Mean Methane Levels reach 1800 ppb

On May 9, the daily mean concentration of carbon dioxide in the atmosphere of Mauna Loa, Hawaii, surpassed 400 parts per million (ppm) for the first time since measurements began in 1958. This is 120 ppm higher than pre-industrial peak levels. This unfortunate milestone was widely reported in the media.

There's another milestone that looks even more threatening than the above one. On the morning of June 16, 2013, methane levels reached an average mean of 1800 parts per billion (ppb). This is more than 1100 ppb higher than levels reached in pre-industrial times (see graph further below).

NOAA image

Vostok ice core analysis shows that temperatures and levels of carbon dioxide and methane have all moved within narrow bands while remaining in sync with each other over the past 400,000 years. Carbon dioxide moved within a band with lower and upper boundaries of respectively 200 and 280 ppm. Methane moved within lower and upper boundaries of respectively 400 and 800 ppb.
Temperatures moved within lower and upper boundaries of respectively -8 and 2 degrees Celsius.

From a historic perspective, greenhouse gas levels have risen abruptly to unprecedented levels. While already at a historic peak, humans have caused emissions of additional greenhouse gases. There's no doubt that such greenhouse gas levels will lead to huge rises in temperatures. The question is how long it will take for temperatures to catch up and rise.

Below is another way of looking at the hockey stick. And of course, further emissions could be added as well, such as nitrous oxide and soot.

Large releases of methane must have taken place numerous times in history, as evidenced by numerous pockmarks, as large as 11 km (6.8 mi) wide.

Importantly, large methane releases in the past did not result in runaway global warming for a number of reasons:

• methane release typically took place gradually over many years, each time allowing a large release of methane to be broken down naturally over the years before another one occurred. 
• Where high levels of methane in the atmosphere persisted and caused a lot of heat to be trapped, this heat could still be coped with due to greater presence of ice acting as a buffer and consuming the heat before it could escalate into runaway temperature rises.

Wikipedia image

Veli Albert Kallio comments:

The problem with ice cores is that if there is too sudden methane surge, then the climate warms very rapidly. This then results the glacier surfaces melting away and the ice core begins to loose regressively surface data if there is too much methane in the air.

Because of this, there has been previous occurrences of high methane, and these were instrumental to bring the ice ages ice sheets to end (Euan Nisbet's Royal Society paper). The key to this is to look at some key anomalies and devise the right experiments to test the hypothesis for methane eruptions as the period to ice ages.

Thus, the current methane melting and 1800 ppm rise is nothing new except that there are no huge Pleistocene glaciers to cool the Arctic Ocean if methane goes to overdrive this time. In fact methane may have been many times higher than that but all surface ice kept melting away and staying regressive until cold water and ice from destabilised ice sheets stopped the supply of methane (it decays fast if supply is cut and temperatures fall back rapidly when seas rose).

The Laurentide Ice Sheet alone was equivalent of 25 Greenland Ice Sheets and the Weischelian and other sheets on top of that. So, the glaciers do not act the same way as fireman to extinguish methane. Runaway global warming is now possibility if the Arctic loses its methane holding capability due to warming.

Further discussion is invited on the following points:

• The large carbon-12 emission anomalies in East Asian historical objects that are dateable by historical knowledge. Discussion about the explanations concocted and why methane emission from permafrost soils and sea beds must be the answer; 

• the much overlooked fact that if there were ever very highly elevated concentrations of air in the Arctic, this would induce strong melting of glaciers which then lack those surface depositions where the air were most CH4 and CO2 laden. Even moderate levels of temperature rise damaged Larsen A, Larsen B, Petermann and Ellesmere glaciers. If huge runaway outgassing came out when Beringia flipped into soil warming, then methane came out really large amounts with CO2.

• Discussion of the experiments how to compensate for the possible lack of "time" in methane elevated periods in the ice cores by alternative experiments to obtain daily, weekly, monthly and yearly emission rates of CH4 and CO2 from the Last Glacial Maximum to the Holocene Thermal Maximum (as daily, weekly, monthly, and yearly sampling of air).

Editor's update: Methane levels go up and down with the seasons, and differ by altitude. As above post shows, mean levels reached 1800 ppb in May 2013 at 586 mb, according to MetOp-2 data. Note that IPCC AR5 gives levels of 1798 ppb in 2010 and 1803 ppb in 2011, as further discussed in later posts such as this one. Also, see historic data as supplied by NOAA below.

Post by Sam Carana.

Research fraud: More scrutiny by administrators is not the answer

I read this piece in the Independent this morning and an icy chill gripped me. Fraudulent researchers have been damaging Britain's scientific reputation and we need to do something. But what? Sadly, it sounds like the plan is to do what is usually done when a moral panic occurs: increase the amount of regulation.



So here is my, very quick, response – I really have lots of other things I should be doing, but this seemed urgent, so apologies for typos etc.



According to the account in the Independent, Universities will not be eligible for research funding unless they sign up to a Concordat for Research Integrity which entails, among other things, that they "will have to demonstrate annually that each team member’s graphs and spreadsheets are precisely correct."



We already have massive regulation around the ethics of research on human participants that works on the assumption that nobody can be trusted, so we all have to do mountains of paperwork to prove we aren't doing anything deceptive or harmful. 



So, you will ask, am I in favour of fraud and sloppiness in research? Of course not. Indeed, I devote a fair part of my blog to criticisms of what I see as dodgy science: typically, not outright fraud, but rather over-hyped or methodologically weak work, which is, to my mind, a far greater problem. I agree we need to think about how to fix science, and that many of our current practices lead to non-replicable findings. I just don't think more scrutiny by administrators is the solution. To start scrutinising datasets is just silly: this is not where the problem lies.



So what would I do? The answers fall into three main categories: incentives, publication practices, and research methods.



Incentives is the big one. I've been arguing for years that our current reward system distorts and damages science. I won't rehearse the arguments again: you can read them here. 
The current Research Excellence Framework is, to my mind, an unnecessary exercise that further incentivizes researchers against doing slow and careful work. My first recommendation is therefore that we ditch the REF and use simpler metrics to allocate research funding to University, freeing up a great deal of time and money, and improving the security of research staff. Currently, we have a situation where research stardom, assessed by REF criteria, is all-important. Instead of valuing papers in top journals, we should be valuing research replicability. 



Publication practices are problematic, mainly because the top journals prioritize exciting results over methodological rigour. There is therefore a strong temptation to do post hoc analyses of data until an exciting result emerges. Pre-registration of research projects has been recommended as a way of dealing with this - see this letter to the Guardian on which I am a signatory. 
It might be even more effective if research funders adopted the practice of requiring researchers to specify the details of their methods and analyses in advance on a publicly-available database. And once the research was done, the publication should contain a link to a site where data are openly available for scrutiny – with appropriate safeguards about conditions for re-use.



As regards research methods, we need better training of scientists to become more aware of the limitations of the methods that they use. Too often statistical training is a dry and inaccessible discipline. All scientists should be taught how to generate random datasets: nothing is quite as good at instilling a proper understanding of p-values as seeing the apparent patterns in data that will inevitably arise if you look hard enough at some random numbers. In addition, not enough researchers receive training in best practices for ensuring quality of data entry, or in exploratory data analysis to check the numbers are coherent and meet assumptions of the analytic approach.



In my original post on expansion of regulators, I suggested that before a new regulation is introduced, there should be a cold-blooded cost-benefit analysis that considers, among other things, the cost of the regulation both in terms of the salaries of people who implement it, and the time and other costs to those affected by it. My concern is that among the 'other costs' is something rather nebulous that could easily get missed. Quite simply, doing good research takes time and mental space of the researchers. Most researchers are geeks who like nothing better than staring at data and thinking about complicated problems. If you require them to spend time satisfying bureaucratic requirements, this saps the spirit and reduces creativity.



I think we can learn much from the way ethics regulations have panned out. When a new system was first introduced in response to the Alder Hey scandal, I'm sure many thought it was a good idea. It has taken several years for the full impact to be appreciated. The problems are documented in a report by the Academy of Medical Sciences, which noted "Urgent changes are required to the regulation and governance of health
research in the UK because unnecessary delays, bureaucracy and
complexity are stifling medical advances, without additional benefits to
patient safety"



If the account in the Independent is to be believed, then the Concordat for Research Integrity could lead to a similar outcome. I'm glad I will retire before the it is fully implemented.

Arctic Sea Ice September 2013 Projections

What will the Arctic Sea Ice look like in September 2013?

Several projections for Arctic sea ice extent are being discussed at places such as ARCUS (Arctic Research Consortium of the United States) and the Arctic Sea Ice Blog. The image below, from ARCUS, shows various projections of September 2013 arctic sea extent (defined as the monthly average for September) with a median value of 4.1 million square kilometers, with quartiles of 3.8 and 4.4 million square kilometers.

Note that sea ice extent in the above projections is defined as area of ocean with at least 15% ice, in line with the way the NSIDC calculates extent. By contrast, the Danish Meteorological Institute includes areas with ice concentration higher than 30% to calculate ice extent.

Rather than looking at the projected average for September, one could also project the minimum value for September 2013. And rather than looking at sea ice extent, one could also look at sea ice area, which differs from sea ice extent as the NSIDC FAQ page describes:

A simplified way to think of extent versus area is to imagine a slice of Swiss cheese. Extent would be a measure of the edges of the slice of cheese and all of the space inside it. Area would be the measure of where there is cheese only, not including the holes. That is why if you compare extent and area in the same time period, extent is always bigger.

Above image shows Sam Carana's projected minimum area of 2 million square km for 2013, based on data by Cryosphere Today and on numerous factors, such as continued warming of the water underneath the ice, stronger cyclones, etc.

Roughly in line with above image, by Wipneus, Sam Carana's projection for Arctic sea ice minimum volume is 2,000 cubic km in September 2013.

Readers are invited to submit comments below with further projections.

Overhyped genetic findings: the case of dyslexia

A press release by Yale University Press Office was recently recycled on the Research Blogging website*, announcing that their researchers had made a major breakthrough. Specifically they said "A new study of the genetic origins of dyslexia and other learning disabilities could allow for earlier diagnoses and more successful interventions, according to researchers at Yale School of Medicine. Many students now are not diagnosed until high school, at which point treatments are less effective." The breathless account by the Press Office is hard to square with the abstract of the paper, which makes no mention of early diagnosis or intervention, but rather focuses on characterising a putative functional risk variant in the DCDC2 gene, named READ1, and establishing its association with reading and language skills.



I've discussed why this kind of thing is problematic in a previous blogpost, but perhaps a figure will help. The point is that in a large sample you can have a statistically strong association between a condition such as dyslexia and a genetic variant, but this does not mean that you can predict who will be dyslexic from their genes.

Proportions with risk variants estimated from Scerri et al (2011)

In this example, based on one of the best-replicated associations in the literature, you can see that most people with dyslexia don't have the risk version of the gene, and most people with the risk version of the gene don't have dyslexia. The effect sizes of individual genetic variants can be very small even when the strength of genetic association is large.



So what about the results from the latest Yale press release? Do they allow for more accurate identification of dyslexia on the basis of genes? In a word, no. I was pleased to see that the authors reported the effect sizes associated with the key genetic variants, which makes it relatively easy to estimate their usefulness in screening. In addition to identifying two sequences in DCDC2 associated with risk of language or reading problems, the authors noted an interaction with a risk version of another gene, KIAA0319, such that children with risk versions in both genes were particularly likely to have problems.  The relevant figure is shown here.

Fig 3A from Powers et al (2013)

There are several points to note from this plot, bearing in mind that dyslexia or SLI would normally only be diagnosed if a child's reading or language scores were at least 1.0 SD below average.

1. For children who have either KIAA0319 or DCDC2 risk variants, but not both, the average score on reading and language measures is at most 0.1 SD below average.


2. For those who have both risk factors together, some tests give scores that are 0.3 SD below average, but this is only a subset of the reading/language measures. On nonword reading, often used as a diagnostic test for dyslexia, there is no evidence of any deficit in those with both risk versions of the genes. On the two language measures, the deficit hovers around 0.15 SD below the mean.


3. The tests that show the largest deficits in those with two risk factors are measures of IQ rather than reading or language. Even here, the degree of impairment in those with two risk factors together indicates that the majority of children with this genotype would not fall in the impaired range.


4. The number of children with the two risk factors together is very small, around 1% of the population.

In sum, I think this is an interesting paper that might help us discover more about how genetic variation works to influence cognitive development by affecting brain function. The authors present the data in a way that allows us to appraise the clinical significance of the findings quite easily. However, the results indicate that, far from indicating translational potential for diagnosis and treatment, genetic effects are subtle and unlikely to be useful for this purpose.



*It is unclear to me whether the Yale University Press Office are actively involved in gatecrashing Research Blogging, or whether this is just an independent 'blogger' who is recycling press releases as if they are blogposts.



Reference

Powers, N., Eicher, J., Butter, F., Kong, Y., Miller, L., Ring, S., Mann, M., & Gruen, J. (2013). Alleles of a Polymorphic ETV6 Binding Site in DCDC2 Confer Risk of Reading and Language Impairment The American Journal of Human Genetics DOI: 10.1016/j.ajhg.2013.05.008

Scerri, T. S., Morris, A. P., Buckingham, L. L., Newbury, D. F., Miller, L. L., Monaco, A. P., . . . Paracchini, S. (2011). DCDC2, KIAA0319 and CMIP are associated with reading-related traits. Biological Psychiatry, 70, 237-245. doi: 10.1016/j.biopsych.2011.02.005
 

Interpreting unexpected significant results

©www.cartoonstock.com

Here's s question for
researchers who use analysis of variance (ANOVA). Suppose I set up a study to
see if one group (e.g. men) differs from another (women) on brain response
to auditory stimuli (e.g. standard sounds vs deviant sounds – a classic
mismatch negativity paradigm). I measure
the brain response at frontal and central electrodes located on two sides of the head. The nerds among my readers will see that I have
here a four-way ANOVA, with one between-subjects factor (sex) and three
within-subjects factors (stimulus, hemisphere, electrode location). My
hypothesis is that women have bigger mismatch effects than men, so I predict an
interaction between sex and stimulus, but the only result significant at p <
.05 is a three-way interaction between sex, stimulus and electrode location. What should I do?

a) Describe this as my
main effect of interest, revising my hypothesis to argue for a site-specific
sex effect

b) Describe the result as
an exploratory finding in need of replication

c) Ignore the result as
it was not predicted and is likely to be a false positive



I'd love to do a survey
to see how people respond to these choices; my guess is many would opt for a)
and few would opt for c). Yet in this situation, the likelihood of the result
being a false positive is very high – much higher than many people realise.   

Many people assume that
if an ANOVA output is significant at the .05 level, there's only a one in
twenty chance of it being a spurious chance effect. We have been taught that we do ANOVA rather
than numerous t-tests because ANOVA adjusts for multiple comparisons. But this
interpretation is quite wrong. ANOVA adjusts for the number of levels within a factor, so, for instance, the probability
of finding a significant effect of group is the same regardless of how many
groups you have. ANOVA makes no
adjustment to p-values for the number of factors and interactions in your
design. The more of these you have, the greater the chance of turning up a
"significant" result.

So, for the example given
above, the probability of finding something
significant at .05, is as follows:

For the four-way ANOVA
example above, we have 15 terms (four
main effects, six 2-way interactions, four 3-way interactions and one 4-way
interaction) and the probability of finding no significant effect is .95^15 =
.46. It follows that the probability of finding something significant is .54.

And for a three-way ANOVA
there are seven terms (three main effects, three 2-way interactions and one
3-way interaction), and p (something significant) = .30.

So, basically, if you do
a four-way ANOVA, and you don't care what results comes out, provided something
is significant, you have a slightly greater than 50% chance of being satisfied. This might seem like an
implausible example: after all who uses ANOVA like this? Well, unfortunately,
this example corresponds rather closely to what often happens in
electrophysiological research using event-related potentials (ERPs). In this field, the interest is often in
comparing a clinical and a control group, and so some results are more
interesting than others: the main effect of group, and the seven interactions
with group are the principal focus of attention. But hypotheses about exactly what will be
found are seldom clearcut: excitement is generated by any p-value associated
with a group term that falls below .05. There's a one in three chance that one
of the terms involving group will have a p-value this low. This means that the
potential for 'false positive psychology' in this field is enormous (Simmons et
al, 2011).

A corollary
of this is that researchers can modify the likelihood of finding a
"significant" result by selecting one ANOVA design rather than
another. Suppose I'm interested in comparing brain responses to standard and
deviant sounds. One way of doing this is to compute the difference between ERPs
to the two auditory stimuli and use this difference score as the dependent
variable:  this reduces my ANOVA from a
4-way to a 3-way design, and gives fewer opportunities for spurious findings. So
you will get a different risk of a false positive,
depending on how you analyse the data.



Another feature of ERP
research is that there is flexibility in how electrodes are handled in an ANOVA
design: since there is symmetry in electrode placement, it is not uncommon to
treat hemisphere as one factor, and electrode site as another. The alternative
is just to treat electrode as a repeated measure. This is not a neutral choice:
the chances of spurious findings is greater if one adopts the first approach,
simply because it adds a factor to the analysis, plus all the interactions with
that factor.

I stumbled across these
insights into ANOVA when I was simulating data using a design adopted in a
recent PLOS One paper that I'd commented on. I was initially interested in looking at the
impact of adopting an unbalanced design in ANOVA: this study had a group factor
with sample sizes of 20, 12 and 12. Unbalanced designs are known to be problematic for repeated measures ANOVA and I initially thought this might be
the reason why simulated random numbers were giving such a lot of
"significant" p-values. However, when I modified the simulation to
use equal sample sizes across groups, the analysis continued to generate far
more low p-values than I had anticipated, and I eventually twigged that this
was because this is what you get if you use 4-way ANOVA. For any one main
effect or interaction, the probability of p < .05 was one in twenty: but the
probability that at least one term in the analysis would give p < .05 was closer
to 50%.

The analytic approach
adopted in the PLOS One paper is pretty standard in the field of ERP. Indeed, I have
seen papers where 5-way or even 6-way
repeated measures ANOVA is used. When
you do an ANOVA and it spews out the results, it's tempting to home in on the
results that achieve the magical significance level of .05 and then formulate
some kind of explanation for the findings. Alas, this is an approach that has
left the field swamped by spurious results.

There have been various
critiques of analytic methods in ERP, but I haven't yet found any that have
focussed on this point. Kilner (2013) has noted the bias that arises when
electrodes or windows are selected for analysis post hoc, on the basis that
they give big effects. Others have noted problems with using electrode as a repeated measure, given that ERPs at different electrodes are often highly
correlated. More generally,
statisticians are urging psychologists to move away from using ANOVA to adopt multi-level modelling, which makes different assumptions and can cope, for
instance, with unbalanced designs. However, we're not going to fix the problem
of "false positive ERP" by adopting a different form of analysis. The
problem is not just with the statistics, but with the use of statistics for what
are, in effect, unconstrained exploratory analyses. Researchers in this field urgently need
educating in the perils of post hoc interpretation of p-values and the
importance of a priori specification of predictions.

I've argued before that
the best way to teach people about statistics is to get them to generate their
own random data sets. In the past, this was difficult, but these days it can be
achieved using free statistical software, R. There's no better way of persuading someone to be less impressed by p
< .05 than to show them just how readily a random dataset can generate
"significant" findings. Those who want to explore this approach may
find my blog on twin analysis in R useful for getting started (you don't need
to get into the twin bits!).

The field of ERP is
particularly at risk of spurious findings because of the way in which ANOVA is
often used, but the problem of false positives is not restricted to this area,
nor indeed to psychology. The mindset of researchers needs to change radically,
with a recognition that our statistical methods only allow us to distinguish
signal from noise in the data if we understand the nature of chance.

Education about
probability is one way forward. Another is to change how we do science to make
a clear distinction between planned and exploratory analyses. This post was
stimulated by a letter that appeared in the Guardian this week on which I was a
signatory. The authors argued that we should encourage a system of
pre-registration of research, to avoid the kind of post hoc interpretation of
findings that is so widespread yet so damaging to science.

Reference

Simmons, Joseph P., Nelson, Leif D., & Simonsohn, Uri (2011). False-positive psychology Psychological Science, 1359-1366 DOI: 10.1037/e636412012-001



This article (Figshare version) can be cited as:
Bishop, Dorothy V M (2014): Interpreting unexpected significant findings. figshare.
http://dx.doi.org/10.6084/m9.figshare.1030406






PS. 2nd July 2013

There's remarkably little coverage of this issue in statistics texts, but Mark Baxter pointed me to a 1996 manual for SYSTAT that does explain it clearly. See: http://www.slideshare.net/deevybishop/multiway-anova-and-spurious-results-syt

The authors noted "Some authors devote entire chapters to fine distinctions between multiple comparison procedures and then illustrate them within a multi-factorial design not corrected for the experiment-wise error rate." 

They recommend doing a Q-Q plot to see if the distribution of p-values is different from expectation, and using Bonferroni correction to guard against type I error.



They also note that the different outputs from an ANOVA are not independent if they are based on the same mean squares denominator, a point that is discussed here:

Hurlburt, R. T., & Spiegel, D. K. (1976). Dependence of F Ratios Sharing a Common Denominator Mean Square. The American Statistician, 30(2), 74-78. doi: 10.2307/2683798

These authors conclude (p 76)

It is important to realize that the appearance of two significant F ratios sharing the same denominator should decrease one's confidence in rejecting either of the null hypotheses. Under the null hypothesis, significance can be attained either by the numerator mean square being "unusually" large, or by the denominator mean square being "unusually" small. When the denominator is small, all F ratios sharing that denominator are more likely to be significant. Thus when two F ratios with a common denominator mean square are both significant, one should realize that both significances may be the result of unusually small error mean squares. This is especially true when the numerator degrees of freedom are not small compared' to the denominator degrees of freedom.

Schizophrenia and child abuse in the media

A couple of weeks ago, the Observer printed a debate headlined “Do we need to change the way we are thinking about mental illness?” I read it with interest, as I happen to think that we do need to change, and that the new Diagnostic and Statistical Manual of the American Psychiatric Association (DSM5) has numerous problems.



The discussion was opened by Simon Wessely, a member of the Royal College of Psychiatrists, who responded No. He didn’t exactly defend the DSM5, but he disagreed with the criticism that it reduces psychiatry to biology. The Yes response was by Oliver James, an author and clinical psychologist, who attacked the medical model of mental illness, noting the importance of experience, especially childhood experience, in causing psychiatric symptoms. I happen to take a middle way here; there’s ample evidence of biological risk factors for many forms of mental illness, but in our contemporary quest for biomarkers, the role of experience is often sidelined. The idea that you might be depressed because bad things have happened to you goes unmentioned in much contemporary research on affective disorders, for instance.



But, rather than getting into that debate, I want to make a more general point about evidence. In his statement, Oliver James came out with some statistics that surprised me. In particular, he said:

13 studies find that more than half of schizophrenics suffered childhood abuse. Another review of 23 studies shows that schizophrenics are at least three times more likely to have been abused than non-schizophrenics. It is becoming apparent that abuse is the major cause of psychoses.

The frustrating thing about this claim is that no sources were given. I don't work in this area, so I thought I’d see if I could track down the articles cited by James. My initial attempt was based on a hasty search of Web of Knowledge on the morning that the article appeared. I described the results of my searches on a blogpost that day, but a commentator pointed out that I'd limited myself to looking at the link between schizophrenia and sexual abuse, whereas James had been referring to childhood abuse in general. I realised that a fair and proper appraisal of his claims should look at this broader category, and accordingly I removed the original post until I could find time to do a more thorough job.



Accurate assessment of child abuse is difficult because it is often hidden away and many cases may be missed. Retrospective accounts of abuse are notoriously hard to validate: false memories can be induced, but true memories may be suppressed. All those writing in this field note the problems of getting accurate data, and the wide variations in rates of abuse reported in the general population, depending on how it is defined. For instance, Fryers and Brugha (2013) noted that for the general population, estimated rates of child physical abuse have ranged between 10% to 31% in males and 6% to 40% of females, and child sexual abuse from 3% to 29% in males and 7% to 36% in females.



Fryers and Brugha focused on prospective, longitudinal studies, taking evidence from over 200 studies. They concluded that “most abuses were associated statistically with almost all classes of disorder (psychosis being largely an exception)”, p 26, and “schizophrenia and closely related syndromes have not generally been much associated with previous child abuse but the picture is not simple.” P.27. They noted that one Australian cohort study found an increase in schizophrenic disorders in children who had been sexually abused, though this was an unusual finding in the context of the research literature as a whole.



Other recent meta-analyses have included case-control studies, where participants are recruited in adulthood, and histories of participants with schizophrenia are compared with those of a control group. Matheson et al (2012) summarised seven studies involving a comparison between patients with schizophrenia and non-psychiatric controls, but definitions of adversity varied widely. In some studies, 'adversity' extended beyond abuse, though physical, sexual and emotional abuse predominated in the definitions. Overall, this review gave results similar to those reported by James, with an adversity rate of 58% in the schizophrenia group and 27% in the controls. This high rate in those with schizophrenia depends, however, on one large study that included adversity factors going beyond abuse, such as having a parent with nervous or emotional problems, or a lot of conflict and tension in the household. In this same study, 91% of controls compared with 71% of those with schizophrenia described their childhood as "happy". If this study is excluded, rates of adversity in those with schizophrenia fall to 28% compared to 8% in controls – still a notable and statistically reliable effect, but with less dramatic absolute rates of adversity than those cited by James.



A larger meta-analysis including a total of 36 studies was conducted by Varese et al (2012), who obtained similar results:  a higher rate of childhood adversities in those who develop psychosis, with an odds ratio estimated at 2.78 (95% CI = 2.34-3.31). Significant associations of similar magnitude were found for all types of adversity other than parental death. The odds ratio is not, however, the same as a risk ratio, so should not be interpreted as indicating that those with schizophrenia are three times more likely to have suffered abuse. I don’t want to downplay the importance of the association, which is nevertheless striking, and supports the authors' conclusion that clinicians should routinely inquire about adverse events in childhood when seeing patients with psychiatric conditions.



Overall, the research literature confirms a reliable association between childhood adversity, including abuse, and schizophrenia in adulthood. The conclusion drawn by James, however, that “abuse is the major cause of psychoses” is not endorsed by any of the academic authors of the reviews I looked at. The complexity of causation in the field of neuroscience and mental health is a topic I hope to return to in a later blogpost, but for the time being, I would recommend another review of this literature by Sideli et al (2012), which discusses possible explanations for links between adversity and psychosis. Most researchers familiar with this area would endorse this quote by Fryers and Brugha (2013), reflecting on our state of knowledge in this area:

From all this work an understanding has emerged of the 'cause' of serious mental illness as complex, varied and multi-factorial, encompassing elements of genetic constitution, childhood experience, characteristics of personality, significant life events, the quality of relationships, economic and social situation, life-style choices such as alcohol and other drugs, and aging. Some of these factors have been elucidated to the point of representing acknowledged risk factors for specific forms of mental illness or mental illness in general, such as familial genes, relative poverty, major trauma, excessive alcohol consumption, extreme negative life-events, poor education, and long-term unemployment.

These may all be experienced in childhood and we do not need research to tell us that poverty, inadequate education and life events such as loss of a parent or displacement as a refugee by war, or trauma such as child sex abuse are bad. Nor should it need evidence of later consequences such as mental illness to argue for the prevention of such situations and experiences. The strongest argument is in terms of human rights. However, the issues are not generally given a high priority and people may think them exaggerated or assume that these things are just part of human life and children get over them anyway. But we should not be willing to accept these as inevitably part of human life, but fight for a better life for our children – and hope thereby for a better life for adults and the whole community.

But to come back to the impetus for the current blogpost, the point I’d really like to make is that if the Observer wants to run articles like this, where scientific evidence is cited, the editor should ask for sources for the evidence, and should provide these with the article. As noted by Prof Michael O'Donovan in a letter to the Observer, Oliver James is "unknown in the scientific community as a researcher into the origins of psychosis". This does not make his opinions worthless, but if he wants to argue his case from the scientific evidence, then we need to know what evidence he is using, just as we would expect for any reputable scientist making such claims. Most readers don't have the resources or skills to trawl through research databases trying to establish whether the evidence is accurately reported or cherry-picked. As O'Donovan points out, confident assertions about childhood causes of schizophrenia can only cause distress to families affected by this condition, and a responsible newspaper should take care to ensure that such claims have a verifiable basis.



References



Fryers, T., & Brugha, T. (2013). Childhood determinants of adult psychiatric disorder. Clinical Practice & Epidemiology in Mental Health, 9 (1), 1-50 DOI: 10.2174/1745017901309010001



Matheson, S. L., Shepherd, A. M., Pinchbeck, R. M., Laurens, K. R., & Carr, V. J. (2013). Childhood adversity in schizophrenia: a systematic meta-analysis. Psychological Medicine, 43(2), 225-238. doi: 10.1017/s0033291712000785



Sideli, L., Mule, A., La Barbera, D., & Murray, R. M. (2012). Do child abuse and maltreatment increase risk of schizophrenia? Psychiatry Investigation, 9, 87-99. doi.org/10.4306/pi.2012.9.2.87


Varese, F., Smeets, F., Drukker, M., Lieverse, R., Lataster, T., Viechtbauer, W., Read, J., van Os, J., & Bentall, R. (2012). Childhood adversities increase the risk of psychosis: A meta-analysis of patient-control, prospective- and cross-sectional cohort studies Schizophrenia Bulletin, 38 (4), 661-671 DOI: 10.1093/schbul/sbs050



Note: Thanks to the anonymous reviewer who noted the problem with my initial analysis.