computers and can run automatically in the background, basically just looking for unexpected side effects.
For example, you could have a subindex,
a drug safety surveillance system which could automatically in a scalable way across millions of people when we have new medicines being rolled out,
actually look to establish that they're safe and do that in a reliable way.
That might be quite cost effective for the government as well to think about.
And also independent from both the companies that make the drugs who who obviously very interested in what side effect profile might be rightly so.
And and also the people who regulate the drugs,
who is doing good at that to be making decisions more quickly that may be them than they hitherto needed to do,
and then basically triangulate that and support it provides the supporting evidence that they need in order to be able to to
look at because you can't look at the when you're in the middle of a pandemic and you're trying to scale a vaccine that,
you know, works, you have to sort of do these things in real time.
There's no point in doing a study two years later and then uncovering something.
You have to have a system in place that allows you to do this as you go along.
And I think one of the one of the other very interesting thing you mentioned about becoming a lot of discussion,
I think there were a lot of concerns about for patients around about the use of data when they didn't know what it's going to be used for.
But in the context of when we were stratifying patients and some of the feedback that we got from patients is that they were
really keen to make sure that their doctor knew that they had conditions or that they had information that was up to date.
They really wanted to know that their data was being used not just for the general good,
but actually to make sure that that the information about their condition was being factored into the risk assessment.
And if they were at risk of needing to have a priority for particular treatment, then then that could be available.
So I think there's been a a sea change in how people have understood data, not just around the risk prediction thing.
I suppose there's a massive increase in health literacy across the population.
I mean, we've now got a population that knows about the names of various viruses and the separate viruses and knows the names
of different vaccines and has some sort of in a way that would have been unthinkable even two years ago to have this.
I think that there's this there's a big step change in how people want to know about information.
They want to be able to assess their risks. I think there's been a big change as well about how we think about other people around this as
well and the need to be considerate about the fact that you might everybody is in the chain.
That might be somebody who's a vulnerable person, which I think some of the Christmas he said early on.
And I think, you know, some of the hope is some of the work that we've done around being able to still
assess people's vulnerability and risk to COVID might help other people.
Be considerate of that particular vulnerable person in the sense that they know that that person is vulnerable.
They might be able to make special concessions and arrangements and things like that to help protect that person.
And as a society,
we can become more considerate about threats and the fact that our actions might actually have consequences for other people and that we
could protect potentially protect them by some of the measures that they've come to know a useful and necessary in these circumstances.
So how was the the code that the QR code would work funded initially so that it was funded by the National Institute for Healthcare Research.
So NIH. Ah, so we had a, we had to get going with the work as soon as we asked to do it.
But so it took a little while for the funding actually to come through, but we knew that it would come through after after some months.
And then that's being funded by that, by the NIH.
And without that, we wouldn't be able to get those.
But what was what did help in the very early stages is that there was a call from the Medical
Sciences Division at the University of Oxford in the right at the beginning of the pandemic for us,
because there was a donor fund being set up.
So we got together very quickly within Oxford.
I think everybody who who does special things, if you like to work with told data linkages sector we will make connections really quickly.
And so we were lucky to get a couple of the early awards that the medical sciences division offered us.
And one of those opposed was to it sounds a small thing,
but actually it enabled us to actually pay for the access fees for some of the datasets
that we needed because there's always costs associated with administering datasets.
So that meant that we could we could actually get that estimate immediately. So there was no delay in getting that dataset.
If we had to wait for the main grant to come through that, the first wave would come and go before we got there.
And then the second one again, which was migrating from somatic installation for,
was that it enabled us to buy some serious quantities of computer software and, and hardware particularly.
So it's a big service that we've been able to set up for researchers.
And so we've got teams of such as now that are locking in.
It's all the data that we have as safely stored.
And in that environment where you go to the data, you don't download data.
So nobody downloads data into the actual emails data.
Only a few people can access it. But what the funding did was basically enables that enabled us to buy some, some high powered computing facilities.
That means that we could get more research seconds and get results out more quickly.
So again, I think that was incredibly lucky being in pay cycles.
That enabled us to do that. And really quickly and I think again,
we were I felt really fortunate to be a donor being and also to get by the
time when the pandemic started very quick and I had met a lot of colleagues,
but then very quickly we were working with a consultant at the intensive care professor,
Sir Peter Horby, for example, who is the chair of the Nervtag group for Sage.
And really any expertise that we needed is available not just in Oxford,
also has a very high concentration of people with very specific expertise from
policy makers like Professor Anthony Harms and the from the Joint Committee
on Vaccinations and the People like Professor Scrutiny develops the monoclonal
antibodies of things that I about which we're now looking at evaluating.
So it's been a really fruitful place to be when you need to get research done really quickly.
It's been an excellent environment. I had a follow up question about the funding,
because you what you mentioned a little while ago that you'd like to continue this approach for other kinds of conditions.
How confident are you that in the light of the success with COVID, that that funding will be forthcoming?
I think it will. I think it will. So one of the things that's happened is that NHS Digital,
it was the first time that they actually ever done a sort of what they call a precision health intervention at scale.
So applying a risk stratification system to a national data set to identify individuals who then said, let's just take it.
For me, it's a special task since that's I'll be doing this sort of thing for other diseases and
COVID this challenging but that similar look to different diseases and ways to the principle.
But for them, it's the first time. And so that they've now started thinking about the whole cohort in approach.
And so one of the things that's happened I think indirectly because of the pandemic is that we perhaps
under a lot of pressure in the NHS for things like early diagnosis of cancer to cuts of screenings.
A lot of the screening processes just ended up being shelved.
So I think we stopped wrestling altogether for some period of time with the pandemic,
and then obviously we start doing those things again and it comes back.
You can't do everybody immediately.
You have to have some sort of mechanism of being able to actually identify people who might be sitting on a waiting list who who are at higher risk,
somebody else on the waiting list, and need that diagnosis more quickly.
And so I know that there are sort of initiatives looking now at whether we can develop the cancer risk stratification into the national system.
And obviously, research always has to come first, and that's always first and centre about developing the tools,
making sure the tools work right and that knowledge and and then that can be engineered.
So I think that that that is likely to be funding bodies such as CII, custom research, UK, MRC, I'll see,
I think are looking at into those areas and I certainly want to some that we were successfully awarded to and what
from the cuts research UK some just for Christmas to look at dynamic assessment of cuts of risk in primary care.
So that is that that's an interesting one I understand what what she said.
So some of it is as as a value it's a subset and it's sorry.
It's like when you go away for three days with people you've never met before.
But in all three we've got a central trust around a particularly thing you might have a particular expertise to offer.
So. So the Social UK has a three day event just before Christmas where they got to get
people who wanted to basically make a step change in the early diagnosis of cancer.
So they included epidemiologists like myself, GP, GP as well, but people who work in secondary care, hospital doctors,
but also engineers and mathematicians and behavioural therapy and psychologists and nursing colleagues,
etc. and then they put you in a room together and basically gets give you a task and then you have to come
up with proposals of something which could which could be a step change in early diagnosis of cancer.
So that I would say that that hasn't yet been announced.
So that will be at some time in that in that in the in the next couple of months the outcomes on that board.
So so that's in the in the risk stratification for the virus cancer and potentially it's an approach that could be used for it.
And looking at is screening for people who have got diabetes and need to have more tailored eye screening schedules.
So some people might need to come every year or two years and people might need to come and be six months.
It could be applied to that breast cancer screening.
So that might be within the crude sort of scheme that we have minute,
which basically says if people between two ages come every so often or maybe three years, like a year,
you could tailor screening intervals to target people who are who've got a family history to come into the system,
think some of this is already done, but not enough in a way that is systematic.
And then from the drug safety side, I think that that's a little challenging.
And I think there's a bigger emphasis on developing new medicines.
And there's less emphasis, I think, on actually checking that that's safe.
However, if you don't have a system that looks at safety, then you could end up with some sort of health scare,
which then means that people don't want to use the medicines that are happening.
So you have to have that education and the.
Has to be. I think in order to get public confidence, professional confidence, it has to be more systematic approach to drug safety.
And so I think that there's there's as ever, there's a need to to ensure that the people who fund things know that these are ongoing needs.
We need to build capacity as well in the system to ensure that the next generation research is coming through.
As some of you have had the research which to completely change by the pandemic,
they weren't able to do the project they start stop doing because if the pandemic took over another people who, you know,
whose research has really exploded in a wonderful way because that they've been rushed at the moment, you know,
in that's in the time where they're asking and answering questions that are of international significance,
and they're doing that at that pace on that scale.
So I think I think it's important also to recognise that a lot of the research that I think has been done in Oxford but probably elsewhere,
is not has been done quickly and effectively in the pandemic.
But it's building on a track record of possibly many years or decades even of of of advances and investment.
And so I think there is an important moment now for the government to be strategic in
how it thinks about funding the but not just the pandemic sciences side of things,
but the the other things I mentioned just a lot about the direct effects.
The pandemic can have an impact just about the COVID itself. So you mentioned the high quality of research in Oxford.
I mean, one consequence of that that I feel that I've perceived is that in normal times it's actually quite competitive.
But you've talked a lot about collaboration between, you know, across disciplines and among people with different interests.
Have you found that distinctively different from your previous experience of working in academic research?
Well, I think it's been incredible, really, because I think when there's a serious threat to health,
which there's got a lot of unknown things where we need to answer questions, you know, in a way, obviously, we don't wish we hadn't had the pandemic,
but it's sets to us with the task and such questions which you can all unite around and be very rapidly sort of like,
okay, I need somebody who's got this expertise or that expertise, and I've just found people fantastically collaborative.
So. So, for example, when I wanted to look at looking at safety of vaccines in pregnancy, for example,
and then you suddenly look around Oxford and you realise you have people here who
are the world experts at looking at pregnancy and maternal inquiries and for death,
for example, or experts in vaccination, etc.
And that you ask people, you know, would you be interested in looking at this question?
Can we can we better quantify risks of vaccination in pregnancy, not just COBRA vaccines, but you just find that everybody says yes.
And can you know, when can we meet them? And the idea is to sort of keep flowing through.
And you just suddenly realise that you've gone from a small project to a really
large programme of work and but it's been fantastic from from my perspective,
I think there's a competitive collaborative thing as well.
And so I think, you know, the fact that people want to it is good to have a bit of a need to get had to put
your research out there so that you can actually communicate your findings quickly.
And you have to sometimes you do have to be first to do to do that well and be
the person who's confirmed the 15th time because that becomes less interesting.
So I think is the overwhelming need from the public and policymakers to get answers out of quickly has sort of tapped into,
I think, the competitive sort of spirit that I think is probably in all of the Oxford academics, really.
But then they've done it in a collaborative in a collaborative way.
And you sometimes find that, you know, you're competing with people and collaborating with them at the same time and that's been fine as well.
So we've been putting in for grants with people and because of hosting a national data, then we have to be fair.
And if people want to put in the bid as well and compete with us with a bid that we're putting in, we support that as well.
So so again, I think it's I think it just opens up your eyes to different ways of working and people
bring different things to the table and they have a different lens and way bouncing,
framing questions and answering questions. So, for example, right now we're doing some work.
You're looking at the safety of these new COVID therapeutics that the monoclonal antibodies that I
mentioned and there were three groups that will be commissioned to do exactly that same question.
So we were all just given the question just before Christmas.
There's a group up in Scotland, there's another group in Oxford as well, led by Dr. Ben Goldacre and A.L. So we were all given the same exam question,
if you like, and then we all produced some protocols, and then we're all meeting on a weekly basis currently to see what answers we're coming up with.
We've got slightly different datasets, we've got slightly different interests of scientific expertise,
and so there's some work that we're confirming because we're both doing the same answers.
And then there's other things that we're doing which are complementary,
and then that will all come up to be concluded, I hope, in April in a sort of report that comes from all of us.
So there's this sort of competitive, collaborative thing where everybody basically benefits,
I think, from from from that sort of working relationship. And is that something you'd like to see continue in the future,
and how likely do you think that is that with once the pressure of this unique emergency is lifted, we're not.
I keep thinking that the pressure has gone and that we've got the other content coming along.
And it just from from that point of view,
the questions are still really from the policymakers point of I think the questions are still very urgent, I hope.
I mean, it's a huge amount of I mean, doing research is a huge amount of fun.
It's just a really interesting thing today.
I mean, I've always enjoyed learning new things and asking questions and advance answer the questions that you ask of the interest to you,
but also interest in interviews to other people as well.
And I think having an environment like that, I sometimes joke about the fact of the research questions.
In Oxford, this one research question generates two more, which generates two, and then it gets a of people together.
And then you've got this enormous list of questions that you need to sort of think about what are the priorities and how can we best answer those?
I think the constraints are going to be time and and then also from is it just come back to funding as well
and making sure that whilst one of the nice things that happened about funding stream in the pandemic,
one of the things I mentioned was the fact that Oxford was so quick off the mark at giving people funding and lockdowns basically quickly.
And that puts us in a competitive position to be able to leverage some of the bigger grants that we then went on to reallocate momentum to to win.
But it's just making sure that that is is matched by the sort of the metrics
that people need to sort of keep for that Chris to be meeting always is it?
There's quite a lot of traditional metrics here, like how many pages will be published,
how much money people get in and and how many students to supervise and how many committees do you sort of sit on?
And those are sort of all very fine, good, bona fide metrics of academic progress.
Then maybe we need to think more about, you know, how many data sets to be shared,
how many patients lives have you potentially changed through the use of your research?
Actually, what impacts have been created? And I think these are things which are just starting to have many questions asked by patients.
Have you actually been able to address in a meaningful way how many lives feature?
I think these are sorts of things that in the last few years we've become more thoughtful about the impacts of measuring
impact and and then making sure that there's that translational mechanism in place so that you can go from some research.
Question I was saying earlier on research question to a tool or something which can
be used to be able to actually build that tool and put it into software and systems.
So I think, you know, again, being in Oxford and having Oxford University innovations is really helpful from that, from that.
And so I hope that it'll it'll continue and I hope that it's sort of that we don't revert back to some of the sort of the old ways really,
where it would take over a year to grant and it took over a year to find out of control on that grant or not,
because certainly in the pandemic situation. Yes.
So so I hope that some of these processes can be speeded up so that we can get more knowledge,
new knowledge out there more quickly, so that they can be more beneficial to patients.
So I've just got a couple of quick questions about your your personal experience of the pandemic.
How did you feel personally threatened by the infection that that you might get it?
And was that something you were fearful about or that people close to you might something like that like everybody?
Well, in the beginning, they had known the nature of the infection about how serious it was.
So I took you know, I took the government guidance extremely seriously and based on my family's as well.
So when we were all supposed to be sitting in our houses going up over what today,
that might be exactly what we did and when we first met wearing masks and we were doing exactly, exactly that.
And so, you know, I think that I think that's responsible behaviour.
I think I did manage to get Cambridge over Christmas having studiously avoided it for the two years last Christmas 21 Christmas 21 Yeah.
Christmas 21 the one that's just happened.
And I think luckily for me, I know I was already vaccinated to them it was it was smiles, it was mild illness, but I think so.
So I think like everybody really.
So that that was the concerns about sort of coming into contact with vulnerable people.
You don't want to be the person who might spread an infection or somebody whose
immune system might not be strong enough to be able to cope with it as well.
So so I think, you know, just like everybody really in the in the face of the unknown and as you get older, as the risks naturally increase.
But, you know, I think the good news is, is that we've now got high levels of immunity in the population.
I think there have been difficult calls that the government would need to make.
And I think largely, you know, either by hook or by crook, we've managed to get where we are reasonably well.
I mean, I think that's I'm sure there will be lessons learned from the pandemic, because as we look back,
as you would expect to of this completely unprecedented times that we've had, and how did the pandemic changed the way you you had to work?
I mean, in terms of things like, you know, going to the office or travelling, I mean, academics tend to do a lot of travelling.
Yeah. So it's completely changed. And so we've gone from sort of and sitting on trains and going down to London and well to having Zoom meetings.
My very, very efficient use of time and almost no time in between some of the and I think
that comes with its own time just as well because I think you miss out on,
on some of the the chats that you have walking into meetings or coming out of meetings or the
moments you have to walk through the beautiful environment in Oxford to the next meeting,
etc. So I think everything is speeded up.
And I think that, you know, I think we're all looking forward to getting back to the sort of the more usual ways of working,
although I don't think it necessarily will happen. I think for some people it's been a welcome change and I've certainly in my work as a GP,
I've certainly got patients who have actually been very relieved not to be doing a three hour commute every day and be able to spend time with family.
Which happens to what's been interesting today and I think making sure that people
can work effectively and comfortably because the still pandemic hasn't finished us,
there are still concerns about whether new variants might emerge, whether that might escape the vaccine.
And so I think there's still a need for sort of watchful caution, I guess.
I think. But I didn't ask you about your work.
I think I sort of assumed that you were no longer working as a GP, but you've continued to practice.
Yes. So I have some for various reasons, but I've continued in my bit to make a general practice.
Rather, throughout the pandemic, I've moved to remote searches, not speaking to people, but for geographical reasons as much as anything.
But and I think that the way the health service is delivered is actually changed, not just in primary care but in secondary carers.
But I think a lot of follow up appointments for patients and so from hospital systems are done by telephone.
I think there's pros and cons of that.
I think we need to get the balance.
The balance might change over time as to how that's all delivered and this capacity issues as well, making sure it is enough workforce and so forth.
But so I think there are challenges ahead and there are more teams in Oxford doing work on the value of remote consultations
and how things have changed basically because of because of the pandemic and what the lessons are for some patients.
I think it's it's more efficient for them not to have to come take an afternoon of work and sit in the GP surgery for it to have
a ten minute appointment when when what they needed was a repeat prescription which could actually be switched at some time.
But, but, but you know, there's pros and cons of these things. I think we just need to have some supply and see how things work out over time.
And do you think I mean, I think everybody found the constraints of the pandemic quite difficult or most of them did.
Do you think the fact that you were able to work on something that was actually addressing the problem supported your own wellbeing?
Well, absolutely. I mean, it's it's it's really I absolutely love doing research.
You know, it's my most privilege. I wish everyone is just brilliant.
It's fantastic to know that some of the questions that we've been asking that day and I just love so.
So for me, actually, having such a high concentration of research has been immense, really, really enjoyable.
And it's one of the things I really enjoy. I think so, yeah.
I think it's been that it's been a really valuable experience, really concentrated.
I think. I don't think I had in the first year of the pandemic.
I don't think I had a clear. We call them at home first gear. And I think it's only recently that I had an auto reply on with my contact details.
If people need to instruct me why, I'm trying to take them on relief.
So I think it's it's yes, it's had its challenges, I think, in that way.
I suppose, particularly if you're in a role where people are expecting you to do something which they need you to do quickly.
Uniquely placed to do it.
I think that's been one one of the issues it's meant you just have to keep going really even WhatsApp code that I'm still sitting on a
conference calls trying to help with the next bit of the national policy on things as I could and has the experience is the last question.
Has the experience led you to think that there are things you'd like to see change in the future in the way research is conducted?
I think one of the amazing things has been about the public engagement research.
And when we were looking at the beginning of the vaccine work that we had this commission,
there was a survey of patients there done by the organisation called HDI UK and I think they identified
like 800 distinct research questions that patients had and wanted to know the answers to and those.
And I think again, some of the questions that people have are not necessarily the ones that researchers are going and doing and that change.
So I think the opportunity really is is to be able to tap into this enormous wealth of questions.
For example, if people want to know if they've had coped already, that they have two vaccines.
Is that as good as actually having had three jabs? What about if you had sort of COVID vaccines and then you had to keep it afterwards?
Is that this is your immunity?
Is that stacking up as well as it would have been had when had you had just in the different order and things like that as well?
And these aren't necessarily questions that would be immediately popping in front of people's minds, but which which potentially it could be.
So I think thing I'd like to see changes continue to change is that close of working
relationship with patients where we can see but can benefit from the questions that
are important to patients and answer those questions in a way that benefit them as
well is also of interest to researchers and you sort of get to the policy makers.
That's okay. That's lovely. I think that's covered everything.
But I can't stop recording now. That.
So what kind of nice and unusual things have happened?
So so I have my very first trip to the House of Lords this Monday as part of the old policy parliamentary group on medical research.
And the visits invited the key scientists who contributed to the pandemic response.
And so I went along, having done the work really on the risk stratification and also vaccine safety.
And then I was really pleased to meet up.
There was a whole group of other Oxford researchers from people that helped make make the vaccine design vaccine test the vaccine.
And of course, we were looking at the safety of the vaccine as well as people who had done the
work to look at the the treatments that might be used in the recovery trial.
And so, again, just with various people and going round and basically hearing everybody's story about what did they do sort of in
the pandemic and how does it all all fit together and how they've actually come to be sort of on that that grouping.
And that was a bit of a call to, I think, to the government again within that,
which was to make sure that it recognised that building on the expertise of all scientists
in this country that has accrued over many years and that does require investment,
does require ongoing investment, and can't just be switched off off and switched on again in the way that investment is there.
So there was a sense of what the purpose of that meeting funding was as well.
So people at the MRC, in Cancer Research, UK and the association, medical charities,
etc., to highlight the importance of, of what I think often goes on in the background.
So then the pending foundational research that is going on will always continue to go on, but just to make sure that, that,
that, that is supported and remains sort of scalable so that hopefully we won't be quite the speed and things that we've done,
but we don't know about the future and there are always the possibilities of of similar
things happening again and we just want to be able to leverage that in the future.
Miss Lovelace. So that was a.