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# Tag: Bad Statistics

## Groep 7

## An Italian CSI drama: social media, a broken legal system, and Micky Mouse statistics

The long building at the top: “Block B” of Umberto I hospital, Lugo
Sector B of “Blocco B” (Google Streetview). Seen from the North.

Bayes rule in odds form.*p* and *d* stand for “prosecution” and “defence” respectively, *H* stands for “Hypothesis”

Minute, hour, weekday, month of deaths

Prediction of vitreous humour K+ concentration 56 hours after death without K+ poisoning
## Condemned by statisticians?

## BOLC (Bureau Verloren Zaken) “reloaded”

## Re-launch of the Bureau of Lost Causes

## ORGANISATION and PLANS

Een lezing van een uur aan kinderen van groep 7.

Eerste versie van een presentatie.

The title of this blog might refer to the very, very famous trials of Amanda Knox in the case of the murder of Meredith Kercher. However, I am writing about a case that is much less known outside of Italy (neither victim nor alleged murderer was a rich American girl). This is the case of Daniela Poggiali, a nurse suspected by the media and accused by prosecution experts of having killed around 90 patients in a two-year killing spree terminated by her arrest in April 2014. She has just been exonerated after a total of three years in prison with a life sentence as well some months of pre-trial detention. This case revolved around statistics of an increased death rate during the shifts of a colourful nurse. I was a scientific expert for the defence, working with an Italian colleague, Julia Mortera (Univ. Rome Tre), later assisted by her colleague Francesco Dotto.

Piet Groeneboom and I worked together on the statistics of the case of Lucia de Berk, see our paper in *Chance* [Reference]. In fact, it was remarkable that the statistical community in the Netherlands got so involved in that case. A Fokke and Sukke cartoon entitled “Fokke and Sukke know it intuitively” had the exchange “The probability that almost all professors of statistics are in agreement … is obviously very small indeed”.

Indeed, it wasn’t. That was one of the high points of my career. Another was Lucia’s final acquittal in 2010, at which the judges took the trouble to say out loud, in public, that the nurses had fought heroically for the lives of their patients; lives squandered, they added, by their doctors’ medical errors.

At that point, I felt we had learnt how to fight miscarriages of justice like that, of which I rapidly became involved in several. So far, however, with rather depressing results. Till a couple of months ago. This story will not have much to do with mathematics. It will have to do with simple descriptive statistics, and I will also mention the phrases “*p*-value” and “Bayes’ rule” a few times. One of the skills of a professional statistician is the abstraction of messy real-world problems involving chance and data. It’s not for everybody. Many mathematical statisticians prefer to prove theorems, just like any other mathematician. In fact, I often do prefer to do that myself, but I like more being able to alternate between the two modes of activity, and I do like sticking my nose into other people’s business, and learning about what goes on in, for instance, law, medicine, or anything else. Each of the two activity modes is a nice therapy for the frustrations which inevitably come with the other.

The Daniela Poggiali case began, for me, soon after the 8th of April, 2014, when it was first reported in international news media. A nurse at the *Umberto I* hospital in the small town of Lugo, not far from Ravenna, had been arrested and was being investigated for serial murder. She had had photos of herself taken laughing, close to the body of a deceased patient, and these “selfies” were soon plastered over the front pages of tabloid media. Pretty soon, they arrived in *The Guardian* and *The New York Times*. The newspapers sometimes suggested she had killed 93 patients, sometimes 31, sometimes it was other large numbers. It was suspected that she had used Potassium Chloride on some of those patients. An ideal murder weapon for a killer nurse since easily available in a hospital, easy to give to a patient who is already hooked up to an IV drip, kills rapidly (cardiac arrest – it is used in America for executions), and after a short time hard to detect. After death, it redistributes itself throughout the body where it becomes indistinguishable from a normal concentration of Potassium.

Many features of the case reminded me strongly of the case of Lucia de Berk in the Netherlands. In fact, it seemed very fishy indeed. I found the name of Daniela’s lawyer in the online Italian newspapers, Google found me an email address, and I sent a message offering support on the statistics of the case. I also got an Italian statistician colleague and good friend, Julia Mortera, interested. Daniela’s lawyer was grateful for our offer of help. The case largely hinged on a statistical analysis of the coincidence between deaths on a hospital ward and Daniela’s shifts there. We were emailed pdfs of scanned pages of a faxed report of around 50 pages containing results of statistical analyses of times of shifts of all the nurses working on the ward, and times of admission and discharge (or death) of all patients, during much of the period 2012 – 2014. There were a further 50 pages (also scanned and faxed) of appendices containing print-outs of the raw data submitted by hospital administrators to police investigators. Two huge messy Excel spreadsheets.

The authors of the report were Prof. Franco Tagliaro (Univ. Verona) and Prof. Rocco Micciolo (Univ. Trento). The two are respectively a pathologist/toxicologist and an epidemiologist. The epidemiologist Micciolo is a professor in a social science department, and member of an interfaculty collaboration for the health sciences. We found out that the senior and more influential author Tagliaro had published many papers on toxicology in the forensic science literature, usually based on empirical studies using data sets provided by forensic institutes. Occasionally, his friend Micciolo turned up in the list of authors and had supplied statistical analyses. Micciolo describes himself as a biostatistician. He has written Italian language textbooks on exploratory data-analysis with the statistical package “R” and is frequently the statistician-coauthor of papers written by scientists from his university in many different fields including medicine and psychology. They both had decent H-indices, their publications were in decent journals, their work was mainstream, useful, “normal science”. They were not amateurs. Or were they?

Daniela Poggiali worked on a very large ward with very many very old patients, many suffering terminal illnesses. Ages ranged from 50 up to 105, mostly around ninety. The ward had about 60 beds and was usually quite fully occupied. Patients tended to stay one to two weeks in the hospital, admitted to the hospital for reasons of acute illness. There was on average one death every day; some days none, some days up to four. Most patients were discharged after several weeks in the hospital to go home or to a nursing home. It was an ordinary “medium care” nursing department (i.e., not an Intensive Care unit).

Some very simple statistics showed that the death rate on days when Poggiali worked was much higher than on days when she did not work. A more refined analysis compared the rate of deaths during the hours she worked with the rate of deaths during the hours she was not at work. Again, her presence “caused” a huge excess, statistically highly significant. A yet more refined analysis compared the rate of deaths while she was at work in the sectors where she was working with the rate in the opposite sectors. What does this mean? The ward was large and spread over two long wings of one floor of a large building, “Blocco B”, probably built in the sixties.

Between the two wings were central “supporting facilities” and also the main stairwell. Each wing consisted of many rooms (each room with several beds), with one long corridor through the whole building, see the floor plan below. Sector A and B rooms were in one wing, first A and then B as you you went down the corridor from the central part of the floor. Sector C and Sector D rooms were in the other wing, opposite to one another on each side of the corridor. Each nurse was usually detailed in her shifts to one sector, or occasionally to the two sectors in one wing. While working in one sector, a nurse could theoretically easily slip into a room in the adjacent sector. Anyway, the nurses often helped one another, so they often could be found in the “wrong sector”, but not often in the “wrong wing”.

Tagliaro and Micciolo (in the sequel: TM) went on to look at the death rates while Daniela was at work in different periods. They noticed that it was higher in 2013 than in 2012, even higher in the first quarter of 2014, then – after Daniela had been fired – it was much, much less. They conjectured that she was killing more and more patients as time went by, till the killing stopped dead on her suspension and arrest

TM certainly knew that, in theory, other factors might be the cause of an increased death rate on Poggiali’s shifts. They were proud of their innovative approach of relating each death that occurred while Daniela was at work to whether it occurred in Daniela’s wing or in the other. They wrote that in this way they had controlled for confounders, taking each death to provide its own “control”. (Similarly, in the case of Lucia de B., statistician Henk Elffers had come up with an innovative approach. In principle, it was not a bad idea, though all it showed was that nurses are different). TM did not control for any other confounding factors at all. In their explanation of their findings to the court, they repeatedly stated categorically that the association they had found must be causal, and Daniela’s presence was the cause. Add to this that their clumsy explanation of p-values might have misled lawyers, journalists and the public. In such a case, a *p*-value is the probability of what you see (more precisely, of at least what you see), assuming pure chance. That is not the same as the probability that pure chance was the cause of what you see – the fallacy of the transposed conditional, also known as “the prosecutor’s fallacy”.

Exercise to the reader: when is this fallacy not a fallacy? Hint: revise your knowledge of *Bayes’ rule*: posterior odds equals prior odds time likelihood ratio.

Bayes rule in odds form.

We asked Tagliaro and Micciolo for the original Excel spreadsheets and for the “R” scripts they had used to process the data. They declined to give them to us, saying this would not be proper since they were confidential. We asked Daniela’s lawyer to ask the court to ask for those computer files on our behalf. The court declined to satisfy our request. We were finally sent just the Excel files by the hospital administration, a week before we were called to give evidence. Fortunately, with a combination of OCR and a lot of painstaking handwork, a wealthy friend of Daniela’s lawyer had already managed to help us get the data files reconstructed. We performed a lot of analyses with the help of a succession of students because extracting what we needed from those spreadsheets was an extraordinarily challenging issue. One kept finding anomalies that had to be fixed in one way or another. Even when we had “clean” spreadsheets, it still was a mess.

Next, we started looking for confounding factors that might explain the difference between Daniela and her colleagues, which certainly was striking and real. But was it perhaps entirely innocent?

Minute, hour, weekday, month of deaths

First of all, simple histograms showed that death rates on that ward varied strongly by month, with big peaks in June and again in December. (January is not high: elderly people stay home in January and keep themselves warm and safe). That is what one should expect. The humid heat and air pollution in the summer; or the damp and cold and the air pollution in the winter, exacerbated by winter flu epidemics. Perhaps Daniela worked more at bad times than at good times? No. It was clear that sectors A+B were different from C+D. Death rates were different, but also the number of beds in each wing was different. Perhaps Daniela was allocated more often to “the more difficult” sections? It was not so clear. Tagliaro and Micciolo computed death rates for the whole ward, or for each wing of the ward, but never took account of the number of patients in each wing nor of the severity of their illnesses.

Most interesting of all was what we found when we looked at the hour of the time of death of patients who died, and the minute of the time of death of patients who died. Patients tended to die at times which were whole hours, “half past” was also quite popular. There was however also a huge peak of deaths between midnight and five minutes past midnight! There were fewer deaths in a couple of hours soon after lunchtime. There were large peaks of deaths around the time of handover between shifts: 7:00 in the morning, 2:00 in the afternoon, 9:00 in the evening. The death rate is higher in the morning than in the afternoon, and higher in the afternoon than at night. When you’re dying (but not in intensive care, when it is very difficult to die at all) you do not die in your sleep at night. You die in the early morning as your vital organs start waking up for the day. Now, also not surprisingly, the number of nurses on a ward is largest in the morning when there is a huge amount of work to do; it’s much less in the afternoon and evening, and it’s even less at night. This means that a full-time nurse typically spends more time in the hospital during morning shifts than during afternoon shifts, and more time during afternoon shifts than during night shifts. The death rate shows the same pattern. Therefore, for every typical full-time nurse, the death rate while they are at work tends to be higher than when they are not at work!

Nurses aren’t authorized to officially register times of death. Only a doctor is authorized to do that. He or she is supposed to write down the time at which they have determined the patient is no longer alive. It seems that they often round that time to whole or half hours. The peak just after midnight is hard to explain. The *date* of death has enormous financial and legal consequences. The peak suggests that those deaths may have occurred anywhere in a huge time window. Whether or not doctors come to the wards on the dot at midnight and fill in forms for any patients who have died in the few hours before is hard to believe

What is now clear is that it is mainly around the hand-over between shifts that deaths get “processed”. Quite a few times of death are so hard to know that they are shunted to five minutes past midnight; many others are located in the hand-over period but might well have occurred earlier.

Some nurses tend to work longer shifts than others. Some conscientiously clock in as early as they are allowed, before their shift starts, and clock out as late as they can after their shift ends. Daniela was such a nurse. Her shifts were indeed statistically significantly longer than those of any of her colleagues. She very often stayed on duty several hours after the official end of the official ten-minute overlap between shifts. There was often a lot to do – one can imagine often involving taking care of the recently deceased. Not the nicest part of the job. Daniela was well known to be a rather conscientious and very hard worker, with a fiery temper, known to play pranks on colleagues or to loudly disagree with doctors for whom she had a healthy disrespect.

Incidentally, the rate of admissions to *Umberto I* hospital tumbled down after the news broke of a serial killer – and the news broke the day after the last day the serial killer was at work, together with the publication of the lurid “selfie”. The rate of deaths was slowly increasing over the two years up to then, as was in fact also the rate of admissions and the occupancy of the ward. A hospital getting slowly more stressed? Taking on more work?

If one finds a correlation between *X* and *Y*, it is a sound principle to suppose that it has a causal explanation. Maybe *X* causes *Y*, maybe *Y* causes *X*, … and maybe *W* causes both *X* and *Y*, or maybe *X* and *Y* both cause *Z* and there has been a selection on the basis of *Z*. In the case of Lucia de B., her association between inexplicable incidents and her presence on the ward was caused by her, since the definition of “unexpected and inexplicable incident” included her being there. She was already known to be a weird person, and it was already clear that there were more deaths than usual on her ward. The actual reason for that was a change of hospital policy, moving patients faster from intensive care to medium care so that they could die at home, rather than in the hospital. If she was not present, then the medical experts always could come up with an explanation for why that death, though perhaps a bit surprising at that moment, was expected to occur soon anyway. But if Lucia was there then they were inclined to believe in foul play because after all there were so many incidents in her shifts.

Julia and I are certain that the difference between Daniela’s death rates and those of other nurses is to a huge extent explainable by the anomalies in the data which we had discovered and by her long working hours.

Some residual difference could be due to the fact that a conscientious nurse actually notices when patients have died, while a lazy nurse keeps a low profile and leaves it to her colleagues to notice, at hand-over. We have been busy fitting sophisticated regression models to the data but this work will be reported in a specialist journal. It does not tell us more than what I have already said. Daniela is different from the other nurses. All the nurses are different. She is extreme in a number of ways: most hours worked, longest shifts worked. We have no idea how the hospital allocated nurses to sectors and patients to sectors. We probably won’t get to know the answer to that, ever. The medical world does not put out its dirty washing for everyone to see.

We wrote a report and gave evidence in person in Ravenna in early 2015. I did not have time to see the wonderful Byzantine mosaics though I was treated to some wonderful meals. I think my department paid for my air ticket. Julia and I worked “*pro deo*“. In our opinion, we totally shredded the statistical work of Tagliaro and Micciolo. The court however did not agree. “The statistical experts for the defence only offered a theoretical discourse while those of the prosecution had scientifically established hard facts”. In retrospect, we should have used stronger language in our report. Tagliaro and Micciolo stated that they had definitively proven that *Daniela’s presence* *caused* 90 or so extra deaths. They stated that this number could definitely not be explained as a chance fluctuation. They stated that, of course, the statistics did not *prove* that she had deliberately *murdered* those patients. We, on the other hand, had used careful scientific language. One begins to understand how it is that experts like Tagliaro and Micciolo are in such high demand by public prosecutors.

There was also toxicological evidence concerning one of the patients and involving K+ Cl–, but we were not involved in that. There was also the “selfie”, there was character evidence. There were allegations of thefts of patients’ personal jewellery. It all added up. Daniela was convicted of just one murder. The statistical evidence provided her motive: she just loved killing people, especially people she didn’t like. No doubt, a forensic psychologist also explained how her personality fitted so well to the actions she was alleged to have done.

Rapidly, the public prosecution started another case based largely on the same or similar evidence but now concerning another patient, with whom Daniela had had a shouting match, five years earlier. In fact, this activity was probably triggered by families of other patients starting civil cases against the hospital. It would also clearly be in the interest of the hospital authorities to get new criminal proceedings against Daniela started. However, Daniela’s lawyers appealed against her first conviction. It was successfully overturned. But then the court of cassation overturned the acquittal. Meantime, the second case led to a conviction, then acquittal on appeal, then cassation. All this time Daniela was in jail. Cassations of cassations meant that Daniela had to be tried again, by yet another appeal court, for the two alleged murders. Julia and I and her young colleague Francesco Dotto got to work again, improving our arguments and our graphics and our formulations of our findings.

At some point, triggered by some discussions with the defence experts on toxicology and pathology, Julia took a glance at Tagliaro’s quite separate report on the toxicological evidence. This led to a breakthrough, as I will now explain.

Tagliaro knew the post-mortem “vitreous humour” potassium concentration of the last patient, a woman who had died on Daniela’s last day. That death had somehow surprised the hospital doctors, or rather, as it later transpired, it didn’t surprise them at all: they had already for three months been looking at the death rates while Daniela was on duty and essentially building up a dossier against her, just waiting for a suitable “last straw”! Moreover, they already had their minds on K+ Cl-, since some had gone missing and then turned up in the wrong place. Finally, Daniela had complained to her colleagues about the really irritating behaviour of that last patient, 73-year-old Rosa Calderoni.

“Vitreous humour” is the transparent, colourless, gelatinous mass that fills your eyeballs. While you are alive, it has a relatively low concentration of potassium. After death, cell walls break down, and potassium concentration throughout the body equalises. Tagliaro had published papers in which he studied the hourly rate of increase in the concentration, using measurements on the bodies of persons who had died at a known time of causes unrelated to potassium chloride poisoning. He even had some fresh corpses on which he could make repeated measurements. His motivation was to use this concentration as a tool to determine the PMI (post-mortem interval) in cases when we have a body and a post-mortem examination but no time of death. In one paper (without Micciolo’s aid) he did a regression analysis, plotting a straight line through a cloud of points (*y* = concentration, *x* = time since death). He had about 60 observations, mostly men, mostly rather young. In a second paper, now with Micciolo, he fitted a parabola and moreover noted that there was an effect of age and of sex. The authors also observed the huge variation around that fitted straight line and concluded that the method was not reliable enough for use in determining the PMI. But this did not deter Tagliaro, when writing his toxicological report on Rosa Calderoni! He knew the potassium concentration at the time of post-mortem, he knew exactly when she died, he had a number for the natural increase per hour after death from his first, linear, regression model. With this, he calculated the concentration at death. Lo and behold: it was a concentration which would have been fatal. He had proved that she had died of potassium chloride poisoning.

Prediction of vitreous humour K+ concentration 56 hours after death without K+ poisoning

Julia and Francesco used the model of the second paper and found out that if you would assume a normal concentration at the time of death, and take account of the variability of the measurements and of the uncertainty in the value of the slope, then the concentration observed at the time of post-mortem was maybe above average, but not surprisingly large at all.

Daniela Poggiali became a free woman. I wish her a big compensation and a long and happy life. She’s quite a character.

Aside from the “couleur locale” of an Italian case, this case had incredibly much similarity with the case of Lucia de Berk. It has many similarities with quite a few other contested serial killer nurse cases, in various countries. According to a *NetFlix* series, in which a whole episode is devoted to Daniela, these horrific cases occur all the time. They are studied by criminologists and forensic psychologists, who have compiled a list of “red flags” intended to help warn hospital authorities. The scientific term here is “health care serial killer”, or HCSK. One of the HCSK red flags is that you have psychiatric problems. Another is that your colleagues think you are really weird. Especially when your colleagues call you an angel of death, that’s a major red flag. The list goes on. These lists are developed in scientific publications in important mainstream journals, and the results are presented in textbooks used in university criminology teaching programs. Of course, you can only scientifically study convicted HCSKs. Your sources of data are newspaper reports, judges’ summings up, the prosecution’s final summary of the case. It is clear that these red flags are the things that convince judges and jurors to deliver a guilty verdict. These are the features that will first make you a suspect, which police investigators will look for, and which will convince the court and the public of your guilt. Amusingly, one of the side effects of the case of Lucia de Berk was contributing a number of entries to this list, for instance, the Stephen King horror murder novels she had at home which were even alleged to have been stolen from the library. Her conviction for the theft of several items still stands. As does Daniela’s: this means that Daniela is not eligible for compensation. In neither case was there any real proof of thefts. Amusingly, one of the side effects of the case of Lucia de Berk was contributing a number of entries to this list. Embarrassingly, her case had to be removed from the collections of known cases after 2011, and the criminologists and forensic psychologists also now mention that statistical evidence of many deaths during the shifts of a nurse is not actually a very good red flag. They have learnt something, too.

Interesting is also the incidence of these cases: less than 1 in a million nurses killing multiple patients per year, according to these researchers. These are researchers who have the phenomenon of HCSKs as their life work, giving them opportunities to write lurid books on serial murder, appear in TV panels and TV documentaries explaining the terrible psychology of these modern-day witches, and to take the stand as prosecution witnesses. Now, that “base rate” is actually rather important, even if only known very roughly. It means that such crimes are very, very unusual. In the Netherlands, one might expect a handful of cases per century; maybe on average 100 deaths in a century. There are actually only about 100 murders altogether in the Netherlands per year. On the other hand, more than 1000 *deaths* every year are due to medical errors. That means that evidence against a nurse suspected of being a HCSK would be very strong indeed before it should convince a rational person that they have a new HCSK on their hands. Lawyers, judges, journalists and the public are unfortunately perhaps not rational persons. They are certainly not good with probability, and not good with Bayes’ rule. (It is not allowed to be used in a UK criminal court, because judges have ruled that jurors cannot possibly understand it).

I am still working on one UK case, Ben Geen. I believe it is yet another example of a typical innocent HCSK scare in a failing hospital leading to a typical unsafe conviction based largely on the usual red flags and a bit of bad luck. At least, I see no reason whatsoever to suppose that Ben Geen was guilty of the crimes for which he is sitting out a life sentence. Meanwhile, a new case is starting up in the UK: Lucy (!) Letby. I sincerely hope not to be involved with that one.

Time for a new generation of nosy statisticians to do some hard work.

**References**

Norman Fenton, Richard D. Gill, David Lagnado, and Martin Neil. *Statistical issues in serial killer nurse cases*. https://arxiv.org/abs/2106.00758.

Alexander R.W. Forrest. Nurses who systematically harm their patients. *Medical Law International*, **1**(4): 411–421, 1995. https://doi.org/10.1177/096853329500100404

Richard D. Gill, Piet Groeneboom, and Peter de Jong. Elementary statistics on trial—the case of Lucia de Berk. *CHANCE*, **31**(4):9–15, 2018. https://doi.org/10.1080/09332480.2018.1549809

Covadonga Palacio, Rossella Gottardo, Vito Cirielli, Giacomo Musile, Yvane Agard, Federica Bortolotti, and Franco Tagliaro. Simultaneous analysis of potassium and ammonium ions in the vitreous humour by capillary electrophoresis and their integrated use to infer the post mortem interval (PMI). *Medicine, Science and the Law*, **61**(1 suppl):96–104, 2021. https://journals.sagepub.com/doi/abs/10.1177/0025802420934239

Nicola Pigaiani, Anna Bertaso, Elio Franco De Palo,Federica Bortolotti, and Franco Tagliaro. Vitreous humor endogenous compounds analysis for post-mortem forensic investigation. *Forensic science international*, **310**:110235, 2020. https://doi.org/10.1016/j.forsciint.2020.110235

Elizabeth Yardley and David Wilson. In search of the ‘angels of death’: Conceptualising the contemporary nurse healthcare serial killer.* Journal of Investigative Psychology and Offender Profiling*, **13**(1):39–55, 2016. https://onlinelibrary.wiley.com/doi/abs/10. 1002/jip.1434

Francesco Dotto, Richard D. Gill and Julia Mortera (2022) Statistical Analyses in the case of an Italian nurse accused of murdering patients. Submitted to “*Law, Probability, Risk*” (Oxford University Press), accepted for publication subject to minor revision; preprint: https://arxiv.org/abs/2202.08895

A Bayesian analysis of the case of Lucia de B.

de Vos, A. F. (2004).

Door statistici veroordeeld? Nederlands Juristenblad, 13, 686-688.

Here, the result of Google-translate by RD Gill; with some “hindsight comments” by him added in square brackets and marked “RDG”.

Would having posterior thoughts

Not be offending the gods?

Only the dinosaur

Had them before

Recall its fate! Revise your odds!

(made for a limerick competition at a Bayesian congress).

The following article was the basis for two full-page articles on Saturday, March 13, 2004 in the science supplement of the NRC (with unfortunately disturbing typos in the ultimate calculation) and in “the Forum” of Trouw (with the expected announcement on the front page that I claimed that the chance that Lucia de B. was wrongly convicted was 80%, which is not the case)

Condemned by statisticians?

Aart F. de Vos

Lucia de Berk [Aart calls her “Lucy” in his article. That’s a bit condescending – RDG] has been sentenced to life imprisonment. Statistical arguments played a role in that, although the influence of this in the media was overestimated. Many people died while she was on duty. Pure chance? The consulted statistician, Henk Elffers, repeated his earlier statement during the current appeal that the probability was 1 in 342 million. I quote from the article “Statisticians do not believe in coincidence” from the Haags Courant of January 30th: “The probability that nine fatal incidents took place in the JKZ during the shifts of the accused by pure chance is nil. (…) It wasn’t chance. I don’t know what it was. As a statistician, I can’t say anything about it. Deciding the cause is up to you”. The rest of the article showed that the judge had great difficulty with this answer, and did not manage to resolve those difficulties.

Many witnesses were then heard who talked about circumstances, plausibility, oddities, improbabilities and undeniably strong associations. The court has to combine all of this and arrive at a wise final judgment. A heavy task, certainly given the legal conceptual system that includes very many elements that have to do with probabilities but has to make do without quantification and without probability theory when combining them.

The crucial question is of course: how likely is it that Lucia de Berk committed murders? Most laypeople will think that Elffers answered that question and that it is practically certain.

This is a misunderstanding. Elffers did not answer that question. Elffers is a classical statistician, and classical statisticians do not make statements about what is actually going on, but only about how unlikely things are if nothing special is going on at all. However, there is another branch of statistics: the Bayesian. I belong to that other camp. And I’ve also been doing calculations. With the following bewildering result:

If the information that Elffers used to reach his 1 in 342 million were the only information on which Lucia de Berk was convicted, I think that, based on a fairly superficial analysis, there would be about an 80% chance of the conviction being wrong.

This article is about this great contrast. It is not an indictment of Elffers, who was extremely modest in the court when interpreting his outcome, nor a plea to acquit Lucia de Berk, because the court uses mainly different arguments, albeit without unequivocal statements of probability, while there is nothing which is absolutely certain. It is a plea to seriously study Bayesian statistics in the Netherlands, and this applies to both mathematicians and lawyers. [As we later discovered, many medical experts’ conclusions that certain deaths were unnatural was caused by their knowledge that Lucia had been present at an impossibly huge number of deaths – RDG]

There is some similarity to the Sally Clark case, which was sentenced to life imprisonment in 1999 in England because two of her sons died shortly after birth. A wonderful analysis can be found in the September 2002 issue of “living mathematics”, an internet magazine (http://plus.maths.org/issue21/features/clark/index.html)

An expert (not a statistician, but a doctor) explained that the chance that such a thing happened “just by chance” in the given circumstances was 1 in 73 million. I quote: “probably the most infamous statistical statement ever made in a British courtroom (…) wrong, irrelevant, biased and totally misleading.” The expert’s statement is completely torn to shreds in said article. Which includes mention of a Bayesian analysis. And a calculation that the probability that she was wrongly convicted was greater than 2/3. In the case of Sally Clark, the expert’s statement was completely wrong on all counts, causing half the nation to fall over him, and Sally Clark, though only after four years, was released. However, the case of Lucia de Berk is infinitely more complicated. Elffers’ statement is, I will argue, not wrong, but it is misleading, and the Netherlands has no jurisprudence, but judgments, and even though they are not directly based on extensive knowledge of probability theory, they are much more reasoned. That does not alter the fact that there is a common element in the Lucy de Berk and Sally Clark cases. [Actually, Elffers’ statement was wrong in its own terms. Had he used the standard and correct way to combine p-values from three separate samples, he would have ended up with a p-value of about 1/1000. Had he verified the data given him by the hospital, it would have been larger still. Had he taken account of heterogeneity between nurses and uncertainty in various estimates, both of which classical statisticians also know how to do too, larger still – RDG]

Bayesian statistics

My calculations are therefore based on alternative statistics, the Bayesian, named after Thomas Bayes, the first to write about “inverse probabilities”. That was in 1763. His discovery did not become really important [in statistics] until after 1960, mainly through the work of Leonard Savage, who proved that when you make decisions under uncertainty you cannot ignore the question of what chances the possible states of truth have (in our case the states “guilty” and “not guilty”). Thomas Bayes taught us how you can learn about that kind of probability from data. Scholars agree on the form of those calculations, which is pure probability theory. However, there is one problem: you have to think about what probabilities you would have given to the possible states before you had seen your data (the prior). And often these are subjective probabilities. And if you have little data, the impact of those subjective probabilities on your final judgment is large. A reason for many classical statisticians to oppose this approach. Certainly in the Netherlands, where statistics is mainly practised by mathematicians, people who are trained to solve problems without wondering what they have to do with reality. After a fanatical struggle over the foundations of statistics for decades (see my piece “the religious war of statisticians” at http://staff.feweb.vu.nl/avos/default.htm) the parties have come closer together. With one exception: the classical hypothesis test (or significance test). Bayesians have fundamental objections to classical hypothesis tests. And Elffers’ statement takes the form of a classical hypothesis test. This is where the foundations debate focuses.

The Lucy Clog case

Following Elffers, who explained his method of calculation in the Nederlands Juristenblad on the basis of a fictional case “Klompsma” which I have also worked through (arriving at totally different conclusions), I want to talk about the fictional case Lucy Clog [“Klomp” is the Dutch word for “clog”; the suffix “-sma” indicates a person from the province of Groningen; this is all rather insulting – RDG]. Lucy Clog is a nurse who has experienced 11 deaths in a period in which on average only one case occurs, but where no further concrete evidence against her can be found. In this case too, Elffers would report an extremely small chance of coincidence in court, about 1 in 100 million [I think that de Vos is thinking of the Poisson(1) chance of at least 11 events. If so, it is actually a factor 10 smaller. Perhaps he should change “11 deaths” into “10 deaths” – RDG]. This is the case where I claim that a guilty conviction, given the information so far together with my assessment of the context, has a chance of about 80% of being wrong.

This requires some calculations. Some of them are complicated, but the most important aspect is not too difficult, although it appears that many people struggle with it. A simple example may make this key point clear.

You are at a party and a stranger starts telling you a whole story about the chance that Lucia de Berk is guilty, and embarks joyfully on complex arithmetical calculations. What do you think: is this a lawyer or a mathematician? If you say a mathematician because lawyers are usually unable to do mathematics, then you fall into a classical trap. You think: a mathematician is good at calculations, while the chance that a lawyer is good at calculations is 10%, so it must be a mathematician. What you forget is that there are 100 times more lawyers than mathematicians. Even if only 10% of lawyers could do this calculating stuff, there would still be 10 times as many lawyers as mathematicians who could do it. So, under these assumptions, the probability is 10/11 that it is a lawyer. To which I must add that (I think) 75% of mathematicians are male but only 40% of lawyers are male, and I did not take this into account. If the word “she” had been in the problem formulation, that would have made a difference.

The same mistake, forgetting the context (more lawyers than mathematicians), can be made in the case of Lucia de Berk. The chance that you are dealing with a murderous nurse is a priori (before you know what is going on) very much smaller than that you are dealing with an innocent nurse. You have to weigh that against the fact that the chance of 11 deaths is many times greater in the case of “murderous” than in the case of “innocent”.

The Bayesian way of performing the calculations in such cases also appears to be intuitively not easy to understand. But if we look back on the example of the party, maybe it is not so difficult at all.

The Bayesian calculation is best not done in terms of chances, but in terms of “odds”, an untranslatable word that does not exist in the Netherlands. Odds of 3 to 7 mean a chance of 3/10 that it is true and 7/10 that it is not. Englishmen understand what this means perfectly well, thanks to horse racing: odds of 3 to 7 means you win 7 if you are right and lose 3 if you are wrong. Chances and odds are two ways to describe the same thing. Another example: odds of 2 to 10 correspond to probabilities of 2/12 and 10/12.

You need two elements for a simple Bayesian calculation. The prior odds and the likelihood ratio. In the example, the prior odds are mathematician vs. lawyer 1 to 100. The likelihood ratio is the probability that a mathematician does calculations (100%) divided by the probability that a lawyer does (10%). So 10 to 1. Bayes’ theorem now says that you must multiply the prior odds (1 : 100) and the likelihood ratio (10 : 1) to get the posterior odds, so they are (1 x 10 : 100 x 1) = (10 : 100) = (1 : 10), corresponding to a probability of 1 / 11 that it is a mathematician and 10/11 that it is a lawyer. Precisely what we found before. The posterior odds are what you can say after the data are known, the prior odds are what you could say before. And the likelihood ratio is the way you learn from data.

Back to the Lucy Clog case. If the chance of 11 deaths is 1 in 100 million when Lucy Clog is innocent, and 1/2 when she is guilty – more about that “1/2” much later – then the likelihood ratio for innocent against guilty is 1 : 50 million. But to calculate the posterior probability of being guilty, you need the prior odds. They follow from the chance that a random nurse will commit murders. I estimate that at 1 to 400,000. There are forty thousand nurses in hospitals in the Netherlands, so that would mean nursing killings once every 10 years. I hope that is an overestimate.

Bayes’ theorem now says that the posterior odds of “innocent” in the event of 11 deaths would be 400,000 to 50 million. That’s 8 : 1000, so a small chance of 8/1008, maybe enough to convict someone. Yet large enough to want to know more. And there is much more worth knowing.

For instance, it is remarkable that nobody saw Lucy doing anything wrong. It is even stranger when further investigation yields no evidence of murder. If you think that there would still be an 80% chance of finding clues in the event of many murders, against of course 0% if it is a coincidence, then the likelihood ratio of the fact “no evidence was found” is 100 : 20 in favour of innocence. Application of the rule shows that we now have odds of 40 : 1000, so a small 4% chance of innocence. Conviction now becomes really questionable. And if the suspect continues to deny, which is more plausible when she is innocent than when she is guilty, say twice as plausible, the odds turn into 80 : 1000, almost 8% chance of innocence.

As an explanation, a way of looking at this that requires less calculation work (but says exactly the same thing) is as follows: It follows from the assumptions that in 20,000 years it occurs 1008 times that 11 deaths occur in a nurse’s shifts: 1,000 of the nurses are guilty and 8 are innocent. Evidence for murder is found for 800 of the guilty nurses, moreover, 100 of the remaining 200 confess. That leaves 100 guilty and 8 innocent among the nurses who did not confess and for whom no evidence for murder was found.

So Lucy Clog must be acquitted. And all the while, I haven’t even talked about doubts about the exact probability of 1 in 100 million that “by chance” 11 people die in so many nurses’ shifts, when on average it would only be 1. This probability would be many times higher in every Bayesian analysis. I estimate, based on experience, that 1 in 2 million would come out. A Bayesian analysis can include uncertainties. Uncertainties about the similarity of circumstances and qualities of nurses, for example. And uncertainties increase the chance of extreme events enormously, the literature contains many interesting examples. As I said, I think that if I had access to the data that Elffers uses, I would not get a chance of 1 in 100 million, but a chance of 1 in 2 million. At least I assume that for the time being; it would not surprise me if it were much higher still!

Preliminary calculations show that it might even be as high as 1 in 100,000. But 1 in 2 million already saves a factor of 50 compared to 1 in 100 million, and my odds would not be 80 to 1000 but 4000 to 1000, so 4 to 1. A chance of 80% to wrongly convict. This is the 80% chance of innocence that I mentioned in the beginning. Unfortunately, it is not possible to explain the factor 50 (or a factor 1000 if the 1 in 100,000 turns out to be correct) from the last step within the framework of this article without resorting to mathematics. [Aart de Vos is probably thinking of Poisson distributions, but adding a hyperprior over the Poisson mean of 1, in order to take account of uncertainty in the true rate of deaths, as well as heterogeneity between nurses, causing some to have shifts with higher death rates than others – RDG]

What I hope has become clear is that you can always add information. “Not being able to find concrete evidence of murder” and “has not confessed” are new pieces of evidence that change the odds. And perhaps there are countless facts to add. In the case of Lucia de Berk, those kinds of facts are there. In the hypothetical case of Lucy Clog, not.

The fact that you can always add information in a Bayesian analysis is the most beautiful aspect of it. From prior odds, you come through data (11 deaths) to posterior odds, and these are again prior odds for the next steps: no concrete evidence for murder, and no confession by our suspect. Virtually all further facts that emerge in a court case can be dealt with in this way in the analysis. Any fact that has a different probability under the hypothesis of guilt than under the hypothesis of innocence contributes. Perhaps the reader has noticed that we only talked about the chances of what actually happened under various hypotheses, never about what could have happened but didn’t. A classic statistical test always talks about the probability of 11 *or more* deaths. That “or more” is irrelevant and misleading according to Bayesians. Incidentally, it is not necessarily easier to just talk about what happened. What is the probability of exactly 11 deaths if Lucy de Clog is guilty? The number of murders, something with a lot of uncertainty about it, determines how many deaths there are, but even though you are fired after 11 deaths, the classical statistician talks about the chance of you committing even more if you are kept on. And that last fact matters for the odds. That’s why I put in a probability of 50%, not 100%, for a murderous nurse killing exactly 11 patients. But that only makes a factor 2 difference.

It should be clear that it is not easy to come to firm statements if there is no convincing evidence. The most famous example, for which many Bayesians have performed calculations, is a murder in California in 1956, committed by a black man with a white woman in a yellow Cadillac. A couple who met this description was taken to court, and many statistical analyses followed. I have done a lot of calculations on this example myself, and have experienced how difficult, but also surprising and satisfying, it is to constantly add new elements.

A whole book is devoted to a similar famous case: “a Probabilistic Analysis of the Sacco and Vanzetti Evidence,” published in 1996 by Jay Kadane, professor of Carnegie Mellon and one of the most prominent Bayesians. If you want to know more, just consult his c.v. on his website http://lib.stat.cmu.edu/~kadane. In the “Statistics and the Law” field alone, he has more than thirty publications to his name, along with hundreds of other articles. This is now a well-developed field in America.

Conclusion?

I have thought for a long time about what the conclusion of this story is, and I have had to revise my opinion several times. And the perhaps surprising conclusion is: the actions of all parties are not that bad, only their rationalization is, to put it mildly, a bit strange. Elffers makes strange calculations but formulates the conclusions in court in such a way that it becomes intuitively clear that he is not giving the answer that the court is looking for. The judge makes judgments that sound as though they are in terms of probabilities but I cannot figure out what the judge’s probabilities are. But when I see what is going on I do get the feeling that it is much more like what is optimal than I would have thought possible, given the absurd rationalisations. The explanation is simple: judges’ actions are based on a process learnt by evolution, judges’ justifications are stuck on afterwards, and learnt through training. In my opinion, the Bayesian method is the only way to balance decisions under uncertainty about actions and rationalization. And that can be very fruitful. But the profit is initially much smaller than people think. What the court does in the case of Lucia de B is surprisingly rational. The 11 deaths are not convincing in themselves, but enough to change the prior odds from 1 in 40,000 to odds from 16 to 5, in short, an order of magnitude in which it is necessary to gather additional information before judging. Exactly what the court does. [de Vos has an optimistic view. He does not realise that the court is being fed false facts by the hospital managers – they tell the truth but not the whole truth; he does not realise that Elffers’ calculation was wrong because de Vos, as a Bayesian, doesn’t know what good classical statisticians do; neither he nor Elffers checks the data and finds out how exactly it was collected; he does not know that the medical experts’ diagnoses are influenced by Elffers’ statistics. Unfortunately, the defence hired a pure probabilist, and a kind of philosopher of probability, neither of whom knew anything about any kind of statistics, whether classical or Bayesian – RDG]

When I made my calculations, I thought at times: I have to go to court. I finally sent the article but I heard nothing more about it. It turned out that the defence had called for a witness who seriously criticized Elffers’ calculations. However, without presenting the solution. [The judge found the defence witness’s criticism incomprehensible, and useless to boot. It contained no constructive elements. But without doing statistics, anybody could see that the coincidence couldn’t be pure chance. It wasn’t: one could say that the data was faked. On the other hand, the judge did understand Elffers perfectly well – RDG].

Maybe I will once again have the opportunity to fully calculate probabilities in the Lucia de Berk case. That could provide new insights. But it is quite a job. In this case, there is much more information than is used here, such as poisonous traces in patients. Here too, it is likely that a Bayesian analysis that takes into account all the uncertainties shows that statements by experts who say something like “it is impossible that there is another explanation than the administration of poison by Lucia de Berk” should be taken with a grain of salt. Experts are usually people who overestimate their certainty. On the other hand, incriminating information can also build up. Ten independent facts that are twice as likely under the hypothesis of guilt change the odds by a factor of 1000. And if it turns out that the toxic traces found in the bodies of five deceased patients are each nine times more likely if Lucia is a murderer than if she isn’t, it saves a factor of nine to the fifth, a small 60,000. Etc, etc

But I think the court is more or less like that. It uses an incomprehensible language, that is, incomprehensible to probabilists, but a language sanctioned by evolution. We have few cases of convictions that were found to be wrong in the Netherlands. [Well! That was a Dutch layperson, writing in 2004. According to Ton Derksen, in the Netherlands about 10% of very long term prisoners (very serious cases) are innocent. It is probably something similar in other jurisdictions – RDG].

If you did the entire process in terms of probability calculations, the resulting debates between prosecutors and lawyers would become endless. And given their poor knowledge of probability, it is also undesirable for the time being. They have their secret language that usually led to reasonable conclusions. Even the chance that Lucia de Berk is guilty cannot be expressed in their language. There is also no law in the Netherlands that defines “legal and convincing evidence” in terms of the chance that a decision is correct. Is that 95%? Or 99%? Judges will maintain that it is 99.99%. But judges are experts.

So I don’t think it’s wise to try to cast the process in terms of probability right now. But perhaps this discussion will produce something in the longer term. Judges who are well informed about the statistical significance of the starting situation and then write down a number for each piece of evidence of prosecutor and defender. The likelihood ratio of each fact must be motivated. At the end, multiply all these numbers together, and have the calculations checked again by a Bayesian statistician. However, I consider this a long-term perspective. I fear (I am not really young anymore) it won’t come in my lifetime.

Het BOLC is weer terug.

10 jaar geleden (in 2010) werd de Nederlandse verpleegster Lucia de Berk bij een nieuw proces vrijgesproken van een aanklacht van 7 moorden en 3 pogingen tot moord in ziekenhuizen in Den Haag in een aantal jaren in de aanloop naar slechts een paar dagen voor de gedenkwaardige datum van “9-11”. De laatste moord zou in de nacht van 4 september 2001 zijn gepleegd. De volgende middag meldden de ziekenhuisautoriteiten een reeks onverklaarbare sterfgevallen aan de gezondheidsinspectie en de politie. Ook plaatsten ze Lucia de B., zoals ze bekend werd in de Nederlandse media, op ‘non-active’. De media meldden dat er ongeveer 30 verdachte sterfgevallen en reanimaties werden onderzocht. De ziekenhuisautoriteiten meldden niet alleen wat volgens hen vreselijke misdaden waren, ze geloofden ook dat ze wisten wie de dader was.

De wielen van gerechtigheid draaien langzaam, dus er was een proces en een veroordeling; een beroep en een nieuw proces en een veroordeling; eindelijk een beroep op het hooggerechtshof. Het duurde tot 2006 voordat de veroordeling (levenslange gevangenisstraf, die in Nederland pas wordt beëindigd als de veroordeelde de gevangenis verlaat in een kist) onherroepelijk wordt. Alleen nieuw bewijs kan het omverwerpen. Nieuwe wetenschappelijke interpretaties van oud bewijs worden niet als nieuw bewijs beschouwd. Er was geen nieuw bewijs.

Maar al, in 2003-2004, maakten sommige mensen met een interne band met het Juliana Kinderziekenhuis zich al zorgen over de zaak. Nadat ze in vertrouwen met de hoogste autoriteiten over hun zorgen hadden gesproken, maar toen ze te horen kregen dat er niets aan te doen was, begonnen ze journalisten te benaderen. Langzaam maar zeker raakten de media weer geïnteresseerd in de zaak – het verhaal was niet meer het verhaal van de vreselijke heks die baby’s en oude mensen zonder duidelijke reden had vermoord, behalve voor het plezier in het doden, maar van een onschuldige persoon die was verminkt door pech, incompetente statistieken en een monsterlijk bureaucratisch systeem dat eens in beweging, niet kon worden gestopt.

Onder de supporters van Metta de Noo en Ton Derksen waren enkele professionele statistici, omdat Lucia’s aanvankelijke veroordeling was gebaseerd op een foutieve statistische analyse van door het ziekenhuis verstrekte onjuiste gegevens en geanalyseerd door amateurs en verkeerd begrepen door advocaten. Anderen waren informatici, sommigen waren ambtenaren op hoog niveau van verschillende overheidsorganen die ontsteld waren over wat ze zagen gebeuren; er waren onafhankelijke wetenschappers, een paar medisch specialisten, een paar mensen met een persoonlijke band met Lucia (maar geen directe familie); en vrienden van zulke mensen. Sommigen van ons werkten vrij intensief samen en werkten met name aan de internetsite voor Lucia, bouwden er een Engelstalige versie van en brachten deze onder de aandacht van wetenschappers over de hele wereld. Toen kranten als de New York Times en The Guardian begonnen te schrijven over een vermeende gerechtelijke dwaling met verkeerd geïnterpreteerde statistieken, ondersteund door opmerkingen van Britse topstatistici, hadden de Nederlandse journalisten nieuws voor de Nederlandse kranten, en dat soort nieuws werd zeker opgemerkt in de gangen van de macht in Den Haag.

Snel vooruit naar 2010, toen rechters niet alleen Lucia onschuldig verklaarden, maar voor de rechtszaal hard-op verklaarden dat Lucia samen met haar collega-verpleegkundigen uiterst professioneel had gevochten om het leven van baby’s te redden die onnodig in gevaar werden gebracht door medische fouten van de medisch specialisten die waren belast met hun zorg. Ze vermeldden ook dat alleen omdat het tijdstip van overlijden van een terminaal zieke persoon niet van tevoren kon worden voorspeld, dit niet betekende dat het noodzakelijkerwijs onverklaarbaar en dus verdacht was.

Enkelen van ons, opgetogen door onze overwinning, besloten om samen te werken en een soort collectief te vormen dat zou kijken naar andere ‘verloren zaken’ met mogelijke justitiele dwalingen waar de wetenschap misbruikt was. Ik had al had mijn eigen onderzoeksactiviteiten omgebogen en gericht op het snelgroeiende veld van forensische statistiek, en ik was al diep betrokken bij de zaak Kevin Sweeney en de zaak van José Booij. Al snel hadden we een website en waren we hard aan het werk, maar kort daarna gebeurde er een opeenvolging van ongelukken. Ten eerste betaalde het ziekenhuis van Lucia een dure advocaat om me onder druk te zetten namens de hoofdkinderarts van het Juliana Children’s Hospital. Ik had namelijk wat persoonlijke informatie over deze persoon (die toevallig de schoonzus was van Metta de Noo en Ton Derksen) geschreven op mijn homepage aan de Universiteit van Leiden. Ik voelde dat het van cruciaal belang was om te begrijpen hoe de zaak tegen Lucia was begonnen en dit had zeker veel te maken met de persoonlijkheden van enkele sleutelfiguren in dat ziekenhuis. Ik schreef ook naar het ziekenhuis en vroeg om meer gegevens over de sterfgevallen en andere incidenten op de afdelingen waar Lucia had gewerkt, om het professionele onafhankelijke statistische onderzoek te voltooien dat had moeten plaatsvinden toen de zaak begon. Ik werd bedreigd en geïntimideerd. Ik vond enige bescherming van mijn eigen universiteit die namens mij dure advocatenkosten betaalde. Mijn advocaat adviseerde me echter al snel om toe te geven door aanstootgevend materiaal van internet te verwijderen, want als dit naar de rechtbank zou gaan, zou het ziekenhuis waarschijnlijk winnen. Ik zou de reputatie van rijke mensen en van een machtige organisatie schaden en ik zou moeten boeten voor de schade die ik had aangericht. Ik moest beloven om deze dingen nooit weer te zeggen en ik zou beboet worden als ze ooit herhaald zou worden door anderen. Ik heb nooit toegegeven aan deze eisen. Later heb ik wel wat gepubliceerd en naar het ziekenhuis opgestuurd. Ze bleven stil. Het was een interessante spel bluf poker.

Ten tweede schreef ik op gewone internetfora enkele zinnen waarin ik José Booij verdedigde, maar die de persoon die haar bij de kinderbescherming had aangegeven ook van schuld verweet. Dat was geen rijk persoon, maar zeker een slim persoon, en ze meldden mij bij de politie. Ik werd verdachte in een geval van vermeende laster. Geïnterviewd door een aardige lokale politieagent. En een paar maanden later kreeg ik een brief van de lokale strafrechter waarin stond dat als ik 200 euro administratiekosten zou betalen, de zaak administratief zou worden afgesloten. Ik hoefde geen schuld te bekennen maar kon ook niet aantekenen dat ik me onschuldig vond.

Dit leidde ertoe dat het Bureau Verloren Zaken zijn activiteiten een tijdje stopzette. Maar het is nu tijd voor een come-back, een “re-boot”. Ondertussen deed ik niet niets, maar raakte ik betrokken bij een half dozijn andere zaken, en leerde ik steeds meer over recht, over forensische statistiek, over wetenschappelijke integriteit, over organisaties, psychologie en sociale media. De BOLC is terug.

**ORGANISATIE en PLANNEN**

Het BOLC is al een paar jaar inactief, maar nu de oprichter de officiële pensioenleeftijd heeft bereikt, “herstart” hij de organisatie. Richard Gill richtte de BOLC op aan de vooravond van de vrijspraak van verpleegster Lucia de Berk in 2006. Een groep vrienden die nauw betrokken waren geweest bij de beweging om Lucia een eerlijk proces te bezorgen, besloten dat ze zo genoten van elkaars gezelschap en zoveel hadden geleerd van de ervaring van de afgelopen jaren, dat ze hun vaardigheden wilden uitproberen op enkele nieuwe cases. We kwamen snel een aantal ernstige problemen tegen en stopten onze website tijdelijk, hoewel de activiteiten in verschillende gevallen werden voortgezet, meer ervaring werd opgedaan, veel werd geleerd.

We vinden dat het tijd is om het opnieuw te proberen, nadat we enkele nuttige lessen hebben geleerd van onze mislukkingen van de afgelopen jaren. Hier is een globaal overzicht van onze plannen.

- Zet een robuuste formele structuur op met een bestuur (voorzitter, secretaris, penningmeester) en een adviesraad. In plaats van het de wetenschappelijke adviesraad te noemen, zoals gebruikelijk in academische organisaties, zou het een morele en / of wijsheidsadviesraad moeten zijn om op de hoogte te worden gehouden van onze activiteiten en ons te laten weten als ze denken dat we van de rails gaan.
- Eventueel een aanvraag indienen om een Stichting te worden. Dit betekent dat we ook zoiets zijn als een vereniging of een club, met een jaarlijkse algemene vergadering. We zouden leden hebben, die misschien ook donaties willen doen, aangezien het runnen van een website en het af en toe in de problemen komen geld kost.
- Schrijf over de zaken waar we de afgelopen jaren bij betrokken zijn geweest, met name: vermeende seriemoordenaars Ben Geen (VK), Daniela Poggiali (Italië); beschuldigingen van wetenschappelijk wangedrag in het geval van het proefschrift van een student van Peter Nijkamp; het geval van de AD Haring-test en de kwaliteit van Dutch New Herring; het geval van Kevin Sweeney.

The BOLC is back. 10 years ago (in 2010) the Dutch nurse Lucia de Berk was acquitted, at a retrial, of a charge of 7 murders and 3 attempted murders at hospitals in the Hague in a number of years leading up to just a few days before the memorable date of “9-11”. The last murder was supposed to have been committed in the night of September 4, 2001. The next afternoon, hospital authorities reported a series of unexplained deaths to the health inspectorate and to the police. They also put Lucia de B., as she became known in the Dutch media, onto “non-active”. The media reported that about 30 suspicious deaths and resuscitations were being investigated. The hospital authorities not only reported what they believed to be terrible crimes, they also believed that they knew who was the perpetrator.

The wheels of justice turn slowly, so there was a trial and a conviction; an appeal and a retrial and a conviction; finally an appeal to the supreme court. It took till 2006 for the conviction (a life sentence, which in the Netherlands is only terminated when the convict leaves prison in a coffin) to become irrevocable. Only new evidence could overturn it. New scientific interpretations of old evidence is not considered new evidence. There was no new evidence.

Yet already, in 2003-2004, some people with an inside connection to the Juliana Children’s Hospital were already getting very concerned about the case. Having spoken of their concerns, in confidence, with the highest authorities, but being informed that nothing could be done, they started to approach journalists. Slowly but surely the media started getting interested in the case again – the story was not anymore the story of the terrible witch who had murdered babies and old people for no apparent reason whatsoever except for the pleasure in killing, but of an innocent person who was mangled by bad luck, incompetent statistics, and a monstrous bureaucratic system which once in motion could not be stopped.

Among the supporters of Metta de Noo and Ton Derksen were a few professional statisticians, because Lucia’s initial conviction had been based on a faulty statistical analysis of faulty data supplied by the hospital and analysed by amateurs and misunderstood by lawyers. Others were computer scientists, some were civil servants at high levels of several government organs appalled at what they saw going on; there were independent scientists, a few medical specialists, a few persons with some personal connection with Lucia; and friends of such people. Some of us worked quite intensively together and in particular worked on the internet site for Lucia, building an English language version of it, and bringing it to the attention of scientists world-wide. When newspapers like the New York Times and The Guardian started writing about an alleged miscarriage of justice in the Netherlands involving wrongly interpreted statistics, supported by comments from top UK statisticians, the Dutch journalists had news for the Dutch newspapers, and that kind of news certainly got noticed in the corridors of power in the Hague.

Fast forward to 2010, when judges not only pronounced Lucia innocent, but actually stated in court that Lucia together with her colleague nurses had fought with utmost professionality to save the lives of babies which were unnecessarily endangered by medical errors of the medical specialists entrusted with their care. They also mentioned that just because the time of a death of a terminally ill person could not be predicted in advance, it did not mean that it was necessarily unexplainable and hence suspicious.

A few of us, exhilarated by our victory, decided to band together and form some sort of collective which would look at other “lost causes” involving possible miscarriages of justice where science had been misused. Aready, I had turned my own research activities to the burgeoning field of forensic statistics, and already I was deeply involved in the Kevin Sweeney case, and the case of José Booij. Soon we had a web-site and were hard at work, but soon after this, a succession of mishaps occurred. Firstly, Lucia’s hospital paid for an expensive lawyer to put pressure on me on behalf of the chief paediatrician of the Juliana Children’s Hospital. I had namely written some information of some personal nature about this person (who coincidentally was the sister-in-law of Metta de Noo and Ton Derksen) on my home page at the University of Leiden. I felt it was crucially in the public interest to understand how the case against Lucia had started and this certainly had a lot to do with personalities of a few key persons at that hospital. I also wrote to the hospital asking for further data on the deaths and other incidents in the wards where Lucia had worked, in order to complete the professional independent statistical investigation which should have taken place when the case started. I was threatened and intimidated. I found some protection from my own university who actually paid expensive lawyer fees on my behalf. However, my lawyer soon advised me to give way by removing offensive material from internet, since if this went to court, the hospital would most likely win. I would be harming the reputation of rich persons and of a powerful organisation, and I would have to pay for the harm I did. Secondly, on some ordinary internet fora I wrote some sentences defending José Booij, but which pointed a finger of blame at the person who had reported her to the police. That was not a rich person, but certainly a clever person, and they reported me to the police. I became a suspect in a case of alleged slander. Got interviewed by a nice local policeman. And a few months later I got a letter from the local criminal courts saying that if I paid 200 Euro administrative fees, the case would be administratively closed.

This led to the Bureau of Lost Causes shutting down its activities for a while. But it is now time for a come-back, a “re-boot”. In the meantime I did not do nothing, but got involved in half a dozen further cases, learning more and more about law, about forensic statistics, about scientific integrity, about organisations, psychology and social media. The BOLC is back.

The BOLC has been dormant for a few years, but now that the founder has reached official retirement age, he is “rebooting” the organisation. Richard Gill founded the BOLC on the eve of nurse Lucia de Berk’s acquittal in 2006. A group of friends who had been closely associated with the movement to get Lucia a fair retrial decided that they so enjoyed one another’s company, and had learnt so much from the experience of the past few years, that they wanted to try out their skills on some new cases. We rapidly ran into some serious problems and temporarily closed down our website, though activities continued on several cases, more experience was gained, a lot was learnt.

We feel it is time to try again, having learnt some useful lessons from our failures of the last few years. Here is a rough outline of our plans.

1. Set up a robust formal structure with an executive board (chairman, secretary, treasurer) and an advisory board. Rather than calling it the scientific advisory board as is common in academic organisations, it should be a moral and/or wisdom advisory board, to be kept informed of our activities and to let us know if they think we are going off the rails.

2. Possibly, make an application to become a foundation (“Stichting”). This means we will also be something like a society or a club, with an annual general meeting. We would have members, who might also like to make donations, since running a web site and occasionally getting into legal trouble costs money.

3. Write about the cases we have been involved in during recent years, in particular: alleged serial killer nurses Ben Geen (UK), Daniela Poggiali (Italy); allegations of scientific misconduct in the case of the PhD thesis of a student of Peter Nijkamp; the case of the AD Herring test and the quality of Dutch New Herring; the case of Kevin Sweeney.

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