The Changing Face of British Suicide

Which jobs are at the highest risk of suicide?

In a fascinating new study, British researchers Roberts, Jaremin and Lloyd show dramatic changes over time. 30 years ago, the worst occupations for suicide were the medical professions. Now, it's blue-collar workers, with coal miners topping the list.

They used official records of UK suicides, comparing 1979-1983 and 2000-2005. Here's the key data (their graphs, my colours)

In the 80s, veterinarians were the most suicidal of all jobs; by 2005, they'd dropped off the Top 30 list entirely. Other healthcare professions, like pharmacists and dentists, likewise disappeared after being high on the rankings.

On the other hand, the most dramatic rise in suicide was among coal miners. They went from #29 to #1, and their rates rose about fourfold. This is not that surprising considering what happened to British coal mining during the 80s...

In general the Top 30 in the 1980s had plenty of white collar workers like engineers, chemical scientists and photographers.

Twenty years later, all of the collars had turned blue. Coal miners were joined by labourers, builders, gardeners, butchers, and others. Strikingly there are no 'professionals' on the modern list, with the interesting exception of musicians and artists (a reflection of the mental illness-creativity link?)

What's more, the correlation between socioeconomic status and suicide rates increased sharply over time. Suicide is now much more of a class issue than it was in the past.

The only constant in this sad picture was the sea: merchant sailors had the #2 spot in 1980, and they kept it in 2005, with the rates almost unchanged. However, this should be taken with a pinch of salt, because the way British suicides at sea are recorded is a bit unusual.

Roberts SE, Jaremin B, and Lloyd K (2012). High-risk occupations for suicide. Psychological medicine, 1-10 PMID: 23098158

Men and Women: From Earth, Not Mars & Venus?

Another day, another debate over how different men and women are, psychologically speaking. Bobbi Carothers and Harry Reis argue that Men and Women Are From Earth.

Their approach is rather interesting.

We sought to empirically determine whether standard gender differences are better conceived as taxonic or dimensional. Although men and women may differ on average in myriad ways, these differences may be dimensional, reflecting different amounts of a given attribute assessed along a single dimension, or qualitative, sorted into fundamentally distinct categories... this difference has considerable importance for understanding the fundamental nature of gender differences.

Using lots of previously published data (13,000 people) and subjecting it to three different methods of statistical "taxometric analysis", they claim that on most psychological measures, there's no evidence that the two sexes are qualitatively different. This is on things like sexual attitudes, personality, and interest in science according to self-report questionnaires.

They give the following hypothetical example to illustrate the idea (my picture based on theirs)

Men are taller than women and also have shorter hair. If you plot a scatterplot of height vs hair length including both genders, you find a negative correlation. However, there is no such correlation within each gender. So gender is a taxon - in this case. There is something qualitatively different between men and women here.

Their argument is that psychological differences between the genders are, in most cases, not because "male" and "female" are two distinct taxons.

So what? I previously covered a paper called The Distance Between Mars and Venus claiming that the difference between men and women on average are larger than previously thought, if you look at all the differences taken together. That's actually consistent with what Carothers and Reis are saying, I think, because it assumes that each of the differences is dimensional and quantitative.

In other words, maybe sexes differ only by a matter of degree, albeit by a larger degree than you'd think at first glance.

All of this leaves open the question of why they differ on average, though. According to yet another study just out, the size of the gap is correlated with the amount of gender inequality in different countries. Women from places where they have much lower incomes, career prospects, etc. compared to men, also endorse more 'feminine' traits.

Personally I consider the question of gender differences largely open because I'm skeptical of self-report questionnaire measures in psychology; objective measures of actual behaviour, stuff like crime statistics, seems to me more interesting.

The fact that the great majority of sex offenders are male, for example, must mean something; I'm not sure what, but I don't think questionnaires will help us find out...

Carothers, B., and Reis, H. (2012). Men and Women Are From Earth: Examining the Latent Structure of Gender. Journal of Personality and Social Psychology DOI: 10.1037/a0030437

Is fMRI About To Get Fifty Times Faster?

According to a paper just published, a new technique of functional MRI scanning (fMRI) could soon allow neuroscientists to measure brain activity far faster: Generalized iNverse imaging (GIN): Ultrafast fMRI with physiological noise correction

Authors Boyacioglu and Barth claim remarkable things for the technique:

We find that the spatial localization of activation for GIN is comparable to an EPI protocol and that maximum z-scores increase significantly... with a high temporal resolution of 50 milliseconds.

EPI, the current standard fMRI sequence, would have a temporal resolution of 2000 or 3000 milliseconds, so it's about 50 times faster.

Other super-fast fMRI methods already exist (e.g. this one I blogged about), but they've generally achieved speed only at a cost: they've had to either sacrifice spatial resolution to achieve that, or limited themselves to scanning only a small fraction of the brain, or have been more subject to random noise and hence less sensitive.

GIN, however, is said to cover the whole brain, with decent spatial resolution and signal-to-noise ratio. The data can be analyzed in exactly the same way as any other kind. So that's up to fifty times faster with no real drawbacks.

That would be truly revolutionary - as the major limitation of fMRI at the moment is that it's much slower than other methods of recording brain activity.

Check it out: this shows brain activation in response to simple visual stimuli, imaged with bog-standard EPI and GIN:

So this is a big deal... if it does work, I'm sure neuroscientists the world over will be lining up to buy Boyacioglu and Barth a GIN and tonic.

How does it work, and is it all it's cracked up to be? Well, I can't really say: the math is beyond me.

In essence, rather than scanning the brain in 3D, slice by slice (like this), GIN only scans one 2D slice, but then manages to reconstruct the rest of the brain in 3D from just that slice, using dark, forbidden magicks... I mean mathematics. The principle is called parallel imaging and it's been around for several years, but with image quality limitations that GIN claims to have overcome.

Perhaps my more technically-inclined readers will have more insightful comments.

Boyacioglu R, and Barth M (2012). Generalized iNverse imaging (GIN): Ultrafast fMRI with physiological noise correction. Magnetic Resonance in Medicine PMID: 23097342

Gene-Guided Antidepressants?

Over the past couple of years, "Big Pharma" has largely moved away from psychiatric drug development. This shift has been widely discussed.

But another trend has been happening over the same time period - or so it seems to me. This is the rise of small companies who offer techniques for diagnosing mental illness, or predicting which drugs will work best. Generally (it seems) partnerships between venture capitalists and psychiatry (ex-)researchers, these enterprises might be dubbed "Little Pharma".

The latest is a company called AssureRx Health, Inc. According to a paper just published, they offer

a pharmacogenomic algorithm designed to improve the safety and efficacy of prescribing antidepressant and antipsychotic medication... based on the genotyping of both copies of five genes.

From this, you end up with a report giving each drug a rating of green, yellow, or red (see above).

The price is not provided on their website.

According to the paper, they gave 26 depressed patients normal treatment at the discretion of their psychiatrist, while 25 got treatment guided by the AssureRx algorithm. It was non-randomized, and unblinded so there's a clear possibility of a placebo effect.

Anyway, the results were...

For the first 4 weeks of treatment, there was no difference between the two groups whatsoever in terms of depression symptom scores - they both improved. But then by week 8, the unguided patients abruptly got worse, while the AssureRx-guided ones continued to benefit. This is an unusual pattern of improvement in an antidepressant trial.

Previously, I wrote about another Little Pharma antidepressant prediction scheme. It used a  different approach, measuring brain electrical activity using a technique called "rEEG", rather than genetics. But the basic idea is the same... and so are the problems.

As I said last time:

There were two groups and they got entirely different sets of drugs. One group also got rEEG-based treatment personalization. That group did better, but that might have nothing to do with the rEEG: they might have done equally well if they'd just been assigned to [those drugs] by flipping a coin. We cannot tell, from these data, whether rEEG offered any benefits at all.

In the AssureRx paper, we can't even tell whether the two groups got different kinds of drugs, because the meds used aren't reported, but if they did differ then that would offer an alternative explanation for the differences in outcome: maybe the 'guidance' just recommended better drugs overall, with the genes being just a sideshow. Or maybe it's a placebo, as I said.

Moving on, I also wrote... 

What's curious is that it would have been very simple to avoid this issue. Just give everyone rEEG, but shuffle the assignments in the control group, so that everyone was guided by someone else's EEG. So you'd give control Patient 2 the drugs that Patient 1 should have got, and vice versa; swap 3 and 4, 5 and 6, etc.

This would be a genuinely controlled test of the personalization, because both groups would get the same kinds of drugs... and it would allow the trial to be double-blind: in this study the investigators knew which group people were in, because it was obvious from the drug choice...

It is odd that Little Pharma never seem to do such real vs. muddled prediction studies, as they'd be really informative as to whether their approach is a helpful innovation as opposed to an expensive, meaningless red herring. Hmm.

Hall-Flavin, D., Winner, J., Allen, J., Jordan, J., Nesheim, R., Snyder, K., Drews, M., Eisterhold, L., Biernacka, J., and Mrazek, D. (2012). Using a pharmacogenomic algorithm to guide the treatment of depression Translational Psychiatry, 2 (10) DOI: 10.1038/tp.2012.99

The Psychology of Edgar Allan Poe

A paper by psychology undergrad Erica Giammarco offers a look at the mind that gave us The Raven and The Masque of the Red Death: Edgar Allan Poe: A Psychological Profile

Poe lost his mother to tuberculosis at the age of 2; he was then adopted, but his foster mother died young as well. He enrolled at the University of Virginia but became involved in gambling and had to ask his foster father for money; they argued and at the age of 20, Poe was cut off from his family. He married, but his wife suffered frequent illnesses, and died at the age of 25 in 1847; by this time Poe was drinking heavily and he died after collapsing 'drunk and delirious' in 1849.

According to Giammarco:

Poe was described as a mischievous child, playing practical jokes on classmates and teachers... One teacher was quoted as saying that Poe had an "...excitable temperament with a great deal of self-esteem." This grandiose self view would remain consistent throughout Poe’s life; however, Poe was defensive and threatened by negative comments. This is consistent with a narcissistic self-view rather than healthy self-esteem.

Although successful in his studies, he did not have many friends and wrote that school was a "miserable" experience. Classmates stated that he was incredibly defensive and did not allow others to get close...

As Poe aged his health deteriorated and he continued to drink heavily. He was described by coworkers and family as chronically melancholic, acquiring the nickname ‘the man who never smiles’... Poe had a great deal of pride, evident in his refusal to accept money when he and his wife were both sick and unable to work...

An examination of the letters Poe wrote to family reveals that he was a dramatic individual. He often used excessive, theatrical language, poignantly captured in his statement, "I do believe God gave me a spark of genius, but He quenched it in misery"

When describing Poe in terms of the Five-Factor Model of personality we can conclude that he would be high on Neuroticism – evident by the constant nervous anxiety he was said to have, as well as his melancholy and irritability. Poe would also be described as being low in Agreeableness and Conscientiousness since he was argumentative, untrusting, and lacked self-control (i.e. his drinking, his failure to pursue education).

Poe actually crops up several times in the medical literature. Other examples of scientific anthropoelogy include...

• Seizures in the life and works of Edgar Allan Poe
• The earliest description of the frontal lobe syndrome in an Edgar Allan Poe tale.
• Informed consent in an Edgar Allen Poe tale

Giammarco, E. (2013). Edgar Allan Poe: A psychological profile Personality and Individual Differences, 54 (1), 3-6 DOI: 10.1016/j.paid.2012.07.027