Citation

Kappes JR, Huber DA, Kirchebner J, Sonnweber M, Günther MP, Lau S. Int. J. Offender Ther. Comp. Criminol. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, SAGE Publishing)

DOI

10.1177/0306624X211062139

PMID

34861802

Abstract

The burden of self-injury among offenders undergoing inpatient treatment in forensic psychiatry is substantial. This exploratory study aims to add to the previously sparse literature on the correlates of self-injury in inpatient forensic patients with schizophrenia spectrum disorders (SSD). Employing a sample of 356 inpatients with SSD treated in a Swiss forensic psychiatry hospital, patient data on 512 potential predictor variables were retrospectively collected via file analysis. The dataset was examined using supervised machine learning to distinguish between patients who had engaged in self-injurious behavior during forensic hospitalization and those who had not. Based on a combination of ten variables, including psychiatric history, criminal history, psychopathology, and pharmacotherapy, the final machine learning model was able to discriminate between self-injury and no self-injury with a balanced accuracy of 68% and a predictive power of AUC = 71%.

RESULTS suggest that forensic psychiatric patients with SSD who self-injured were younger both at the time of onset and at the time of first entry into the federal criminal record. They exhibited more severe psychopathological symptoms at the time of admission, including higher levels of depression and anxiety and greater difficulty with abstract reasoning. Of all the predictors identified, symptoms of depression and anxiety may be the most promising treatment targets for the prevention of self-injury in inpatient forensic patients with SSD due to their modifiability and should be further substantiated in future studies.

Language: en

Keywords

offending; self-harm; forensic psychiatry; machine learning; schizophrenia