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Journal Article

Citation

Fazel S, Wolf A, Vazquez-Montes MDLA, Fanshawe TR. Sci. Rep. 2019; 9(1): e841.

Affiliation

Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.

Copyright

(Copyright © 2019, Nature Publishing Group)

DOI

10.1038/s41598-018-37539-x

PMID

30696902

Abstract

Scalable and transparent methods for risk assessment are increasingly required in criminal justice to inform decisions about sentencing, release, parole, and probation. However, few such approaches exist and their validation in external settings is typically lacking. A total national sample of all offenders (9072 released from prisoners and 6329 individuals on probation) from 2011-2012 in the Netherlands were followed up for violent and any reoffending over 2 years. The sample was mostly male (nā€‰=ā€‰574 [6%] were female prisoners and nā€‰=ā€‰784 [12%] were female probationers), and median ages were 30 in the prison sample and 34 in those on probation. Predictors for a scalable risk assessment tool (OxRec) were extracted from a routinely collected dataset used by criminal justice agencies, and outcomes from official criminal registers. OxRec's predictive performance in terms of discrimination and calibration was tested. Reoffending rates in the Dutch prisoner cohort were 16% for 2-year violent reoffending and 44% for 2-year any reoffending, with lower rates in the probation sample. Discrimination as measured by the c-index was moderate, at 0.68 (95% CI: 0.66-0.70) for 2-year violent reoffending in prisoners and between 0.65 and 0.68 for other outcomes and the probation sample. The model required recalibration, after which calibration performance was adequate (e.g. calibration in the large was 1.0 for all scenarios). A recalibrated model for OxRec can be used in the Netherlands for individuals released from prison and individuals on probation to stratify their risk of future violent and any reoffending. The approach that we outline can be considered for external validations of criminal justice and clinical risk models.


Language: en

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