Thank you very much and welcome everybody to PHINS 2018.
As Gerry said there is a big team of us that have worked on this project
and we were intending that Wendy would present some of this but frankly all the fault is
mine
and we didn't get as far along as we wanted and so we are not able to present all of the
analysis that Wendy has done.
So I am going to present the bit that we are able to present today.
So, in essence we are going to present some of the preliminary results today of a large
systematic review of international mortality inequality trends.
So the background to this.
Health inequalities in Scotland are known to be wider than in the rest of western and
Central Europe.
So I will just illustrate that quickly, so this is relative index of inequality
so relative index of inequalities in mortality for men across Europe, so there is Scotland there.
So you will see that Scotland has one of the higher rates and indeed you need to
go to Hungary or the Czech Republic to find relative inequalities that are higher than
in Scotland.
Very similar picture for women.
There again is Scotland but in fact relative mortality inequalities for women are higher
than even in Eastern Europe.
So Scotland has huge problem with health inequalities and therefore it is rightly a policy priority here
So in order to inform the policy processes in Scotland and beyond it is important we
understand which nations have experienced improvements or worsenings in their health inequality
trends
and then understand what are the causal factors underlying those trends.
So we can inform policy and practice.
This particular study aimed to describe those trends in inequalities in mortality
and life expectancy amongst adults in high and upper-middle income countries across a
range of measurements of socioeconomic position.
So that means ranking people by social class, or educational attainment or income or area
of deprivation.
And then to examine how mortality inequalities relate to a range of socioeconomic exposures and political
exposures at the national level.
I'm going to tell you a bit about the methods just because there are so many studies in
this area that could be included so we had to narrow it down.
So we did take a systematic review approach which as Julie Arnot, our Knowledge Services
Expert, will tell you was a very comprehensive search
and had a whole range of databases and grey literature.
We then included studies that looked at all-cause mortality, so I'm not going to talk about
cause specific mortality or wellbeing or other measures of health
so just mortality and all-cause mortality.
But also including life expectancy or survival.
We only included countries with a population of greater than 1 million inhabitants, so
you will not see any data at all for Liechtenstein.
In 2010 we defined countries with either a high income or upper-middle income.
That encompasses most countries who have data to be fair in this but obviously the
results are more generalisable to Scotland from that kind of cohort of countries.
We were quiet happy to take data ranked by area deprivation, educational attainment,
social class, occupation, income or wealth or indeed parental education, parental social
class etc. for longitudinal studies.
We also needed to have enough data within the studies to be able to calculate something
called the Relative Index of Inequality or the Slope Index of Inequality.
And I'll tell you a wee bit about that in a second.
So that also meant that some studies had to be excluded for that reason.
We also excluded studies that didn't attempt to be representative of the gross population
so the studies that I'm going to show you are fairly representative of the country that
it is pertaining to.
We excluded studies looking at only children and young people and where the ranking of
socioeconomic position is not possible
so for example some countries there is a lot of studies that look at ethnic differences in health, but of course ethnicity
isn't rankable so we weren't able to include that as measure of health inequalities as we've defined here.
We also excluded studies that were over-adjusted.
So if they showed an inequality in health but then adjusted for things like self-rated health
That was seen as over-adjustment for our purposes, we didn't want to explain those inequalities
by individual level factors.
We did critical appraisal of all of the studies using the Modified Hamilton Tool really looking
for sources of bias and then we calculated the SII and RII.
So just to rehearse that this is some data from Scotland in 2015 as you can expect
the mortality rates in the least deprived areas
are much less in the most deprived areas.
To calculate this you put a best fit regression line across that and how steep that slope is
tells you how bad the inequalities are,
To calculate the SII you look at the top and the bottom of that slope and that is your
SII so in this case it would be about 600 deaths per hundred thousand per year.
To calculate the RII you have to look at the ratio of how many time worse the bottom is
from the top and then divide by the average.
It's a bit of arithmetical thing we do that to take account of secular trends in countries
so that over time as countries improve
or get worse in terms of their overall life expectancy or mortality we can account for
that in the measure.
So to do that you take the average across the whole population you get that average
and divide one by the other and in this case it gives you 1.33.
So that's the two measures I am going to focus on throughout this presentation.
In terms of exposures data we looked at a wide range of different aspects of social
and political exposure,
so welfare state is really important and how generous the welfare state is we know that
and we wanted to include that in our analysis.
We looked at Human Freedoms Index, measure of the actualities of human rights.
The Gender Inequalities Index,
The Index of Economic Freedom,
which is really about how liberal the market is in a country,
we looked a measures of Democracy and also then also measures of public spending on health
and social security.
And then we also included a number of economic measures.
GPD per capita and income inequality.
Okay, so results.
well we found over 17,000 citations which came up in our searches of that we narrowed it
down to 247 studies that we included with that,
fortunately or unfortunately that resulted in 3,106 individual data points.
Which is just a vast amount of data and that is why we have not finished this project yet
and I am just going to present you some of these internal results.
The other problem we have got by having so much data is that there are so many different
ways of cutting the data up an exploring it
So you can look at it separately for men and for women or for the total population, you
can look at relative inequalities or absolute inequalities. i.e. RII or the SII
You can look at life expectancy, survival and mortality and you can't really merge these
things easily
and there is all the different measures of social-economic position so class, education etc etc.
Furthermore it is further complicated by inequalities in health tend to vary by age,
so they tend to be higher in young working age adults and they get smaller as you get
towards the extremes of age.
So none of these can be directly compared.
So there are so many results I need to focus in.
I'm going to start by talking really about male mortality Relative Index of Inequality by educational attainment
and I am going to focus on these results mostly because that is where most of the results are.
I am going to show you a lot of data quite quickly now, don't get scared I'll talk you through it.
So that is all the data for men for all ages by educational attainment results are obviously
really clear to everyone, yeah you all know what is going on?
So, the higher up this scale you are the worse it is, so the higher the inequalities the
lower the better,
along the bottom is time so you've got from 1960 to 2020 here and I will keep these axis
the same throughout.
So a few things to note, this is an example of a single study that shows three or four
data points by age group.
And that is just highlighted there to illustrate that, that is your young working age, that's
late working age
and that's retirement age and you see the inequalities vary across the life course.
And you see that a lot with studies so there is quite a lot there and in the mid 2000 just
showing different age groups in populations.
As you go on we get more data, so there is very little data prior to 1980 available but
from 1990 onwards we have a vast array of data for a vast array of countries.
And there is also a general trend that taking all of these countries and all of this data
together in general inequalities seem to be going up in this measure.
But lets get into it in a bit more detail.
I've just taken out the point data so this is now only the trends data so you can see
we've lost a lot from the last slide.
And then if we take the higher qualities study only,
so there is a lot of studies in there where there is a really high risk of bias so again
that reduces our numbers down quite vastly
and then if I just show you the stable of improving trends,
so suddenly the graph becomes much much clearer because unfortunately we don't have so many
examples of relative inequalities that are improving,
but we do have some.
So, our countries here are, Finland, Denmark, France, some parts of the USA, England, Wales, Sweden, Denmark
Some of these results aren't particularly spectacular and is basically a flat line for
five or ten years.
But for some of them we do have good examples of improvements.
I'm going to talk you through some of those improvements later on.
But before that I just wanted to show you some of the data for females.
So again this is female mortality, Relative Index of Inequality by education so again
there is all the data the messy data set that I showed you about
very similar trends to men, usually most of these studies have male and female data included
within them.
Again if we just show you the trends data that cuts out a lot and then if you take out
the higher quality studies again, much fewer studies available.
So I am just going to show you some selected examples of worsening trends here, we have
so many examples of trends that are getting worse on this measure that I would be there
all day trying to explain them.
So just to take some concrete examples.
So there is Estonia, Hungary and New Zealand and I will talk you through some of those
causal pathways for those later on.
So if we turn now to try to explain some of those trends again these results are preliminary
got to make that point again.
And we will do, I say we, Alastair Layland will do some multi level modeling on this
once we have the data set entirely clean for all of these measures.
But one of the problems that we have had is that the data set is so very messy so we've
got all these different trends by age, social economic position
and then there is lots of missing data for both our outcomes measures and our exposures too.
So again illustrative case studies here, I am going to use Estonia as an example of Eastern Europe,
we are fairly confident that inequalities and mortality across much of Eastern Europe
increased during the 1990s so I will use that as an example.
And I want to use Finland and France as examples of long high quality data sets which have
had periods of improvement
and just to give some illustrations about some of the factors of what may lie behind
that improvement in relative inequalities.
So, I've just pulled out those three lines so there's France in Blue, Finland in Red here and then Estonia
So you can see that relative inequalities for France by occupation and social class between
the 1970s and early 1990s improved for working age men,
similar picture for Finland, working age men between 1970 and about 1985.
Again it's quite a substantial improvement in Relative Index of Inequality.
And there is an example of Eastern Europe, we could have used just about any country
in Eastern Europe to be honest over that time period.
But that's Estonia for working age men between 1989 and 2001, quite a marked rise in inequalities.
So first of all we are going to talk about income and inequality the good thing about
the income and inequality data sets is that they are complete,
they go back a long, long way so I am more certain about these results than I am about
some of the others.
So there is the income inequality graph for Finland, so higher up this scale is more unequal
lower down this scale is more equal.
In theory it runs from 0 to 100 but it's almost impossible to get values that are that extreme
the most unequal countries of the world are about 50 or 60 so places like Brazil or some parts of Sub-Saharan Africa
But most equal countries are about 20.
So you can see that Finland is a relatively equal country over the whole time series but
there was a marked improvement in the 1960s, 70s
flat lined in the 1980s and then increased slightly thereafter.
So what's of relevance here, is those two time periods that I've highlighted
so at the same time as the Relative Index of Inequality for Mortality improves and gets narrower through the 1970s and early 1980s
that is the same period where the income inequalities have also improved in Finland.
So that is some good evidence that these two may be related.
That is strengthened by the fact that if you take France again their income
inequalities are higher throughout the whole time period sitting between 30 and 35 fairly
flat until about 1983 or 84
which is I think roughly when Francois Mitterrand came into power and declined slightly thereafter
and then was stable.
Again if we look at that time period it corresponds with decline in mortality in equalities in France
and we see a decline in income equalities overall with a blip in the middle.
So again a suggestion that the two maybe related then finally to look at Estonia so we don't have such a long time series for Estonia as
it was part of the USSR prior to that
but again you can see a marked rise in income inequalities to become the highest of these
three examples.
And again that rise in income inequalities corresponds to the rise in health inequalities
over the same time period.
So we think that income inequality is probably a good predictor and a good cause in health
inequalities.
And it works both ways so an improvement in income inequalities seems to lead to an improvement
in health inequalities and vice versa.
To look at the next set of data, I am going to look at an index of democracy, so this
is a less complete data set,
so it is based on the Polity 2 index, just show you the data.
So 10 here is very Democratic and 1 is very Undemocratic.
You can see that Finland throughout this time period was very democratic
France which may surprise some of you, I had to go back and check my history here,
was actually fairly undemocratic post WWII, it didn't have a free press, there was various
militaristic leaders for a long period of time.
But obviously their democracy improved over time still not reaching the heavy heights
of Finland though.
When will we ever be like Scandinavia I hear you ask.
And then Estonia again, fairly undemocratic in the early 1990s and then improving thereafter.
So again to try and look at the trends so that time period of France becoming more Democratic
corresponds with the same time period of France become less unequal in terms of its mortality.
For Estonia again pretty unclear here, very undemocratic at the start of the time series.
The change doesn't really happen until after the mortality inequalities have happened.
So again it's not out with keeping with the idea that better Democracy improves health inequalities
And then for Finland they have just been very democratic throughout and perhaps just having
that very sustained democratic society
could contribute to better health inequalities.
Okay the last example that I am going to give you is about economic freedoms so this is
about how marketised the economy is.
So there is the data for three example countries again so in this example 10 is more marketised,
so a more free market system and 1 would be a sort of communist state if you like.
So, again during this period here that France has we don't have the whole data series so
again we have a lot of missing data in terms of our exposures.
But for the period that we do have France has a fairly heavily regulated market.
And that seems to correspond to a time when health inequalities are narrowing.
For Estonia we have got a very short time series very difficult to see very much about that
But for Finland again, we are missing a lot of the early time series that would be of interest
but what we have got does suggest that a more regulated market does seem to be correlated
with an improvement with Health Inequalities.
These results are all very tentative and once we give the data to Alistair to do the multi
level model
we will be much more certain about whether these trends are real and whether there is a causal relationship.
But hopefully that give some illustration at the moment.
So to summarise what I have said health inequalities are greatest for young adults and for men, compared to women.
and there have been large increases in health inequalities in many nations during the 1980s
and 1990s .
But it is not true to say that no where declines.
So some people have claimed in academic literature recently that relative inequalities are just
not worth talking as they will not be reduced
but I have shown today that there are a number of counties that have shown a decline in relative
inequalities so therefore it is possible given the right social and political context.
There are lots of caveats to this data so we do see divergent trends for different measures
of socioeconomic position.
So for example one measure for social economic inequalities in mortality in class might go
in a different direction than it might for educational attainment
and that is quite problematic as we are trying to explain these in a coherent way.
It's also true that the quality of the data really matters, so the more biased data often
goes in a completely different direction from the better quality data.
But our earlier analysis does suggest that income inequality, democracy and market regulation
are important.
So our next steps are multilevel modelling that I have suggested.
But we will also do case studies of countries trying to pull together all the health inequalities
data for that country and then looking at the social backdrop to that.
And particularly focusing on what policy mixes are the most effective and ineffective at
reducing inequalities in health.
And then we also want to understand much better why the different measures of social economic
position diverge,
do they measures some sort of different class and social process in society.
And then of course we still have quality checks to do but we will make this data set widely available for others as well.
I just want to thank my co-authors Wendy, Vittal, David, Aaron, Julie and Alistair.
If you do want more information then just email me, or send me a direct message on Twitter.
If you want to see more about Protocol it's registered at the University of York and the CRD center
and all the details of the analysis and what we have got planned are listed there.
Thank you very much.