Stigma and the Biological Model of Mental Illness

For many years, the psychiatric profession has been pushing the idea of mental illness as biological illness, despite the fact that there is no blood test or other biological test for any psychiatric condition. The National Alliance on Mental Illness says on its web site: "Mental illnesses are biologically based brain disorders. They cannot be overcome through 'will power' and are not related to a person’s 'character' or intelligence." NAMI thus invokes the biological theory of mental illness as a way to counter the stigma that so often accompanies mental illness. But what if a belief in biological models actually worsens stigma or degrades people's belief in a positive prognosis?

A 2001 study by John Read and Niki Harré (and I quote) "confirmed previous findings (contrary to the assumption on which most destigmatisation programmes are based) that biological and genetic causal beliefs are related to negative attitudes, including perceptions that 'mental patients' are dangerous, antisocial and unpredictable, and reluctance to become romantically involved with them."

A small but interesting 2007 study by D.C. Lam and P.M. Salkovskis reported:

Anxious and depressed patients (n=49) were randomly allocated to three experimental conditions. Prior to watching a video of a person suffering from panic disorder, participants were told either that research indicated that panic was caused by biological factors, by psychological factors or the cause was unclear (control condition). Those in the biological condition were significantly more pessimistic about the patient's prospects for recovery and rated risks as higher compared to those in the psychological condition. The results call into question the widely accepted practice of promoting biological/disease explanations of mental health problems.

In 2009, Brett J. Deacon and Grayson L. Baird replicated the Lam & Salkovskis results using a group of 90 undergraduates who were were asked to imagine feeling depressed (and seeking help from a doctor who diagnosed them with major depressive disorder), then received either a chemical imbalance explanation or a biopsychosocial explanation for their symptoms. The students then filled out surveys to rate each explanation’s credibility and provide information on stigma, prognosis, and treatment expectancies. The upshot:

Compared to the biopsychosocial model, the chemical imbalance model was associated with significantly less self-stigma but also significantly lower credibility, a worse expected prognosis, and the perception that psychosocial interventions would be ineffective. The chemical imbalance explanation appears to reduce blame at the cost of fostering pessimism about recovery and the efficacy of nonbiological treatments.

Sheila Mehta and Amerigo Farina (1997) found that college students delivered electric shocks of higher intensity and duration to an allegedly mentally ill confederate when the person’s symptoms were described as caused by biological rather than psychosocial factors. While allegedly mentally ill confederates were held less responsible for their performance on a learning task when their disorder was attributed to biological as opposed to psychosocial factors, nevertheless a biological explanation did not improve the likeability, perceived dangerousness of the confederate, or willingness of the observer to engage in a social relationship with the confederate.

Studies of these sorts provde suggestive evidence that the biological model, whether it's a valid model or not, does little to mitigate stigma and may actually worsen attitudes toward recovery. As the authors of a 2014 paper in Psychiatry Research said: "A growing body of studies shows that although biogenetic explanations reduce blame, they tend to reinforce prognostic pessimism and harsher treatment of people with schizophrenia." If these studies are correct, it indicates that NAMI, NIMH, the APA (with its "biological" revision of the DSM), and other groups are potentially doing harm to the very people they intend to help.

My 384-page mental illness memoir, Of Two Minds, goes into depth on issues surrounding depression, schizophrenia, drugs, and therapeutic alternatives, with 300+ footnotes to the literature and countless personal narratives to shed further light on these and other issues. Please join the mailing list to get updates. Thanks!

Is Abilify Effective for Depression?

Abilify is the top-selling prescription drug in the U.S., a ranking it has held for a couple of years now. What's astonishing is that this drug was originally approved as (and only used as) an antipsychotic. My wife, who has schizoaffective disorder, is an experienced user of Abilify, Haldol, Latuda, Zyprexa, and Saprhis. She will attest to the antipsychotic properties of Abilify (and I'll show a graph for efficacy further below). But treatment of psychosis is not what made Abilify the top-selling drug in the U.S. Abilify is now widely prescribed as an adjunctive drug for depression; plus other uses, both on-label and off-label.

The manufacturer says Abilify is indicated for:

• Use as an add-on treatment to an antidepressant for adults with Major Depressive Disorder who have had an inadequate response to antidepressant therapy
• Treatment of manic or mixed episodes associated with Bipolar I Disorder in adults and in pediatric patients 10 to 17 years of age
• Treatment of schizophrenia in adults and in adolescents 13 to 17 years of age
• Treatment of irritability associated with Autistic Disorder in pediatric patients 6 to 17 years of age

Physicians and consumers alike have bought into the "Abilify as an adjunct" idea in a big way. Why has this "adjunct" modality become so popular? The main reason is that regular antidepressants simply aren't very effective, and people in a desperate mental state naturally want to know what else can be done other than switching from one ineffective drug to another.

But how good is Abilify, really, as an adjunct to antidepressants? Here is the graph touted on the Abilify website:

Abilify's efficacy (as an adjunct to antidepressants), versus placebo. (Click to enlarge.) The vertical scale plots average change (decrease) in patient scores on the Montgomery–Åsberg Depression Rating Scale.

This graph pools data from three studies. It shows that the average drop in MADRS score for patients on placebo-plus-antidepressant is a drop of 6.2 points after 6 weeks (from a starting average score of 26). When Abilify is added to the mix, the average score drop is 9.4 points. So if you add Abilify, you can expect a 3.2-point drop in your MADRS score that you wouldn't otherwise have had.

But if you look at the MADRS test, a change in just one answer (e.g., better sleep) could easily produce a 3-point change in your score. What you need to ask yourself is whether this magnitude of change is clinically significant. If you found a dollar bill on the sidewalk, your MADRS score would probably change by 3 points (briefly, of course). If your tax refund was $100 larger than you expected, it would probably mean a 3-point change on MADRS. The point is, the magnitude of change we're talking about here is small. It may be statistically significant, but is it clinically significant?

Abilify's primary use is in treatment of schizophrenia symptoms. Here's the efficacy graph for improvement on the PANSS scale, a common rating system for assessing schizophrenia symptoms:

Abilify efficacy with respect to schizophrenia symptoms (placebo in grey).

This is a much different graph. It shows what anyone would call good separation from placebo: five to eight times bigger change, with Abilify, than with placebo alone.

Abilify costs over a thousand dollars a month (10 mg, 30 pills) if you were to have to pay for it out of pocket. For most patients, this cost is hidden by insurance (or Medicare, in my wife's case). If you were taking Abilify for depression (as an adjunct) and had to pay for it out of pocket, you'd probably find the cost depressing (to the tune of much more than a 3-point drop in MADRS). Still, desperate people will do desperate things. Adding Abilify is "one more thing to try." So by all means try it. But don't expect miracles. If you see any effect at all, it's likely to be a rather small one.

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Where Are the Schizophrenia Genes?

Schizophrenia is another one of those conditions (like alcoholism) that we're constantly being told is highly heritable, based on the results of twin studies and various other findings (from studies that pre-date the DNA-sequencing era). Studies "proving" the heritability of schizophrenia are, in general, rarely questioned, or even reviewed critically, due to the fact that a genetic basis for schizophrenia makes so much intuitive sense and agrees so well with ordinary experience. Everybody knows someone who "has schizophrenia in their family," and it very much does seem to travel in families.

The fact that something travels in families is not automatically indicative of a genetic connection, but most people aren't ready to believe such a thing since it goes against common sense. Nevetheless, in the past few decades, a great deal of work (much of it quite compelling) has gone into the study of things like intergenerational trauma, with researchers finding, for example, that the course of PTSD in modern-day Israeli military veterans is much different for soldiers whose parents or grandparents were Holocaust survivors. (I talk about intergenerational trauma in Of Two Minds. In fact, there's a whole chapter on Trauma.)

Social workers and criminologists have known for years that childhood physical and sexual abuse tend to be intergenerational. Yet no one seriously suggests there's a "child abuse gene" or a "child molestation gene." A lot of things that aren't purely genetic are trans-generational; familial. Schizophrenia itself may be in this category. The links between childhood trauma and schizophrenia are quite strong and well known. Indeed, the literature shows that the associations between childhood trauma and schizophrenia are actually as strong as or stronger than the associations between childhood trauma and other disorders that we know have a high correspondence to childhood trauma (including depression, PTSD, anxiety disorders, dissociative disorders, eating disorders, personality disorders, substance abuse, and sexual dysfunction, among others).

Still, the search for "schizophrenia genes" goes on, and every so often we hear claims for this or that group of scientists having discovered some new genetic feature that confers high risk for schizophrenia. The latest of these reports comes from the Schizophrenia Working Group of the Psychiatric Genomics Consortium, which published a piece in Nature Letters in July 2014 called "Biological insights from 108 schizophrenia-associated genetic loci." The report trumpets the finding of "108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported." This is a genome-wide association study, subject to the usual GWAS caveats.

Of the 108 loci, 75% were associated with protein-coding genes, a very high number for a GWAS study, and quite a few genes involve physiologically relevant functions.

On the surface, it sounds promising.

Where things start to fall down is in determining how much heritability risk the 108 loci can account for. The authors said: "Assuming a liability-threshold model, a lifetime risk of 1%, independent SNP effects, and adjusting for case-control ascertainment, RPS" (risk profile scores) "now explain about 7% of variation on the liability scale to schizophrenia across the samples, about half of which (3.4%) is explained by genome-wide significant loci." To parse that: We know the lifetime risk of schizophrenia is 1%. If we total up the disease risk attributable to all of the 128 markers found in the study, we can explain 7% of schizophrenia risk. But if we limit ourselves to the 108 markers that met criteria for genome-wide significance (a stringent test intended to reduce the risk of false positives), we can explain only a little more than 3% of schizophrenia risk.

So once again, it's a mixed bag: a genome-wide association study (GWAS) that's filled with interesting findings (some of which may prove to be important), but that fails, quite miserably, to explain heritability.

Many people have commented on the possible reasons why genome-wide association studies have been hit-or-miss in their ability to explain heritability, why they so often find SNPs (single nucleotide polymorphisms) that don't map to genes, why the results often don't replicate well, etc. I think if we're honest, we have to admit that GWAS isn't always the right tool for the job. It's a great tool, but you have to remember how it works. GWAS attempts to match fairly common (i.e., occurring in 1% or more of the population) single nucleotide polymorphisms (which you can think of as point mutations) in databases that have been specially built to accommodate the most common polymorphisms. In a GWAS, you aren't doing deep sequencing of people's genes; you're looking for markers that cover a rather tiny percent of the genome. These markers occur commonly, which almost by definition prevents them from being very serious defects, because serious genetic defects are rendered rare by evolution (natural selection removes them from the gene pool over time). Therefore, in a technique that, by design, looks mainly at commonly occurring SNPs, which are overwhelmingly neutral (from an evolutionary standpoint), we should not be surprised to find that the markers that get spotted in GWAS (as genome-wide significant) are essentially neutral mutations without much phenotypic effect.

Unfortunately, we're at an awkward point in the history of science, because technology has given us some powerful DNA analysis tools (and the computers needed to crunch the data), but we're not quite yet at the point where detailed, deep genetic sequencing of whole human genomes is economically feasible for a study that includes, say, a thousand cases and a thousand controls. (Right now, it costs about $10,000 to do the necessary sequencing.) When a human genome can be reliably sequenced for under $2000, we'll have the tools to do really serious, deep genetic studies of hard-to-pin-down diseases. Whether we'll have the computer power needed to do the necessary statistical analysis on the scale of large (N > 1000) case-control studies is another matter; remember that in GWAS we're dealing with perhaps 500,000 data points per genome whereas in deep sequencing we're generating billions of data points per genome. It'll probably be doable using Amazon AWS/EC2 infrastructure (or Google's compute-cloud). Another reason to invest in Amazon, probably.

I'm in the process of writing a 100,000-word book on mental illness. It's evidence-based; part science, part memoir, with over 200 footnotes (so you can refer to the scientific literature yourself). To follow the progress of the book, check back here often, and also consider adding your name to our mailing list. Thanks!

Winner's Curse

Before saying anything more about genetic studies, I want to talk about a problem that's quite common in scientific studies. It's colloquially (and somewhat inappropriately, since the analogy doesn't hold 100%) known as the "winner's curse."

In economics, the idea of "winner's curse" comes from the observation that the winner of an auction often overpays. Maybe you've noticed this phenomenon yourself, if you've participated in auctions? It's not just that people get into a frenzy of bidding and drive prices too high. That's not really the core idea here. The core idea is more like this: Suppose a laptop goes on auction and there are ten people in the room. All will bid on it. Every one of the ten persons has an estimate (a top bid) in mind for the true value of the laptop. The average of those ten estimates might be (let's say) $300. However, an average bid doesn't win an auction, does it? The highest bid does. And that might be considerably in excess of $300.

The "winner's curse" phenomenon comes into play in science a lot, because (for example) when a systematic review is done for various studies that found the "effect size" for a given type of medical treatment, the effect size often turns out to be larger in smaller studies. In theory, a treatment should give roughly the same effect size in all different sizes of study. If aspirin is a successful treatment for headaches 50% of the time, a study involving 30 people should show that, and a study involving 1000 people should show that. Instead, what you often see is (here I'm making numbers up) aspirin works 60% of the time in a small study and 50% of the time in a large study.

You can see how this might happen. Suppose four different teams of scientists (who don't talk to each other) decide to investigate the effectiveness of a vitamin milkshake as a hangover cure. And suppose that the "true effectiveness" of this "cure" (over a large enough number of studies and subjects) is actually about 20%. However, our four teams, working with very small study populations (hence, a high potential for statistical noise) find effectiveness of 11%, 13%, 22%, and 30%. The teams that got the lowest numbers probably won't publish their results. The team that got 30% probably will.

This sort of thing happens in science a lot and helps explain why, for example, some of the early twins studies on schizophrenia found high concordance rates (over 60%) for schizophrenia in identical twins versus fraternal twins, in relatively small studies, whereas later, much larger studies have found rates as low as 11%.

"Winner's curse" is a well-known ascertainment problem in science that affects studies of many kinds, including some of the recent large genome-wide associataion studies that have produced findings that failed to replicate when other teams decided to do similar sorts of investigations. I'll be talking more about that about soon.

Note: For a more technical discussion of "winner's curse" in genetic studies and what can be done about it, see Sebastian Zöllner and Jonathan K. Pritchard, "Overcoming the Winner’s Curse: Estimating Penetrance Parameters from Case-Control Data," Am J Hum Genet. Apr 2007; 80(4): 605–615.

I'm working on a no-nonsense, "skewer the sacred cows" book about mental illness. If you'd like to follow the progress of the book, sign up for the mailing list, and be sure to check back here regularly. Thanks!