The World Mental Health Missionaries?

Is research on the global distribution of mental health problems a kind of modern-day missionary work?

Maybe, says Australia's Dr Stephen Rosenman in a provocative paper: Cause for caution: culture,sensitivity and the World Mental Health Survey Initiative.

The World Mental Health Survey (WMHS) is a huge World Health Organization project that aims to measure the rates of various psychiatric disorders in countries around the world. The WMHS has produced a great deal of data, but Rosenman points out that this assumes that people all over the world suffer from the same psychiatric disorders (and display them in the same ways) as the Americans and Europeans about whom the diagnostic manual was originally written.

The surveys translated the diagnostic criteria into the local languages, of course, but that doesn't mean they were appropriate to the local cultures.

He suggests that all this is a bit like missionaries who went around translating the Bible and trying to convince people to read it -

Looked at with a less admiring eye, the [WMHS] resembles in some ways the missionary movements of the last two centuries. Like the missionaries, the organisers are committed, selfless people of extraordinary goodwill who have come to poor countries from cultures at the apogee of their wealth, prestige and intellectual power.

They bring an evolved and highly developed system of thought. They set about delivering the fruits of that to the people. The survey initiative has engaged the leaders of the profession in the countries and, in a sense, has converted them to this view of psychopathology.

It is difficult to know if their success is due to the power of the ideas they brought, or the power and prestige of the cultures they came from, or from their technique of taking over both the centre and the contours of the beliefs of a culture. Missionaries brought a ‘colonisation of consciousness’... etc.

He does goes on to say though, "I do not want to push the missionary analogy too far" which is wise I think; there are important differences and other analogies are equally apt.

The paper's a good read though. It refers to Crazy Like Us, a book I'm fond of.

Although Rosenman doesn't cite another important source (cough cough): he points out that the WMHS national estimates of rates of depression don't correlate at all with national suicide rates, which is seriously odd -

According to the CIDI [the psychiatric interview used in the WMHS], Japan, for example, has one-third the rate of mood disorders (3.1%) seen in the USA (9.6%). At the same time, Japan’s suicide rate (20.3/100,000) is twice that of the USA (10.8/100,000). Suicide rates seem to have almost no relationship with CIDI diagnoses of affective disorder... Suicide, of course, is complexly shaped by the culture but are we to believe that answers to the CIDI are any less culturally determined and which is to be considered the better index of disorder?

I made the very same point using the very same datasets in 2009 (although I looked at 'all mental illness' rather than 'mood disorders').

Rosenman, S. (2012). Cause for caution: culture, sensitivity and the World Mental Health Survey Initiative Australasian Psychiatry, 20 (1), 14-19 DOI: 10.1177/1039856211430149

Take Your Placebos, Or Die

People who take their medication as directed are less likely to die - even when that "medication" is just a sugar pill.

This is the surprising finding of a paper just published, Adherence to placebo and mortality in the Beta Blocker Evaluation of Survival Trial (BEST). 

BEST was a clinical trial of beta blockers, drugs used in certain kinds of heart disease. The patients were aged about 60 and they all suffered from heart failure. Everyone was randomly assigned to get a beta blocker or placebo, then followed up for 3 years to see how they did.

Here's the big finding: in the placebo group of 1174 patients, the people who took all of their placebo pills on time (the good adherers), were significantly less likely to die than the patients who missed lots of doses. People who took over 75% as directed were 40% less likely to die than those with less than 75% adherence:

That's pretty interesting. The pills were placebos - they can't have had any benefit. So what's going on?

It gets even better. You might be tempted to write off these results as obvious: "Clearly, people who follow the study instructions are just 'healthy' people in other ways - maybe they take more exercise, eat better, etc. and that's what protects them."

Certainly, that's what I'd have said.

But what's remarkable is that when the authors corrected the statistics for all the confounding variables they measured - including things like age, gender, ethnicity, smoking, body mass index and blood pressure - it barely changed the effect. Some of the factors did correlate with adherence, but not in a way that it could explain the adherence effect on mortality.

This isn't the first study to find this effect. The authors themselves have already reported it, as have other researchers going back decades (many of which also tried, and failed, to explain it through confounding factors.) They say that it's unlikely to be a case of publication bias.

So what we have is a large effect, which cannot be causal, yet which can't be explained by any obvious confounds. Logically then, it must be the result of a confound (or more than one) that aren't obvious.

This is an important lesson. It's common for someone to do a study and find an interesting / scary / controversial correlation between two things. Often one is some kind of lifestyle factor, diet, environmental exposure, or whatever, and the other is some nasty disease. "And it wasn't explained by confounds!", such studies often conclude.

What the placebo adherence effect demonstrates is that there may be confounds no-one has thought of. They might even be impossible to measure. And if these mystery confounds can literally kill you, they can probably cause all kinds of other effects too.

In other words this illustrates the truism that correlation is not causation - not even when you're really sure it is...

Pressman, A., Avins, A., Neuhaus, J., Ackerson, L., and Rudd, P. (2012). Adherence to placebo and mortality in the Beta Blocker Evaluation of Survival Trial (BEST) Contemporary Clinical Trials DOI: 10.1016/j.cct.2011.12.003

Men and Women - Alien Personalities?

How different are men and women? Are they from two different planets?

In the cleverly-titled The Distance Between Mars and Venus, the authors argue that personality-wise, the differences between men and women have been underestimated by previous studies because they used simplistic statistics.

Traditional studies of gender and personality have given some men and some women a personality quiz, and calculated the average male and female scores on the different aspects of personality.

When you do this you find that there are differences, but that the standardized effect sizes are fairly small, which means that there is a lot of overlap. Even on measures where men score above women on average, lots of men score below the female average, and vice versa, like this:

Traditional studies of overall gender differences have looked to see the differences between the average man and woman on each personality aspect, and thenaveraged the differences on each scale to get an "overall difference" score. Which comes out as fairly small.

The authors of the new paper say that this approach fails to capture the true difference and they give a helpful analogy of why:

Consider two fictional towns, Lowtown and Hightown. The distance between the two towns can be measured on three (orthogonal) dimensions: longitude, latitude, and altitude. Hightown is 3,000 feet higher than Lowtown, and they are located 3 miles apart in the north-south direction and 3 miles apart east-west.

What is the overall distance between Hightown and Lowtown? The average of the three measures is 2.2 miles, but it is easy to see that this is the wrong answer. The actual distance is the Euclidean distance, i.e. 4.3 miles – almost twice the "average" value.

The main novel argument of this paper is that if you calculate the distance (technically the Mahalanobis distance) in 'personality space' between men and women then you get a larger value than if you just average the differences on each measure.

The paper also uses a couple of other methods that increase the effect sizes, namely using 15 different personality measures instead of the more common Big 5, and adjusting the differences upwards to take account of the fact that quizzes only imperfectly measure underlying 'latent' personality traits.

I don't want to get into the debate over how valid the underlying data are (a 1993 sample of over 10,000 American adults, used to standardize the 16PF questionnaire). There are lots of technical comments here. I'm going to focus on the distance method.

It's a very interesting approach and certainly raises questions about merits of the old approach, which when you think about it, does seem a bit crude. But I'm not sure that the average person is talking about distance in a hypothetical space when they talk about "personality differences".

As an analogy, consider the dog breeds Labrador and Golden Retriever. These are regarded as being pretty similar kinds of dog. On any given feature, the average differences are small, at least compared to the diversity of other breeds. They're roughly the same size, much the same build, coat type etc.

They are distinct breeds. This surely means that when you take all of the differences together, they define distinct regions of "dog space" (which has dozens or hundreds of dimensions), with little or no overlap.

Yet they are still regarded as similar. "Similar" and "distinct" are not mutually exclusive. In fact, isn't the definition of 'distinct yet similar' that two things separate in some kind of feature-space, but don't differ much on any one measure?

So I would say that these data show that, while men and women may be distinguishable in personality, they could still be similar. This is something of a semantic point but not "merely" semantic: it changes the interpretation of the numbers.

J. S. Hyde, who is most associated with the view that gender differences are small, makes a similar (or do I mean distinct?) point in her comment on the paper:

The gender difference found is along a dimension in multivariate space that is a linear combination of the original variables transformed into latent variables...[but] the resulting dimension here is uninterpretible. What does it mean to say that there are large gender differences on this undefined dimension in 15-dimensional space created from latent variables? The authors call it global personality, but what does that mean?

Her questioning of what the direction along which men and women differ means, is (I think) the same question I'm asking about whether it disproves the idea of "similarity", in the ordinary sense of the term.

Finally, take a step back and the whole debate seems a bit circular because, by definition, "personality" means "things that differ between individual people". Things we are have in common aren't even in the picture. Two groups could differ in personality space but still be very close in the much larger space of "possible creatures". There's no personality trait for 'being human'.

Del Giudice, M., Booth, T., and Irwing, P. (2012). The Distance Between Mars and Venus: Measuring Global Sex Differences in Personality PLoS ONE, 7 (1) DOI: 10.1371/journal.pone.0029265

One in Four Revisited

In a recent Telegraph article, professional contrarian Brendan O'Neill argues against the idea that one in four people experience mental illness - and indeed against the idea that one in four people are bullied, abused or whatever else:

Can it really be true that a quarter of Brits are bullied or beaten up at home or are mentally ill, or is this simply a case of social campaigners exaggerating how bad life is in order that they can continue to make headlines, make an impact, and get funding? I reckon it's the latter. Next time you see the "one in four" figure, be very sceptical – it's probably Dickensian-style doom-mongering disguised as social research, where the aim is to convince us, against the evidence of our own eyes and ears, that loads of the people we encounter everyday are basket cases in need of rescue.

I say "argues against", but he doesn't actually provide any arguments. He just links to the claims and says they're silly.

As Neuroskeptic readers know, I am myself skeptical of the idea that one in four people are mentally ill, but I'm skeptical of it because I've looked at the evidence and it doesn't support that figure. Actually, if you take the available evidence at face value, it says that the true figure for the lifetime prevalence is much higher than one in four. I don't think those figures are very useful however because of various methodological issues.

So in my view we just don't know how many people are mentally ill, largely because we don't have any clear definition of what "mentally ill" means. But that doesn't mean we can just assume that it can't possibly be one in four just because "our own eyes and ears" tell us that most people are not "basket cases".

Much mental illness goes undiagnosed and unnoticed, and I'd imagine also that Brendan O'Neill and the kind of people who read him don't tend to "encounter everyday" people from groups such as the unemployed, the elderly and so forth, in whom the rates are higher.

But even beyond that, it's a silly argument because of selection bias. If you as a healthy person encounter someone everyday, chances are they're not severely ill - mentally or physically - because if they were, they'd be less likely to be around in places for you to encounter. Unless you're a doctor or whatever, you live your life in the world of healthy people.

It's like saying that you don't believe children or the elderly exist, because in your life as a working age adult, you never meet any of them.