Can we predict dangerousness in
our patients?
Seena Fazel
Wellcome Senior Research Fellow, University of Oxford &
Honorary Consultant Forensic Psychiatrist, Oxford Health



Background and context
What is the association between severe
mental illness and violence?
How can we predict violence in patients
with severe mental illness?



Public health impact of interpersonal
violence
Mortality and morbidity
Increasing numbers of secure hospital
beds and prisoners
Deaths by cause, estimates for 2004
(total deaths, % total)
Disability Adjusted
Life Years
(DALYs)
Prison population England & Wales
90000
80000
70000
60000
50000
40000
30000
20000
10000
0
1990
2005
Reinstitutionalization
Priebe, 2008
Khiroya, 2009
Cost of mental health services in 2009/10
Figure 2b - Risk estimates with substance abuse comorbidity
Figure 6a – Risk estimates for violence in men with schizophrenia
comorbid with substance abuse compared with risk in men with
substance abuse (without psychosis) reported in the same study
The problem of risk assessment




Current approaches are expensive,
resource-intensive, and not scalable
Risk assessment has mixed evidence
for predictive ability
Guru-like system of occasional training
120+ structured instruments
National Confidential Inquiry datahomicides by psychiatric patients
Risk assessment tools – research
questions





How do these measures compare with
other medical technologies?
Is there an authorship effect?
Which are the best ones?
Are they more useful for some people
than others?
Does their predictive validity change
using different study designs?
Design-related biases?
Lijmer, JAMA 1999
Authorship effects?
Bekelman, JAMA 2003
New review and meta-analysis



81 samples involving 26,426 individuals
Replication studies from 1 January 1995 to 1 January
2011
Diagnostic odds ratio (DOR), sensitivity, specificity,
area under the curve (AUC), positive predictive value
(PPV), negative predictive value (NPV), and the
number needed to detain (NND) to prevent one
offence were calculated
Other prognostic tools


AUCs from cardiovascular prognostic
tools similar: Framingham 0.57-0.86,
SCORE 0.65-0.85, QRISK 0.76-0.79
DORs for diagnostic tests considerably
higher
Summary



Violent risk prediction cannot be done
accurately
Cannot be used as sole determinants of
sentencing, release or discharge
Violence risk assessment instruments have
moderate PPVs, higher NPVs
Implications




Screen out those at low risk
Mixture of clinical judgement and
evidence-based clinical prediction rules
Risk management
We cannot predict on individual patient
basis