in this video today I'm going to be talking about research that brings
together biological and social data to answer important questions about health
and society this is sometimes called bio social research so in the first video
what I'm going to be talking about is what kinds of biological data we might
have in social surveys that enables us to do this kind of research and then
give examples of research questions that we could answer with these data and show
how they might contribute to understanding of health and to policy
it's important to know I'm just talking about what are called biomarkers so
indicators of biological processes I'm not talking about genetics today there's
a second part of the talk another video in which I'll talk about how you analyze
these biological data but in this video I'm really focusing on why we might want
to do it I think Pam heard really sums this up well why she says is that in
medical research often people tend to have one single variable to capture the
whole of our social life are you poor or not and in social sciences what we often
do is to have a single variable that tries to capture every aspect of
people's health over the last 12 months have you been good fair or poor but
neither of these ways of measuring people's social lives or people's health
are really adequate to capture the richness of them what we really need is
data surveys that bring together the rich dimensions of people's health and
the rich experiences of people's social and economics lives in order to get a
much better understanding of how society impacts on health and health impacts on
people's ability to contribute to society when we have data that's
theoretically based a Pyrrhic lis well measured on both health and society then
we can create a better understanding these two concepts and we can identify
how to improve them through policy so the kinds of data I want to talk about
are called biomarkers a biomarker is a characteristic that's objectively
measured and evaluates the kind of whole of a biological process it can be all
sorts of things so it could be somebody's height someone's blood
pressure or measures extracted from blood or other tissues such as saliva or
hair what's really important to remember is although these are objective and
therefore not subject to self-report bias and other kinds of biases because
people are telling us about things there might still be measurement error so if
you think about measuring height if I respondent was really tall and our
interview really small they might not be able to reach the top of their head so
they might be at not be able to see what the measurement says very accurately if
I respondent had a large bun or tall hat or shoes then that might give us an
inaccurate measure of their height with blood pressure often when you go to the
doctors to get your blood pressure measured that can actually make your
blood pressure get higher it's known as the white coat syndrome and it's exactly
the same in surveys the first blood pressure measure we take often is much
higher than normal for that person so when we analyze blood pressure data we
might ignore the first measure and just use the second one or we might take an
average across them with blood assuming the person's consented and the person
taking it has taken the blood well then we need to think about how it's
processed in terms of the quality of the data that we might get so for example
often blood sent through the post and then frozen and defrosted and analyzed
and all of those processes might harm the quality of the blood and hence the
measures that we can take from it and these are the sorts of things we need to
think about when using these data so yes they were objective and that gives us
lots of benefits which I'll talk about in a minute but they still might have
so what do biomarkers measure well the first most obvious thing when you go to
your doctor they might give you a blood test to measure whether or not you have
a particular disease so there's an measure in our blood which tests whether
or not we're intolerant to sugar hba1c and that's an indicator of whether or
not we have diabetes and there were a range of blood tests that would tell the
doctors and and surveys whether or not someone has a disease the next thing we
look for in biomarkers is risk factors so you haven't actually got a disease
yet but you're kind of on the pathway towards them so for example high blood
pressure or high cholesterol a really big risk factors for heart disease so in
surveys we might be looking for those to be able to identify earlier in the
pathway on the road towards somebody getting heart disease if we're
interested in how people's social lives affect their health then what we're
really interested in is how social stress might kind of get under the skin
in order to affect your health and then we're interested in things like cortisol
which is often called a stress hormone which rises when you're in under stress
and then kind of circulates around your body to make you fight or flight but if
that doesn't happen if you're under stress a lot of the time in your
cortisol is rising then that might start to damage some of your organs and other
systems in ways that lead to long term health problems another stress pathway
that we often look at is based on inflammatory markers these are analytes
in the blood that show an inflammation you might get them when you have an
infection like a cold or if you bump into something can get a bruise these
are perfectly normal kind of physiological responses to infection but
again if stress carries on for a long time then that might lead to great
damage to your physiological systems in ways that are harmful and lead to later
health problems the fourth sort of biomarkers are novel
markers they're things that our biologists
fermenting with in order to better capture how our systems work and they
might hold a lot of promise for understanding social and biological
factors or they might not lead to anything so a key area for development
is what's known as biological aging so can we identify simple ways of measuring
the way our bodies age faster than chronologically we are because those are
the people that we might want to give the most help so ten years ago we all
focused on something called telomere length which is the size the end of your
chromosome which protects your DNA from damage now we're thinking much more
about epigenetics and that's how the environment affects whether your genes
get switched on or off in ways that are good or bad for your health
there were also a whole range of omics so for example proteomics and that's
what happens when your genes tell your body to do something often what they're
doing is creating a protein that goes on to kind of lead to an effect in your
body so there's a whole range of new things all the time that biologists are
looking at that we in the social sciences might be able to make use of
having indicators of all these different risk factors and diseases is really
helpful but one thing we often want to do is kind of catch the whole of
somebody's health and so these factors are built into risk scores so a couple
of really famous ones are the Framingham risk score for heart disease or
allostatic load which is a measure of cumulative burden on your physiological
system due to stress and I'll talk about those a little bit in part two of this
presentation so why is it that we might want to include biomarkers in social
science research so first of all as I hope you've got a sense from the kind of
indicators I've talked about they might give us earlier more precise measures of
people's health before people are actually aware that they're ill so an
analyte in your blood might be heightened but you might not actually
experience symptoms their objective as I said before so they're not subject to
things like self-report bias and so I'm going to give a couple of examples of
how this might benefit us first for thinking about how people's
health changes over their lifespan and secondly thinking about how biomarkers
together with what people tell us about their health might help us understand
why people go to the doctors or not or why people take medication that they
need or not secondly I want to talk about that issue around how social life
gets under the skin so what are the pathways through which your social life
your income your family and all those sorts of things might affect your health
understanding the biological processes might help us with that and the whole
reason for doing this is if we understand these things better we can
identify intervention points for policies to improve people's health so
the first thing to note is that there's a natural kind of shape to our bio
markers over our life so if you think about something like your lung function
when you're a child your lung function capacity develops and eventually it
reaches a peak and then during middle age that kind of stays at that peak or
maybe gradually declines and then an older age it declines a bit steeper but
if you've been disadvantaged in some way or perhaps been ill as a child that peak
might be lower you might peak earlier the decline might
be steeper and all of these things help us to think about at what stage in the
life course we might want to intervene to promote health to perhaps lessen that
decline or create a higher peak so one example of this is grip strength so grip
strengths measured with a machine that you squeeze really hard to see how
strong it is and perhaps not surprisingly grip strength as you can
see in the graph is much higher for men than for women your grip strength is a
really good indicator of frailty in later life and over early mortality and
so it's really good to measure this in populations and to see the shape of that
development over life so you can see not only a do men have higher grip strength
than women but they peak slightly later but then they
decline is slightly stronger and so those sorts of things can help us to
think about well when might we want to intervene if we wanted to change this
pattern differently in men than women
the second example I want to give is about how thinking about in a survey
what people tell us about their health and then what measures in their blood
also tell us helps us to understand the way people behave when they're ill so
why might they differ well first of all if you've got a raised level of
something in your blood you might not be aware of it or you might just feel a bit
under the weather and put it down to kind of all sorts of things rather than
being potential illness even if you are aware that you feel under the weather
whether or not you go to the GP and kind of try to do something about it and
might depend on all sorts of factors how busy you are if your fight and what
might be wrong with you all sorts of things and then whether you tell the
survey about these things might depend on the rapport you develop with the
interviewer or how truthfully you want to be about how ill or healthy you are
understanding these different things using biomarkers can help us identify
whether somebody has a need for health care independent of their awareness
their health seeking behavior their willingness to report things but putting
this information together can help us understand who manages their health
problems well and who manage this them badly and needs more help so in this
graph what we're looking at is a range of different ways of measuring whether
or not somebody has diabetes so the first column on the left is the blood
analyte that tells us whether or not someone has diabetes which is known as
hba1c and as you can see about 4% of women and 6% of men have diabetes
according to their blood levels the next column tells us whether they told us in
the survey that they had diabetes slightly more women told us they had
diabetes than have the raised blood level but slightly less men told us they
had diabetes than those with the raised blood level which might suggest that
perhaps aren't seeking help and don't know that
they have this condition the third column from the left is about whether or
not you're actually on diabetes medication now you don't need to be with
diabetes some people have diabetes and control it perfectly well through
exercise and diet but as you can see there's a sort of similar level slightly
less in both cases for men and women of those people on medication to those
people who have died or who tell us they have diabetes the final column is
somebody who has any one of those three things so either they have a high blood
and a light that tells us that they have diabetes or they've actually told us
they've got diabetes or they're on medication for diabetes so you can see
that looking at one of those measures on its own really doesn't tell us enough
about who has diabetes and needs care when you put these different things
together you can start to identify those people who need more health care so the
bottom bar of these two stacked bar charts tells us about those people who
know they have diabetes but their blood sugar levels are still too high and that
suggests they're managing it poorly the next bar up tells us those people whose
blood levels are too high but aren't aware or at least haven't told us that
they have diabetes and in both of those cases you can see that's more for men
than for women so that suggests there's something there about men who either
don't manage their condition very well or don't go to their GP and get it
diagnosed the next spar up on both men and women is those people actually who
know they have diabetes and their blood sugar levels at the right level so
that's suggest they're managing it really well and that seems to be about
the same for men and women and at the very top of the stacked bar are those
people who are on medications for diabetes but don't tell us it in the
survey so that suggests there may be some degree of under reporting in
self-report data about diabetes why people don't tell us they have diabetes
when they clearly are intreated fret we don't really know but
that's something for people who design surveys to think about but in terms of
healthcare you can see that we've identified a number of groups here who
might need some support and if we look at those groups by their education level
for example we can see that those people who aren't managing their diabetes very
well those whose blood sugar levels are still raised seem to be those with less
education and so that might help us to think about how we might target care for
them the final example I want to give in this video is about how biomarkers can
help us think about the pathways between people's social lives and their health
in a different way so the example I want to give is about work and health there's
lots and lots of research that shows us that work or people who work are much
more healthy than people who are unemployed and so we assume from that
that returning to work it's going to improve your health but is all work good
good for health again there's this assumption that and a lot of government
policies that's trying to encourage people with health problems or in an
unemployment back to work assumes that's going to be in good for their health
when we look at the literature about this it's often based on self-report
data so people are asked questions about their job and they might say they don't
have much control or it's not very interesting and things like that and
then they also might say that their Health's not very good so that might
actually not be about the relationship between those two things but perhaps
this person is a pessimistic person anyway but if we use biomarker data we
take away that self-report bias problem because biomarkers are objective
measures of people's health and then we can investigate whether or not there's
still this relation relationship between going back to work and health
improvement so what researchers in this study did is they looked at people's
employment in one wave and then they looked at their employment and health a
year later to see whether or not health improved and they're measuring how
with allostatic load which is as I mentioned earlier a measure of how
stress over a long period of time might impact on your physiological systems and
in part two I'll talk about how to measure that but here I just want to
illustrate this point so in this graph what we see is how people whose remain
unemployed on the left hand side compared with people who go back to work
in different kinds of jobs so if you remain unemployed between two waves of
data collection your allostatic load is about two point five if you go back to a
good job where you have control and you are satisfied with your work the next
bar along that's a much lower allostatic load which is a good thing if you go
back to a job where there's one adverse thing about it then that's better than
being unemployed but naturally enough not as good as going back to a job where
everything is really good for you but finally the far right hand column shows
if you go back to a job that has multiple adverse things about it so you
don't have much control you're not satisfied a whole range of things like
that and that seems to be much worse for you than remaining unemployed so this
shows us that actually not all work is good for us and it may be in thinking
about policies that encourage people back to work both for kind of social
reasons to have employment but also for health reasons we need to think about
the kinds of work that people go back to this was just an illustration of how
biomarkers help us to think about the pathways between social factors and
health a little differently thank you
you
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