Recording even as you do it. Okay.
Could you just start by saying your name and what unit of the university you work in at the moment?
Sure. My name is Sarah Hallett.
I'm an associate professor in Health Informatics and a senior research fellow in Biomedical data Science in the Department of Orthopaedics,
Rheumatology, Musculoskeletal sciences. And within that, the centre of statistics and medicine.
And there's also a unit, I think correct me if I'm wrong, that's got the words planetary health in it.
Is that correct? That is my research group that I lead.
So I'm sort of principal investigator, investigator for the Planetary Health Informatics Group.
So that's my own sort of research body. Mm hmm.
So I'd like to talk a little bit more about that later. But just first of all, very briefly,
can you just give me an account of how you got interested in your subject and what the main milestones in your career have been so far?
Um, I trained in engineering, so I'm an engineer by qualification, and I studied engineering science in Pakistan.
And later on through a good scholarship, which is limited to Oxford,
I had the opportunity to undertake my doctoral studies at the engineering department at Oxford,
specialising in biomedical engineering, because that was an is the area of engineering that most interested me and continues to do so.
And then that's the basis of my training.
And my research has since evolved into the field of health informatics.
If you. Okay. And. And yeah. So I'd like to get back to the title of your research group, Planetary Health, which intrigues me.
I mean, I understand that you work in the general area of health informatics data and digital health.
No, digital health isn't quite the way yet in informatics.
And but planetary health seems to take it wider than simply looking at the health of human beings.
Absolutely. And that's the entire idea. It's.
It's still very much human health.
But taking into account not just our, you know, the health of our bodies, but the impact of our environment and more broadly, the climate.
The impact of the changes in all of that on human health.
And that's what. Planetary health, which is, I have to say, a still nascent, still nascent field within within the big field of health informatics.
So I should do it within the field of health care,
know how to promote planetary health within the world of global health or public health is a relatively recent concept.
It has been expedited through, you know,
an accelerated awareness of all the climatic changes around us and the recognition of the impacts of a warming world on women.
So that's what planetary health is about. Why?
Why we so. Within planet within.
Within health care. It was quite clear to see that if we are to take the field of informatics forward,
which is how can we make use of tools and techniques like statistics and artificial intelligence to explore big data from health care,
whether that is at a population level or a patient level to solve global problems.
Then in order to do that, a lot has been done by explore,
leveraging or harnessing just data that is limited to kind of the biology or the genome of the human body.
Less so. Maybe the idea of taking into account environmental climatic links has been explored.
Why we are now able to start looking into this is because we now have as we know, there's been a data explosion.
We live in the age of big data and data driven analytics made possible because, you know, we are seeing.
Terabytes of data come through daily care and so on.
But also because climate data has become basically freely available,
you can basically leverage satellite imagery data and try and glean some friends from it.
So my group is what our niche is, is trying to kind of do something which is not without its challenges,
which is and we mesh environment and climate information together with what
we have available in the form of data on health and derive insights from it.
By using data describing tools like artificial intelligence and so on to understanding both just generally the health gaps,
but also the impact of climate change is an interesting guess.
Have you got an example you can give? Not counting?
CO But we haven't quite got to that yet. But from from your from your previous work,
have you got an example where you can show how incorporating this climate data has has led to new insights?
So yeah, a couple of studies both from because you know, by by virtue of the way it works, the research is not limited to any kind of geography.
So I can give you an example of a study that we've we are doing on on data from children who move from place to place.
So we have know, for example, that we've been looking at over a million children moving from place to place in Europe, trying to see.
By. By following their movement and their health profiles.
Can we learn retrospectively the impact of, let's say, more green environments versus more brown environments or more urban rural environments?
On on health impacts of children as they grew.
And obviously you can do this as a clinical trial because you can sort of you can't randomly allocate children to good and bad environments,
but with access to routinely collected health information such as those from GP's and with
access to environmental and chronic climatic level indicators such as from satellite imagery,
we are now being able to kind of retrospectively study the movement of children.
Now that's one example from Europe in South Asia, because my background is in Pakistan,
So I have some collaborations there and what we're one of the one of the applications is to look at.
A big problem is pollution.
So South Asia is home to at least two cities like Karachi and Bombay, which are the top three polluted cities.
Air pollution rise in the world is a big problem. And so we're looking at brick kilns and they produce a lot of air pollution, but also heat.
So the air pollution is affecting people's respiratory health in real time and the heat.
Is obviously contributing to global warming as well.
A lot of these industries are not regularised.
They are not officially. They're not in the official sort of radar.
So can we use artificial intelligence to remotely identify such you know,
it is such industries geographically where they are geographically located over vast expanses of rural areas.
And so we've shown how you can basically start to do that with artificial intelligence.
And then that can serve as as an input for policymakers and lawmakers wants to then do monitoring or surveillance or policing of that activity.
And you can you can link that to the health data from from that region as well, Can you?
Yes, exactly. Yes.
So So more on the kind of maybe.
Yes, we can. And then just maybe sort of substantiated with this slightly with another study.
But a related kind of example is that we've started to look at a lot has been done, as I was saying, on health,
in health indicators know there are studies like the global burden of disease and so on which have been tracking for the last 20,
30 years how it is the burden incidence of various diseases around the world at a global level.
And what we're doing is to now map that to environmental indicators.
And this is something that can be done at a global level. We are doing it for South Asian Middle East to try and see, as the health indicators have,
as the burden of disease has gone up over the last 30 years.
Is there a relationship or is it a correlation between that and the kind of the increase in some of the climatic indicators like global land,
surface temperatures, alcohol, dioxide and so on? So that's another study.
And we're beginning to see some interesting correlation, you know, in different parts of South Asia for that.
And that's a that's a really good introduction to the that the scope of your your research and where you get your data and so on.
So I think that's now arrived at the COVID pandemic.
Can can you remember where you were or how you first came to hear that there was an outbreak of respiratory disease.
Yeah, I mean, yeah, yeah, yeah. Very clear like it was yesterday.
It was in fact in this very, very space that I am with today's call only because it's Friday afternoon and I had to go in for something to my home.
I was right here. My mother had just landed in from Pakistan for her annual summer break, I think.
And obviously Boris Johnson was announcing on the TV behind us on the 20th or something of March 2020 that we're all going to have to stay at home.
And the world started to sort of close in.
But that's just, you know, you know, you have these big sporadic memories and that's my first memory of lockdown, lockdown in the UK.
I had, incidentally, just I was just coming back to work after my second maternity leave during that same time as well.
So what that meant was that I, I came back to work after a nine month maternity leave in, in the middle of a lockdown situation, i.e., working.
Having to work from home. And because of the lockdown restrictions and nursery closures, etc.
I had a nine year old primary school child and a nine month old.
A baby at home as well. And so that. That is my second kind of recollection of what lockdown and COVID meant,
but what it meant in terms of work and how it impacted research and how the how,
like so many other researchers around Oxford built around the academic community,
around the world, things had to be pivoted, felt around COVID, not just because it had to be done,
but because we wanted to contribute what we could from our area of work to.
Driving rapid. Evidence around.
So where, for example,
folks in the Jenner Institute were doing all of the important stuff like the vaccine and all what we were able to do because we work with large scale,
routinely collected health data, health information such as from GP's in hospitals and pharmacies and registries and all of that.
We tried and used or gleaned all of that information.
We tried and kind of apply analytical tools that we usually do the same methodology that we work with,
which is in statistics and informatics to do routinely collected data, which is also something that we we work with.
But to answer COVID related questions,
So what is the clinical risk profile of what increases your risk of contracting COVID or being infected with COVID?
And of course, two, three, two and a half years down the lane, our answers to that question are very different than much.
We have a lot greater understanding of it. More than biological and epidemiology levels.
But at that time, you know, we were trying to kind of basically dig out from data, available data what we could about, you know,
for example, just having a history of respiratory illnesses, obesity, cardiovascular disease put you at a high risk of having COVID.
Once you've had COVID, what are you most at high risk of in terms of outcomes?
So who's, you know, at high risk of mortality, hospitalisations, etc., etc.
And in some of this work, even informed policies like the government's sort of shielding policy.
The the e-mails policy on to go ahead with the the the mutual trials on sorry not the EMEA is guidance on European Medicines Agency.
Yes the European Medicines Agency guidance on hydroxychloroquine which was one of the trials on drugs under trial in W.H.O.,
ended up stopping the hydroxychloroquine trial. Based on that,
even though the US President had been saying something completely different to our work once epidemiology questions
using dramatic tools and techniques and big health data is what we were doing during during the pandemic.
You know, can you can you tell me something about the because obviously you were using data from lots and lots and lots of different countries,
which presumably was collected using different criteria. And and there wasn't standardisation from one country to another.
But I understand that you participated in various international collaborations and
consortia which helped to solve that problem so that the data was comparable to Italy.
I mean, this is a world that was led predominantly by Professor Peter Alzheimer,
Professor of Pharmacology, device Epidemiology department, as I am, and yes, in a nutshell,
to consortia like the observational Data Science initiated,
which the global community working in dramatics applied to the all to be ready to go and of course, like everybody else.
This consortia had been working for a long time on an underlying problem, which is which pre-dates COVID, which is how do you make heterogeneous,
patchy health data from different settings and context compatible, interoperable,
harmonised and be able to talk to each other so that they can be data and then be used or compared?
Any any kind of a apples to apples manner.
And we were lucky enough to be able to leverage the sort of the methodology that had been developed around this question and apply it to COVID,
because from COVID, we were able to kind of leverage data from all sort of contributing data partners.
This model operates on a kind of contributory basis.
So it comes within it falls within what you might call Federated Network Analytics.
The kind of the spirit behind that is that.
Data owners anywhere in the world who are interested in contributing their data to generate evidence and do research.
They can basically map their data to be a Sandy data model, which is one model of data sharing,
and that enables them to then participate in network study, to collaborate a lot of questions around it.
And that was the kind of classical or the perfect opportunity for many collaborators around the world with either.
We were either data hoarders or data orders or had access to relevant data to be able to kind of contribute to that.
And produce results that were globally important but also relevant for their specific geographies and regions.
And did you have a particular focus yourself within that context on on what kind
of data or what from what geographical regions you were you were looking for?
If you if you're talking about kind of the work we did. In in the kind of observational data space where this is a pandemic.
And certainly we quite quickly came to the realisation of was that.
A lot of these data that we were that we was possibly in leverage.
In the form of an adverse study or an exclusion.
Gentleman's reports were coming from, let's say, the global north, if you will.
I'm not a big fan of kind of. Is the appropriate terminology.
But a lot of the a lot of the data is coming from this intense period where it's even going to be more accurate from higher income settings.
In higher income settings we get from Northwest or from from places like.
China and South Korea. So to question one of the efforts we did do was to try and leverage by inviting
data partners from some kind of resource limited settings around the world
from where we knew from previous collaboration there was just as good quality data to participate in this kind of international activity around COVID.
And in a sense, as many people would say, that, you know, there were some silver linings to COVID,
and one of that was accelerated research, lots of things that couldn't have happened.
I mean, the vaccine is what's a better example than the vaccine being developed in three months or.
But that is another example with previously there would have been lots of issues and lots of issues and lots of bureaucracy and loopholes.
Look, sorry to get through. It was possible and there was interest in motivation from our collaborators in regions like Brazil and Pakistan.
Specifically to join this goal during the journey of harmonising data to international standards and be able to kind of generate evidence,
do research from that. And so that's been an interesting and maybe interesting success story.
In the sense also that beyond COVID, that sort of collaboration and.
Culture of evidence based research for evidence generation and evidence based decision making has the support of continued.
And I hope that that's something that can great. That's very good news.
And whether other COVID related projects that you particularly led on.
And we think now. So there is a number of studies who went around the simulation space and there was some studies within the university.
And in other words, more and more internationally, we were looking at all sorts of things like risk factors,
treatments, vaccines around the ideas, questions of efficacy and safety.
Effectiveness and on. That's all.
That's. That's what I'm thinking off the top of my head. And Oh, right.
I'm just looking at the time, so I'm just going to switch to your own sort of personal experience of the pandemic.
So you told me your situation you were just back from maternity leave and shop within your four walls at home.
How threatened did you feel by the infection itself, by the possibility of catching the infection yourself?
I think just as everyone else knew, there was so little knowledge at home, especially early on in the pandemic.
More kind of reason for apprehension. When you combine the lesson information we have, the more we tend to panic and so on.
All the panic is what's the word I would use. I think there was a sense of the seriousness and the gravity of the situation.
At the same time, I think I distinctly remember the sense of reassurance as well in knowing that this is we're all in it.
It's not something that is. It's just I mean, I don't have to go through this alone.
We're all in it. You know, it's it's you know, the prime minister has been affected of this country.
People around the world are being affected. It's not something that Kuwait in itself wasn't bringing inequity.
It did, however, I have to say. Highlight existing and underlying inequities in our health care systems and more broadly in our societies.
We saw that through how various countries responded and the border restrictions imposed and so on.
But that's probably a whole other topic for another day.
But on the whole, there was a kind of a mixed mixed sense of obviously some apprehension,
but also some some sort of reassurance in the sense that we weren't going to.
Interesting now that you had previously asked about what is so.
In fact, I forgot to mention one of our ongoing studies is about.
Inequities in health care. And with regards to bias in data and bias in our technology and how that can play
out in a few bias in the health care that people receive and that in our society.
And for that we are in partnership with the data reaching Q2 and looking at for the
first time an in-depth study of health care records of with regards to ethnicity.
From everyone in England and Wales. So that's that's, that's also something really special in how it played out in terms of COVID 19.
And that's also an area that we're looking at as well.
Mm hmm. So I think we've more or less ran out of time.
You mentioned earlier that the the work that you did with low and middle income countries
had had long term benefits in terms of a greater willingness to collaborate and so on.
Are there any other ways in which, as you say, as a sort of silver lining to the pandemic,
your work has been accelerated or changed or made broader as a result of the work that you did during COVID?
I think yes. I think it made us realise as researchers the plasticity and.
The versatility of our work and how we can be adaptable.
To the need of the new global need of the day, if you will.
And. Yeah, I mean, I. That's just kind of a more high level thing.
I don't know if you can think of be more specific recognition or flying effect, if I can think of anything.
Okay. I think that's great. Thank you very much indeed.
It's a pleasure to talk to you. And I will turn to recording.