In a paper called “Rarity of Respiratory Arrest,” Richard Gill writes:
Statistical analysis of monthly rates of events in around 20 hospitals and over a period of about 10 years shows that respiratory arrest, though about five times less frequent than cardio-respiratory arrest, is a common occurrence in the Emergency Department of a typical smaller UK hospital.
He has lots of detailed and commonsensical (but hardly routine) data analysis. Those of you who read my recent posts (here and here) on the World Cup might be interested in this, because it has lots of practical details of statistical analysis but of a different sort (and it’s a topic that’s more important than soccer). The analysis is interesting and clearly written.
But the subject seems pretty specialized, no? Why am I sharing with you an analysis of respiratory arrests (whatever that is) in emergency departments? The background is a possibly huge miscarriage of justice.
Here’s how Gill told it to me in an email:
I’m wondering if you can do anything to help Ben Geen – a British nurse sentenced for 30 years for a heap of crimes that were not committed by anyone. Show the world that in the good hands, statistics can actually be useful!
There are important statistical issues and they need to be made known to the public.
There is in fact an international epidemic of falsely accused health care serial killers.
In Netherlands: Lucia de Berk
In the UK: Colin Norris, Ben Geen
In Canada: Susan Nelles
In the US: https://en.wikipedia.org/wiki/Ann_Arbor_Hospital_Murders
So I [Gill] got involved in the Ben Geen case (asked by defence lawyer to write an expert statistical report). This is it
I became convinced that most of what the media repeated (snippets of over the top info from the prosecution, out of context, misinterpreted) was lies, and actually the real evidence was overwhelmingly strong that Ben was completely, totally innocent.
Here’s the scientific side of the story. It’s connected to the law of small numbers (Poisson and super Poisson variation) and to the Baader-Meinhof effect (observer bias). And to the psycho-social dynamics in a present-day, dysfunctional (financially threatened, badly run) hospital.
In the UK Colin Norris and Ben Geen are in 30 year sentences and absolutely clearly, they are completely innocent. Since no one was murdered there never will be a confession by the true murderer. Because there were no murders there won’t ever be new evidence pointing in a different direction. There will never be a new fact so the system will never allow the cases to be reviewed. Since the medical profession was complicent in putting those guys away no medical doctor will ever say a word to compromise his esteemed colleagues.
What is going on? why this international epidemic of falsely accused “health care serial killers”?
Answer: in the UK: the scare which followed Shipman triggered increased paranoia in the National Health Service. Already stressed, overburdened, underfunded … managers, nurses, specialists all with different interests, under one roof in a hospital … different social classes, lack of communication
So here’s the ingredients for a Lucia / Ben / Colin:
(1) a dysfunctional hospital (chaos, stress, short-cuts being taken)
(2) a nurse who is different from the other nurses. Stands up in the crowd. Different sex or age or class. More than average intelligence. Big mouth, critical.
(3) something goes wrong. Someone dies and everyone is surprised. (Why surprised: because of wrong diagnosis, disinformation, ….)
(4) Something clicks in someone’s mind (a paranoid doctor) and the link is made between the scary nurse and the event
(5) Something else clicks in … we had a lot more cases like that recently (eg. the seasonal bump in respiratory arrests. 7 this month but usually 0, 1 or 2)
(6) The spectre of a serial killer has now taken possession of the minds of the first doctor who got alarmed and he or she rapidly spreads the virus to his close colleagues. They start looking at the other recent cases and letting their minds fall back to other odd things which happened in recent months and stuck in their minds. The scary nurse also stuck in their mind and they connect the two. They go trawling and soon they have 20 or 30 “incidents” which are now bothering them. They check each one for any sign of involvement of the scary nurse and if he’s involved the incident quickly takes on a very sinister look. On the other hand if he was on a week’s vacation then obviously everything must have been OK and the case is forgotten.
(7) Another conference, gather some dossiers – half a dozen very suspicious cases to report to the police to begin with. The process of “retelling” the medical history of these “star cases” has already started. Everyone who was involved and does know something about the screw-ups and mistakes says nothing about them but confirms the fears of the others. That’s a relief – there was a killer around, it wasn’t my prescription mistake or oversight of some complicating condition. The dossiers which will go to the police (and importantly, the layman’s summary, written by the coordinating doctor) does contain “truth” but not the *whole truth*. And there is lots of truth which is not even in hospital dossiers (culture of lying, of covering up for mistakes).
(8) The police are called it, the arrest, there is of course an announcement inside the hospital and there has to be an announcement to the press. Now of course the director of the hospital is in control – probably misinformed by his doctors, obviously having to show his “damage control” capacities and to minimize any bad PR for his hospital. The whole thing explodes out of control and the media feeding frenzy starts. Witch hunt, and then witch trial.
Then of course there is also the bad luck. The *syringe*, in Ben’s case, which clinches his guilt to anyone who nowadays does a quick Google search.
This is what Wendy Hesketh (a lawyer who is writing a book on the topic) wrote to me:
“I agree with your view on the “politics” behind incidences of death in the medical arena; that there is a culture endorsing collective lying”
“Inquries into medico-crime or medical malpractice in the UK see to have been commandeered for political purposes too: rather than investigate the scale of the actual problem at hand; or learn lessons on how to avoid it in future, the inquiries seem designed only to push through current health policy”
“The “Establishment” want the public to believe that, since the Shipman case, it is now easier to detect when a health professional kills (or sexually assaults) a patient. It’s good if the public think there will never be “another Shipman” and Ben Geen and Colin Norris being jailed for 30 years apiece sent out that message; as has the string of doctors convicted of sexual assault but statistics have shown that a GP would have to have a killing rate to rival Shipman’s in order to have any chance of coming to the attention of the criminal justice system. In fact, the case of Northumberland GP, Dr. David Moor, who openly admitted in the media to killing (sorry, “helping to die”) around 300 patients in the media (he wasn’t “caught”) reflects this. I argue in my book that it is not easier to detect a medico-killer now since Shipman, but it is much more difficult for an innocent person to defend themselves once accused of medico-murder.”
Indeed, the rate of serial killers in the UK’s National Health Service must be tiny and if there are good ones around they won’t even be noticed.
Yet is is so so easy in a failing health care organization for the suspicion to arise that there is one around. And once the chances are aligned and the triggering event has happened there is no going back. The thing snowballs. The “victim” has no chance.
Chance events are clustered!!! Pure chance gives little bunches of tightly clustered events with big gaps between them. When chances are changing (e.g. seasonal variation, changes in hospital policies, staffing, new personel with new habits when filling in woefully inadequate diagnosis forms) then the phenomenon is stronger still!
eg three airliners crashed within a couple of days this week!!!
How odd is a cluster of cases? Well by the law of *small* numbers (Poisson and even super-Poisson variation – Poisson means pure chance .. super-Poisson means pure chance but with the “chance per day” slowly varying in time) “short intervals between crashes are more likely than long ones”. (actually – very short, and very long, intervals, are both common. Pure chance means that accidents are *not* uniformly spread out in time. They are clustered. Big gap, cluster, biggish gap, smallish cluster… that’s pure randomness!!!)
Then there is the Baader-Meinhof phenomenon
[I replaced an earlier link for this which pointed to a flaky news site -- AG]
“Baader-Meinhof is the phenomenon where one happens upon some obscure piece of information– often an unfamiliar word or name– and soon afterwards encounters the same subject again, often repeatedly. Anytime the phrase “That’s so weird, I just heard about that the other day” would be appropriate, the utterer is hip-deep in Baader-Meinhof.”
Another name for this is *observer bias*. You (a medical doctor having to fill in a diagnose for a patient in a standard form, which is totally inadequate for the complexity of medicine) saw one case which they had to give a rather unusual label to, and the next weeks that “unusual diagnosis” will suddenly come up several times.
Well, Professor Jane Hutton (Warwick university, UK) wrote all these things in her expert report for the appeal 6 years ago but the judge said that such kind of statistical evidence “is barely more than common sense” so refused the request for her to tell this common sense out loud in court.
OK, it’s me again. I haven’t looked at this case in any detail and so can’t add anything of substance to Gill’s analysis. But what I will say is, if Gill is correct, this example demonstrates both the dangers and the potential of statistics. The danger because it is statistical analysis that has been used to convict Geen (both in court and in the “court of public opinion” as measured by what can be found on Google). The potential because a careful statistical analysis reveals the problems with the case (again, I’m relying on Gill’s report here; that is, my comments here are conditional on Gill’s report being reasonable).
Just to be clear, I’m not not saying that statistical arguments cannot or should not be used to make public health decisions. Indeed, I was involved last year in a case in which the local public health department made a recommendation based on statistical evidence, and this recommendation was questioned, and I (at the request of the public health department, and for no compensation) wrote a brief concurrence saying why I did not agree with the critics. So I am not saying that any statistical argument can be shot down, or that the (inevitable) reliance of any argument on assumptions makes that argument suspect. What I am doing is passing along Richard Gill’s analysis in this particular case, where he has found it possible for people to draw conclusions from noise, to the extent of, in his view, sending an innocent person to jail for 30 years.