ArticleFrontiers in psychiatry2023
Offenders and non-offenders with schizophrenia spectrum disorders: Do they really differ in known risk factors for aggression?
Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Forensic mental health service use in early psychosis: A scoping review.Social psychiatry and psychiatric epidemiology · 2026Review
- Delusional themes and forensic status in schizophrenia spectrum disorders: a machine learning based discriminative study.Frontiers in psychology · 2026Article
- Differences between criminal offender versus non-offender female patients with schizophrenia spectrum disorder: a retrospective cohort study.Archives of women's mental health · 2025Article
- Determinants of compulsory admission in detainees with acute psychiatric symptoms in the French speaking counties of Switzerland.BMC psychiatry · 2025Article
- Construction of a troublemaking risk assessment tool for patients with severe mental disorders in community of China.Scientific reports · 2025Article
- Sociodemographic Variables in Offender and Non-Offender Patients Diagnosed with Schizophrenia Spectrum Disorders-An Explorative Analysis Using Machine Learning.Healthcare (Basel, Switzerland) · 2024Article
- Offenders and non-offenders with schizophrenia spectrum disorders: the crime-preventive potential of sufficient embedment in the mental healthcare and support system.Frontiers in psychiatry · 2023Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: Individuals with schizophrenia spectrum disorders (SSD) have an elevated risk for aggressive behavior, and several factors contributing to this risk have been identified, e. g. comorbid substance use disorders. From this knowledge, it could be inferred that offender patients show a higher expression of said risk factors than non-offender patients. Yet, there is a lack of comparative studies between those two groups, and findings gathered from one of the two are not directly applicable to the other due to numerous structural differences. The aim of this study therefore was to identify key differences in offender patients and non-offender patients regarding aggressive behavior through application of supervised machine learning, and to quantify the performance of the model. Methods: For this purpose, we applied seven different (ML) algorithms on a dataset comprising 370 offender patients and a comparison group of 370 non-offender patients, both with a schizophrenia spectrum disorder. Results: With a balanced accuracy of 79.9%, an AUC of 0.87, a sensitivity of 77.3% and a specificity of 82.5%, gradient boosting emerged as best performing model and was able to correctly identify offender patients in over 4/5 the cases. Out of 69 possible predictor variables, the following emerged as the ones with the most indicative power in distinguishing between the two groups: olanzapine equivalent dose at the time of discharge from the referenced hospitalization, failures during temporary leave, being born outside of Switzerland, lack of compulsory school graduation, out- and inpatient treatment(s) prior to the referenced hospitalization, physical or neurological illness as well as medication compliance. Discussion: Interestingly, both factors related to psychopathology and to the frequency and expression of aggression itself did not yield a high indicative power in the interplay of variables, thus suggesting that while they individually contribute to aggression as a negative outcome, they are compensable through certain interventions. The findings contribute to our understanding of differences between offenders and non-offenders with SSD, showing that previously described risk factors of aggression may be counteracted through sufficient treatment and integration in the mental health care system.
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