Showing posts with label #autism #epidemiology. Show all posts
Showing posts with label #autism #epidemiology. Show all posts
Wednesday, 1 January 2020
Research funders need to embrace slow science
Uta Frith courted controversy earlier this year when she published an opinion piece in which she advocated for Slow Science, including the radical suggestion that researchers should be limited in the number of papers they publish each year. This idea has been mooted before, but has never taken root: the famous Chaos in the Brickyard paper by Bernard Forscher dates back to 1963, and David Colquhoun has suggested restricting the number of publications by scientists as a solution more than once on his blog (here and here).
Over the past couple of weeks I've been thinking further about this, because I've been doing some bibliometric searches. This was in part prompted by the need to correct and clarify an analysis I had written up in 2010, about the amount of research on different neurodevelopmental disorders. I had previously noted the remarkable amount of research on autism and ADHD compared to other behaviourally-defined conditions. A check of recent databases shows no slowing in the rate of research. A search for publications with autism or autistic in the title yielded 2251 papers published in 2010; in 2019, this has risen to 6562. We can roughly halve this number if we restrict attention to the Web of Science Core database and search only for articles (not reviews, editorials, conference proceedings etc). This gives 1135 articles published in 2010 and 3075 published in 2019. That's around 8 papers every day for 2019.
We're spending huge amounts to generate these outputs. I looked at NIH Reporter, which provides a simple interface where you can enter search terms to identify grants funded by the National Institutes of Health. For the fiscal year 2018-2019 there were 1709 projects with the keyword 'autism or autistic', with a total spend of $890 million. And of course, NIH is not the only source of research funding.
Within the field of developmental neuropsychology, autism is the most extreme example of research expansion, but if we look at adult disorders, this level of research activity is by no means unique. My searches found that this year there were 6 papers published every day on schizophrenia, 15 per day on depression, and 11 per day on Alzheimer's disease.
These are serious and common conditions and it is right that we fund research into them – if we could improve understanding and reduce their negative impacts, it would make a dramatic difference to many lives. The problem is information overload. Nobody, however nerdy and committed, could possibly keep abreast of the literature. And we're not just getting more information, the information is also more complex. I reckon I could probably understand the majority of papers on autism that were published when I started out in research years ago. That proportion has gone down and down with time, as methods get ever more complex. So we're spending increasing amounts of money to produce more and more research that is less and less comprehensible. Something has to give, and I like the proposal that we should all slow down.
But is it possible? If you want to get your hands on research funding, you need to demonstrate that you're likely to make good use of it. Publication track record provides objective evidence that you can do credible research, so researchers are focused on publishing papers. And they typically have a short time-frame in which to demonstrate productivity.
A key message from Uta's piece is that we need to stop confusing quantity with quality. When this topic has been discussed on social media, I've noted that many ECRs take the view that when you come to apply for grants or jobs, a large number of publications is seen as a good thing, and therefore Slow Science would damage the prospects of ECRs. That is not my experience. It's possible that there are differences in practice between different countries and subject areas, but in the UK the emphasis is much more on quality than quantity of publications, so a strategy of focusing on quality rather than quantity would be advantageous. Indeed, most of our major funders use proposal forms that ask applicants to list their N top publications, rather than a complete CV. This will disadvantage anyone who has sliced their oeuvre into lots of little papers, rather than writing a few substantial pieces. Similarly, in the UK Research Excellence Framework, researchers from an institution are required to submit their outputs, but there is a limited number that can be submitted – a restriction that was introduced many years ago to incentivise a focus on quality rather than quantity.
The same people who are outraged at reducing the number of publications often rail against the stress of working in the current system – and rightly so. After all, at some point in the research cycle, at least one person has to devote serious thought to the design, analysis and write-up. Each of these stages inevitably takes far longer than we anticipate – and then there is time needed to respond to reviewers. The quality and impact of research can be enhanced by pre-registration and making scripts and data open, but extra time needs to be budgeted for this. Indeed, lack of time is a common reason cited for not doing open science. Researchers who feel that to succeed they have to write numerous papers every year are bound to cut corners, and then burn out from stress. It makes far more sense to work slowly and carefully to produce a realistic number of strong papers that have been carefully planned, implemented and written up.
It's clear that science and scientists would benefit if we take things more slowly, but the major barrier is a lack of researcher confidence. Those who allocate funds to research have a vested interest in ensuring they get the best return from their investment – not a tsunami of papers that overwhelm us, but a smaller number of reports of high-quality, replicable and credible findings. If things are to change we need funders to do more to communicate to researchers that they will be evaluated on quality rather than quantity of outputs.
Tuesday, 21 June 2011
Autism diagnosis and hyper-systemizing parents: Nottingham vs. Eindhoven
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| Family life in Eindhoven?
© cartoonstock.com
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As the researchers themselves note, the hyper-systemizing account is not the only possible explanation for their result. Crucially, the study relied on counting diagnoses from school records, rather than screening the population in a standard fashion. Although it’s not hard to recognise a case of classic Kanner autism, there’s far more disagreement about diagnoses for children with milder symptoms. As I argued on a Guardian blog, unless we have clear objective criteria for diagnosis, it’s hard to compare one prevalence rate with another. The different numbers could just reflect local expertise, policy or practice in diagnosing autism.
One limitation of the published study is that the researchers are quoted as saying that their study was prompted by anecdotal reports that autism was abnormally common in Eindhoven. While it is worth checking out if the anecdote is accurate, this makes Eindhoven less than ideal for testing the hyper-systemizing hypothesis, as it potentially capitalises on a chance blip. What would be better would be a study with clear a priori predictions, based solely on levels of IT industries in different cities. In theory, it should be possible to do this using publicly-available data from the UK published by the Department of Children, Schools and Families. This dataset has the advantage of being comprehensive, unlike the school report data from the Eindhoven study, which relied on schools providing the data (- the response rate was 75% for Eindhoven but only 50% for Haarlem and 46% for Utrecht). A recent report by Lindsay (2011) presented some data from this UK database on numbers of children with Special Educational Needs categorised as having Speech, Language and Communication Needs (SLCN) or Autistic Spectrum Disorder (ASD). The numbers of children with these labels varied massively from place to place, as shown in Table 1.
Table 1: Percentages of children with SEN diagnosed with SLCN or ASD;
Data from local authorities with the highest or lowest % of either diagnosis
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I have no idea whether the number of IT experts is higher in Nottingham than Leeds, but it’s noteworthy that places where you might expect high levels of hyper-systemizing, such as the university towns of Oxford and Cambridge, don’t feature among the places with very high rates of ASD diagnoses. Lindsay also points out that “the two neighbouring authorities of Nottingham and Nottinghamshire have almost identical prevalence rates for SLCN and ASD, despite one being a large city, and the other a shire county” (p. 143). Clearly, my intuitions are no substitute for real data, and it’s also important to note that the data in Table 1 are not population prevalence figures, but instead are proportions of children who have already been identified as having Special Educational Needs. One would need to use the frequencies of ASD in the population as a whole to test the hypothesis of a correlation between level of IT industry and rates of autism. The data are available, and this might seem like a nice project for someone to do, except for a major problem. As Lindsay emphasised, it is impossible to conclude from the UK data whether prevalence really do vary across the country, because the definitions of disorders are inconsistent from one area to another. It’s possible that criteria for autism are more standardised in the Netherlands than in the UK, but the UK data make me suspect that it’s just not possible to draw meaningful conclusions about prevalence from data based on educational records.
References
Bishop, D. V. M., Maybery, M., Maley, A., Wong, D., Hill, W., & Hallmayer, J. (2004). Using self-report to identify the broad phenotype in parents of children with autistic spectrum disorders: a study using the Autism-Spectrum Quotient. Journal of Child Psychology and Psychiatry, 45, 1431-1436.
Lindsay, G. (2011). The collection and analysis of data on children with speech, language and communication needs: The challenge to education and health services. Child Language Teaching & Therapy, 27(2), 135-150.
Roelfsema, M., Hoekstra, R., Allison, C., Wheelwright, S., Brayne, C., Matthews, F., & Baron-Cohen, S. (2011). Are Autism Spectrum Conditions More Prevalent in an Information-Technology Region? A School-Based Study of Three Regions in the Netherlands Journal of Autism and Developmental Disorders DOI: 10.1007/s10803-011-1302-1
Lindsay, G. (2011). The collection and analysis of data on children with speech, language and communication needs: The challenge to education and health services. Child Language Teaching & Therapy, 27(2), 135-150.
Roelfsema, M., Hoekstra, R., Allison, C., Wheelwright, S., Brayne, C., Matthews, F., & Baron-Cohen, S. (2011). Are Autism Spectrum Conditions More Prevalent in an Information-Technology Region? A School-Based Study of Three Regions in the Netherlands Journal of Autism and Developmental Disorders DOI: 10.1007/s10803-011-1302-1
Wheelwright, S., Auyeung, B., Allison, C., & Baron-Cohen, S. (2010). Defining the broader, medium and narrow autism phenotype among parents using the Autism Spectrum Quotient (AQ). Molecular Autism, 1(1), 1-9.
Windham, G. C., Fessel, K., & Grether, J. K. (2009). Autism spectrum disorders in relation to parental occupation in technical fields. Autism Research, 2(4), 183-191.
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