title: Prediction of violent reoffending on release from prison: derivation and external validation of a scalable tool.
abstract: BACKGROUND
  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 inefficiency in identifying those who would benefit from interventions to reduce this risk, we developed and validated a clinical prediction rule to determine the risk of violent offending 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 reoffending for the cohort. First, we developed a derivation model to determine the strength of prespecified, 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 reoffending at 1 and 2 years using cutoffs of 10% for 1-year risk and 20% for 2-year risk.
  
  
  RESULTS
  We identified a cohort of 47 326 prisoners released in Sweden between 2001 and 2009, with 11 263 incidents of violent reoffending during this period. We developed a 14-item derivation model to predict violent reoffending 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 reoffending at 1 year, sensitivity was 76% (95% CI 73-79) and specificity 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 specificity 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 reoffending 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.
  
  
  CONCLUSIONS
  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 offending, and those at high risk of violent reoffending who might benefit from drug and alcohol treatment. Further assessments in other populations and countries are needed.
  
  
  BACKGROUND
  Wellcome Trust, the Swedish Research Council, and the Swedish Research Council for Health, Working Life and Welfare.
authors.affiliation: Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK. Electronic address: seena.fazel@psych.ox.ac.uk.
authors.forename: Seena
authors.initials: S
authors.lastname: Fazel
authors.affiliation: Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Chang
authors.affiliation: Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
authors.forename: Thomas
authors.initials: T
authors.lastname: Fanshawe
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Research and Evaluation Department, Swedish Prison and Probation Administration, Sweden.
authors.forename: Niklas
authors.initials: N
authors.lastname: Långström
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Paul
authors.initials: P
authors.lastname: Lichtenstein
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Medical Sciences, Örebro University, Stockholm, Sweden.
authors.forename: Henrik
authors.initials: H
authors.lastname: Larsson
authors.affiliation: School of Population and Health Sciences, University of Birmingham, UK.
authors.forename: Susan
authors.initials: S
authors.lastname: Mallett
mesh_terms: D000328:Adult; D015331:Cohort Studies; D003415:Crime / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011329:Prisoners / Q000523:psychology* / Q000706:statistics & numerical data; D012042:Registries; D015203:Reproducibility of Results; D012307:Risk Factors; D013548:Sweden; D014754:Violence / Q000706:statistics & numerical data*