My talk today is titled.
And when our health inequalities unfair.
I will, I will explain how this connects
to this course.
So it's a usual disclaimer.
I just to represent my view and then no one else
answer this talk.
The focus is equity considerations
in the design
and conduct of clinical research.
So here when I say clinical research
I use that term very broad way.
So anything to do with any human health.
And then I give a lot of example of the intervention
studies and then clinical trials.
But what I talk about today
really applies to any other kind of like
observational studies and the other designs too.
And then it's not just that medicine person.
It could be you can extend this discussion
to social behavioral research to.
And then I want to
make two points, in this talk.
First is that the equity considerations are not a
checkbox.
They're woven into principles for ethical,
clinical research.
So I suppose you're, quite familiar with,
these eight principles for ethical clinical research.
So when I say equity, it's not like
I want to add one more consideration to the
eight.
It's more like for every thing, every one of them,
you have ethical considerations.
And then the interesting thing is that
then it's not like I when I say it's not checkbox,
which means that then it's not like okay,
for this one
principle there's then the ethics consideration
check.
Another one check. It's not like that.
It's not that simple.
So if you think about ethical considerations in one,
that might lead to the other principle.
So it's kind of woven into and today
because of that time considerations
and that I just focus on these three.
And then the
second point I want to make is the bottom one
to make equity constellations explicit and meaningful.
It is important to think carefully
about when health inequalities are unfair.
So probably kind of what what do I mean?
You know, I hope this becomes clear as I
talk.
So today
this talk is guided by three questions.
First, who should be in the study and why?
Second, which health inequalities are unfair and why?
And then finally, how do equity considerations influence this
study?
Design, analysis, plan and reporting.
And then this is not the easy question.
It's like I mean we have a clear answer.
And you can memorize it and sometimes answers your
meaning
and you'll have to pick.
So it's you know,
it's not like an easy but and I hope
that then that by the end of this talk,
I can convince you, Nathan, it's important to examine these
questions.
So let's start.
Who should be in a study and then why
and then why?
The reason I think that there are three.
We can think about participation,
biology and societal concern.
And then I'll go one by one.
The first it's participation.
Probably familiar to you from previous lectures.
So consideration here is the fair
distribution of benefits and the burden.
So as you probably already heard,
what it means to participate in studies has changed
from bearing the burden to accessing to innovations.
So it used to be that, the participation
in the clinical trials
is the special thing that you needed.
The justification and then study
participants are usually very similar homogeneous people.
And now it's shifted that then that the default is
that then everybody should be able to participate.
And if you exclude them you need justification.
So I think that this in itself is in the quite interesting
history
of how our views changed.
But here, if, that could be one reason
that then the why you want to, involve,
diverse population in your study
or because, you know, you care about fair
distribution of benefits and a burden.
But that's not the only reason
why you might want to think about
different kinds of people in your study.
And then here I give you biology and societal
concern.
So for biology,
the thinking is that, that you are interested in in
your study,
the some causal, relationship x causes Y.
And you might believe for whatever reason
that then that,
causality may work differently in different groups
of human beings because of biological
differences.
So here I want to give an example of the
dual
vascular disease.
This is kind of infographics,
from the American Heart Association.
So heart attack symptoms,
between men and women
probably this is now, you know, they quite,
pushed out a lot so that some people know
that men and women have a quite different
symptoms of the heart attack.
You can see I don't go over, details,
but just looking at it.
Women have a longer list than the men.
So you can imagine that.
Then you just study men.
You miss what's happening in women.
And it's not just the symptoms.
That's different.
And then it's underlying, like, a
biological mechanisms could be different.
So this is on the, one of the recent studies.
Again, I'm not gonna go into the details
because this is not a medical talk, but,
it shows that
in that probably there's some important hormonal
and the molecular mechanisms difference between men and
women,
although we are talking about exactly the same disease.
So how that manifests in the body
is biologically different
between men and women. Women.
So that is a good reason to suspect that in a well,
we cannot just study men, which we traditionally did.
We might miss a lot of things.
And we cannot say that causal relationship, generalize
to women.
So there's a good reason that then that
you should study both of these groups.
So it's just in the one of the examples
of the biology drives
your interest to have different kind of people in your study.
Another one is the societal concern.
So here again, we might be interested
in some of the causal relationship.
And you might expect that
that relationship works differently
for different kinds of people because of how we organize our
society.
So one way
to think about, think about this is that there might be
differential
intervention effects by social group characteristics.
So here example is that, the effects of women's
group participation on neonatal mortality
rate by marginalization.
So let me explain a bit more.
So this is taken from the meta analysis.
So there are many different studies
countries they focus on that.
And then I'm just focusing on one country
without Nepal.
So intervention is that an older some pregnant
women,
participating in a, some women's group intervention while they're
pregnant.
And then, the comparison
is an a waste intervention and then result and then
outcome.
We are interested in interested in
is that the neonatal mortality of the,
the babies of the women, if we participate
or not participate in the intervention.
And then
here the investigators thought that maybe this
intervention,
have a different effect depending on the,
degree of marginalization here.
In this study, marginalization is the,
define lowest literacy.
So most marginalized group, is
the women who were little bit and poor,
and the less marginalized is the less, the rest of the
women.
So the right hand side, you see, you know,
I can't use it.
So you see that the dotted line is the control
group,
and the only line is the intervention.
And then you see on the vertical line
is the, neonatal mortality rate.
So going up higher is then the high
mortality rate, which is not good.
So you want to be lower and you see year one.
And then you okay.
So here we see that then that for the control group
the intervention works for the year
one and two
because they decline is lower than the dotted line.
But interestingly for the marginalized group, if
you just
look at the first year there doesn't seem
to be any intervention effect.
But year two is a big, big difference.
So if you don't suspect that, then the maybe this intervene in
effect
is different depending on the different kinds of
people.
You can design this study and you would totally miss that.
So that's one way to kind of think, okay,
when I, design the study,
maybe I need to include different
kinds of women,
based on the marginalization, because I expect this.
Right. So that's one way.
The other is the bottom one.
So you might expect that, differential intervention
effects
within the traditionally excluded groups.
So here I want to give you an example of
the,
evidence of a high intensity lifestyle based program
for obesity treatment in primary care
clinics in low income neighborhoods.
So now we are just focusing on traditionally excluded group,
which is that low income neighborhood.
So here is the study.
So population is the primary care
patients living in low income.
Neighborhood intervention is a high intensity
lifestyle based program for obesity
treatment.
Usually these things, include like a, you know, diet
and nutrition advice and then the physical exercise
programs
and all those kind of things, or combine
comparison and then this intensive program
and the usual care, usual care could be just doctors advice.
They're not in the intervention program.
Outcome is that on the percentage
change of their body weight.
And the major findings is that in
in two years, how much body weight change?
So here you see that the income intensive lifestyle
group had the, the biggest amount of weight
loss.
So it seems to be working and, and it's good
that the navigation in this study,
the study population is entirely
those people who were traditionally
not included, you know, excluded in that,
the studies and the we don't pay attention
so much.
So this is a good results.
And then you would imagine that the, this,
low income neighborhoods,
they share one characteristic which is low income.
But in the they are different kinds of
people.
So there might be some different intervention effects within
them.
So this study authors did just that.
So how about by race?
So in this neighborhood, their black, people, non-black
people.
And then are there some differences?
And yes, they found the difference.
So here you see the, the, solid line is in this graph
is the usual care dotted line is the intervention group.
So for most intervention groups it works.
But interestingly a nice a bit unfortunately
the effect of the intervention is a bit less
for black population than the other race.
Right.
So if you design this way you could kind of tease out the
this and different, intervention effect.
So so
I introduce you like a for societal concern.
One could be that then the differential
intervention effect.
But social group characteristic that some people study.
The other is that that you can exclusively focus
on
the traditionally excluded group,
which is the obesity intervention study.
So primary interest here is that on the
why we're interested in this and societal concern
is that, it's driven from the concern
for unfairness or inequity.
So these groups, when we think about them,
there's
something wrong about it, ethically and morally.
That should be the reason why
we want to study.
So here's a good,
time to talk about terminology.
So I've been saying the equity inequality,
I saw many different terms.
And it's super confusing because they have many similar
terms.
And unfortunately researchers don't use consistently.
And then also when you talk to philosophers,
public health people, they use differently.
So this is I would claim that and this is
the way, you know, people should use.
But this is how I make sense of it.
And I hope it makes sense to you.
And then in my advice is the to understand
this now.
And when you talk to people about equity,
you you have a conversation that the
what do you mean equity.
And in addition, to make it clear
that they know what you're talking about,
it's just super confusing in the field.
I've been working in this field for 20 years and it's
unfortunately
still like that.
So the way I, it makes sense to me
is that the,
health inequalities, these are differences
between groups or individuals, just differences.
If you don't have any anything, it's just differences.
And then
among them, their health inequities.
And when I say health inequities,
which is unfair,
how differences between groups or across
individuals.
As you can imagine, unfair.
You can define differently
and maybe depends on how you feel.
But the difference here is that in health,
inequalities are not ethically moral.
It's just a gigantic category of
differences.
And when you start to think about unfairness
ethically problematic, then it's inequity.
And then often people use health disparities.
And that's how, can use a lot in the United
States.
And this is how I understand it.
So it's one way to define what is unfair.
So disparities I have differences associated
with
some socially meaningful characteristics
often indicative of social disadvantage.
So for example health disparities
or health differences by race
income education disability status.
And those grades kind of indicate social
disadvantage.
And then how is equity
which I use as the absence of health
inequities.
If you forget everything
about what I say today, I hope this is the one that then
to you,
you know, in your dream it shows up sometime.
So I kind of made a big thing about the non societal concern.
And then we talk about,
you know, inequity disparities difference
and so I carry on that.
And now it comes to the second guiding
question of which these are unfair.
And why.
Because in that
I give you the kind of distinction between
how is inequality and inequity.
And inequity is the unfair inequalities.
But what do you mean by unfair?
So, this stepping back,
including diverse study subjects for societal
concern,
that kind of study often is operationalized to include or
focus on
historically overlooked social groups.
So, as I already mentioned in the Health
Disparities group select,
this signifies social disadvantage or power
imbalance.
So in sample.
So what are those groups I already kind of behind it.
You know, NIH guidelines,
says that the minority minority group
is the readily
identifiable subset of the US population,
which
which is distinguished by either racial, ethnic, and or cultural
heritage.
So that's the definition.
And then also there's this thing called a progress
class.
This is just acronym.
So you can see what the progress stands for
here.
So this is some kind developed
by some researchers 2003 and Cochrane,
a Campbell collaboration, which is they are
establishing method for the,
the systematic reviews and a meta analysis
so that there are many different,
single studies.
But we need to look
all those evidence together and what we can say and how to
do it.
So that's the kind of groups needed.
And then they took on to, maybe equity
considerations are important.
And they promoted this a progress class.
So if we are interested in certain groups
for societal concern,
these are the groups. So you can look at them
and then you
hear whatever way you look in the literature.
The common thread is what I call binary approach.
So this is how we think.
So they say health, you know, whatever health outcome you look
for,
it could be life expectancy, mortality,
health equity, quality of life,
cancer prevalence, whatever.
You can take a look.
It's the health you can measure.
And going up there's a healthy going down.
There's a less healthy.
And then you have these social groups
you're interested in.
And then I quote attribute by attribute
like income, race, ethnicity,
sexual orientation, disability status,
whatever you're interested in.
You can have groups.
So this is how people typically think about it.
It's okay.
But in it, I think there's some challenges
of this way of centering.
And you know, I just list it for you.
So, now
these are kind of groups that indicate social
disadvantage.
That list is growing longer and longer every year,
which in itself is not not a bad thing
because I think we are more sensitive
about those issues.
But in them list is getting longer,
meaning that, there are many, many groups
every year.
And then also in that,
the picture I show you, you know,
it looks as if,
if you belong to that group, your health
is exactly the same within the group.
And of course it's not the case. Right.
So the earlier I showed you that the mapping that the
obesity, study,
you went to the lower, income neighborhood, but in the
there the racial difference.
So some there's some differences between them.
But it's like a this way of looking at
is that then
the intersectionality are often overlooked.
And then if you start to pay attention
to intersectionality, for example, the income and in
the race,
then you have more number of people
to look into.
So that group is getting more and more.
And then also interestingly, the group definition itself
could change.
So I can think about sex, you know,
when I go into graduate school, like,
yeah, sex is a binary variable.
And then now is like a low maybe not.
It's a continuous.
And you have to measure differently.
So that group definition itself could change.
And then often those reasons why the selected group
characteristics
signifies concerns for unfairness
and inequity, are often just intriguing.
Well, it's a social use advantage.
I mean, you don't go anything beyond.
So then what's happening is that there's so many
different groups.
We think it's important.
And then we describe inequality this way
and the multiple way and then bits of lots of
information.
And then a simple question like are we making any progress
to reduce those inequalities.
We don't know because there's so many.
And then how can we make a kind of synthesize account?
How can we synthesize those results.
We don't know.
So I think that then a deeper philosophical
discussion is
we need to include a diverse set of subjects for societal
concern.
So as I already mentioned in some examples, for social
groups
of concern, racial, ethnic minorities,
sexual gender, minorities,
people with low income, low education
and disabilities, these are just examples.
But then you have to ask which groups to choose.
And then and right now researchers
just pick up four very intricate sets.
But I think that and then maybe we can do
a little bit deeper.
We say, you know, philosophy for X and Y.
One is concerned about these social groups.
So here I got the one example of the Eric lectures work.
So maybe you're concerned about recognition of people
then it's a good reason to pick
racial, ethnic or asexual or gender minorities.
So something to do with and discrimination
that you might pick does.
Or maybe you're concerned about resources,
how resources are distributed.
So you might go to more class based groups
like a people with low income education
or maybe
concerns for representation, like a political representation.
That's your reasons.
And people with disability.
It's just an example. But you can see that.
Then you go some deeper than that
might give you some reason that why you pick those groups
or I add concerns for the current
or historical situation.
So, I'm from Canada, so I feel more
comfortable to give you an example here.
Canadian example, which is indigenous population
in Canada past ten years, indigenous
populations.
And what Canada did to them became
just so important.
And and now it's like, well, they're
rich indigenous people, highly educated, politically,
quite strongly influential indigenous people.
But it doesn't matter if you have indigenous heritage.
We have to be worried about
because of that historical reason,
what we did as a country.
So that's just no question other than then you have to
measure.
So that could be one good reason.
So I just want to have that kind of conversation going
a little bit for the empirical researchers
to show these kind of results.
So that's my hope that then, people
start to talk about it more.
So the final, the guiding question.
So now I hope you kind of have us some,
you know, ideas of what this equity considerations are.
But then how do equity considerations
influence the study
design analysis plan and reporting.
So again,
you have to go back to the reason
why you wanted to include
diverse population and participation
biology and societal concerns.
And then I want to walk you through.
But here is that just a stepping back.
The traditional study is that you have a population
in your study subject intervention group,
the non intervention group.
And then you want to see the outcome
and how that's different.
So this is typical way to do and I'm sorry
the example
hypothetical example I assigned a reading Cookson's
work.
So I want to walk through you with this example.
Populations that low income country
intervention is an antenatal dietary
medication and supplementation program,
much like, you know, pregnant women.
A the
the intervention study that we talked about earlier,
comparisons are babies whose mothers received the
intervention
and the babies whose mothers did not receive the
intervention.
And then we want to know that birth weight,
so traditional
study is and usually we look at an average treatment
effect.
So here you see that that's the density.
Like a what's the, the, birth weight.
And then here's the worst way going that way.
And then it's the, what's the distribution of it
and then a control and then untreated it's a
different color
and then their different, distribution
and then and that dotted line is the average.
So you analyze the difference between this, that means
and you report what's the difference between
them.
So that's the traditional way
that the, the all the typical intervention
studies we do.
So how does it look if you have equity
concentrations.
So the reason why you're interested
in encouraging diverse population is that
participation.
We already talked about this right then.
And then if
fair subject selection is a primary concern
here,
and then if there's no reason to suspect
or the interest in potentially
differential intervention effects
due to biology or societal concern,
no additional considerations for this study.
Design analysis on reporting, maybe necessary.
All you have to worry about is that the
you go to the diverse population
and you make sure that the study subjects,
selection is fair
and then you just do business as usual.
But if it is biology, remember that
in a cardiovascular disease, maybe men
and the women there are different,
then it has a big implication
for study design, analysis plan and
reporting.
So you need to make sure you include
both groups, men and women.
In that example you have to run the study
like among them intervention works.
And then you have to do the separate
analysis.
If you are a quantitative person,
it's a stratified
analysis, we say, and you have to report
separately.
Otherwise you don't know if men and women have a
different, effect,
societal concern, which is the equity,
then yes,
you have to do a bit differently.
And there's an equity consideration.
People start to use different terms,
like in health equity relevant
randomized trials or equity informed informative
methods.
And they're two different approaches.
One is focusing on the traditionally excluded
good
and the other is in assessing differences
across social groups.
So focusing on traditionally escalate group,
which is not that different in terms of the study design.
It's just me. You have to go to those.
And that traditionally excluded groups
like all the example is that's in there,
the researchers who went to the low income
neighborhood
and then they selected people, and then the rest is business,
as usual. Right.
So now these distributions,
came from that particular a group.
And then you see intervention effect,
just like the traditional study.
It's just a population is different here,
but a bit more complex.
This and assessing differences across social groups.
Now you have to go to the population.
Well representing that social characteristic of
interest.
So if we are interested in income
you'll have to have
a very low income people.
And in high income people you cannot
just have a certain income people.
Right.
So you do that and then when you run
the study, make sure you measure it.
You have to measure income.
Every income is your interest race.
If race is the interest and the analysis
the examine the intervention effect
across those characteristics in a way that.
So the study would look like this.
So this is the again that cookson's study.
But the null income rank is added.
So the each day low income to high income.
Right. And then what's the birth weight.
And then
here you can see for that low birth weight
what's the difference
between intervention and non intervention group.
And then a middle.
And in a high income high.
So you can at the different
that level of income how the intervention effect is
different.
And you can report that.
So for this hypothetical study it's a wonderful study because.
And then the lower the income is the greater the intervention
effect is.
If you data measure income you can't do this right.
And so you need to plan in advance
of what you want to look for
and how to analyze it and how to report.
So there's some challenges
for equity relevant and informative
studies.
One is sample size because on that those are the
groups
indicating the social disadvantage
are usually very small.
So large sample size is needed for typically
small groups.
We are interested in
minority groups and the racial minority
groups.
And by definition minority is a small group.
So you have to increase the number.
But as long as diverse socioeconomic
characteristics are reported,
studies can be equity relevant, informative, post hoc,
meaning that only if you report it
and then if you
if you measure if you report it and if you publish the study
later, people can go back and then check it again.
And that's not there.
Many quite exciting quantitative methods
how to do it
so that combining data from many studies
does the meta analysis.
So your study might be very small,
but it makes you collect and combine
and later.
And then also there's a method called the Bayesian methods.
And that overcome the small sample size issues.
So at least it's wonderful if you can design
a very large study.
That's enough, power to detect those.
And small differences that small groups, if you cannot
still measure those characteristics,
then you can help, other researchers
to kind of combining the power later
cost could be the issue because and
and now you have to go to those groups
that then traditionally not invited.
Therefore they may not be very willing
to participate.
But in interestingly NIH guidelines,
it doesn't a cost cannot be the reason.
And so what happens is that, researchers
write their grants
and then they justify, you know, we want to do this study.
And if it is more expensive equation
and they want to go into that,
those difficult to reach population
for their societal concern.
And I
sure will do it because it's important.
So cost should not be the reason I think that's ideal.
And in practice maybe that state consideration
to make.
But in that I think it's a good thing to
think about.
And then, as you could see in my little
diagram,
this equity relevance study is again,
fairly complex in how you design it.
So the summary,
we asked in three guiding questions
who should be in the study and, and why.
They're different reasons and which health
inequalities are unfair and and why.
And then how do equity considerations
influence
the study design analysis plan and reporting.
And then I hope the the two key points
I wanted to make are now kind of clear
equity considerations are not the
checkbox.
And they are woven into the principles
for ethical clinical research
and to make equity considerations
explicit and meaningful.
It is important to think carefully about
when inequalities around their.
Okay. Thank you very much.