Offenders and non-offenders with schizophrenia spectrum disorders: the crime-preventive potential of sufficient embedment in the mental healthcare and support system - Machetanz, Lena; Hofmann, Andreas B.; Möhrke, Jan; Kirchebner, Johannes (Frontiers in Psychiatry, 2023)
Suffering from a schizophrenia spectrum disorder (SSD) has been well established as risk factor for offending. However, the majority of patients with an SSD do not show aggressive or criminal behavior. Yet, there is little research on clinical key features distinguishing offender from nonoffender patients. Previous results point to poorer impulse control, higher levels of excitement, tension and hostility, and worse overall cognitive functioning in offender populations. Our objective was to detect the most indicative distinguishing clinical features between forensic and general psychiatric patients with SSD based on the course of illness and the referenced hospitalization in order to facilitate a better understanding of the relationship between violent and non-violent offending and SSD. Our study population consisted of forensic psychiatric patients (FPP) with a diagnosis of F2x (ICD-10) or 295.x (ICD-9) and a control group of general psychiatric patients (GPP) with the same diagnosis, totaling to 740 patients. Patients were evaluated regarding their medical (and, if applicable, criminal) history and the referenced psychiatric hospitalization. Supervised machine learning was used to exploratively evaluate predictor variables and their interplay and rank them in accordance with their discriminative power. Out of 194 possible predictor variables, the following 6 turned out to have the highest influence on the model: olanzapine equivalent at discharge from the referenced hospitalization, a history of antipsychotic prescription, a history of antidepressant, benzodiazepine or mood stabilizer prescription, medication compliance, outpatient treatment(s) in the past, and the necessity of Offenders and non-offenders with schizophrenia spectrum disorders: Identifying clinical key differences through application of supervised machine learning compulsory measures. Out of the 7 algorithms applied, gradient boosting emerged as most suitable with an AUC of 0.86 and a balanced accuracy of 77.5%. Our study aimed to identify the most influential illness-related predictors distinguishing between FPP and GPP with SSD, thus shedding light on key differences between the two groups. To our knowledge, this is the first study to compare a homogenous sample of FPP and GPP with SSD regarding their symptom severity and course of illness using sophisticated statistical approaches with the possibility of evaluating the interplay of all factors at play.