Lessons from Lucia
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The document discusses healthcare serial killers, focusing on the case of nurses who commit murders within hospital settings, illustrating insights into their characteristics and behaviors. It details a proposed 'red flag checklist' that could assist in identifying potential offenders, as well as critiques the legal proceedings in the prominent case of Lucia de Berk, who was wrongly convicted. The analysis concludes with an examination of systemic failures in the medical and legal communities surrounding cases of alleged murder by healthcare professionals.
Lessons from Lucia
- 2. Overview of Lecture • Background • Theory (statistical paradigms) • Lucia • Conclusions
- 3. Background • Serial killer nurses: is there an epidemic? • Victorino Chua (UK) • Daniela Poggiali (It) • Nils H. (Germany) • Ben Geen (UK) • … • But perhaps also an epidemic of falsely convicted innocent nurses !
- 4. Academic (?) research • Katherine Ramsland (2007) Inside the minds of health care serial killers: why they kill • ElizabethYardley and David Wilson (2014) In Search of the ‘Angels of Death’: Conceptualising the Contemporary Nurse Healthcare Serial Killer “Red flag check-list”: inspired (in part) by the case of Lucia de Berk … from the time when everyone knew she was guilty Sources: newspaper reports and prosecution documents
- 5. was necessary. For example, through discussion, we interpreted regularly as meaning a pat- tern of employment that was out of the ordinary, and which might have seen the nurse set- tling at a job for only a few months and then moving on. However, it is also clear that at some later stage this checklist should be further refined and scrutinised, especially as it Charles Cullen 11 Kimberley Saenz 9 Kristen Gilbert 8 Robert Rubane Diaz 8 Sonia Caleffi 8 Beverley Allitt 7 Cecile Bombeek 7 Vickie Dawn Jackson 7 Aino Nykopp-Koski 6 Orville Lynn Majors 6 Benjamin Geen 5 Petr Zalenka 5 Christine Malevre 4 Irene Becker 4 Stephan Letter 4 Colin Norris 2 Table 15. Prevalence of items on ‘red flag’ checklist Item N % of cases in which this item was present 1. Moves from one hospital to another 6 38 2. Secretive/difficult personal relationships 7 43 3. History of mental instability/depression 10 63 4. Predicts when someone will die 4 25 5. Makes odd comments/claims to be ‘jinxed’ 3 19 6. Likes to talk about death/odd behaviours when someone dies 1 6 7. Higher incidences of death on his/her shift 15 94 8. Seems inordinately enthused about his/her skills 6 38 9. Makes inconsistent statements when challenged about deaths 3 19 10. Prefers nightshifts—fewer colleagues about 6 38 11. Associated with incidents at other hospitals 6 38 12. Been involved with other criminal activities 7 43 13. Makes colleagues anxious/suspicious 9 56 14. Craves attention 7 43 15. Tries to prevent others checking on his/her patients 0 – 16. Hangs around during investigations of deaths 0 – 17. In possession of drugs at home/in locker 8 50 18. Lied about personal information 0 – 19. In possession of books about poison/serial murder 1 6 20. Has had disciplinary problems 4 25 21. Appears to have a personality disorder 8 50 22. Has a substance abuse problem 3 19 Note: % ≠ 100 as all cases had at least one checklist item Copyright © 2014 John Wiley & Sons, Ltd. J. Investig. Psych. Offender Profil. (2014) DOI: 10.1002/jip In Search of the ‘Angels of Death’: Conceptualising the Contemporary Nurse Healthcare Serial Killer ELIZABETH YARDLEY* and DAVID WILSON Birmingham City University, Centre for Applied Criminology, City North Campus, Franchise Street, Perry Barr, Birmingham, B42 2SU, United Kingdom Abstract Focusing specifically upon nurses who commit serial murder within a hospital setting, this paper aims to establish insights into this particular subcategory of healthcare serial killer. In addition, the paper aims to test the usefulness of an existing checklist of behaviours among this group of serial murderers. Drawing upon existing lists of healthcare serial killers produced by other scholars as well as legal records and an online news archive, we identified and researched healthcare serial killer nurses, collating socio demographic and criminological data and applying the aforementioned checklist to each case. Our find- ings suggest that to date, the label ‘healthcare serial killer’ has been applied in too loose a manner, making the understanding of this phenomenon problematic. In further refining the definition and identifying the socio-demographic and criminological characteristics of the victims, perpetrators and crimes, we have developed more specific and therefore useful insights for practitioners and identified a potentially useful checklist which, with revisions, could contribute towards preventative strategies and interventions. Copyright © 2014 John Wiley & Sons, Ltd. Key words: healthcare serial killers; nurses; hospitals INTRODUCTION Healthcare serial killers (HSKs), sometimes also known as medical murderers (Hickey, 2010), have emerged as a common concern within popular culture and true crime, with various books, documentaries and films all eager to understand what might have motivated, for example, Beverly Allitt, Harold Shipman or Charles Cullen to have taken the lives of their patients (Davies, 1993; Graeber, 2013; Peters, 2005). However, whilst the HSK has been emerging into popular consciousness, academic, criminological research about this type of offender remains somewhat underdeveloped. Even so, a relatively small body of work has begun to iden- tify, map and describe cases of healthcare serial murder (see, for example, Ramsland, 2007; *Correspondence to: Elizabeth Yardley, Birmingham City University, Centre for Applied Criminology, City North Campus, Franchise Street, Perry Barr, Birmingham B42 2SU, UK. E-mail: elizabeth.yardley@bcu.ac.uk Journal of Investigative Psychology and Offender Profiling J. Investig. Psych. Offender Profil. (2014) Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/jip.1434 Copyright © 2014 John Wiley & Sons, Ltd.
- 6. Theory: statistical paradigms • Bayes (one person statistics) • Frequentist (two person, collaborative statistics) • Likelihood (avoiding the issue) NB two paradigms of probability: “subjective” (Bayesian), “objective” (frequentist)
- 7. Bayes’ rule • Posterior odds = prior odds * likelihood ratio • Likelihood ratio = Prob( data | HP ) : Prob( data | HD ) Bayesian/frequentist peaceful coexistence theorem: {Decision theoretic admissible} = {Bayesian (for some prior)}
- 8. Current research: use of Bayes net (aka graphical model) • Bayesian model of (probabilistic) causality • Bayesian means “probability as degree of belief” (epistemological, not ontological) • Statistical correlations “explained” by causal dependence on past events • Some of those events are known, others unknown • Computations: with GeNIe, HUGIN Lite, or in R
- 9. wid and Evett (1997) consider a fictitious burglary case, described ows: An unknown number of o↵enders entered commercial premises late at night through a hole which they cut in a metal grille. In- side, they were confronted by a security guard who was able to set o↵ an alarm before one of the intruders punched him in the face, causing his nose to bleed. The intruders left from the front of the building just as a police patrol car was arriving and they dispersed on foot, their getaway car having made o↵ at the first sound of the alarm. The security guard said that there were four men but the light was too poor for him to describe them and he was confused because of the blow he had received. The police in the patrol car saw the o↵enders only from a considerable distance away. They searched the surrounding area and, about 10 minutes later, one of them found the suspect trying to “hot wire” a car in an alley about a quarter of a mile from the incident. Example: Dawid and Evett (1997)
- 10. Example (ctd) : Dawid and Evett (1997) At the scene, a tuft of red fibres was found on the jagged end of one of the cut edges of the grille. Blood samples were taken from the guard and the suspect. The suspect denied having anything to do with the o↵ence. He was wearing a jumper and jeans which were taken for examination. A spray pattern of blood was found on the front and right sleeve of the suspect’s jumper. The blood type was di↵erent from that of the suspect, but the same as that from the security guard. The tuft from the scene was found to be red acrylic. The suspect’s jumper was red acrylic. The tuft was indistinguishable from the fibres of the jumper by eye, microspectrofluorimetry and thin layer chro- matography (TLC). The jumper was well worn and had several holes, though none could clearly be said to be a possible origin for the tuft. In this example there are three general kinds of evidence: eye-witne J. Mortera A. P. Dawid (2006), Probability and Evidence, Research Report No. 264, Department of Statistical Science, University College London.
- 11. Example: Dawid and Evett (1997) Squares = observed = evidence; circles = not observed; C = hypothesis of interest
- 12. Dawid and Evett (1997) blood, and fibre; and for each kind a variety of individual evidential items. We can summarise the salient features of the evidence against the suspect as follows: EYEWITNESS G : The evidence of the security guard W : The evidence of the police o cer who arrested the suspect BLOOD R : The bloodstain in the form of a spray on the suspect’s jumper X1: Suspect’s blood type X2: Guard’s blood type Y2: Blood type of blood spray on jumper FIBRES X3: Properties of the suspect’s jumper Y1: Properties of fibre tuft The uncertain hypotheses and variables that enter are: HYPOTHESES C: Whether the suspect was or was not one of the o↵enders 11 We can summarise the salient features of the evidence against the suspect as follows: EYEWITNESS G : The evidence of the security guard W : The evidence of the police o cer who arrested the suspect BLOOD R : The bloodstain in the form of a spray on the suspect’s jumper X1: Suspect’s blood type X2: Guard’s blood type Y2: Blood type of blood spray on jumper FIBRES X3: Properties of the suspect’s jumper Y1: Properties of fibre tuft The uncertain hypotheses and variables that enter are: HYPOTHESES C: Whether the suspect was or was not one of the o↵enders 11 A: The identity of the person who left the fibres on the grille B: The identity of the person who punched the guard N: The number of o↵enders
- 13. Of these the specific charge before the court is C = true; the others are included to provide a complete account of the problem. Figure 1: Bayesian network for burglary example Dawid and Evett (1997) J. Mortera A. P. Dawid (2006), Probability and Evidence, Research Report No. 264, Department of Statistical Science, University College London.
- 14. Example: prior to entering evidence
- 15. Example: posterior to entering evidence
- 16. Lucia
- 17. Shifts Court dataCourt dataCourt data CorrectedCorrectedCorrectedCorrected JKZ MCU-1 incidentincident incidentincident Oct ’00 – Sept ’01 with without with with Lucia with 9 b133 b7 b13 Lucia without 0 b887 b4 b88 RKZ-42 Aug – Nov ’97 Lucia with b6 b52 b5 b5 Lucia without b9 272 10 27 RKZ-41 Aug – Nov ’97 Lucia with 1 bb0 1 bb Lucia without 4 361 4 35 Lucia: the data
- 18. LuciadeB. Reconstructie van een gerechtelijke dwaling LuciadeB. Reconstructievaneengerechtelijkedwaling n Magazines Ton Derksen TonDerksen
- 19. Lucia: time-line • Sept. 4, 2001,“unexpected” death of Amber • 2003: life sentence for 4 murders and 2 attempts; proof: statistical • 2004: life sentence of 7 murders and 3 attempts; proof: medical • 2006: confirmed by supreme court • 2006: publication of book by Ton Derksen (philosopher of science) • 2006: case submitted to special committee for review of exceptional possibly unsafe convictions
- 20. • 2008: CEAS reports death of Amber natural, recommends reopening • 2008:“advocate-general” to supreme court admits there is no “novum”, commissions further investigations • 2009:AG recommends case is reopened (with “novum” if required: former key pathologist agrees with new findings – he had less information at his disposal • 2009: supreme court accepts, case is reopened • 2010: not-guilty verdict (all deaths natural; nurses behaviour exemplary; medical errors)
- 21. Lucia: likelihood ratio • Hypothesis of the prosecution: (most of the) Lucia incidents are murders or attempted murders • Hypothesis of the defence: the events are natural and would have happened anyway • Prob(data|HP):Prob(data|HD)=1:1
- 22. Lucia: the original statistical analysis • Frequentist approach; hypothesis test; null hypothesis = “balls in vases” • For each of three data sets, court’s statistician computed the “p-value” P(as extreme as Lucia or more | balls in vases model) • For JKZ MCU-I, he multiplied by 26 (= # nurses worked on the ward that year) • Product of three p-values = 1 in 342 million
- 23. Lucia: the defense • Judge:“what is the probability the coincidence is due to chance?” • Defence 1.There are so many different probability models, you cannot compute a probability • Defence 2. Multiplying p-values is wrong (reductio ad absurdam) • Judges:“we are not here to do thought experiments, but to determine facts” • Judges:“The verdict of the court does not depend on a statistical computation of probabilities”
- 24. Lucia: the defense • Judge:“what is the probability the coincidence is due to chance?” • Defence 1.There are so many different probability models, you cannot compute a probability • Defence 2. Multiplying p-values is wrong (reductio ad absurdam) • Judges:“we are not here to do thought experiments, but to determine facts” • Judges:“The verdict of the court does not depend on a statistical computation of probabilities”
- 25. No one checked the data! • Three children responsible for multiple identical events, some in Lucia’s shifts, some not • No consistent definition of “incident” • No consistent definition of “time of incident” • The data suggested the hypothesis • No-one studied the “normal” situation (clusters of events, clusters of shifts are normal)
- 26. Shifts Court dataCourt dataCourt data Corrected dataCorrected dataCorrected dataCorrected data JKZ MCU-1 incidentincident incidentincident Oct ’00 – Sept ’01 with without with without Lucia with 9 b133 b7 b135 Lucia without 0 b887 b4 b883 RKZ-42 Aug – Nov ’97 Lucia with b6 b52 b5 b53 Lucia without b9 272 10 273 RKZ-41 Aug – Nov ’97 Lucia with 1 bb0 1 bb2 Lucia without 4 361 4 359 1
- 27. Some p-values • Cochran-Mantel-Haenszel test & Elffers’ post-hoc correction 1 in 916 • Ultimate stratification 11 days at JKZ with both incident & Lucia on duty 1 in 25 • Gamma(1) heterogeneity over Poisson intensity JKZ, RKZ pooled 1 in 25
- 28. Aftermath • Since 2010, no more media interest • The legal system got the blame, the taxpayer paid the bill • There have been reforms, improvements, communication between legal and scientific communities • Medical community is silent
- 29. Interview with president Council for Justice • “The system worked fine” • “Murderers who escape conviction usually confess on their deathbed”
- 30. What really happened? • In Dutch hospitals: 2000 deaths per year due to avoidable medical errors; culture of denial; frequent communication failures • During 9 months up to 4 Sept. 2001, there was gossip about Lucia among nurses and specialists • Medical errors by specialists were being associated with Lucia • Director and top medical staff (but not all), under oath: there was no suspicion till 4 Sept. 2001
- 31. • No suspicion at all till 4 September, 2001? Director Paul Smits reported 10 unnatural deaths and suspicious reanimations, over last year, within 15 minutes of being informed of death of Amber, and on the very same day • Strange fact: these 10 “incidents” were also reported to Health Inspectorate. Conclusion: nothing wrong. • 4 medical specialists, it appears, have lied to police and to courts (and to one another) concerning the treatment of their own patients What really happened?
- 32. Key case: baby Amber • Baby Amber did not die of digoxin poisoning • In fact the circumstances of her death are completely consistent with a “natural” process • Lucia did not have opportunity (doctors were with the baby at the time when the court had determined she must have acted) • It seems there might have been digoxin in the body, but it did not play any role in her death, and there are many innocent explanations for how it got there … if it was there at all
- 33. HCSK’s • Once a hospital has “identified” a HCSK, the suspect has no chance any more • Lucia got accused through a combination of unlucky coincidences • She got exonerated through another combination of lucky coincidences … and a lot of very hard work of very many “outsiders”
- 34. gill@math.leidenuniv.nl http://www.math.leidenuniv.nl/~gill Conclusions • Forensic statistics is in its infancy • It requires non-standard paradigms and will need new methodology • Multiparty statistics • Nuisance parameters • Model the forensic investigation process • Communication of statistical ideas to non- statisticians is going to be the bottle-neck