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The  role  of  mental  illness  in  recidivism  Seena  Fazel,    

Department  of  Psychiatry,    University  of  Oxford  

 

Outline    

•  Background  –  recidivism  and  role  of  mental  illness  

•  New  study    •  ImplicaJons  •  Risk  assessment    

ReconvicJon  rates  are  high  

Fazel  &  Wolf,  PLoS  One  2015  

Re-­‐offending  rates  not  decreased  over  Jme  in  E&W    

Ministry  of  JusJce,  StaJsJcs  bulleJn  2012.  

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Previous  reviews  downplay  clinical  factors    Are  psychoJc  disorders  associated  with  recidivism?    

Fazel,  Scz  Bull  2012  

New  study    

•  Swedish  populaJon    •  High  quality  naJonal  registers  that  can  be  linked  accurately    

•  Similar  rates  of  violent  reoffending,  mental  illness  and  substance  abuse  in  prisoners;  lower  rates  of  incarceraJon    

Methods Population and data sources

INCLUSION CRITERIA

•  All prison releases between 2000 and 2009

•  Age 15-75

FOLLOW UP

•  Violent crime at 1,2 and 5 years

SWEDISH REGISTERS

•  Total Population Register

•  Patient Register

•  Cause of Death Register

•  National Crime Register

•  Longitudinal Integration Database

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QuesJons  

•  Is  there  an  associaJon  between  mental  disorders  and  violent  reoffending?    

•  To  what  extent  is  this  associaJon  confounded?  And  by  what?    

•  Are  there  differences  by  individual  diagnoses?    

 

Sample  descripJve  staJsJcs

  Male Female

Number  of  individuals 43840 3486

Any  psychiatric  disorder  (%) 42.3 64.1

Alcohol  abuse  (%) 21.2 27.8

Drug  abuse  (%) 21.9 41.3

Personality  disorder  (%) 5.3 10.1

ADHD  (%) 1.3 1.5

Other  developmental  or  childhood  disorder  (%) 2.2 4.0

Schizophrenia  spectrum  disorder  (%) 2.8 3.7

Bipolar  disorder  (%) 0.5 1.0

Depression  (%) 5.8 12.0

Anxiety  disorder  (%) 7.4 15.3

0.00

0.20

0.40

0.60

0.80

1.00

Priso

ners

witho

ut re

offen

ding

0 2 4 6 8 10Year after release

Male prisoner without psychiatric disorderMale prisoner with psychiatric disorderFemale prisoner without psychiatric disorderFemale prisoner with psychiatric disorder

Kaplan-Meier survival estimates

Higher  rates  of  violent  recidivism     Probability  of  violent  reoffending  by  Jme  aaer  release

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Non-­‐specific  links?    

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Findings  are  generalizable  to  other  countries     ImplicaJons    

•  NaJonal  violence  prevenJon  strategies  should  include  prison  health  

•  Very  lidle  familial  confounding  suggesJng  causality    

•  Non-­‐specificity  by  diagnosis  suggests  shared  mechanisms  (emoJonal  dysregulaJon?)  

•  MulJmorbidity  important    

Overall  implicaJons  

•  ExplanaJons  of  reoffending  –  criminological,  psychological,  medical/public  health    

•  What  next  for  risk  assessment?    •  What  next  for  risk  management?    

New  Scien1st,  2013  

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Coid,  JFFP  2011  

And  what  does  high  risk  mean?    

Singh,  BJP  2014  

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Problems  

•  Costly  (direct  and  indirect)  •  Time  consuming  •  Not  transparent  •  PublicaJon  bias  •  Evidence  base  is  variable      •  Guru-­‐like  system  of  training    •  Meaningless  cut-­‐offs?    •  Don’t  improve  outcomes    

So  lets  do  it  differently  and  properly  

•  What  precedents  are  there?    

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So  lets  do  it  differently  and  properly  

•  What  precedents  are  there?    •  Develop  web  based  calculator,  free  to  use,  requires  no  training    

Methods Population and data sources

INCLUSION CRITERIA

•  All prison releases between 2000 and 2009

•  Age 15-75

FOLLOW UP

•  Violent crime within 12/24 months

•  Any crime within 12/24 months

SWEDISH REGISTERS

•  Total Population Register

•  Patient Register

•  Cause of Death Register

•  National Crime Register

•  Longitudinal Integration Database

Methods Samples & Analyses

DERIVATION

•  Cox regression

•  Risk factor groups

•  Low / Medium / High 10% 50%

•  All pre-specified

VALIDATION

•  Sensitivity/Specificity

•  PPV/NPV

•  AUC

•  Brier score

SAMPLES

•  47 326 prisoners

•  Derivation sample (n= 37 100)

•  Validation sample (n= 10 226)

Index violent crime, previous violent crime, length of incarceration

CRIMINOGENIC

Sex, age, immigrant status, marital status, educational level, income, deprivation

SOCIO-DEMOGRAPHIC

Homicide, assault, robbery, arson, sexual offense, illegal threat or intimidation

VIOLENT CRIME

Alcohol or drug disorders, mental illness, developmental or childhood disorders

CLINICAL

Methods Risk factors and event

18 % within 24 months

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Results Included risk factors

MALE 93 %

MEAN AGE 36 yrs

IMMIGRANT STATUS 31 %

LENGTH OF INCARCERATION

VIOLENT INDEX OFFENCE 38 %

PREVIOUS VIOLENT CRIME 53 %

UNMARRIED 65 %

EDUCATION

EMPLOYMENT 25 %

DISPOSABLE INCOME

NEIGHBOURHOOD DEPRIVATION

ALCOHOL USE DISORDER 22 %

ANY MENTAL DISORDER 22 %

DEVELOPMENTAL/CHILDHOOD DIS 3 %

1

2

3

DRUG USE DISORDER 23 %

ANY SEVERE MENTAL DISORDER 3 %

Variable Adjusted Odds Ratio

Sex (female) 0.51 (0.45-0.57)

Age (per 5 years) 0.84 (0.83-0.85)

Immigrant status 0.97 (0.92-1.02)

Length of incarceration 6-11 months 12-23 months 24+ months  

0.85 (0.81-0.90) 0.69 (0.63-0.75) 0.55 (0.48-0.64)

Violent index offence 1.53 (1.46-1.59)

Previous violent crime 2.41 (2.29-2.54)

Unmarried 1.08 (1.02-1.15)

Education level 9-11 years 12+ years

0.83 (0.79-0.87) 0.65 (0.57-0.75)

Results Risk factor odds ratios

Variable Adjusted Odds Ratio

Employment 0.68 (0.63-0.72)

Disposable income Zero Low (<20º) Medium (20º - 80º) High (>80º)

1.69 (1.11-2.57) 1.68 (1.11-2.53) 1.45 (0.96-2.19) 1.57 (0.92-2.67)

Neighbourhood deprivation 1.03 (1.01-1.04)

Alcohol use disorder 1.41 (1.33-1.49)

Drug use disorder 1.51 (1.44-1.59)

Any mental disorder 1.09 (1.03-1.15)

Any severe mental disorder 1.10 (0.99-1.22)

Results Validation and web tool

NPV PPV

Specificity

99 %

Sensitivity

37 %

70 % 67 %

c-index (AUC)

.76

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Results Comparison with other tools

SENSITIVITY 67%

SPECIFICITY 70%

POSITIVE PREDICTIVE VALUE 37%

NEGATIVE PREDICTIVE VALUE 99%

AREA UNDER THE CURVE 0.76

SENSITIVITY 92%

SPECIFICITY 36%

POSITIVE PREDICTIVE VALUE 41%

NEGATIVE PREDICTIVE VALUE 91%

AREA UNDER THE CURVE 0.72

VIOLENCE TOOLS Mean values for HCR-20, SARA,

SAVRY, and VRAG*

OXRECOXFORD RISK OFRECIDIVISM TOOL

*Fazel et al., BMJ 2012

Conclusions  •  Scalable  tool  •  Overall  similar  predicJve  abiliJes  to  current  approaches  

•  More  effecJve  targeJng  than  current  tools  –  sensiJvity  67%    

•  IdenJfies  those  with  drug  and  alcohol  needs,  and  mental  health  problems  

•  Simple  way  of  providing  BASELINE  risk    •  Needs  to  be  complemented  with  more  individualised  needs  assessments    

Published paper

www.thelancet.com/psychiatry Vol 3 June 2016 535

Articles

Lancet Psychiatry 2016; 3: 535–43

Published OnlineApril 13, 2016http://dx.doi.org/10.1016/S2215-0366(16)00103-6

See Comment page 493

Department of Psychiatry, Warneford Hospital (Prof S Fazel MD, Z Chang PhD) and Department of Primary Care Health Sciences (T Fanshawe PhD), University of Oxford, Oxford, UK; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Z Chang, Prof N Långström PhD, Prof P Lichtenstein PhD, Prof H Larsson PhD); Department of Medical Sciences, Örebro University, Stockholm, Sweden (Prof H Larsson); Research and Evaluation Department, Swedish Prison and Probation Administration, Sweden (Prof N Långström); and School of Population and Health Sciences, University of Birmingham, UK (S Mallett PhD)

Correspondence to:Prof Seena Fazel, Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, OX3 7JX, UK.seena.fazel@psych.ox.ac.uk

Prediction of violent reo! ending on release from prison: derivation and external validation of a scalable toolSeena Fazel, Zheng Chang, Thomas Fanshawe, Niklas Långström, Paul Lichtenstein, Henrik Larsson, Susan Mallett

SummaryBackground More than 30 million people are released from prison worldwide every year, who include a group at high risk of perpetrating interpersonal violence. Because there is considerable inconsistency and ineffi ciency in identifying those who would benefi t from interventions to reduce this risk, we developed and validated a clinical prediction rule to determine the risk of violent off ending in released prisoners.

Methods We did a cohort study of a population of released prisoners in Sweden. Through linkage of population-based registers, we developed predictive models for violent reoff ending for the cohort. First, we developed a derivation model to determine the strength of prespecifi ed, routinely obtained criminal history, sociodemographic, and clinical risk factors using multivariable Cox proportional hazard regression, and then tested them in an external validation. We measured discrimination and calibration for prediction of our primary outcome of violent reoff ending at 1 and 2 years using cutoff s of 10% for 1-year risk and 20% for 2-year risk.

Findings We identifi ed a cohort of 47 326 prisoners released in Sweden between 2001 and 2009, with 11 263 incidents of violent reoff ending during this period. We developed a 14-item derivation model to predict violent reoff ending and tested it in an external validation (assigning 37 100 individuals to the derivation sample and 10 226 to the validation sample). The model showed good measures of discrimination (Harrell’s c-index 0·74) and calibration. For risk of violent reoff ending at 1 year, sensitivity was 76% (95% CI 73–79) and specifi city was 61% (95% CI 60–62). Positive and negative predictive values were 21% (95% CI 19–22) and 95% (95% CI 94–96), respectively. At 2 years, sensitivity was 67% (95% CI 64–69) and specifi city was 70% (95% CI 69–72). Positive and negative predictive values were 37% (95% CI 35–39) and 89% (95% CI 88–90), respectively. Of individuals with a predicted risk of violent reoff ending of 50% or more, 88% had drug and alcohol use disorders. We used the model to generate a simple, web-based, risk calculator (OxRec) that is free to use.

Interpretation We have developed a prediction model in a Swedish prison population that can assist with decision making on release by identifying those who are at low risk of future violent off ending, and those at high risk of violent reoff ending who might benefi t from drug and alcohol treatment. Further assessments in other populations and countries are needed.

Funding Wellcome Trust, the Swedish Research Council, and the Swedish Research Council for Health, Working Life and Welfare.

Copyright © Fazel et al. Open Access article distributed under the terms of CC BY.

IntroductionTo reduce the mortality and morbidity burden associated with interpersonal violence, the identifi cation of, and intervention with, prisoners at high risk of perpetrating violence provides an approach with considerable public health and safety benefi ts.1 Repeat off ending rates remain high in many high-income countries,2 and have not followed the downward trend of violence reported in these countries.3 In England and Wales, prisoners have been reconvicted at a 2-year rate of 55%–60% for the past decade.3 With about 30 million individuals entering and leaving prison per year worldwide,4 the contribution of this population to societal violence is high, and an estimated 20% of all arrests in the USA,5 and 18% of new crimes in the UK,6 are by former prisoners.

To identify individuals who are at the highest risk of reoff ending and most in need of interventions to reduce future criminality, criminal justice agencies in most

high-income and middle-income countries have used actuarial and clinically informed decision aids. These aids assist with decisions about sentencing, entry into specifi c programmes for prison treatment and aftercare, and the timing of release from detention and need for supervision on release. More than 300 of these risk assessment tools exist, but they are limited by low to moderate accuracy,7 fi nancial and non-fi nancial competing interests aff ecting the research evidence,8 and inconsistent defi nitions of risk classifi cations.9 Typically, these tools identify prisoners at low, medium, and high risk of repeat off ending on the basis of an assessment weighted towards historical non-modifi able factors. Many of the tools are expensive to use and require training to administer. Another problem is that they are usually developed without predetermined protocols, the use of which enhances transparency and reduces bias by clarifying key elements in research design before data acquisition or analysis, and hence increasing

Results Summary  •  High  rates  of  recidivism  •  Previous  reviews  of  role  of  mental  illness  misleading  

•  New  research  using  large  sample,  novel  design,  reliable  exposures  and  hard  outcomes    

•  All  mental  disorders  associated  with  violent  recidivism  

•  NaJonal  violence  strategies,  prison  health  services,  and  risk  assessment  need  review  in  light  of  this    

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References  (open  access)  •  Fazel  S,  Chang  Z,  Fanshawe  T,  Långström  N,  Lichtenstein  P,  Larsson  H,  Malled  S.  

PredicJon  of  violent  reoffending  on  release  from  prison:  derivaJon  and  external  validaJon  of  a  scalable  tool.  Lancet  Psychiatry  2016;  3:  535-­‐543.  PMID:  26946390  

•  Chang  Z,  Larsson  H,  Lichtenstein  P,  Fazel  S.  Psychiatric  disorders  and  violent  reoffending:  a  naJonal  cohort  study  of  convicted  prisoners  in  Sweden.  Lancet  Psychiatry  2015;  2:  891-­‐900.  PMID:  26342957.  

•  OxRec:  www.oxrisk.com/oxrec  

Acknowledgements

Achim Wolf Psychiatry, Oxford

Zheng Chang Karolinska Institutet

Henrik Larsson Karolinska Institutet

Paul Lichtenstein Karolinska Institutet

Susan Mallett University of Birmingham

seena.fazel@psych.ox.ac.uk Twitter: @seenafazel

Johan Zetterqvist Karolinska Institutet

Niklas Långström Karolinska Institutet

Thomas Fanshawe Primary Care, Oxford