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So could you start just by saying your name and your current position and affiliation?

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Sure. I'm Jennifer Beam Dowd and I'm a professor of demography and population health and

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the Department of Sociology and the Leverhulme Centre for Demographic Science.

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That's great. Thank you. And without telling me your entire life history,

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you just go over how you first got interested in this area of research and what

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your main kind of staging post have been on the way to your current position.

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Yeah, sure. I guess prior to COVID, I was doing a lot of research on Let's go back.

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I'm going way, way, way, way back. Okay. Yeah, I would.

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I yeah. I was just, I guess going to signpost the topic.

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A lot of work on the social determinants of health and that I guess for me started in graduate school.

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I was actually studying economics and public policy and really interested in improving people's lives, you know, through the study of economics.

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And I kind of shifted a bit to health as an outcome, thinking about how inequalities in health are kind of the ultimate injustice.

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When, when I learned that, you know,

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people with less education and income actually have lower life expectancy and worse health throughout their lives.

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It kind of a light bulb went off that this you know, this is the ultimate form of inequality.

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And I decided I wanted to devote my research to that.

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So that was graduate school at Princeton University.

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So I studied demography and economics and then drifted a little bit more into to health.

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After that, I did a postdoctoral research fellowship at the University of Michigan,

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and that's where I linked up with people from a lot of different disciplines.

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So I worked closely with Alison Aiello, who's an infectious disease epidemiologist,

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and she was kind of interested in also social determinants of health, but trying to understand the biology underneath it.

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So, you know, what exactly is getting under the skin when people are getting sick because of their social class?

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And so we kind of really bonded on that. We both have that passion to understand the mechanisms.

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And so we actually studied the immune system and different infections and how

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they were related to the social environment and especially chronic stress.

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And so that's what got me kind of knee deep and learning a lot more biology than I had been trained to do as a social scientist.

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But I really, really value that interdisciplinary collaborations and I've tried to replicate that ever since.

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So I don't like kind of being pigeonholed, I guess, in one single discipline.

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And so that was a really great program. It was actually called the Robert Wood Johnson Health and Society Scholars,

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and it was meant to build these collaborations to solve, you know, big health problems across.

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You know, we're bringing people in from lots of different disciplines. So that was a really valuable training for me.

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And then I became an assistant professor at the City University of New York.

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In both epidemiology and demography, they had a school of Public Health and Demography Centre and yes,

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I guess toiled away on also trying to to unpack some of these biological mechanisms responsible for health inequalities.

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And so, yeah, that eventually, I guess brought me over to England.

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I worked at King's College London for a while and their Department of Global Health and Social Medicine and

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then came to Oxford in November of 2019 with the founding of the Leverhulme Centre for Demographic Science.

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And the rest is history because obviously, yeah, the world changed a lot within just a few months there.

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So demography, I mean here it sits within the social sciences in the way the university divides itself up, sits within social science.

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Yeah, but from what you've been saying, it sounds as though it draws on data from a much wider field than just the social sciences.

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It definitely does, and I think our centre centres at Oxford are typically embedded in a department,

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but we've been very consciously interdisciplinary from the beginning and have a lot of collaborators in genetics and the Big Data Institute.

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And so we absolutely kind of want to blur those lines as much as possible,

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and it really is kind of a toolbox that I think that you can apply to lots of different problems, demography.

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So no, it doesn't belong to sociology or any one discipline for sure.

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Perhaps we should define demography at this. That is always a very challenging question when I'm teaching demography.

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Ah. I was just. Just talking about this, actually.

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I'd say the most traditional demography is, you know, thinking about population dynamics and population change and you know,

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historically that very much kind of men, population size, age distribution.

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And so, you know, the three things that really affect that are, you know, births.

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So demographers love to study fertility deaths, you know, people exiting the population.

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So we study a lot about mortality and migration, people moving across these borders.

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So those are kind of the three the three big things that demography was built on.

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But it really became a way to think about all of these life transitions that affect those things.

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So obviously, you know, partnering and getting married are very important for understanding and predicting fertility.

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So there's a lot of family demography that understands family formation and change.

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And similarly, you know, mortality trends are really about health.

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That's happening way before people die.

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So, you know, it's really a much larger body of research about what determines the pace of ageing in humans and what can we do to change that.

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And with migration also, you know, what motivates people to to migrate or it's about so it's not just about measuring the outcomes,

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but understanding that the human decisions that go into contributing to those, those behaviours.

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So it really kind of has builds I think up and up and up to a,

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a very wide umbrella as a quantitative social science where you can think about and answer a lot of questions.

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But I guess the key thing is it always tries to tie those individual level decisions then to how that affects patterns,

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you know, at the population level. So what's going on with fertility or mortality trends?

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And and so that's where it's a really nice toolkit and I think was especially helpful during the COVID pandemic with all the data coming out.

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Well, before we get to the COVID pandemic, what were the specific areas of health that you were focusing on in your research?

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Yeah, so as I said, I was actually very interested in how chronic stress and immune function might actually

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explain why people with lower socioeconomic status seem to have accelerated ageing.

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So, you know, it depends on what populations you look at,

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but life expectancy can differ by about ten years across from the top to the bottom of social groups,

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even in relatively high income countries like the U.S. or the UK.

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And that's kind of a staggering, you know, gap.

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And taking a look at things like health behaviours like smoking or obesity, it seems like maybe that would explain some of that gap.

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But it those things actually don't explain as much as we might expect.

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And so there there was a search for kind of other explanations.

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And there's this idea that, you know, just being in a lower social status comes with a lot of chronic wear and tear.

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You know, it's even just daily hassles of your car breaking down.

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But obviously there's a lot more stress for things.

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You know, we're being worried about, you know, losing your job and not having a safety net or savings.

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You can think of all sorts of stressors that that do just kind of accumulate over time and might, you know, literally weather people down.

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And so we were using data that incorporates biological measures and social data to kind of track,

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you know, how people's immune systems were reacting to the social environment.

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So measuring things like inflammation, which is kind of a marker of ageing these days, that's become quite popular.

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And also chronic infections were something that we studied.

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So there's this whole class of of herpes viruses which you probably have heard of and but there's, you know, something like that called cold sores.

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Yeah. So yeah, people, I think immediately think of herpes simplex one and two, which calls cause cold sores.

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But there's actually varicella.

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What causes chickenpox and shingles is also in this class of viruses.

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The one we study a lot is called cytomegalovirus.

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And it's not that well known because most people have it.

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Over 90% of the population would test positive for antibodies, but.

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The the all of these herpes viruses, the key features that they kind of stay with you for life.

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You don't ever fully get rid of them. They they establish latency in their hosts.

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And that's why you can get shingles and reactivation of cold sores and things.

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So they're unusual in that way, but it means you can kind of measure antibody response to them as an indirect

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marker of how well your immune system is keeping everything under control.

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And so we were able to to use that in some national surveys and show big differences

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by level of education and income in people's ability to control these infections,

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also associated with inflammation and, you know, actually premature mortality.

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We were able to show that in some population surveys.

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And so that that was, you know, stuff we did prior to the pandemic.

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But it became it became kind of very important to think about once COVID was was out into the world,

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we did start seeing very socially patterned kind of susceptibility to severe disease.

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And, you know, some of what we immediately thought of based on our work was that there was

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already a lot of underlying vulnerability in certain groups because their yeah.

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Their immunity is just has been worn down a bit over their, their lifetimes.

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And so I think that did contribute to the differential burden that we saw once once the pandemic arrived.

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So before we get to that, how do you collect data?

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You don't yourselves run labs, presumably? Yeah, no.

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Demographers, I guess, are known for repurposing data collected for other sort, you know, other purposes.

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And going back to kind of church records were how we measured births and deaths, you know, hundreds of years ago and building on from that.

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And so there's a lot of administrative data that we use, just simply the vital records that are now collected,

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but also a lot of what we call population surveys that might be run by different governments or just a lot of research centres have ongoing studies.

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The British cohort studies are a great example.

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They're currently run out of UCL and funded by the Government, but are the funding agencies of the Government that they've been following people?

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The first one was the 1946 birth cohort and it was maybe everyone born in one week in England in 1946,

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and those have continued from 1958 to 1970 and they continue to add them.

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So that sort of data is what we typically analyse.

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And so some demographers are involved in those studies and collecting them and adding new questions and new, you know, health.

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There's a lot of health data and a lot of these surveys, including biological measures,

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where we collect blood or do MRI, you know, brain scans to see what's going on with dementia and cognitive decline.

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So there's a lot of rich data that's already out there, I guess.

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So it's more unusual for an individual investigator to kind of go collect their own instead of just

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collaborating with some of these big existing surveys that are happening with UK Biobank just out of interest.

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I don't other members of the centre here to do work with that because they they've you know,

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do a combination of social science and genetics and yeah, it's an amazing resource for that.

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But no, it's not, that's not part of my work. So I think we finally arrived.

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But do you remember just from a personal point of view,

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where you were or when you first heard that there was something going on in China that looked like these areas?

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That's a good question. I assume I was following the news by early January of 2020, you know, but I think I'm a very optimistic person.

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So, you know, you often saw some of that news pop up and things would kind of fizzle out.

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So, you know, I think the very first time maybe you saw a new virus is being reported, you're like, okay, we'll see.

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We'll see where that goes. Kind of like the monkeypox right now. So that's a bit alarming, but we won't get overly excited yet.

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So, you know, I think I didn't immediately have all those alarm bells going off, but pretty quickly throughout, yeah,

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probably by the end of January, we kind of looked around and thought we should figure out a way to get some of this data.

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And we did have some graduate students from from China who were saying, you know,

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we said this is where they're posting some of the daily numbers and we should try to.

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So we set something up to kind of scrape the data off the websites,

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just that we just had a sense this would be important and valuable and we should capture the data while we can.

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And of course, other people, you know, created these dashboards later on and ended up doing that.

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But that's the first thing I remember doing, doing as an organised group here.

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And by February you know with, with things taking off in Italy, I guess that was more towards the end of February.

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But we had several postdocs from Italy, some of whom had kind of gone back.

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I think when they I don't know why, I can't remember why they decided going back would be a good idea.

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But they and one of whom was actually in the Lombardy region, where it was, it was quite serious.

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So so we started getting kind of reports back and decided as a group that we

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had to mobilise and try to understand what was going on with mortality data.

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We'll just get together in a room and say, Yeah, no, we did it exactly.

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We, we, we planned a meeting. And at that point the centre had just launched in November 2019.

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So we were a pretty small group of faculty and a few postdocs.

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And so we just said yeah we so we organised a meeting in our small conference

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room and said anyone who's just kind of interested in brainstorming come.

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And it was, yeah, it was indeed a really for us a historic meeting because we,

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we were looking at the death data from Italy and we noticed a very strong I mean, it's it's so I guess obvious now with COVID.

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But at the time, all of this was new raw data coming out.

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So it was very strongly associated with age.

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So like all of the deaths were, most of the deaths were happening above age 65 in Italy at that time.

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If you looked at the data coming out and demographers, you know, like I said, love to study population composition.

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And one of the huge issues of the past few decades in rich countries has been population ageing and

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the fact that a higher and higher percentage of the population is above a certain age like 65.

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And this has been kind of a population transformation for the last, like I said, several decades.

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And as a function of people living longer is one thing, but also with huge drops in fertility compared to what we know.

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People used to have lots of kids in rich countries are having many fewer children.

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So that just means there's much larger cohorts that are ageing and not as large of of young cohorts underneath to kind of support them.

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So this has economic concerns. Yeah, it has important economic consequences, but it also, you know, just kind of rang a bell for us that,

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you know, Italy is actually one of the oldest populations in the world.

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It was second only to Japan in the proportion of the population above age 65, which was I think it was about 23% when we were looking in 2020.

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And so yeah, we wrote a, you know, we felt like a pretty simple paper just outlining how important to the age

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composition of populations might be for the ultimate burden of COVID mortality,

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just based on that simple, very steep association between age and COVID deaths.

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And we did some different scenarios.

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And because this was, you know, the very beginning that hadn't Italy was the first place really outside of Wuhan that was seeing a lot of deaths.

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So it was kind of all speculation of. What would happen if this spread around the world.

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And so we did simulations of countries with different age structures and how that would translate into the total mortality burden,

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you know, making some estimates of what percentage of the population might get infected.

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And so it was a very kind of, you know, simple scenario based exercise,

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but it really dramatically kind of showed how even with the same infection rate,

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countries that are older are going to just see a lot a lot more mortality.

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And so that I also remember that because we finished that up literally on March 13th,

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we were all still in the office and we're like, okay, we've got the draft done.

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And we made some nice data visualisations. And I was in charge of kind of finishing that up over the weekend.

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We got it in the form of a real draft and I think we submitted it to the Journal then too.

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But we're like, okay, we should tweet this, you know, we should put it out, out into the world.

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And some of us were on Twitter, but we weren't like real Twitter professionals or aficionados at that point.

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So I went on Twitter Sunday morning, March 15th, and did a thread of our paper just kind of describing describing the results.

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And we also actually had some data in there from our colleague in Italy showing the difference in the the outbreak curves for Lombardy versus Bergamo,

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I think it was so one region had actually locked down several days earlier.

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And we kind of created this figure that looked like the flatten the curve, the famous flatten the curve thing from the 1918 pandemic.

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And it was just really striking because it really looked like if you cut it off early, it did succeed in flattening that curve.

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So that was part of this paper and went out on Twitter.

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And I just I think I might have done it on my phone. I don't remember.

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I just remember sitting in an in a recliner on a Sunday morning drinking coffee, trying to get this out into the world.

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And we sent it off.

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And then the rest of the day I went on a walk or whatever, you know, and my phone was blowing up with notifications.

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And, you know, for whatever reason, that especially the flatten the curve data was really compelling to people.

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And it it kind of went viral on Twitter and got, I guess, over a million views, not not retweets.

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But if you look at the reach, it went pretty far that day and all of a sudden, yeah,

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the media started calling and we kind of realised the government calling it that.

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So yeah. And I'll just say then of course everyone kind of remembers that weekend because that was, I mean that's when we,

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we left the office and never came back and I don't know if we realised that was going to happen at the time.

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I think I think the U.K. lockdown actually happened. Yeah.

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A little bit later than than some other people. But, you know, it's such a whirlwind.

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We I'm trying to remember we very quickly did also do a follow up analysis of how it would affect the U.K. domestically based on because,

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you know, there's actually big differences in age and composition even within countries.

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And, you know, urban areas like London are actually quite young compared to some areas.

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So we did a paper mapping hospital capacity because also at the time you probably remember it was all about how many beds and ventilators are there.

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So we found some measures of that and combined it with the age structure of different local areas to say,

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here's areas that look prone to having more strain just based on the demographics.

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And yes, we do get a lot of inquiries from local area governments trying to help them kind of anticipate and understand their risk.

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So we were doing a lot of consultation there and internationally we were yeah,

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just doing a lot of not only media but the World Bank and other organisations were in touch

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because at that time it was kind of trying to anticipate where things would be particularly bad.

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And I guess I should have said the other element we focussed on in that first paper was that Italy had a high level of intergenerational co resonance,

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that it wasn't, you know,

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just the old age structure that they had a lot of old people is that those old people were in daily contact with a lot of younger people.

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And anecdotally our colleagues were saying, you know, it probably came in to Milan as an international hub,

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which I think I think we do know that's kind of how the virus first got there.

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And a lot of young professionals in Milan still commute home to the village.

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Ages and have all these meals and live with their older family members.

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So we we just talked about that in that paper as a potential, you know, something that can accelerate transmission to the vulnerable elderly groups.

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And I think that also struck a chord, especially for countries where that, you know, living situation is quite common.

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And it did make a lot of Italian news.

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There was one headline that said, you know, wired, wired deaths so bad in Italy, blame the families, says Oxford University.

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I know obviously I was in Italian and I saw I was like, does that say blame the families?

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Says Oxford University? And that wasn't really our message.

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We were just trying to highlight yeah, highlight things that that we should everyone should be looking out for to try to anticipate vulnerabilities.

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And I think I think that did play out a bit also when, you know,

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it wasn't too long before the outbreak kind of hit the U.S. and New York City was hit extremely hard in that first wave.

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And it wasn't a lot of immigrant populations that also, you know,

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where the the young people were in the service sector or something, having a lot of contact.

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And then households where lots of generations lived together were particularly vulnerable.

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So I think that that was another important demographic factor that did indeed end up being important.

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And obviously we all had to figure out ways to adjust our lives to kind of protect those most vulnerable groups.

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But that was something that we tried to raise the alarm about early on.

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And was that data feeding into government policy in the UK at that stage?

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At the local level it definitely was. I mean our our whole centre had a lot of different people who got pulled into things.

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So the director Melinda Mills, ended up being pulled on to Sage Committees and doing a lot of reports.

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And, and so I guess the one that I do know kind of contributed to some policy in April or May.

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We also then I think quickly after this paper got a lot of attention and seemed and

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there was also just so much raw data coming out and people trying to make sense of it.

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And that's actually I think demographers are kind of good at that and making connections

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between the individual level data and what's going on at the population level.

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So we continued to think about ways in which we could contribute.

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And of course, we all thought that lockdown would be a very time limited thing, which I mean, I guess it was the first one.

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But, you know, we didn't know another one was in the works later, but we so we were like, what can we do to minimise spread,

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you know, once lockdown restrictions are eased, you know, what would be strategic ways to continue to protect people?

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So so the second group paper that we worked on that ended up being quite important was a paper on we call it the bubble paper, the pod paper.

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But it did kind of the science and the social network simulation of why, you know,

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strategies like having a pod where you limit your social contacts to a few key people kind of limits the spread.

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And it's more complicated than that, but the paper is really nice with some data visualisations.

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But the core idea is kind of when you limit those, those bridges, you know that if I, you know,

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if I have a lot of social contact with one group, but then I'm also going to an entirely new group once a week to hang out with them.

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Then you have this possibility for for bringing the virus between two groups.

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Whereas if you can really kind of keep those bubbles or pods more isolated, it can have a really big effect on limiting the spread.

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So do you think you coined the term bubble for that too? Like I that did become a term.

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Yeah, it did become. Yeah, I'm not sure.

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I'm not sure we can claim I think I don't think we completely invented the idea of that and I think, I think the word had existed.

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But, but we like kind of laid out the science of it.

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And, and it was still quite early. This was like April.

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So it did suddenly that also got taken up very quickly and it was, you know,

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quoted in or it was in the parliamentary evidence right before the support bubble, I guess policy got got released.

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So that was so we did feel like that was having an immediate impact and then we got lots of contacts later on because this was also.

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Then we had this summer, which was a bit of a lull,

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but then schools were reopening in the autumn and there were just a lot of people reaching out about the best way to manage that.

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So the idea of kind of cohorting cohorting and keeping kids in their bubbles, we definitely, you know, we're helping to consult with that.

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Different universities in the U.S. are also contacting me about that.

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So, yeah, we can't take credit for the entire idea of kind of outlining the science behind it and some of the optimal strategies,

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I think in that paper did have a big impact.

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And again, it just it felt like we really urgently wanted to figure out things that people needed to know.

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It just felt very different from normal research because it was a lot of our research doesn't have immediate applications.

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And so this the urgency of this was was quite new.

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I mean, I know that that happened to people in a lot of different fields, but I don't know whose Melinda was leading on this.

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But you did some work on masks as well. On face masks that yeah.

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Yeah, I wasn't directly involved in that, but that I think that was part of her contributions to Sage and other of that.

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I think she was doing some things for the British Academy as well.

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And the world. Yeah, yeah. Okay. I guess it was the Royal Society.

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That's right. And right.

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That was just another really hot button issue that summer of 2020 was thinking about face masks and that.

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So yeah, I've, I was doing other science communication work which we can maybe talk about definitely needs to at least talk about that.

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But no, I think that that review, that issue kind of did a big meta analysis to review on the evidence for for face masks.

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And it really was time when that came out.

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It was not long before the government did finally come out and recommend them in public spaces.

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So yeah, it was just a very it's very strange for social scientists to see their work just have seemingly have that impact within a matter of days.

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But it was a very strange time for sure.

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No, you said that it was very different from research you've done for up on those grounds.

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Something else I'm interested in is. The notion of working as a team, which I mean, clearly you are a centre, you have a collaborative ethos,

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but did you find that working on these coded projects was an even more extreme example of that?

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Absolutely. No, it was. I mean, in retrospect, that part was.

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Almost magical because we were a new and new group that had recently come together.

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But that, you know, starting with that first paper and collaboration and then being thrown into just the urgency of contributing,

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somehow, I just think everyone really wanted, you know, it was, yeah, kind of like a war effort.

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I think people everywhere wanted to figure out how they could contribute and suddenly the nerds could contribute by,

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you know, understanding just the absolute flood of data that was coming out.

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So we were then in constant contact, even though we we all left the office on March 13th and never came back, we set up the Slack channel right away.

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We're having zooms just all the time. Um, it, I guess I felt,

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I felt like the team came together and it felt like we've been working together forever and incredibly bonded in a short amount of time to try to,

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you know, to try to answer these really important questions.

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And everyone just kind of pitched in at that point. It was I think it was a great outlet for people, too.

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I mean, it's hard to remember now, but everyone was really disconcerted about what's happening in the world and it was,

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you know, quite stressful to have your kids home homeschooling.

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There were so many stressors in people's lives, but just really worried about people's health.

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And and so, yeah, having a productive outlet where you felt like you could contribute, I think was was really important to all of us.

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And we absolutely couldn't have pulled it off without the team because individually it just it would have taken one of us a long time to,

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to collect the right data to figure out the best way to to pull it all together.

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And we could just do it in such a short amount of time working together.

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And it was that part just seemed effortless. It was just like everyone coalesced around this common, common need and and made stuff happen.

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So did that extend beyond these walls into the rest of the rest of the UK and the rest of the world?

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You know, yeah. I mean, we did a lot of work together.

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I, you know, I think a lot of our research was just with this group.

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I'm trying to think, yeah, most of it came out of the centre, you know, which then continued to grow.

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So we, um, I guess, you know, some, some things kept happening during the pandemic as far as hiring.

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And within a few months we also had some new people and wrote some important papers on the impact of COVID,

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on life expectancy in the UK and measuring excess mortality and things.

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And those papers also ended up being, I mean, not as immediately policy relevant because they're,

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they're more, you know, taking stock of what has been the toll so far of the pandemic.

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But what were the main messages of those papers?

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The main messages, the let's say I'm trying to think the first one was looking at excess mortality in in the UK.

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And first of all, I think that concept was I find it interesting, it's a very demographic concept,

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excess mortality that suddenly became like it's just a really useful tool I think during the pandemic for explaining to people what was going on.

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So, you know, probably I don't know if this was as much of an issue here in the UK,

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but I also followed a lot of the US media and social media and there was a concern that we were over counting COVID deaths.

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You know, a lot of people are convinced about this. They would have died in yeah, they would have died anyway.

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They were dying with COVID, not of COVID.

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And then even in the US there was kind of more cynical interpretations of, Oh, they want, you know, to count these deaths.

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So even if you come in with a motorcycle accident but you test positive, they're they're counting you as COVID deaths.

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And and this was like a pervasive myth. You know, I think it still is a somewhat pervasive piece of misinformation.

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And, you know, that it's a hard to, you know, hard to completely counter that, like doctors were coming out saying,

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you know, it would be highly unethical of us to mess with death certificates or,

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you know, so like that's not happening because there was also in the US, of course,

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this idea that there were financial incentives for doctors to put this down.

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So there were, you know, lots of conspiracy theories about that.

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But excess mortality, which demographers often use in natural disasters or things to try to we're we're not counting you know,

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we don't know if people. Died, you know, specifically an earthquake or some major or if there's a heat wave,

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there are often things that happen where we see elevated deaths and wars or wars.

337
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Yeah, exactly. But the benefit is you're comparing the deaths to some kind of baseline of how many deaths would have been expected.

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So it's often, you know, the last five the average of the last five years, say, is the most kind of basic example of what the counterfactual would be.

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And so it really is asking that counterfactual question.

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If COVID hadn't happened, you know, how many deaths would have been expected.

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And we'll look at how many deaths. But I guess the key feature is it's deaths from any cause.

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So we're not just counting COVID deaths. We're saying we actually do a pretty good job of measuring all deaths because in developed countries,

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you need a death certificate for, you know, all sorts of administrative reasons so we can get a really good count of total deaths.

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And then that tells us how many more deaths have happened above and beyond what we would have expected.

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And so that was I guess that was our first paper, but it was also something that I used to communicate a lot to general audiences about why,

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you know, COVID actually was causing a serious a serious burden of mortality.

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And we were actually undercounting COVID deaths for the most part, especially in the beginning when testing was not as widely available.

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So so we showed we showed that the high levels of excess mortality in the first half of 2020, we kind of looked at what ages were most hit.

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You know, not surprisingly, it was older ages, but on a relative basis, you know, compared to their normal mortality,

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the percentage increases in deaths were actually kind of high for even, you know, sort of 40 year olds.

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So it wasn't leaving people, you know, completely unscathed.

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And you could also see a much higher burden for for men compared to women.

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And that is something that has remained consistent as well.

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And then, you know, after 2020, you know, passed,

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we kind of took stock of the entire year for life expectancy and also looked across 29 other high income countries,

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mostly because they had the complete data that is, you know, by age and sex and a very fine,

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fine grained way that's necessary to calculate life expectancy.

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And there were just really dramatic losses. I think in the UK it was about one year loss in life expectancy.

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The U.S. was a real outlier with about a two year loss in life expectancy.

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And we were able to reach across the whole population. Across the whole population, yeah.

361
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Yeah. And so, you know, life expectancy we don't have to dig into, but it really is, you know,

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people think they know what it means, but it's really a snapshot of current mortality.

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It's it's not a prediction of an individual's life span.

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So it's kind of a tricky concept to communicate to the public.

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But we did try to put the context like a shock of that nature had not been seen since World War two, basically to mortality.

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So, you know, this this was a really sizeable hit to mortality and we've since updated that for 2021.

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And a lot of countries have bounced back.

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You know, 2021 was a bit of a mixed bag because we had the vaccine rollout.

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But, you know, January before that really happened was still quite, quite deadly of 2021.

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But we've had new variants that brought a lot more transmission.

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And so we saw a much wider variety of experiences across the high income countries in 21, with some getting even worse.

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Eastern Europe was hit much harder and in 2021 the U.S. did just as bad badly in 2021.

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And you could really see kind of the divergence in countries that probably had better

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vaccine uptake and more efficient rollouts and compared to those that didn't.

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So, yeah, I'd say the the experiences globally are starting to diverge,

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which I think makes sense with a lot of differences in the responses and vaccine uptake has been kind of fascinating also from my point of view,

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from thinking about the decisions made around vaccine policy.

378
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So going back to your original interest in socioeconomic determinants of health and mortality, yeah.

379
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How fine grained were you able to be in looking at who who was dying?

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Who is dying? Yeah, you know that I think a lot of that work remains to be done because partly because of the way the UK collects their data.

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In the US level of education and race, ethnicity or actually on death certificates.

382
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So kind of very quickly that sort of those estimates could be, you know, churned out, whereas in the UK it's that's not the case.

383
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There is a spot for occupation on death certificates, but it's missing out on a large percentage of them, so it's a little less useful.

384
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So you were kind of relying on data from from the OAS, the Office of National Statistics,

385
00:42:07,190 --> 00:42:13,000
to link to previous census data or do some other type of data linkages.

386
00:42:13,010 --> 00:42:17,899
So so they started putting out reports that that looked at some of this.

387
00:42:17,900 --> 00:42:23,450
And not surprisingly, there were really big race ethnic differences in the UK.

388
00:42:23,450 --> 00:42:29,060
And some of that of course was, you know, London getting hit so hard in the first wave and that being,

389
00:42:30,350 --> 00:42:34,310
you know, where a lot of ethnic minorities are living.

390
00:42:34,340 --> 00:42:43,129
So part of that is just kind of the different pattern of the pandemic itself that it started in those big internationally connected cities.

391
00:42:43,130 --> 00:42:47,000
And then it took longer to get to the more rural areas.

392
00:42:48,290 --> 00:42:53,719
But occupations seem to be quite important and, you know, not surprisingly at all,

393
00:42:53,720 --> 00:43:02,870
but your ability to socially distance and work from home is tremendously different depending on your socioeconomic status.

394
00:43:03,700 --> 00:43:12,139
And so that yes, that came out very clearly in the data that certain occupations were much more dangerous than others,

395
00:43:12,140 --> 00:43:18,830
especially in those early waves. And it's presumably mostly public facing roles and mostly public facing roles.

396
00:43:18,830 --> 00:43:24,950
Yeah, there was a long list, but, you know, things like bus drivers, but food service processors.

397
00:43:24,950 --> 00:43:26,269
Yes, meat processors.

398
00:43:26,270 --> 00:43:34,130
Because that environment also apparently was very conducive, very human was like and close contact was very conducive to transmission.

399
00:43:35,180 --> 00:43:41,959
So I think that it's been interesting that in real time you could kind of see the social determinants of health.

400
00:43:41,960 --> 00:43:49,010
It was like a new health threat. And, you know, there's all these different mechanisms that might contribute to that ultimate inequality,

401
00:43:49,670 --> 00:43:56,479
you know, so with race, ethnicity, even, and you know, that was also a function of living arrangements as well.

402
00:43:56,480 --> 00:44:05,930
So there there were a lot of intergenerational households and probably just more crowded in urban areas and households.

403
00:44:05,930 --> 00:44:11,989
So all of those things seemed to to fit in, especially in those first couple of waves.

404
00:44:11,990 --> 00:44:19,729
It's interesting. The different waves have had somewhat different patterning, I think depending on yeah.

405
00:44:19,730 --> 00:44:24,379
Geographically where the what was going on or you know, what the initial, you know,

406
00:44:24,380 --> 00:44:29,209
sometimes it was kids going back to school that seemed to to start these surges.

407
00:44:29,210 --> 00:44:33,980
And so so there's some interesting patterns over time as well.

408
00:44:33,980 --> 00:44:35,750
But yes, the bottom line, I think,

409
00:44:35,750 --> 00:44:45,470
and certainly in the UK and the US was a really strong social patterning ultimately of of the severe disease and mortality.

410
00:44:46,250 --> 00:44:54,860
And so a lot of that was occupational exposure, but probably a lot of it was also, you know, kind of more serious underlying conditions.

411
00:44:54,860 --> 00:44:58,249
So that's something we'll continue to explore,

412
00:44:58,250 --> 00:45:08,000
to try to understand the differences in the U.S. There's a lot more obesity and diabetes than other countries,

413
00:45:09,110 --> 00:45:15,229
and especially even in those younger Middle Ages, you know, like 50 to 64,

414
00:45:15,230 --> 00:45:22,100
there were actually a lot more deaths in the US in that age group than there have been in the rest of Europe and in the UK.

415
00:45:22,100 --> 00:45:28,190
So trying to understand those cross-country differences is also something we're very interested in.

416
00:45:28,490 --> 00:45:35,300
There's going to be a lot of research on methods, a lot of mortality research around COVID for a very long time.

417
00:45:35,930 --> 00:45:44,000
Yes, I do. I mean, how optimistic are you? Are you optimistic at all that that understanding, which in some ways is not news?

418
00:45:44,390 --> 00:45:47,450
We had the Black Report in 1982. We've had Michael Marmot study.

419
00:45:47,450 --> 00:45:51,860
Oh, you know, yeah. The economic differences make a difference to health outcomes.

420
00:45:53,930 --> 00:45:57,770
Are we any closer, do you think, to getting policy changes that will address that?

421
00:45:57,920 --> 00:46:06,710
Oh, that's yeah, that's a great point because England has been at the forefront of highlighting these these social determinants of health.

422
00:46:08,810 --> 00:46:14,719
You know, I work kind of knee deep in this, and so I've been thinking about it a lot.

423
00:46:14,720 --> 00:46:24,110
And I guess I remain pessimistic in the sense that it's very hard to break all of these mechanisms that tie your social class to health.

424
00:46:24,110 --> 00:46:33,950
It's yeah, there is a famous paper by Bruce Link and Joe Fallon called The, you know, social factors is the fundamental cause of disease.

425
00:46:34,050 --> 00:46:39,120
That, you know, the situation can change, the mechanisms can change.

426
00:46:39,120 --> 00:46:46,200
But there's always, you know, because people with more resources can or just social capital education.

427
00:46:46,650 --> 00:46:53,250
You know, when a new risk emerges like COVID, they're able to protect themselves better through a variety of ways.

428
00:46:53,250 --> 00:46:57,000
So. So I agree. It's it's not surprising that we saw that.

429
00:46:58,170 --> 00:47:04,229
And so I guess I'm pessimistic that we can ever eliminate these relative inequalities.

430
00:47:04,230 --> 00:47:10,950
But I always remain hopeful that we can still improve everyone's outcomes in health by,

431
00:47:12,060 --> 00:47:16,680
you know, still giving them better resources to to protect themselves.

432
00:47:17,070 --> 00:47:26,610
And certainly we've seen improvements in educational attainment that do typically lead to better health over over a lifetime.

433
00:47:27,720 --> 00:47:34,830
But I do think, you know, social safety nets and basic kind of income supports have also been shown to make a big difference,

434
00:47:35,310 --> 00:47:40,550
especially in kids lives and ultimately their their health in later life.

435
00:47:40,560 --> 00:47:47,940
So, yeah, I'm up I'm optimistic, I guess, about helping people in absolute terms.

436
00:47:48,300 --> 00:47:55,770
I guess eliminating inequalities is is a nice a nice goal for us to always keep in mind.

437
00:47:55,770 --> 00:48:01,950
But that's probably. Yeah, there's inequalities even in, you know, one of the things we study are these primate,

438
00:48:03,210 --> 00:48:08,440
you know, primate groups that still have a social rank within themselves and monkeys with monkeys.

439
00:48:08,460 --> 00:48:09,720
Yeah. Sorry. There's.

440
00:48:09,720 --> 00:48:18,570
Yeah, there's studies of baboons and rhesus monkeys that show very similar social gradients in health, you know, within these groups that don't have,

441
00:48:18,570 --> 00:48:26,940
you know, diplomas or degrees or money, you know, so, you know, inequality is is very hard to completely eliminate.

442
00:48:26,940 --> 00:48:34,739
But at the same time, we've greatly improved people's health status overall, you know, across this century.

443
00:48:34,740 --> 00:48:38,459
And I think about those successes, you know, it's not just vaccines.

444
00:48:38,460 --> 00:48:44,850
It's been sanitation and huge public health investments like that, nutrition.

445
00:48:45,270 --> 00:48:52,589
And then, you know, more recently, we've made just huge improvements in cardiovascular disease and less smoking and statins.

446
00:48:52,590 --> 00:48:58,709
And, you know, so these are things that have really moved the needle on health for everyone.

447
00:48:58,710 --> 00:49:03,540
And then but I do think we need to always look at the ways in which that that can

448
00:49:03,540 --> 00:49:08,820
sometimes exacerbate inequalities and what we can do to minimise those the best we can.

449
00:49:09,300 --> 00:49:15,480
So you talked about helping people to protect their own health and presumably that communication work you talked about doing with.

450
00:49:15,490 --> 00:49:20,490
Yeah. Is part of that. So so tell me a little bit about what you've been able to do to.

451
00:49:20,760 --> 00:49:25,110
Yeah. Medicating with it with the general public. Okay. Yeah, I would love to talk about that.

452
00:49:25,110 --> 00:49:28,140
And that was, you know, honestly, completely parallel to the,

453
00:49:28,680 --> 00:49:34,020
the research we're doing at the centre, but basically the same I think it was the same day,

454
00:49:34,020 --> 00:49:39,299
March 15th, some colleagues in the US who I'd worked,

455
00:49:39,300 --> 00:49:45,000
academic colleagues had worked with actually in this Robert Wood Johnson Health and Society Scholars program.

456
00:49:45,000 --> 00:49:48,930
So they were also interdisciplinary population.

457
00:49:48,930 --> 00:49:57,540
Health scientists reached out, I guess via email or social media and said, we're starting this web page to answer questions about COVID.

458
00:49:57,540 --> 00:50:00,839
And they had seen me tweeting and doing stuff about it.

459
00:50:00,840 --> 00:50:07,840
So they're like, Can you can you help out maybe just for a couple of weeks, just like answering a couple questions, you know, over.

460
00:50:07,920 --> 00:50:10,370
I was like, sure, yeah, I'm I'm all in.

461
00:50:10,410 --> 00:50:21,270
It was that really sense of all hands on deck and urgency and so first created a Facebook and Instagram page called Dear Pandemic.

462
00:50:21,450 --> 00:50:25,889
And that was I didn't come up with the name, but my colleague did.

463
00:50:25,890 --> 00:50:30,660
And that was it was kind of a riff off the Dear Abby advice columns in the past.

464
00:50:30,660 --> 00:50:38,550
So it was and the real impetus was that a lot of us were getting questions from our family and friends about what the heck should I be doing?

465
00:50:39,000 --> 00:50:46,770
As I mean, we all remember just that sudden crescendo of like, this is real and what is, you know, what is going on?

466
00:50:46,770 --> 00:50:53,190
Should they cancel my travel? Am I going to catch this from, you know, my groceries?

467
00:50:53,190 --> 00:50:59,370
Do I need to wipe them down with alcohol? Like people were really, you know, very basic questions.

468
00:50:59,370 --> 00:51:07,560
And so we realised putting that out on to social media would be much more efficient than us all responding to emails from our family and friends.

469
00:51:08,400 --> 00:51:18,570
But that was really our audience then, you know, in our mind was people that we loved and needing answers to very basic questions.

470
00:51:18,870 --> 00:51:20,519
But yes, in a very practical way.

471
00:51:20,520 --> 00:51:26,429
Like it's, you know, they might have been interested in the epidemiology of COVID and how is that moving around the world.

472
00:51:26,430 --> 00:51:31,229
But they really wanted to know what actions do I need to take in my life?

473
00:51:31,230 --> 00:51:39,780
And so we saw a real gap in trends. Eating just I mean, all of the flood of information that was coming out and people, you know,

474
00:51:39,930 --> 00:51:45,299
trying to, you know, read the Internet and figure out what that meant for their daily lives.

475
00:51:45,300 --> 00:51:49,580
So the daily press conferences. The daily press conferences, yeah.

476
00:51:50,580 --> 00:51:55,709
And we we decided we were well placed to fill that gap because we were really good at reading

477
00:51:55,710 --> 00:52:00,840
across tons of disciplines and kind of summarising and synthesising that information.

478
00:52:00,840 --> 00:52:10,770
And so that's how it kicked off it. Quickly, quickly kind of grew as a social media presence just way faster than we expected.

479
00:52:11,540 --> 00:52:17,020
And again, like a lot of media, then because we suddenly then were deemed to be, you know,

480
00:52:17,040 --> 00:52:22,739
experts in all of this, and there was just such a hunger for information, people who can talk about this stuff.

481
00:52:22,740 --> 00:52:31,770
And you gave yourselves quite a provocative name. We did. Yeah. Well, we a follower an early, very early follower, made a comment again.

482
00:52:31,770 --> 00:52:39,960
It was, you know, some kind of advice for giving early on. And he wrote, I trust anything those nerdy girls have to say.

483
00:52:40,440 --> 00:52:43,890
And somehow, yes, we thought that's that's perfect.

484
00:52:45,030 --> 00:52:48,959
Yeah. And there's been like debate about why are we calling ourselves girls or, you know,

485
00:52:48,960 --> 00:52:54,180
we're women and scientists, but we actually like the tone of being approachable.

486
00:52:54,180 --> 00:53:02,819
And, you know, we didn't want this to be feel like we were public health, you know, scolds or academics trying to give people advice.

487
00:53:02,820 --> 00:53:13,920
We actually liked the idea that, no, we're actually your nerdy mom next door that you trust to go ask these questions and not to judge you so much,

488
00:53:13,920 --> 00:53:17,820
but to really give you the bottom line, like, what's the what's the real deal here?

489
00:53:18,210 --> 00:53:25,500
And so that was our tone from very early on. We kind of wanted to be approachable, communicate very plainly.

490
00:53:25,500 --> 00:53:28,860
You know, what's what's the bottom line of this research?

491
00:53:29,820 --> 00:53:34,649
You should. Yeah, there was so much about whether people should wear masks, but, you know, transmission.

492
00:53:34,650 --> 00:53:36,959
I mean, we have a question box.

493
00:53:36,960 --> 00:53:43,290
That was another thing that came up really early is we wanted it to be a bi directional communication with our community,

494
00:53:43,300 --> 00:53:48,629
so we weren't making up the questions. Yeah, I mean, you know, we often were if there was breaking news or something,

495
00:53:48,630 --> 00:53:56,850
but we had this question box that mostly kind of structured what we what we wrote about and also responding to comments,

496
00:53:57,270 --> 00:54:03,870
you know, we really tried to pour ourselves into it to alleviate people's concerns.

497
00:54:04,350 --> 00:54:07,799
And and we also noticed quite quickly that, you know,

498
00:54:07,800 --> 00:54:14,700
there was it was filling this specific niche because a lot of science news, you know, was meant to kind of,

499
00:54:15,060 --> 00:54:19,320
you know, you know, to get media clicks, you kind of have to make it sound scary,

500
00:54:19,320 --> 00:54:23,100
like, oh, the, you know, the new variant or, you know, the monkeypox.

501
00:54:23,100 --> 00:54:29,940
The way it's happening now, it kind of raises the emotional valence is is kind of what the media wants to do.

502
00:54:29,940 --> 00:54:36,749
So we kind of noticed over time that a lot of our posts were kind of like, yeah, you know, we're keeping an eye on this.

503
00:54:36,750 --> 00:54:42,840
But no, this isn't something you need to really you know, viruses do evolve all the time.

504
00:54:42,840 --> 00:54:50,909
So just the fact that there is this change does not mean that it's suddenly, you know, going to be a very different ballgame.

505
00:54:50,910 --> 00:54:57,120
And so a lot of a lot of what we do is kind of manage anxiety to people were really fearful.

506
00:54:58,170 --> 00:55:08,680
And so we saw our role as kind of, you know, being that empathetic again but knowledgeable, you know, friend that could explain stuff and yeah.

507
00:55:08,830 --> 00:55:14,280
So it really grew and we, you know, wrote a couple of posts a day.

508
00:55:14,280 --> 00:55:19,769
We're still going after two years, let's say, or two weeks. Yeah, two weeks has become two years.

509
00:55:19,770 --> 00:55:23,490
It just really grew as a social media presence.

510
00:55:24,540 --> 00:55:35,040
All sorts of things have happened. The W.H.O. just it just came out today, chose us as a case study for kind of crisis, pandemic science,

511
00:55:35,040 --> 00:55:40,559
communication, and we're going to be making a handbook for them about how to replicate this effort.

512
00:55:40,560 --> 00:55:47,010
But, you know, I think we also saw that as the pandemic wore on, that there were a lot of challenges in science communication,

513
00:55:47,010 --> 00:55:54,089
and that I think the UK did actually a very good job every time I watched all the

514
00:55:54,090 --> 00:55:58,290
press conferences and Chris Whitty and Patrick Vallance were excellent communicators,

515
00:55:58,290 --> 00:56:01,439
I thought the US had some challenges.

516
00:56:01,440 --> 00:56:08,790
Obviously the first part of the pandemic was during the Trump administration and the CDC had very little ability,

517
00:56:08,790 --> 00:56:17,699
I think, to directly communicate with the public. So there were there were a lot of of missteps around the world and then a complete kind of,

518
00:56:17,700 --> 00:56:22,850
I think, loss of trust and in some public facing science communication.

519
00:56:22,860 --> 00:56:25,739
So we decided that was really important.

520
00:56:25,740 --> 00:56:33,840
Part of our what we were trying to accomplish, too, was to develop this trusted relationship with our followers and.

521
00:56:33,920 --> 00:56:37,780
Then your followers are mostly in the US, would you say? I mostly.

522
00:56:37,790 --> 00:56:41,629
And it seemed, yeah. It felt to me as if you were reaching out.

523
00:56:41,630 --> 00:56:46,490
Reaching out to us. Yeah. So, yeah. And I guess I should back up and say so.

524
00:56:46,490 --> 00:56:51,620
Our little band of nerdy girls grew to probably about a core group of ten at first.

525
00:56:52,340 --> 00:56:57,229
And yes, we were all kind of connected in the US, so that was the original social network.

526
00:56:57,230 --> 00:57:05,129
And so that was, I think a lot of the Facebook audience initially snowballed from our own contacts and things.

527
00:57:05,130 --> 00:57:14,120
So yeah, it has, it has grown internationally, but definitely percentage wise the US is the bulk of our followers and we also a few

528
00:57:14,120 --> 00:57:20,329
months later started as a mere Spanish language site called Carry the Pandemia,

529
00:57:20,330 --> 00:57:24,260
which is actually has tons of followers all through Latin America.

530
00:57:24,260 --> 00:57:29,270
We kind of intended it for migrant communities in the US, but it somehow took off.

531
00:57:29,270 --> 00:57:33,260
And in Latin America where there's a lot of misinformation about COVID.

532
00:57:35,150 --> 00:57:39,860
So where is that going? So that was the the original group.

533
00:57:41,330 --> 00:57:46,670
Yeah, most mostly in the US, but I think there was a lot of interest.

534
00:57:46,670 --> 00:57:53,329
I kind of did a little series of postcards from England because there is there was a tie, at least for a time.

535
00:57:53,330 --> 00:57:57,860
There were things happening here maybe two or three weeks before they would happen in the

536
00:57:58,280 --> 00:58:04,310
U.S. So I felt like I was always giving little warning shots for what was about to happen.

537
00:58:05,360 --> 00:58:14,149
And but I think it's a good point. I think we also our followers, if we you can't see so much on Facebook demographics, but I think not surprisingly,

538
00:58:14,150 --> 00:58:20,510
it's a lot of women in our rough age group, you know, there is a span, but kind of the bulk is in that.

539
00:58:20,810 --> 00:58:25,190
And so we realised there was a lot of women like taking care of their families during the pandemic too.

540
00:58:25,190 --> 00:58:31,759
Like women are often the information seekers and feel responsible for keeping their families safe.

541
00:58:31,760 --> 00:58:38,149
So, you know, it's not that we would love to reach everyone and I guess a more diverse demographic.

542
00:58:38,150 --> 00:58:47,450
And we do have plenty of men who follow us. But I think there's something that makes sense about getting that practical information, you know,

543
00:58:47,510 --> 00:58:52,999
to to the women and kind of helping disseminate that not only in their families, but in their communities.

544
00:58:53,000 --> 00:58:58,400
You know, we got so much feedback, people bringing this, you know, to help their schools reopen or,

545
00:58:58,790 --> 00:59:06,199
you know, all sorts of decisions that were being made in communities. And so that's where we felt like our reach was really amplified that, you know,

546
00:59:06,200 --> 00:59:14,120
there was a bit of we started on social media, too, and Facebook to get, you know, a direct link to family and friends.

547
00:59:14,120 --> 00:59:19,970
But, you know, Facebook came under fire a bit during the pandemic for circulating misinformation.

548
00:59:20,630 --> 00:59:29,060
And so we're you know, we had some some moments where we're like, are we, you know, you know, why are we using Facebook as our main platform?

549
00:59:29,060 --> 00:59:32,600
Is this you know, is this something we shouldn't be doing?

550
00:59:32,600 --> 00:59:38,749
But we really felt it was important to kind of combat the misinformation on its home turf.

551
00:59:38,750 --> 00:59:44,030
And people a lot of people just get their news and information from social media.

552
00:59:44,030 --> 00:59:47,960
So we felt like you really have to meet people where they are.

553
00:59:48,440 --> 00:59:53,179
And then the power of that social media is people share it with their family and friends.

554
00:59:53,180 --> 01:00:01,700
So like shares are the thing that we kind of use as our metric because yes, if someone trust your posts,

555
01:00:01,730 --> 01:00:06,470
you know enough to share it with family and friends, that's a strong signal.

556
01:00:06,710 --> 01:00:11,660
And we know that like in kind of health behaviour research that people really trust,

557
01:00:12,110 --> 01:00:16,459
they trust information from their, their own network and their, their friends.

558
01:00:16,460 --> 01:00:26,210
So, so we want to be that trusted messenger and then from which that information kind of emanates out into other social networks and communities.

559
01:00:26,210 --> 01:00:37,340
And that's been kind of our, our real goal and also just giving people tools to combat these conspiracy theories and misinformation.

560
01:00:37,340 --> 01:00:43,700
Like often you might have someone come and say, Oh yeah, we're over counting those COVID deaths,

561
01:00:43,700 --> 01:00:49,519
but you wouldn't necessarily know how to, you know, counter that kind of argument.

562
01:00:49,520 --> 01:00:55,489
And so, you know, we made we had ready made posts that explain this and people were so grateful.

563
01:00:55,490 --> 01:01:00,319
They're like, Oh, thank goodness, I've been hearing this and now I have something to share with people.

564
01:01:00,320 --> 01:01:07,430
So yeah, we do think it had kind of this multiplicative impact by just being shared.

565
01:01:07,430 --> 01:01:13,880
And that's that's where we hope we can continue to do is kind of yeah become help people be

566
01:01:14,180 --> 01:01:19,730
nodes of trust in their own social networks is kind of the model that we're trying to create.

567
01:01:20,510 --> 01:01:28,750
Then you could see how well obviously W.H.O. see. Yeah, and you could use that model to address obesity or heart health or.

568
01:01:29,060 --> 01:01:33,230
Yeah. Conditions. No, exactly. We kind of, you know, as it went.

569
01:01:33,440 --> 01:01:38,749
We never thought it would be going on this long, but unfortunately we got COVID and kept going.

570
01:01:38,750 --> 01:01:43,580
But we were more and more trying to transition to non-covid topics just because

571
01:01:43,970 --> 01:01:48,379
we do feel like there's a real lack of access to accessible information.

572
01:01:48,380 --> 01:01:55,450
There's, you know, if you go, you know, to the W.H.O. or a government website to look up, you know,

573
01:01:55,520 --> 01:01:59,989
some some guidance on a public health thing, it's it's really dense and difficult to read.

574
01:01:59,990 --> 01:02:08,660
So there's really a need, I think, to empower people with information that they can actually consume and use in their daily lives.

575
01:02:08,660 --> 01:02:14,660
And so we do want to continue to provide that for a variety of other health and medical topics.

576
01:02:14,990 --> 01:02:23,270
And also because we've established trust, we think with our current follower base, we noticed this when the vaccines, you know, came out.

577
01:02:23,270 --> 01:02:27,909
We'd already been publishing and writing for four months by then.

578
01:02:27,910 --> 01:02:34,040
And when the the vaccines, the trial, you know, came out in November, and then we're like, okay, you know,

579
01:02:34,040 --> 01:02:42,319
our major task is going to be to educate people now on the vaccines and, you know, alleviate people's concerns.

580
01:02:42,320 --> 01:02:45,620
So we did a lot of writing on the vaccines.

581
01:02:45,620 --> 01:02:52,819
And some people I just remember with this one comment that was like, oh, you know, we try to make things as non-technical as possible.

582
01:02:52,820 --> 01:02:59,570
Sometimes it's hard. And there was one comment from someone, you know, Oh, you know, thanks so much for this.

583
01:02:59,570 --> 01:03:04,459
I can't you know, I can't always follow all of the technical details.

584
01:03:04,460 --> 01:03:11,450
But, you know, but you've built you know, I've been following you for months and you've built that trust with me.

585
01:03:11,450 --> 01:03:15,109
So I know I know that I can trust that these vaccines are safe.

586
01:03:15,110 --> 01:03:18,919
Because you're telling me that's that's the bottom line is that they're safe.

587
01:03:18,920 --> 01:03:21,799
And that just really hit us, you know, that, you know,

588
01:03:21,800 --> 01:03:30,110
people have actually now put their trust in us and it gives us an ability then to hopefully be there for the next crisis,

589
01:03:30,110 --> 01:03:32,780
which I hope is not anytime too soon.

590
01:03:32,780 --> 01:03:41,809
But you kind of need to foster those relationships, I think, during normal times so that you can be there and ramp that up during a crisis.

591
01:03:41,810 --> 01:03:47,750
So so we're kind of fine if things lay low for a while and people don't need us, I think we would be very happy.

592
01:03:47,750 --> 01:03:53,960
But we want we want to continue to be there in the event that that we need to get important information out.

593
01:03:54,230 --> 01:04:00,530
And there are plenty of kind of more every day. You're right. Science and health topics that that need covering.

594
01:04:01,070 --> 01:04:04,520
So so that's definitely part of the ongoing plan.

595
01:04:05,420 --> 01:04:12,229
We want to democratise information a bit more. It's not it shouldn't be only academics who have a good sense of what's going on.

596
01:04:12,230 --> 01:04:17,660
This stuff. It's it's actually really hard to evaluate evidence on your own.

597
01:04:17,660 --> 01:04:21,980
And we know that Dr. Google is not not that reliable.

598
01:04:22,220 --> 01:04:27,920
So we want to be everyone's doctor Google who can actually, you know, put together the pieces for people.

599
01:04:28,400 --> 01:04:34,840
And people did, I mean, to a much greater extent than before to try to get across all this information, didn't they.

600
01:04:34,970 --> 01:04:39,020
Yeah, it was like, oh number and. Oh I know. Yeah I know.

601
01:04:39,020 --> 01:04:40,940
There was something much. Yeah.

602
01:04:40,940 --> 01:04:50,209
No, the public has gotten quite, quite an education and I should say that was another piece like a lot of it was very specific topics, you know,

603
01:04:50,210 --> 01:04:54,500
explaining, you know, airborne transmission or something or, you know,

604
01:04:54,500 --> 01:05:00,200
if you're in a car, which windows should you roll down for the airflow to be best?

605
01:05:00,200 --> 01:05:03,499
There were you know, there were just tons of things that we covered.

606
01:05:03,500 --> 01:05:08,659
But we also wanted to educate people on how to read science news themselves.

607
01:05:08,660 --> 01:05:16,129
So we have kind of some different series on, you know, just fallacies to look out for or,

608
01:05:16,130 --> 01:05:21,140
you know, how to better interpret scientific research, design and results.

609
01:05:21,140 --> 01:05:25,240
And that's something we also think is really important going forward.

610
01:05:25,510 --> 01:05:28,250
It's an education in science literacy. Yes, we want to.

611
01:05:28,290 --> 01:05:33,139
We want to that's one of our kind of two aims is to kind of curate this information for people,

612
01:05:33,140 --> 01:05:36,920
but also empower them to be able to do it a bit better themselves.

613
01:05:37,820 --> 01:05:41,730
That's terrific. Right.

614
01:05:43,940 --> 01:05:49,009
But I will. Yeah, I'll just say that was one of the best experiences because this group of women

615
01:05:49,010 --> 01:05:53,319
just came together and supported each other also personally because I mean,

616
01:05:53,320 --> 01:05:58,190
know you could friends, everyone only we all had kind of some random connections to each other.

617
01:05:58,190 --> 01:06:01,790
So there were some maybe pairs of people who had good friendships.

618
01:06:01,790 --> 01:06:05,449
But no, we mostly all bonded over that experience.

619
01:06:05,450 --> 01:06:07,249
And it is another thing that, I mean,

620
01:06:07,250 --> 01:06:16,639
we'll just be bonded for life because it was so intense and just so many hours and this was all volunteer as well.

621
01:06:16,640 --> 01:06:20,330
I mean, everyone just did it outside of their normal jobs.

622
01:06:20,330 --> 01:06:27,650
And again, that didn't feel unusual at the time because it was the pandemic in an emergency and everyone wanted to do everything that they could.

623
01:06:28,910 --> 01:06:34,820
But I think thinking about the sustainability of of that, it's not something that's typically rewarded in the,

624
01:06:35,000 --> 01:06:39,739
you know, traditional academic structures kind of doing that kind of outreach.

625
01:06:39,740 --> 01:06:46,639
And and so that's been just one of the challenges as it's gone on longer and longer to figure out how to do that.

626
01:06:46,640 --> 01:06:54,110
But I think with the pandemic, you know, universities were very supportive in general of of all of those efforts.

627
01:06:54,110 --> 01:07:03,400
But I think we need to find a way to build that in more permanently, as you know, to have some some way to reward that type of work in academia.

628
01:07:03,740 --> 01:07:06,920
I really second that I've seen a lot.

629
01:07:07,400 --> 01:07:18,020
Yeah. So I'm just moving into the sort of final stages which are more about how the whole experience impacted on you personally.

630
01:07:18,860 --> 01:07:25,220
So first of all, how did having to be away from the office impact on what you personally were able to do?

631
01:07:27,200 --> 01:07:35,629
You know, I was shocked at how well we were together in those first few months because of the technology, you know, that we all have learned.

632
01:07:35,630 --> 01:07:44,840
So Zoom and slack or our go tos. But we were really in constant communication for from those first few months our research group.

633
01:07:44,840 --> 01:07:50,299
And so I didn't I think I didn't really feel it until, you know, more like a year.

634
01:07:50,300 --> 01:07:55,850
And and I think then you really I felt us kind of go not going our separate ways,

635
01:07:55,850 --> 01:08:04,370
but the way we had just coalesced as a team, people started pursuing more of their own kind of not side projects.

636
01:08:04,370 --> 01:08:08,839
People were all working on important stuff, but kind of that feeling of unity dissipated a little bit,

637
01:08:08,840 --> 01:08:13,790
I think, for not seeing each other in person, because it really it truly is.

638
01:08:13,790 --> 01:08:20,989
All of these conversations that you have in the hallway and coffee and lunch that generate new ideas.

639
01:08:20,990 --> 01:08:26,240
And so, yeah, I think we had a lot of energy and ideas to start with and then but the working

640
01:08:26,240 --> 01:08:31,010
from home after a while you lose that generation of the new the new ideas.

641
01:08:31,010 --> 01:08:38,630
And so it's been great to be back, I guess, since October 21, much more in person.

642
01:08:40,340 --> 01:08:45,620
But I also but I, I was kind of amazing just, I felt really lucky, obviously,

643
01:08:45,620 --> 01:08:53,599
that our jobs are something that we can do remotely and still mostly do, I guess, 90%.

644
01:08:53,600 --> 01:09:00,500
Well, and it just it's the longer term I think where it suffers with those relationships and creating.

645
01:09:00,980 --> 01:09:06,500
Yeah, that new, that new energy is what suffers. But it was just felt like go, go, go.

646
01:09:06,500 --> 01:09:11,750
I'm sure, I'm sure everyone you talk to is, was hardly sleeping during the pandemic.

647
01:09:11,750 --> 01:09:16,280
I can't. Well, I have another question about your working hours. I can't even imagine.

648
01:09:17,240 --> 01:09:20,540
Yes, it was just those two.

649
01:09:20,810 --> 01:09:30,260
I felt like 24, seven. I never took a weekend off for probably until very recently, you know, because on top of the research and the outreach media,

650
01:09:30,260 --> 01:09:39,469
we're just calling all the time and I by no means was doing a lot of that compared to others, but it was not something I'd ever done pre-COVID.

651
01:09:39,470 --> 01:09:44,360
So initially it felt really outside of my comfort zone.

652
01:09:44,360 --> 01:09:47,629
And did you get any training? Any training? No.

653
01:09:47,630 --> 01:09:52,580
No. Like during COVID? No, I think I had a training like in my postdoc years ago.

654
01:09:52,580 --> 01:09:59,569
But no, I just kind of dove in because, again, it was I mean, it's great because in normal times,

655
01:09:59,570 --> 01:10:03,379
I would have really hesitated and been insecure about talking to the media.

656
01:10:03,380 --> 01:10:07,340
And you're just like, I have to do this. This is an emergency.

657
01:10:07,340 --> 01:10:15,139
And so in that way, it was a real time of personal growth because you you're just thrown into situations

658
01:10:15,140 --> 01:10:18,830
you never would have been thrown into and you have to rise to the occasion or not.

659
01:10:18,830 --> 01:10:23,149
So so I'm grateful in that sense for the personal development,

660
01:10:23,150 --> 01:10:29,060
that that sense of urgency just overcame all of my insecurities and fears about trying new things.

661
01:10:29,540 --> 01:10:39,919
But, you know, I felt a responsibility to, you know, really prepare and try to clearly communicate and do a good job with with any media that I did.

662
01:10:39,920 --> 01:10:43,100
And, you know, that world is just, like, so hectic.

663
01:10:43,210 --> 01:10:50,230
I can't even imagine. They just want to call you shortly before and talk you into going on live TV in like 30 minutes.

664
01:10:50,230 --> 01:10:55,270
And, you know, so I had to kind of learn the system a bit and realise that.

665
01:10:55,270 --> 01:11:03,970
And then also in the first few months it felt very much like being in the media was getting out important information that again kind of like this,

666
01:11:04,270 --> 01:11:09,820
what we were doing under pandemic, it's like people just want to know how to keep their families safe.

667
01:11:10,900 --> 01:11:15,219
You know, I remember Christmas that first Christmas, too, and things were getting bad again.

668
01:11:15,220 --> 01:11:19,629
And like, you know, I really wanted I didn't want to ruin people's holidays,

669
01:11:19,630 --> 01:11:25,290
but I really wanted people to know, like the vaccines are coming very soon.

670
01:11:25,300 --> 01:11:31,510
Like, just like plan, plan to, you know, you want all your loved ones to be there for the next holiday.

671
01:11:31,510 --> 01:11:36,280
So, you know, I just remember some of that messaging seemed very important.

672
01:11:36,280 --> 01:11:39,909
But as time went on, you got a sense that, you know,

673
01:11:39,910 --> 01:11:48,940
people were wanting to they wanted to ask you to be on TV to critique the government or pit you against some anti-mask person.

674
01:11:48,940 --> 01:11:55,569
And so, you know, I think I kind of tried to find an equilibrium where I was like, no, I'm going to be that boring.

675
01:11:55,570 --> 01:12:02,260
You know, they're not going to use me in that way. Like I need to do it on my terms to get good information out there.

676
01:12:02,260 --> 01:12:11,140
But I don't want to be baited into some saying specific things about the government or, you know, fighting with conspiracy theories.

677
01:12:11,150 --> 01:12:13,090
So that was a big learning curve.

678
01:12:13,090 --> 01:12:20,770
But I'm still really grateful that I was able to get, you know, contribute to getting some information out there at pivotal times.

679
01:12:20,770 --> 01:12:27,429
There is a lot around schools that I kind of spoke about on the radio and things when schools were opening up.

680
01:12:27,430 --> 01:12:33,390
And I was I still remain puzzled at why masks were such as masks in schools.

681
01:12:33,510 --> 01:12:40,450
You know, people were so reluctant to do that here, you know, I mean, it's the context has changed over the whole pandemic.

682
01:12:40,450 --> 01:12:48,189
But early on that for some reason there were not many masks in schools and England was quite unusual compared to other countries in that.

683
01:12:48,190 --> 01:12:58,930
So, um, you know, yeah, I did some public writing on that and put all these hot button issues can end up getting a lot of negative attention as well.

684
01:12:59,260 --> 01:13:07,630
But I'll say I didn't have a bad problem with trolls or I know a lot of people like Melinda mills got,

685
01:13:07,810 --> 01:13:10,810
you know, death threats and things from being on TV.

686
01:13:10,810 --> 01:13:17,139
And so a lot of people suffered from that side of the public facing communication.

687
01:13:17,140 --> 01:13:26,430
But I think for better or for worse, found some kind of slightly under the radar where people did not attack me personally.

688
01:13:26,440 --> 01:13:33,069
So that did I am grateful for that enabled to be yeah I was fairly I was going to say we actually

689
01:13:33,070 --> 01:13:40,000
did steer pretty clear so there's always kind of in your comments section you know some

690
01:13:40,000 --> 01:13:45,909
some bad actors although I don't think we get a lot we we we curated a little and can block

691
01:13:45,910 --> 01:13:51,069
people who are terrible but we never really got personal attacks or things of that sort.

692
01:13:51,070 --> 01:13:54,100
I think being, you know, kind of a group that was, you know,

693
01:13:54,100 --> 01:13:58,419
our individual identities weren't so visible from that I think protected us

694
01:13:58,420 --> 01:14:02,469
because we did have some colleagues who had kind of started their own pages.

695
01:14:02,470 --> 01:14:07,720
We're doing parallel things and then we all kind of joined together, not not formally,

696
01:14:07,720 --> 01:14:12,220
but we had like the support group and communicated back channel to support each other.

697
01:14:12,670 --> 01:14:19,959
And some of those people who are more individuals really did get attacked and you know yeah this well

698
01:14:19,960 --> 01:14:24,490
one particular I don't need to tell the whole story about a woman in Texas who is an epidemiologist,

699
01:14:24,490 --> 01:14:29,590
but also the wife of a Christian minister and in a big church.

700
01:14:29,590 --> 01:14:35,560
And, you know, she was making recommendations about, you know, not attending services and things,

701
01:14:36,040 --> 01:14:41,260
you know, ways that and this is really in the time when it was really dire like that.

702
01:14:41,590 --> 01:14:49,479
People shouldn't be gathering. And she her family had to move like two times for getting like really serious threats.

703
01:14:49,480 --> 01:14:55,809
And so a lot of people took on a lot of personal pain and risk.

704
01:14:55,810 --> 01:14:59,860
And I still yeah, I'm really grateful to the people that did that.

705
01:15:00,460 --> 01:15:05,140
And I was just very fortunate that I did not experience anything like that.

706
01:15:05,840 --> 01:15:10,540
Did you feel personally threatened by the risk of infection being in that state?

707
01:15:11,410 --> 01:15:17,379
That's a good question. You know, no, I think I'm kind of a healthy at risk demographic.

708
01:15:17,380 --> 01:15:27,040
Yeah, I'm kind of a health optimist. But my we live with my mother in law, who's now 88, and we are a multigenerational family.

709
01:15:27,040 --> 01:15:32,409
So she's 88 now. So I guess, yeah, 86 at the onset.

710
01:15:32,410 --> 01:15:37,000
And and then I had three teenage kids in the household.

711
01:15:37,000 --> 01:15:41,950
So I was very worried about any of us bringing it to my mother in law.

712
01:15:42,990 --> 01:15:47,040
But no, I think like at a personal level, I was never that afraid of it.

713
01:15:47,040 --> 01:15:51,060
But I think I think I'm just a how I'm always a health optimist in general.

714
01:15:51,420 --> 01:15:56,489
So I wasn't taking it lightly. But no, I wasn't. I didn't feel personally scared for myself.

715
01:15:56,490 --> 01:16:00,180
It was really. Yeah. About my family. Yeah.

716
01:16:00,180 --> 01:16:03,860
And my family in the US I Yeah.

717
01:16:04,050 --> 01:16:10,470
Have older parents. I was, I was worried about exactly the demographic that I, I knew was most at risk.

718
01:16:18,150 --> 01:16:24,750
Is there. So has the work you did during the pandemic raised questions that you were interested in exploring in the future?

719
01:16:25,350 --> 01:16:34,770
Oh, yes. Well, I think the the funny thing is how well, it kind of dovetailed with things I was already doing.

720
01:16:34,770 --> 01:16:40,799
So in some ways it's not it's not a big shift. But no, I was so obsessed with not obsessed,

721
01:16:40,800 --> 01:16:47,760
but like I've done so much reading and that was just the other things that I guess ramped up in probably February of 2020.

722
01:16:47,760 --> 01:16:55,499
To Rewind. I just remember voraciously having to read everything that was coming out and all the preprints and scientific papers.

723
01:16:55,500 --> 01:17:04,290
So I did spend an enormous amount of time staying on top of the literature, whether it was the vaccine efficacy, the the immune response,

724
01:17:04,290 --> 01:17:15,120
like I was reading everything and I feel like that has put me in a good position to continue to kind of do this interdisciplinary work on, on COVID.

725
01:17:15,990 --> 01:17:24,209
And it's I think what I really want to focus in on is the long term implications of of the pandemic for population health.

726
01:17:24,210 --> 01:17:29,610
So we're continuing to look at, you know, changes in life expectancy.

727
01:17:30,600 --> 01:17:36,839
You know, it could be a temporary shock where we we go back to kind of normal levels of of mortality.

728
01:17:36,840 --> 01:17:44,909
But, you know, I think all of us are concerned about long COVID, not just like long COVID as people having symptoms,

729
01:17:44,910 --> 01:17:50,040
but, you know, the actual damage from people who had severe disease, there's lung scarring.

730
01:17:50,430 --> 01:17:58,530
The risk of having heart attack and stroke and other events in the year following an infection has been shown to be pretty severely elevated.

731
01:17:58,530 --> 01:18:07,920
So I think there's big question marks about how long kind of the scars of the pandemic on population health are going to last.

732
01:18:08,870 --> 01:18:18,599
And that's something, you know, that is what demography and population health is all about, is trying to understand these trends in population health.

733
01:18:18,600 --> 01:18:26,429
I think there's also a lot of research that I did prior to COVID on the impacts of early life exposures on your later life health.

734
01:18:26,430 --> 01:18:33,299
And so we still study the 1918 flu like people who were in in utero in 1918

735
01:18:33,300 --> 01:18:37,830
and how that affects their risk of heart attack and dementia later in life.

736
01:18:37,830 --> 01:18:44,430
So, you know, I know that we've tried to say it's mild in kids and I so much hope that that is true.

737
01:18:44,430 --> 01:18:49,649
But I think we really need to look at the long term effects of the infection, even,

738
01:18:49,650 --> 01:18:53,820
you know, from people infected at younger ages and just really keep an eye on that.

739
01:18:54,300 --> 01:19:00,209
And so, yeah, no, it's absolutely going to be something that that I keep doing.

740
01:19:00,210 --> 01:19:09,090
And I had actually gotten an e r c consolidator grant project that I wrote before COVID to look at mortality

741
01:19:09,090 --> 01:19:16,379
trends in the UK and Europe and try to understand the causes of kind of stalling life expectancy.

742
01:19:16,380 --> 01:19:25,920
We've seen improvements really slowing down and so so I was already kind of planning to do that and try to understand the trends before COVID.

743
01:19:25,920 --> 01:19:32,940
But this will obviously be just, you know, something that will is going to be important for the next hundred years and understanding

744
01:19:33,390 --> 01:19:38,610
mortality trends because it it has it's it's what I call kind of this cohort exposure,

745
01:19:38,610 --> 01:19:49,169
you know, like the kids, you know, in utero or born today are still going to be like they've they've had this big exposure to an unusual pathogen.

746
01:19:49,170 --> 01:19:55,110
And they'll carry that to some degree, you know, through their health history for the rest of their lives.

747
01:19:55,110 --> 01:20:00,659
So so I'll have to be kind of forward looking, thinking about how COVID will affect those trends.

748
01:20:00,660 --> 01:20:09,870
And I'm also looking back to try to understand what what were the major impacts on mortality of the cohorts that are entering old age today.

749
01:20:10,800 --> 01:20:14,820
So it really does all all kind of tie together. So you've got your career.

750
01:20:14,970 --> 01:20:19,410
I have plenty, but I'm never going to run out of research topics, I'm quite sure.

751
01:20:20,040 --> 01:20:26,490
And has the experience changed your attitude or your approach to your work and the things you'd like to see change in the future?

752
01:20:26,880 --> 01:20:40,350
I, I think as we were discussing, I, I, I came into, I guess I, I came in to graduate school and research, really wanting to,

753
01:20:40,770 --> 01:20:49,530
to change the world like I was really that early, you know, young twenties who I am going to completely solve poverty and inequality.

754
01:20:49,530 --> 01:20:54,299
And so I came in very bullish on making a difference.

755
01:20:54,300 --> 01:20:56,700
And I mean, I kind of fell in love with research.

756
01:20:56,700 --> 01:21:03,480
And then you also, you know, life intervenes and it's not as easy to traipse around the world and do hands on things.

757
01:21:04,620 --> 01:21:11,339
So I think I didn't realise how much I kind of missed the idea that my work would have have an impact.

758
01:21:11,340 --> 01:21:16,920
So, you know, you know, working on health, I was always able to say, no, this, this, this does have.

759
01:21:16,960 --> 01:21:26,020
Immediate applications. I'm not doing something completely theoretical, but, you know, the you know, describing these biological pathways.

760
01:21:26,350 --> 01:21:30,459
There wasn't a lot of policy relevance to some of the work I was doing.

761
01:21:30,460 --> 01:21:35,290
So I guess it changed me in the sense that I realised, yes,

762
01:21:35,290 --> 01:21:43,449
being connected to changing people's lives in real time is something that I want to continue to do with my work.

763
01:21:43,450 --> 01:21:52,989
So that sort of connection to translation and just public outreach is something that I'm not going to kind of go back into my cocoon.

764
01:21:52,990 --> 01:21:59,559
I feel like I feel like it's now a responsibility and it's almost it's almost irresponsible to do

765
01:21:59,560 --> 01:22:06,670
this kind of work on a topic like health and not figure out a way to make it real in people's lives.

766
01:22:06,670 --> 01:22:10,030
So it's definitely it kind of brought me back, I think,

767
01:22:10,030 --> 01:22:20,140
to my my lofty goals of youth and finding a connection between between the science and and making people's lives better.

768
01:22:21,640 --> 01:22:22,630
Thank you very much.