I had a paper on predicting a more frequent event (death by suicide) rejected a week or so ago. A quite expert reviewer noted that there had been …. five reviews recently. Which all make the same point.
If some investigative reporter shoved a microphone and camera in my face and asked “how is it mental health professionals can’t predict this stuff better?” my answer would be:
“I will bet a bazillion dollars, right now, that the last patient I saw in my office today will not go rent a hotel room outside a country music concert and start shooting everyone in the crowd from his window tonight.”
Do you understand?
Let’s unpack this a little more. The number of variables that contribute to an incident like the Las Vegas concert shooting over the weekend, (or Gabrielle Giffords shooting, or Aurora Colorado, or Newtown, or…) are so complex, so unweighted, so numerous, so difficult to tease out every interaction, that even if you had the most sophisticated super computer and plugged in every piece of data we have on every individual in the US, you could not predict who will be the next Jared Loughner.
It is a matter of specificity vs sensitivity. I could, with the help of some super smart colleagues develop an algorithm that would be very sensitive at detecting those who are at an elevated risk for behaving this way. But how elevated? Imminent? Probably in the next 5 days? Maybe in the next 5 years? And if you employed such an algorithm you would detain an enormous amount of people who are simply pinging my test as risky above some level higher than the baseline population. But that brings us to the next point.
I work in a higher risk area, so I don’t gamble. The problem is that our tools cannot be that precise, because of Bayes’ Theorem. To expand, using a classic example.
The probability that a patient has HIV is 0.001 and the diagnostic test for HIV can detect the virus with a probability of 0.98. Given that the chance of a false positive is 6%, what is the probability that a patient who has already tested positive really has HIV?
Let’s first formulate the problem in formal terms. Let DD be the event that the person has the disease, then DcDc denotes the event that the person doesn’t have the disease. Let YY be the event that the test gives the positive result (person has the disease as per the test diagnostic) and NN be the event that the test gives the negative result.
Now let’s write down the given information.P(D)=0.001
P(D)=0.001
P(Y|D)=0.98
P(Y|D)=0.98
P(Y|Dc)=0.06
P(Y|Dc)=0.06We have to find P(D|Y)P(D|Y).
Now we’ll use Bayes’ theorem to find the required probability.P(D|Y)=P(Y|D)P(D)P(Y)P(D|Y)=P(Y|D)P(D)P(Y).
P(Y)=P(Y?(D?Dc))=P(Y?D)+P(Y?Dc)=P(Y|D)P(D)+P(Y|Dc)P(Dc)
P(Y)=P(Y?(D?Dc))=P(Y?D)+P(Y?Dc)=P(Y|D)P(D)+P(Y|Dc)P(Dc)
as DD and DcDc are mutually exclusive events and together form a partition of the sample space. Using the given values, we have
P(Y)=0.98×0.001+0.06×0.999=0.06092
P(Y)=0.98×0.001+0.06×0.999=0.06092Therefore,
P(Y|D)=0.98×0.0010.06092=0.016
The prevalence of suicide in the general population is about 1:10 000. The prevalence of murder is a tenth of that. The prevalence of mass murder… is lower, and I am unsure if anyone has done the math. With events at this low rate of occurrence, the chance is almost certain that any person who is considered high risk is not going to do such an act. But there is also a false negative rate: some people who you think will never do such an event will do such, and there are more of them in most societies than people who are considered “high risk” and detained under various laws. Scott’s perfect test, at this low rate, will be… useless.
I think most people, on some gut level understand this, even if they don’t understand the math.
So what does feeling so helpless make them focus on? Guns.
It seems to me that many of these mass shootings (but not all) would not have happened if it were not that the perpetrators were taking prescribed psychoactive drugs (usually SSRIs or chemical relatives) leading to a kind of akathisia (considered as a pathological state of mind, rather.than a movement disorder).
So, the increased incidence of such events is a consequence of the vastly increased psychoactive drug prescriptions, and this can be known – even when the specifics of each event cannot be predicted.
Mass shooting is rarer than but causally the same basic phenomenon as the increase in out-of-the-blue suicide attempted by violent means (eg hanging, jumping) – which has become shockingly common among young teens on medication (almost never seen in the old days 40 years ago when teens were not given drugs) – and also celebrities.
No. SSRIs can and do cause more akathisia than atypical antipsychotics because SSRIs are used for anxiety disorders and depression.
There is no data. Psychological autopsy of a case series would be a good project for a forensic psychiatrist.
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On the matter of SSRIs. The black box labels on these medications are driven by lawyers, and in that environment zero risk can be tolerated.
When an RTC (randomized clinical trial) is conducted on a new drug, if just one person of 500 participants claims, over a 6 month study that they had “stomach pain” then that becomes an associated “side effect” of the medication, even though it probably did not “cause” the problem.
This is a Black Swan event. It is not predictable. To ask for predictions, even after the fact, is folly.
There was always a risk, but one cannot predict the actualization of the risk.
To treat it as mental illness is itself foolish. The mass murder was an act of evil, and the denial of malicious will prevents us from recognizing the real needs and vulnerabilities of our society.
This is where religion is needed – to recognize there is good and evil; that a sovereign and good will is guiding events even where we are helpless; and that we are part of the problem and must seek aid from the only One who can give it.