It took me a long time to realize that the implicit theory underlying a lot of work on health inequality was the classic American idea of Structural-Functionalism, developed by people like Talcott Parsons and Robert Merton. This was dozy of me, as decades ago Gordon Marshall had pointed out that the Registrar-General's Social Class schema used in research on health inequality in UK was based on, as he saw it, outdated functionalist and eugenic notions.
When the national statistics office for England & Wales adopted a new measure of social class for the 2001 Census and all other official statistics from then onward, I did notice that people had trouble using it. There seemed to be a constant attempt to drag the meaning of the measure away from "employment relations and conditions", which was its theoretical basis, back toward something like "manual/ non-manual". I have already written about this all over the place, including in previous blogs. So I won't bind on about it too much more here.
But recent exhaustive international comparative research has given the idea new legs. It turns out that over the last 30-40 years, Italy and Spain have stubbornly retained the smallest differences in mortality and life expectancy between those in the most and the least advantaged social circumstances, whether these were measured by income, education or social class. I well remember the total shock 25 years ago when this was first discovered. Italy & Spain don't have the lowest income inequality for example, or the most egalitarian welfare states. In fact, health inequality in most studies is higher in the egalitarianNordic nations. For many years the standard explanation was that the smoking epidemic was delayed in these Mediterranean nations: smoking was slow to become concentrated among people with lower income and less advantaged occupations.
However, as the years rolled by, it began to seem less likely that the narrower health gaps in the Mediterranean nations could be purely due to smoking. So an alternative explanation began to emerge.
According to this idea, the size of the differences in health & life expectancy between more & less socially advantaged groups might be due to the ways in which their home nations allocate people into these groups. Accroding the the Functionalist theory, societies like the Norway & Sweden are more meritocratic. Education is available to everyone up to an advanced level. This helps to make sure that the fittest & most able people are channeled into those jobs that are most essential for the "functioning" of society, like senior management, judges, military and political leaders. Regardless of origin family, the fittest and most intelligent people will be channelled through the schools and universities into these important positions. In order to motivate the fittest people to aspire to these destinations, salaries & status are high. As fitness for high position is only partly determined by genetic inheritance from the parents, this process is important to make sure that "good functioning" is ensured. A society cannot just rely on allocating the sons (and it would be sons) of the powerful into powerful positions themselves. There will have to be a turnover such that the less fit sons of more advantaged families in one generation are filtered out by the education system and replaced by the fitter sons of the less advantaged. So the less fit fall down the social ladder and contribute to worse health in the less advantaged social groups, and vice versa. Michael Young wrote about this many decades ago in his book "The Rise of the Meritocracy", depicting an eventual dystopia in which society was divided into extremes of health, intelligence, and income.
There is some evidence in favour of this idea
https://academic.oup.com/eurpub/article/23/6/1010/439677
In addition, there were large increases in health inequality in England and Wales duriing the 1950s-1980s, a time when the numbers of middle class jobs increased enormously, with an associated increase in social mobility (although this mobility took place during the work career, not through the educaiton system). One might argue that the increase in mortality among older working men in unskilled manual jobs (which was the main source of the rising inequality) resulted from an unfit group being left behind.
https://tinyurl.com/u4vazyg
On the other hand, extensive sociological research has documented a pretty hefty role for the social class, income and education level of the parents in determining those of the children, even after taking coognitive variables ("intelligence") into account. If anything, the expansion of higher education in the UK, for example, has mainly benefited middle class children. Almost 100% of middle class children now go to University and get degrees, which must therefore cover a wide spectrum of the ability range.
So there is a certain amlunt of evidence out there that can be used to test this idea further. But a lot remains to be done.
Friday, 17 January 2020
Wednesday, 23 October 2019
Eugenic thinking is not dead if you know where to look
There has recently been a lot of
dicussion about the award of a prestigious fellowship at Cambridge University
to Dr Noah Carl. I am not at all familiar with Dr Carl's work so not in a
position to make any comment at all on whether it give comfort and succour to
racists or eugenicists at the expense of scientific accuracy. Agreement broke
out, at least on my Twitter time line, that critics have every opportunity to
test rigorously his work as he uses openly available data. Which is an
excellent argument for open data, but that is another story (see my earlier
blog "In God we trust, the rest must bring data").
One direction in which the Twitter
exchange led, however, resulted in me feelinig that a lot of sociologits have
been leading somewhat sheltered live in terms of their familiarity with the
influence of eugenic ideas in neighbouring disciplines. I work with
epidemiologists. And the first thing one notices turning back to sociological
literature is that no sociologist has used the Registrar General's Social Class
schema (RG) for, oh, 30 years at least. Long ago, Gordon Marshall wrote of the
"eugenic assumptions" that underlie this classification and I have
also written a couple of blogs about this ("What is wrong with
"SES""). But in social epidemiology, after a move in a different
drection up to about 10 years ago , the use of measures and thinking that is
based on the same ideas as the RG classification has come roaring back.
All this has coincided with the
rise and rise of genetic epidemiology. Let me give a couple of quotes from
recent papers. Here is one from a paper in a high ranked journal called "Intelligence
in youth and all-cause-mortality: systematic review with meta-analysis"
published in 2011:
"Twin studies to determine the extent to which intelligence
shares genetic and environmental causes with health, education, and social
class, in predicting mortality, will also help to inform this issue. With
evidence of associations between cognitive performance and education showing
substantial heritability, it is possible that these variables may share some
genetic effects in predicting death."
Perhaps the recent paper that is
being most discussed is called “Gene discovery and polygenic prediction from
a genome-wide association study of educational attainment in 1.1 million
individuals” published in Nature Genetics this year. Here is a quote
from its Conclusions
For research in
social science and epidemiology, the polygenic scores
that we construct—which explain 11–13% and 7–10% of the variance in educational
attainment and cognitive performance, respectively—will prove useful across at
least three types of applications. First, by examining
associations between the scores and high-quality
measures of endophenotypes, researchers may
be able to disentangle the mechanisms by which genetic
factors affect educational attainment and cognitive phenotypes.”
The authors of this study were eager to avoid
misinterpretation of their findings. On the website of the Social Science Genetic
Consortium https://www.thessgac.org/ (stongly
recommended) are a large number of "FAQs" that had been raised about
the study, with associated sensible comments for example:
"it is important to
keep in mind that the score fails to predict the vast majority
(89%) of variation in years of education across individuals. Many of those with
low polygenic scores go on to achieve high levels of education, and a large
proportion of those with high polygenic scores do not complete college.
Thus, an important message
of this paper and our earlier papers is that DNA does not “determine”
an individual’s level of education, for multiple reasons: First, it is
estimated that, at least in the environments in which we have been measuring
it, the additive effects of common genetic variants will only ever predict
about 20% of the variance in educational attainment across individuals. Second, today’s polygenic
score is only able to predict a little more than half of that 20% (11
percentage points).
Thus
it seemed that “social genetics” (a term I hate) are too fascinating for many researchers
to avoid, is being carried out by responsible people. And indeed, the GWAS
studies have revealed important differences to the twin studies that have been
carried on for many years .
But I guess it was never going to stop
there. And in the last week or so a new paper has appeared: Genetic
consequences of social stratification in Great Britain by Abdel
Abdellaoui and colleagues, available at http://dx.doi.org/10.1101/457515 .
In their words:
We show that the
geographic clustering of genome-wide trait-associated alleles is related to
recent geographic movement of people and that the resulting regional genetic
patterns are associated with regional socioeconomic and cultural outcomes......
The strongest clustering was observed for Educational Attainment (EA). Among
the rest of the geographically clustered traits are body dimensions,
personality dimensions, and physical and mental health traits. ... Our results
show that people with a genetic predisposition for higher cognitive abilities
are leaving these (deprived) regions, likely attracted by better educational or
occupational opportunities in other regions. In fact, the people who were born
in coal mining areas and migrated to better neighbourhoods have higher average
EA polygenic scores than people born outside of these regions. The regional
clustering of cognitive abilities that follows may further affect the economic
development of neighbourhoods.
The
authors do go on to speculate that social policies might need to be devised to
lower the tendency of geographical mobility to increase differences in
deprivation between areas. If those with higher genetic scores for educational
attainment did not have to travel to more prosperous areas in order to get
better jobs and increase their income, the argument goes, this might slow down
the tendency of health inequalities between areas to widen over time.
A
clearly written cautionary commentary has also appeared in Nature at https://www.nature.com/articles/d41586-019-03171-6
But it is hard to escape the impression
of an element of dog-whistling in this literature. At the very least, sociologists
need to be on their toes in respect of it.
Monday, 21 October 2019
An interesting papear has recenty appeared in the Journal of Epidemology and Community Health by Murray et al, entitled Inequalities in time from stopping paid work to death: findings from the ONS
Longitudinal Study, 2001 to 2011
open access version at https://discovery.ucl.ac.uk/id/eprint/10082463/1/Murray%2020190821_JECH_R2_manuscript_CLEAN%20copy.pdf
At first sight it might appear puzzling that the bottom line of the paper is that people who had worked in less advantaged, lower skilled & lower status jobs (defined by the Registrar General's class schema, which is conceptually based on skills and status) actually lived longer after leaving the labour force than those whose jobs were of higher skill and status. To cite the paper itself:
"LS members who had worked in lower social classes lived a greater number of years after they stopped work, with more time per decreasing social class."
How to make sense of this? Actually, a similar issue was also a big puzzle for the authors of the first book that decribed the contribution of the LS to the study of health inequality. To the amazement of almost everyone involved, the earliest analysis showed no social class gradient in health, or even a hint of a reverse gradient. Did this mean that all the previous research on social class differences in health including those in the Black Report, had been biased by either artefact or selection?
And what does this have to do with Murray et al.'s paper?
To cut a long story short, it turned out that high status, high skilled jobs also benefit from less arduous working conditions. Just think about the difference between a building worker and a university lecturer. Lets say both of them start to get angina pectoris. The lecturer, first of all, will probably not be affected by narowing of her coronary arteries as soon because she does not need to do much heavy lifting, digging, or other activities that equire increaed cardiac output. And when she does start to notice, it is not too hard to keep her job if she also needs to do less of these kinds of things (moving furniture, lifting large piles of books or files...). By the time she even notices that her cardiovascular function is declining, let alone has to give up her job, her disease is far more advanced. Contrast to the building worker whose daily work activities will cause pain earlier in the natural history of the disease, and incapacitate her (or him) more quickly. Let's say the disease process is exactly the same in them both. The building worker will not die sooner than the lecturer but will have had to give up work earlier.
In other words, it is not that the people in the less skilled, lower status jobs died later, but that they had to give up work earlier in the disease process.
This phenomenon eventually resulted in a convention when analyzing social class differences in health in longitudinal follow up such as that in the LS, to allow several years to pass to "allow selection to wear off" before comparing the death rates in the different classes. After the 1st 5 years, the sicker people in the more advantaged social positions died and the social class inequalities in mortality risk re-appeared. All subsequent papers using the LS have used this adjustment, but often explain it in a rather abbreviated way (let me know if anyone wants references). With this adjustment, the class differences in mortality look about the same as in the previous, cross sectional ("unlinked") studies from 1951 onwards.
These comments are no criticism of the JECH paper, whose objectives were to show that extending the pension age is less fair to people in more advantaged social classes. But it does show the dangers in assuming that the nominal definitions of social position need to be taken with care. The social forces at work here are not derived from status or skills but from working conditions, which, while collinear to each other, do not measure the same thing.
open access version at https://discovery.ucl.ac.uk/id/eprint/10082463/1/Murray%2020190821_JECH_R2_manuscript_CLEAN%20copy.pdf
At first sight it might appear puzzling that the bottom line of the paper is that people who had worked in less advantaged, lower skilled & lower status jobs (defined by the Registrar General's class schema, which is conceptually based on skills and status) actually lived longer after leaving the labour force than those whose jobs were of higher skill and status. To cite the paper itself:
"LS members who had worked in lower social classes lived a greater number of years after they stopped work, with more time per decreasing social class."
How to make sense of this? Actually, a similar issue was also a big puzzle for the authors of the first book that decribed the contribution of the LS to the study of health inequality. To the amazement of almost everyone involved, the earliest analysis showed no social class gradient in health, or even a hint of a reverse gradient. Did this mean that all the previous research on social class differences in health including those in the Black Report, had been biased by either artefact or selection?
And what does this have to do with Murray et al.'s paper?
To cut a long story short, it turned out that high status, high skilled jobs also benefit from less arduous working conditions. Just think about the difference between a building worker and a university lecturer. Lets say both of them start to get angina pectoris. The lecturer, first of all, will probably not be affected by narowing of her coronary arteries as soon because she does not need to do much heavy lifting, digging, or other activities that equire increaed cardiac output. And when she does start to notice, it is not too hard to keep her job if she also needs to do less of these kinds of things (moving furniture, lifting large piles of books or files...). By the time she even notices that her cardiovascular function is declining, let alone has to give up her job, her disease is far more advanced. Contrast to the building worker whose daily work activities will cause pain earlier in the natural history of the disease, and incapacitate her (or him) more quickly. Let's say the disease process is exactly the same in them both. The building worker will not die sooner than the lecturer but will have had to give up work earlier.
In other words, it is not that the people in the less skilled, lower status jobs died later, but that they had to give up work earlier in the disease process.
This phenomenon eventually resulted in a convention when analyzing social class differences in health in longitudinal follow up such as that in the LS, to allow several years to pass to "allow selection to wear off" before comparing the death rates in the different classes. After the 1st 5 years, the sicker people in the more advantaged social positions died and the social class inequalities in mortality risk re-appeared. All subsequent papers using the LS have used this adjustment, but often explain it in a rather abbreviated way (let me know if anyone wants references). With this adjustment, the class differences in mortality look about the same as in the previous, cross sectional ("unlinked") studies from 1951 onwards.
These comments are no criticism of the JECH paper, whose objectives were to show that extending the pension age is less fair to people in more advantaged social classes. But it does show the dangers in assuming that the nominal definitions of social position need to be taken with care. The social forces at work here are not derived from status or skills but from working conditions, which, while collinear to each other, do not measure the same thing.
Monday, 19 March 2018
Understanding the research-policy relationship: the role of "technical interests"
In their excellent LSE Impact blog, on the relationship between research and policy
http://blogs.lse.ac.uk/impactofsocialsciences/2018/03/12/one-way-mutually-constitutive-or-two-autonomous-spheres-what-is-the-relationship-between-research-and-policy/
Christina Boswell & Kat Smith talk about "mutual constitution" of the 2 spheres. This is a huge step forward in the present understanding.
Many years ago, in a differently labelled social-scientific enterprise, scholars proposed the notion of "partisan mutual adjustment" as a guide to understanding this relationship. That was why the title of my book on thetopic was "Authorities and Partisans". The findings of the project that gave rise to the book were totally at odds with any of my hypotheses. Scientists (the "Authorities") often behaved like partisans and participants in policy debate played a very important role in the consititution of knoowledge claims as facts. As Boswell & Smith point out, this kind of thinking can help us to a much more sophisticated understanding of a present day obsession: "Impact". It is in fact rather coounter producitve to work with an image of science as a hammer having "impact" on policy in any simple way.
There was in my opinion one element left out from the blog, however. Of course there is not space to go through everything in a short article. But I am gonig to try & fill this out, using a lot of extracts from my paper "Do we need a Strong Programme in Medical Sociology" I wont give the URL because it is still paywalled about 100 years after it was published. Oe reason I want to do this is that since both my research and even Kat's great PhD work was done, genetic research has risen to much greater prominance and I believe that "technical interests" are very much in play here. For example, there are no clear policy implications to, for example, finding such as educational attainment being partly due to a bunch of genes. In general, genetic research has little potential for impact and yet it received massive funding and gets published in the highest impact (i the other sense) journals. Interestingly, private biotech companies have been withdrawing fundinig from genetic research as they realise how ittle infleunce genes actually have on health conditions. This leaves, of course, the massive ideological benefiit of allowing governments to cut back on (obviously) educational spending, or indeed to implement policies that discourage childbearing in poorer people. I dont want to play this down at all. But the "technical interest", that centres on those who build the large expensive machinery needed to sequence genes and those who develop methods to make sense of the resulting data, are aso I think very important and somewhat neglected.
By technical interest is meant the interest of occupational subgroups in creating a continuing market for the specific techniques and forms of expertise of which they can claim 'ownership'. For example, experts in the anatomy of plants were threatened, as Nicolson (1984; 1989) has shown, by a move towards the use of plant ecology as the most important form of classifying plants and understanding soil fertility. Another example, not yet studied systematically"*, might be drawn from the recent history of reproductive technology. Obstetrics has faced some degree of crisis due to the compression of childbearing into women's healthiest years. The resulting decline in the amount of medical intervention required is arguably one reason for the rapid growth of interest in infertility. Although far fewer women are affected, those who see their infertility as a problem that doctors can solve require a great deal of medical intervention over an extended period of time.
The model for studying how the production of scientific knowledge is influenced by technical interests is one which regards scientists as actively 'doing interest-work', that is, mobilising a variety of other social groups (and being mobilised by them). These processes of enrolment and alliance profoundly influence the knowledge claims made by scientists. Some of these claims go on to become facts.
In his work on biometrics, Mackenzie (1981) compares biometry in the early 1900s to a new political party which has to build its network of supporters and gain resources for its tasks. In order to do this, biometry, or any other new and/ or struggling discipline, must demonstrate the utility of its ideas and measures to more powerful interest-groups and/or groups or individuals with command of resources. Accordingly, biometry linked itself to the Eugenics movement, as Mackenzie shows.
A scientific team quite often finds itself in the position of having a solution to a problem no one has, or a product with no market. So as well as translating their products to fit the interests of powerful groups, scientists may work at translating the goals of these groups to fit better with what the scientists have on offer. 'If you give greater priority to this issue' a group of scientists may argue with a struggling sub-profession (for example a medical sub-specialty), 'we have a hot new technique (or machine) which will enable your weak, low-status segment of the profession of medicine to gain far more influence and status.' In such cases it is not only a case of selling one's idea or device as better suited to existing purposes of other groups, but of persuading other groups that they have interests they didn't even know about before.
Aronson (1982) gives an excellent example of scientists being enrolled by other interest groups and the resultant shaping of knowledge claims in her study of nutrition science in the 1870s and 1880s in the United States. At this time: liberal economists and statisticians hoped to end class conflict by developing objective criteria of the adequacy of wages At the same time, the young discipline of nutrition science was struggling to establish its credentials as a respectable science worthy of funded teaching and research posts in US universities. The upshot of the negotiations between interested groups was that: the alliance between nutrition science and labour statistics gave birth to the definition of nutrition as a "social problem" inextricably tied to labour reform. Accordingly, the first dietary survey conducted in the USA, in 1885, concluded that:
"existing wages would be adequate if workers learned to eat scientifically"
Thus the leading exponent of the new science, Atwater, could claim that nutrition:
"could solve the labour problem . . . within the existing class structure and without decreasing the profits of capital and therefore deserved public support."
Atwater correctly interpreted the interests of various powerful groups involved in both the policy debate on 'the labour problem', the debate on nutritional requirements, and the debate on the status of nutrition as an academic discipline.
These are just a few examples of the wide range of "technical" and "professional interests" that have linked scientists and political interest groups
Aronson, N. (1982) Nutrition as a social problem: a case study of entrepreneurial
strategy in science. Social Problems, 29, 474-487.
Mackenzie. D. (1981) Statistics in Britain 1865-1930: The Social Construction of
Scientific Knowledge. Edinburgh University Press.
Nicolson. M. (1984) The Development of Plant Ecology. 1790-1960. Unpublished
Ph.D. Thesis, University of Edinburgh.
Nicolson. M. (1989) National styles, divergent classifications: a history of French and
American plant ecology, in L. Hargen. R. A. Jones. A. Pickering (eds.).
Knowledge an
http://blogs.lse.ac.uk/impactofsocialsciences/2018/03/12/one-way-mutually-constitutive-or-two-autonomous-spheres-what-is-the-relationship-between-research-and-policy/
Christina Boswell & Kat Smith talk about "mutual constitution" of the 2 spheres. This is a huge step forward in the present understanding.
Many years ago, in a differently labelled social-scientific enterprise, scholars proposed the notion of "partisan mutual adjustment" as a guide to understanding this relationship. That was why the title of my book on thetopic was "Authorities and Partisans". The findings of the project that gave rise to the book were totally at odds with any of my hypotheses. Scientists (the "Authorities") often behaved like partisans and participants in policy debate played a very important role in the consititution of knoowledge claims as facts. As Boswell & Smith point out, this kind of thinking can help us to a much more sophisticated understanding of a present day obsession: "Impact". It is in fact rather coounter producitve to work with an image of science as a hammer having "impact" on policy in any simple way.
There was in my opinion one element left out from the blog, however. Of course there is not space to go through everything in a short article. But I am gonig to try & fill this out, using a lot of extracts from my paper "Do we need a Strong Programme in Medical Sociology" I wont give the URL because it is still paywalled about 100 years after it was published. Oe reason I want to do this is that since both my research and even Kat's great PhD work was done, genetic research has risen to much greater prominance and I believe that "technical interests" are very much in play here. For example, there are no clear policy implications to, for example, finding such as educational attainment being partly due to a bunch of genes. In general, genetic research has little potential for impact and yet it received massive funding and gets published in the highest impact (i the other sense) journals. Interestingly, private biotech companies have been withdrawing fundinig from genetic research as they realise how ittle infleunce genes actually have on health conditions. This leaves, of course, the massive ideological benefiit of allowing governments to cut back on (obviously) educational spending, or indeed to implement policies that discourage childbearing in poorer people. I dont want to play this down at all. But the "technical interest", that centres on those who build the large expensive machinery needed to sequence genes and those who develop methods to make sense of the resulting data, are aso I think very important and somewhat neglected.
By technical interest is meant the interest of occupational subgroups in creating a continuing market for the specific techniques and forms of expertise of which they can claim 'ownership'. For example, experts in the anatomy of plants were threatened, as Nicolson (1984; 1989) has shown, by a move towards the use of plant ecology as the most important form of classifying plants and understanding soil fertility. Another example, not yet studied systematically"*, might be drawn from the recent history of reproductive technology. Obstetrics has faced some degree of crisis due to the compression of childbearing into women's healthiest years. The resulting decline in the amount of medical intervention required is arguably one reason for the rapid growth of interest in infertility. Although far fewer women are affected, those who see their infertility as a problem that doctors can solve require a great deal of medical intervention over an extended period of time.
The model for studying how the production of scientific knowledge is influenced by technical interests is one which regards scientists as actively 'doing interest-work', that is, mobilising a variety of other social groups (and being mobilised by them). These processes of enrolment and alliance profoundly influence the knowledge claims made by scientists. Some of these claims go on to become facts.
In his work on biometrics, Mackenzie (1981) compares biometry in the early 1900s to a new political party which has to build its network of supporters and gain resources for its tasks. In order to do this, biometry, or any other new and/ or struggling discipline, must demonstrate the utility of its ideas and measures to more powerful interest-groups and/or groups or individuals with command of resources. Accordingly, biometry linked itself to the Eugenics movement, as Mackenzie shows.
A scientific team quite often finds itself in the position of having a solution to a problem no one has, or a product with no market. So as well as translating their products to fit the interests of powerful groups, scientists may work at translating the goals of these groups to fit better with what the scientists have on offer. 'If you give greater priority to this issue' a group of scientists may argue with a struggling sub-profession (for example a medical sub-specialty), 'we have a hot new technique (or machine) which will enable your weak, low-status segment of the profession of medicine to gain far more influence and status.' In such cases it is not only a case of selling one's idea or device as better suited to existing purposes of other groups, but of persuading other groups that they have interests they didn't even know about before.
Aronson (1982) gives an excellent example of scientists being enrolled by other interest groups and the resultant shaping of knowledge claims in her study of nutrition science in the 1870s and 1880s in the United States. At this time: liberal economists and statisticians hoped to end class conflict by developing objective criteria of the adequacy of wages At the same time, the young discipline of nutrition science was struggling to establish its credentials as a respectable science worthy of funded teaching and research posts in US universities. The upshot of the negotiations between interested groups was that: the alliance between nutrition science and labour statistics gave birth to the definition of nutrition as a "social problem" inextricably tied to labour reform. Accordingly, the first dietary survey conducted in the USA, in 1885, concluded that:
"existing wages would be adequate if workers learned to eat scientifically"
Thus the leading exponent of the new science, Atwater, could claim that nutrition:
"could solve the labour problem . . . within the existing class structure and without decreasing the profits of capital and therefore deserved public support."
Atwater correctly interpreted the interests of various powerful groups involved in both the policy debate on 'the labour problem', the debate on nutritional requirements, and the debate on the status of nutrition as an academic discipline.
These are just a few examples of the wide range of "technical" and "professional interests" that have linked scientists and political interest groups
Aronson, N. (1982) Nutrition as a social problem: a case study of entrepreneurial
strategy in science. Social Problems, 29, 474-487.
Mackenzie. D. (1981) Statistics in Britain 1865-1930: The Social Construction of
Scientific Knowledge. Edinburgh University Press.
Nicolson. M. (1984) The Development of Plant Ecology. 1790-1960. Unpublished
Ph.D. Thesis, University of Edinburgh.
Nicolson. M. (1989) National styles, divergent classifications: a history of French and
American plant ecology, in L. Hargen. R. A. Jones. A. Pickering (eds.).
Knowledge an
Tuesday, 24 October 2017
What have political interference in research & Harvey Weinstein got in common (updated)
I don't suppose anyone will have trouble guessing the answer: neither the existence of sex pests in show business (for heavens sake) nor the existence of political considerations in academic work are anything new.
In the case of abuse in show business, what has changed is the awareness of it as a problem that should not be tolerated. This is a big step forward. Universities, it also turns out, are far from innoocent in this respect. Some senior academics have this weird mixture of power and charisma based on their knowledge and communication skill. In medicine, this "transference" problem is so well known that there are severe sanctions for taking sexual advantage of patients. Even if it might seem like something consensual, the power imbalance makes it abusive. In academe, however, sleeping with your students seems to be more accepted. At least there is no strict code of ethics, or wasnt the last time I looked. A BSA working group on this issue, 20 or so years ago now, decided not to develop such a code, to the dismay of someone I know with training in medicine who was a member of the working group. This is not to say we thought a lecturer should never develop an emotional relationship with a student, only that it must no take place at the same time as the power relationship of teacher-student. As another friend once said of an academic we both knew "Lets face it dear, if he was the window cleaner you wouldn't look twice".
In the case of academic work, it is to be greatly welcomed that we now have things like "Retraction Watch" working to expose misconduct. And that people are speaking up more about harassment of all kinds. I think this is a similar develoment. It comes at a time when the incentives for misconduct are massively inceasing, but I have remarked on Twitter that it is nothing completely new. So here are some stories about the way things have been over the last 30 years of my career.
In the 1980s there was a lively debate on whether or not unemployment (during the Thatcher recession) was harmful to health. Could it be that sicker or less "fit" people were more likely to become and remain unemployed? Any apparent association between the two wold then be "confounded" by pre-existing poor health. A series of 3 studies seemed to show that this was not true, that unemployment itself was harmful. There were not many holds barred in this competition between what you might say were more versus less politically sophisticated groups and individuals. I was told during this time that one way an academic could get research money was to say "Dear Minister, we can prove that unemployment does not cause ill health or mortality". Other people admitted to having taken up the topic because it made it easier to get published. The topic was a bandwagon. There is more detail on this story in my book "Authorities and Partisans". But the denoument was remarkable: an MRC programme grant that had produced some of these findings was cut from 5 years to 2. That had never happened before and I don't think it ever happened again. This was in 1987.
Most people may not remember that Thatcher commissioned a review of the (then ) Social Science Research Council. The result was a change in title of the UKs major social science funding body to "Economic and Social Research Council" (the ESRC of today). In this way the title of the funding did not contain the word "Science". Political interference? Naaahhh.
Some colleagues set out to try & make me understand that a lot of interference in research was not "Political", it was small-p stuff that was done by those who sat on powerful funding bodies. There were funding bodies, and indeed, journals, that one just did not bother with during the incumbency of certain people on their governing board or editoria committees, because you knew it would be a waste of time. A Scottish Office civil servant admitted duriing a seminar I attended that during the Thatcher years there were some eminent Profs who just would never be granted funding from government sources. But other eminent figures would vote down research projects or papers because their own results might be threatened, which had nothing to do with Politics.
When I applied for money to do my own first project on unemployment & health, I had come to a more subtle understanding of this kind of thing. I included a good-ish chunk of text about how, now that we had longitudinal data, we could investigate more closely the selection processes that might produce the appearance of poor health among the unemployed (sicker people being "selected" for unemployment). We knew the health status of participants in these studies from birth, and we knew their hisotires of employment and unemployment. But silly me! I had not anicipated that there would be assessors of the proposal who were very much outside this ideological camp. One fair minded senior colleague, who was asked to be a referee on the proposal, was a little shocked at my approach (he had supervised my PhD, I was mortified). But being fair minded, he did not turn it down. This was around 1993. We got all our papers published, which began to show that the association between unemployment and health needs to be seen not as "either selection or causation" but rather that many unemployed people had had previously more adverse life courses, which could increase their propensity to both illness and unemployment. This work met with very little response, either hostile or friendly. At that time (mid 1990s) Cox regression with time varying co-ordinates was a bit heavy going for the average reader . More important perhaps was that there was a change of government in 1997. When, during the mid 2000s, I went to present some of the results at the Social Exclusion Unit I was asked why all our models adjusted for the height of the parents. I explained this was the only way we had to control for any genetic influences on who became unemployed. The politicans present, to their everlasting credit, were absolutely horrifed at the notion anyone would think unemployment was genetic. I do wonder if members of the present government would have the same reaction.
Which brings me to the last story I have energy to talk about today. I am privileged to be involved in a large EU funded project on health inequality during the life course . One of the "work packages" concerns policy implications and policy impact. So the project leader, a distinguished public health academic, who also knows a lot about "omics" (genomics, proteoics etc), invited a participant from a very well known health policy institute to help the project with policy impact. At our last meeting our policy expert was critical of the little genetic work that had yet been done (it will be, it just takes awhile) and of the strong social justice theme in the project overall. "All the policy makers want to hear is about genetic influences" we were told. This story illustrates the importance that has to be given to things like a totally oversimplified view of genetics now in the age of Impact. Admittedly 20 years ago we had to show we had "taken account " of genetic influences in order to get past referees, but our Unemployment & Health project was not assessed on Impact .It got the highest possible grade in the assessment of its Final Report to the ESRC, which it would not today because we were pretty conservative in drawing media attention to it. And what will happen to the EU projects Impact when it does publish results of proper genetic research on health inequality, which will show miniscule influences? We shall see.
Oh, yes, cant resist one last tale. Around 1997, someone I had worked with for years was a speaker at a workshop in Manchester on Health Inequality, to talk about the genetics of health inequality. I had been invited to this but didn't feel like going to Manchester (tilting trains make me sick). So I phoned my long term colleague to ask if he would like me to turn up & give some moral support. "You misunderstand" he responded "I am talking in favour of a genetic effect". I was stunned. "If I get an OR of 1.4 for some social determinant" he explained" I only get that paper in JECH. If I get that same result for a gene, I get it in Nature". Go figure.
In the case of abuse in show business, what has changed is the awareness of it as a problem that should not be tolerated. This is a big step forward. Universities, it also turns out, are far from innoocent in this respect. Some senior academics have this weird mixture of power and charisma based on their knowledge and communication skill. In medicine, this "transference" problem is so well known that there are severe sanctions for taking sexual advantage of patients. Even if it might seem like something consensual, the power imbalance makes it abusive. In academe, however, sleeping with your students seems to be more accepted. At least there is no strict code of ethics, or wasnt the last time I looked. A BSA working group on this issue, 20 or so years ago now, decided not to develop such a code, to the dismay of someone I know with training in medicine who was a member of the working group. This is not to say we thought a lecturer should never develop an emotional relationship with a student, only that it must no take place at the same time as the power relationship of teacher-student. As another friend once said of an academic we both knew "Lets face it dear, if he was the window cleaner you wouldn't look twice".
In the case of academic work, it is to be greatly welcomed that we now have things like "Retraction Watch" working to expose misconduct. And that people are speaking up more about harassment of all kinds. I think this is a similar develoment. It comes at a time when the incentives for misconduct are massively inceasing, but I have remarked on Twitter that it is nothing completely new. So here are some stories about the way things have been over the last 30 years of my career.
In the 1980s there was a lively debate on whether or not unemployment (during the Thatcher recession) was harmful to health. Could it be that sicker or less "fit" people were more likely to become and remain unemployed? Any apparent association between the two wold then be "confounded" by pre-existing poor health. A series of 3 studies seemed to show that this was not true, that unemployment itself was harmful. There were not many holds barred in this competition between what you might say were more versus less politically sophisticated groups and individuals. I was told during this time that one way an academic could get research money was to say "Dear Minister, we can prove that unemployment does not cause ill health or mortality". Other people admitted to having taken up the topic because it made it easier to get published. The topic was a bandwagon. There is more detail on this story in my book "Authorities and Partisans". But the denoument was remarkable: an MRC programme grant that had produced some of these findings was cut from 5 years to 2. That had never happened before and I don't think it ever happened again. This was in 1987.
Most people may not remember that Thatcher commissioned a review of the (then ) Social Science Research Council. The result was a change in title of the UKs major social science funding body to "Economic and Social Research Council" (the ESRC of today). In this way the title of the funding did not contain the word "Science". Political interference? Naaahhh.
Some colleagues set out to try & make me understand that a lot of interference in research was not "Political", it was small-p stuff that was done by those who sat on powerful funding bodies. There were funding bodies, and indeed, journals, that one just did not bother with during the incumbency of certain people on their governing board or editoria committees, because you knew it would be a waste of time. A Scottish Office civil servant admitted duriing a seminar I attended that during the Thatcher years there were some eminent Profs who just would never be granted funding from government sources. But other eminent figures would vote down research projects or papers because their own results might be threatened, which had nothing to do with Politics.
When I applied for money to do my own first project on unemployment & health, I had come to a more subtle understanding of this kind of thing. I included a good-ish chunk of text about how, now that we had longitudinal data, we could investigate more closely the selection processes that might produce the appearance of poor health among the unemployed (sicker people being "selected" for unemployment). We knew the health status of participants in these studies from birth, and we knew their hisotires of employment and unemployment. But silly me! I had not anicipated that there would be assessors of the proposal who were very much outside this ideological camp. One fair minded senior colleague, who was asked to be a referee on the proposal, was a little shocked at my approach (he had supervised my PhD, I was mortified). But being fair minded, he did not turn it down. This was around 1993. We got all our papers published, which began to show that the association between unemployment and health needs to be seen not as "either selection or causation" but rather that many unemployed people had had previously more adverse life courses, which could increase their propensity to both illness and unemployment. This work met with very little response, either hostile or friendly. At that time (mid 1990s) Cox regression with time varying co-ordinates was a bit heavy going for the average reader . More important perhaps was that there was a change of government in 1997. When, during the mid 2000s, I went to present some of the results at the Social Exclusion Unit I was asked why all our models adjusted for the height of the parents. I explained this was the only way we had to control for any genetic influences on who became unemployed. The politicans present, to their everlasting credit, were absolutely horrifed at the notion anyone would think unemployment was genetic. I do wonder if members of the present government would have the same reaction.
Which brings me to the last story I have energy to talk about today. I am privileged to be involved in a large EU funded project on health inequality during the life course . One of the "work packages" concerns policy implications and policy impact. So the project leader, a distinguished public health academic, who also knows a lot about "omics" (genomics, proteoics etc), invited a participant from a very well known health policy institute to help the project with policy impact. At our last meeting our policy expert was critical of the little genetic work that had yet been done (it will be, it just takes awhile) and of the strong social justice theme in the project overall. "All the policy makers want to hear is about genetic influences" we were told. This story illustrates the importance that has to be given to things like a totally oversimplified view of genetics now in the age of Impact. Admittedly 20 years ago we had to show we had "taken account " of genetic influences in order to get past referees, but our Unemployment & Health project was not assessed on Impact .It got the highest possible grade in the assessment of its Final Report to the ESRC, which it would not today because we were pretty conservative in drawing media attention to it. And what will happen to the EU projects Impact when it does publish results of proper genetic research on health inequality, which will show miniscule influences? We shall see.
Oh, yes, cant resist one last tale. Around 1997, someone I had worked with for years was a speaker at a workshop in Manchester on Health Inequality, to talk about the genetics of health inequality. I had been invited to this but didn't feel like going to Manchester (tilting trains make me sick). So I phoned my long term colleague to ask if he would like me to turn up & give some moral support. "You misunderstand" he responded "I am talking in favour of a genetic effect". I was stunned. "If I get an OR of 1.4 for some social determinant" he explained" I only get that paper in JECH. If I get that same result for a gene, I get it in Nature". Go figure.
Monday, 31 July 2017
What happened to health inequality in England & Wales after 1991?
Someone asked me what has happened to health inequality between social class groups (not areas) between 1991 and the present time. I said I would post something about this, taken from my book "Health Inequality: An Introduction".
To start with, lets have a look at what happened to social class difference in mortality between 1931 an 1991. This is a unique series of data, the likes of which does not exist anywhere else in the world. If you want to know what a Standardized Mortality Ratio is you will need to buy the book :-). You culd look it up i Wikipedia I guess, but my explanation is specially designed for people who don't like algebra formulas (because I don't either). At a very simple level, an SMR of 100 means that group has around the average level of mortality for the whole population of that age and sex. More than 100 is higher mortality (bad) and less than 100 means lower mortality (good).
These figures only refer to England and Wales because of the way in which health statistics and censuses are organised in the United Kingdom.
Table 1: Health
inequality i n England and Wales,
1931-1991 : Standardized Mortality Ratios by Registrar-General's Social
Class (RGSC) i n men aged 15-64
RGSC
|
1931
|
1951
|
1961
|
1971
|
1981 *
|
1991 *
|
I: Professional
|
90
|
86
|
76
|
77
|
66
|
66
|
II: Managerial
1991 Il l routi ne
|
94
|
92
|
81
|
81
|
76
|
72
|
IIIN: Routine non-
manual (1991)
|
100
|
|||||
Ill : Routine non manual & skilled manual
(1931-1981)
|
97
|
101
|
100
|
104
|
103
|
|
IIIM: Skilled manual
|
117
|
|||||
IV semi-skilled manual
|
102
|
104
|
103
|
114
|
116
|
116
|
V unskilled manual
|
111
|
118
|
143
|
137
|
1 66
|
189
|
*ages 20-64
Source: (Wilkinson,
1986) ( 1 986: 2, table I . I ); (Drever, Bunting and Harding, 1997): 98, table 8.2)
Table 2: Trend in inequality in mortality
between 1970s and 2001-2 using old and new social class measurements. England
and Wales, men age 25-64. Directly age standardized rates per 100,000
1970-72
|
1979-83
|
1991-93
|
2001-03
|
2010
|
|
RGSC I
|
500
|
373
|
280
|
||
RGSC V
|
897
|
910
|
806
|
||
Rate Ratio
|
1.8
|
2.4
|
2.9
|
||
NS-SeC 1.1
|
182
|
128
|
|||
NS-SeC 7
|
513
|
458
|
|||
Rate Ratio
|
2.8
|
2.8
|
Sources: (White,
2007; Office for National Statistics, 2012)
The second table is obviously very different to the first one. . It has been simplified drastically by only including the most and least advantaged occupational classes at each time point. Like table 1 however, what makes it possible to calculate these figures is having a numerator (numbers who die) taken from official death records, and a denominator taken from Censuses or other official statistics.
What the figures seem to show is that the difference in mortality risk between the most advantaged (RGSC I and NS-SeC 1.1) and the least advantaged (RGSC V and NS-SeC 7) social classes slightly fell and then stabilised to a situation where working age men in the least advantaged social class had around 2.8 times the risk of early death of those in the most advantaged class. We do need to be cautious about these digures of course because both the definition of social class and the sources from which the data have been derived have changed.
Drever, F.,
Bunting, J. and Harding, D. (1997), Male mortality from major causes of death.
In F. Drever and M. Whitehead (eds), Health Inequality, 122-142. London:
HMSO.
Office for
National Statistics (2012), Intercensal Mortality Rates by NSSEC, 2001-2010.
London: Office for National Statistics.
White, C.,
Glickman, M., Johnson, B. and Corbin, T. (2007), Social inequalities in adult
male mortality by the National Statstics Socio-economic classification, England
and Wales, 2001-03. Health Statistics Quarterly 36, 6-23.
Wilkinson,
R.G. (1986), Income and mortality. In R.G. Wilkinson (ed), Class and health:
research and longitudinal data, London: Tavistock.
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