Offenders and non-offenders with schizophrenia spectrum disorders: the crime-preventive potential of sufficient embedment in the mental healthcare and support system

Abstract

BackgroundSuffering from schizophrenia spectrum disorder (SSD) has been well-established as a 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 non-offender patients. Previous results point to poorer impulse control, higher levels of excitement, tension, and hostility, and worse overall cognitive functioning in offender populations. This study aimed 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 offenses and SSD.MethodsOur study population consisted of forensic psychiatric patients (FPPs) with a diagnosis of F2x (ICD-10) or 295.x (ICD-9) and a control group of general psychiatric patients (GPPs) with the same diagnosis, totaling 740 patients. Patients were evaluated regarding their medical (and, if applicable, criminal) history and the referenced psychiatric hospitalization. Supervised machine learning (ML) was used to exploratively evaluate predictor variables and their interplay and rank them in accordance with their discriminative power.ResultsOut 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 compulsory measures. Out of the seven algorithms applied, gradient boosting emerged as the most suitable, with an AUC of 0.86 and a balanced accuracy of 77.5%.DiscussionOur 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 highly sophisticated statistical approaches with the possibility of evaluating the interplay of all factors at play.

Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review

“…Most of the included studies utilized only sociodemographic and clinical features of patients to predict VB. Of these studies, five evaluated a large number of features (over 100 features) as predictors ( 39 , 41 , 45 , 49 , 50 ). Tzeng et al.…”

“…Furthermore, Machetanz et al. (2022, 2023) in two separate studies evaluated the differences between offender and non-offender SSD patients regarding psychiatric pr e scription patterns and illness-related factors ( 46 , 49 ). Also, ten studies analyzed the relationship between different rating tools scores and VB in patients with SSD ( 36 , 38 , 39 , 41 , 43 , 45 , 46 , 48 – 50 ), including the Brief Psychiatric Rating Scale (BPRS) ( 38 , 43 , 52 ), the Psychopathy Checklist: Screening Version (PCL-SV), the Historical, Clinical and Risk management (HCR-20) scale ( 38 , 53 ), The Barratt Impulsiveness Scale version 11 (BIS-11) ( 38 , 54 ), the Positive And Negative Symptom Scale (PANSS) ( 36 , 39 , 41 , 43 , 45 , 46 , 48 – 50 , 55 ), the Social Disability Screening Schedule (SDSS) ( 43 ), Insight and Treatment Attitude Questionnaire (ITAQ) ( 47 , 56 ), Family Adaptation, Partnership, Growth, Affection and Resolve (APGAR) ( 47 , 57 ), Social Support Rating Scale (SSRS) ( 47 , 58 ), and Family Burden Scale of Disease (FBS) ( 47 , 59 ).…”

“…Furthermore, only sixteen studies acknowledged the implementation of imputation methods on their respective training set data ( 39 , 41 , 45 , 46 , 49 – 51 ). Imputation methods refers to techniques for estimating or imputing missing values within datasets to enhance overall completeness and analytical suitability ( 66 ).…”

“…Imputation methods refers to techniques for estimating or imputing missing values within datasets to enhance overall completeness and analytical suitability ( 66 ). Notably, 5 studies opted for a common practice wherein missing continuous values were imputed with the mean observed values pertaining to the respective variable, while categorical variables underwent replacement with the mode of observed values ( 39 , 41 , 45 , 46 , 49 , 50 ). However, one study imputed missing continuous variables with either the observed mean or median values, concurrently addressing missing categorical variables based on the mode of observed values ( 51 ).…”

“…Some studies suggest an increased risk of VB associated with positive symptoms ( 35 , 43 ), while others propose a diminishing impact of specific positive symptoms, including delusion of persecution and auditory hallucination, on VB occurrence ( 40 ). Furthermore, various studies reported that daily dosage of prescribed olanzapine-equivalent at the time of discharge from previous psychiatric hospitalization of SSD patients can predict the occurrence of VB among them ( 39 , 45 , 46 , 48 , 49 ). However, their results were divergent, with four studies demonstrating a positive association between the olanzapine-equivalent dosage and risk of VB ( 39 , 46 , 48 , 49 ), and one study reporting a negative association ( 45 ).…”