Violent and non-violent offending in patients with schizophrenia: Exploring influences and differences via machine learning

Objectives: The link between schizophrenia and violent offending has long been the subject of research with significant impact on mental health policy, clinical practice and public perception of the dangerousness of people with psychiatric disorders. The present study attempts to identify factors that differentiate between violent and non-violent offenders based on a unique sample of 370 forensic offender patients with schizophrenia spectrum disorder by employing machine learning algorithms and an extensive set of variables. Methods: Using machine learning algorithms, 519 variables were explored in order to differentiate violent and non-violent offenders. To minimize the risk of overfitting, the dataset was split, employing variable filtering, machine learning model building and selection embedded in a nested resampling approach on one subset. The best model was then selected, and the most important variables applied on the second data subset. Results: Ten factors regarding criminal and psychiatric history as well as clinical, developmental, and social factors were identified to be most influential in differentiating between violent and non-violent offenders and are discussed in light of prior research on this topic. With an AUC of 0.76, a sensitivity of 72% and a specificity of 62%, a correct classification into violent and non-violent offences could be determined in almost three quarters of cases. Conclusions: Our findings expand current research on the factors influencing violent offending in patients with SSD, which is crucial for the development of preventive and therapeutic strategies that could potentially reduce the prevalence of violence in this population. Limitations, clinical relevance and future directions are discussed. (C) 2021 The Author(s). Published by Elsevier Inc

Exploring Characteristics of Homicide Offenders With Schizophrenia Spectrum Disorders Via Machine Learning

The link between schizophrenia and homicide has long been the subject of research with significant impact on mental health policy, clinical practice, and public perception of people with psychiatric disorders. The present study investigates factors contributing to completed homicides committed by offenders diagnosed with schizophrenia referred to a Swiss forensic institution, using machine learning algorithms. Data were collected from 370 inpatients at the Centre for Inpatient Forensic Therapy at the Zurich University Hospital of Psychiatry. A total of 519 variables were explored to differentiate homicidal and other (violent and non-violent) offenders. The dataset was split employing variable filtering, model building, and selection embedded in a nested resampling approach. Ten factors regarding criminal and psychiatric history and clinical factors were identified to be influential in differentiating between homicidal and other offenders. Findings expand the research on influential factors for completed homicide in patients with schizophrenia. Limitations, clinical relevance, and future directions are discussed

Measurement of salivary cortisol in two New World primate species

Funding: R.S. was supported by the Austrian Science Fund (FWF, Young Independent Researcher Group (YIRG) grant; Grant Number ZK 66) and ERC Grant 230604 SOMACCA (to W. Tecumseh Fitch).Glucocorticoids (GCs) are mammalian steroid hormones involved in a variety of physiological processes, including metabolism, the immune response, and cardiovascular functions. Due to their link to the physiological stress response, GC measurement is a valuable tool for conservation and welfare assessment in animal populations. GC levels can be measured from different matrices, such as urine and feces. Moreover, especially in captive settings, measuring GCs from saliva samples proved particularly useful as those samples can be collected non-invasively and easily from trained animals. Salivary GC levels can be measured using a variety of analytical methods, such as enzyme immunoassays. However, it is crucial to validate the analytical method for each specific application and species when using a new matrix. Using high-pressure liquid chromatography and a cortisol enzyme immunoassay, we show that the main glucocorticoids secreted in the saliva of squirrel monkeys and brown capuchin monkeys are cortisol and cortisone. Our biological validation found the expected salivary cortisol level to decline throughout the day. Our findings support the reliability of salivary cortisol measurements and their potential to be used as a valid tool in research and welfare assessment for these non-human primates.Publisher PDFPeer reviewe

Exploring substance use as rule‐violating behaviour during inpatient treatment of offender patients with schizophrenia

Background: Rule-violating behaviour in the form of substance misuse has been studied primarily within the context of prison settings, but not in forensic psychiatric settings. Aims: Our aim was to explore factors that are associated with substance misuse during hospitalisation in patients among those patients in a Swiss forensic psychiatric inpatient unit who were suffering from a disorder along the schizophrenia spectrum. Methods: From a database of demographic, clinical and offending data on all residents at any time between 1982 and 2016 in the forensic psychiatric hospital in Zurich, 364 cases fulfilled diagnostic criteria for schizophrenia or a schizophrenia-like illness and formed our sample. Any confirmed use of alcohol or illicit substances during admission (yes/no) was the dependent variable. Its relationship to all 507 other variables was explored by machine learning. To counteract overfitting, data were divided into training and validation set. The best model from the training set was tested on the validation set. Results: Substance use as a secure hospital inpatient was unusual (15, 14%). Prior substance use disorder accounted for so much of the variance (AUC 0.92) that it was noted but excluded from further models. In the resulting model of best fit, variables related to rule breaking, younger age overall and at onset of schizophrenia and nature of offending behaviour, substance misuse as a minor and having records of complications in prior psychiatric treatment were associated with substance misuse during hospitalisation, as was length of inpatient treatment. In the initial model the AUC was 0.92. Even after removal of substance use disorder from the final model, performance indicators were meaningful with a balanced accuracy of 67.95, an AUC of 0.735, a sensitivity of 81.48% and a specificity of 57.58%. Conclusions: Substance misuse in secure forensic psychiatric hospitals is unusual but worthy of clinical and research consideration because of its association with other rule violations and longer hospitalisation. More knowledge is needed about effective interventions and rehabilitation for this group. Keywords: rule-violations; schizophrenia; substance misus

Different needs in patients with schizophrenia spectrum disorders who behave aggressively towards others depend on gender: a latent class analysis approach

Background There is limited research with inconsistent findings on differences between female and male offender patients with a schizophrenia spectrum disorder (SSD), who behave aggressively towards others. This study aimed to analyse inhomogeneities in the dataset and to explore, if gender can account for those. Methods Latent class analysis was used to analyse a mixed forensic dataset consisting of 31 female and 329 male offender patients with SSD, who were accused or convicted of a criminal offence and were admitted to forensic psychiatric inpatient treatment between 1982 and 2016 in Switzerland. Results Two homogenous subgroups were identified among SSD symptoms and offence characteristics in forensic SSD patients that can be attributed to gender. Despite an overall less severe criminal and medical history, the female-dominated class was more likely to receive longer prison terms, similarly high antipsychotic dosages, and was less likely to benefit from inpatient treatment. Earlier findings were confirmed and extended in terms of socio-demographic variables, diseases and criminal history, comorbidities (including substance use), the types of offences committed in the past and as index offence, accountability assumed in court, punishment adjudicated, antipsychotic treatment received, and the development of symptoms during psychiatric inpatient treatment. Conclusions Female offender patients with schizophrenia might need a more tailored approach in prevention, assessment and treatment to diminish tendencies of inequity shown in this study

Stress - Psychose - Delinquenz - Identifikation von relevanten Stressoren zur Einschätzung der Delinquenzschwere bei Schizophrenie

In Hinblick auf mögliche Entstehungsbedingungen von Kriminalität bei PatientInnen mit Schizophrenie wurde versucht, spezifische Faktoren zu identifizieren, die mit delinquentem Verhalten und der Schwere der Delinquenz in Verbindung gebracht werden können. Dabei sollte der Zusammenhang von psychischer Krankheit und schwerer Delinquenz auf Grundlage von Stress betrachtet werden. Es wurde hierfür vor allem auf die Arbeit von Robert Agnew (1992, 2006, 2007) Bezug genommen, dessen General Strain Theory Faktoren beschreibt, die als förderlich für Delinquenz angenommen werden. Die Stichprobe bestand aus 370 StraftäterInnen mit einer F2.-Diagnose (ICD-10), die zwischen 1982 und 2016 in einer Schweizer forensischen Psychiatrie untergebracht waren. Die Daten zur Demografie, Kindheits- und Jugendvariablen, zur psychiatrischen und kriminellen Vorgeschichte und zum Anlassdelikt wurden retrospektiv aus den Krankenakten zusammengetragen. Die Analyse erfolgte mittels Multipler Imputationsverfahren und Logistischen Regressionsanalysen. Es konnte ein signifikanter Einfluss der Anzahl der Stressoren auf die Wahrscheinlichkeit für die Begehung eines schweren Deliktes festgestellt werden. Weiters konnte ein Regressionsmodell generiert werden, das eine Reihe an Stressoren identifiziert, die als einflussreich auf die Wahrscheinlichkeit für die Begehung eines schweren Deliktes angenommen werden dürfen. Zu diesen Stressoren zählen z.B. Armut, Gewalterfahrungen durch die Bezugsperson und Arbeitslosigkeit. Es ergibt sich das Bild einer Summierung von Stressoren über den Lebensverlauf und eine Teilung der Delinquenz in schwere und leichte Delikte auf Grundlage des individuellen Umgangs mit Stressoren

Factors and predictors of length of stay in offenders diagnosed with schizophrenia - a machine-learning-based approach

Background: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the application of a new statistical methodology better accommodating this data structure. The present study attempts to investigate factors contributing to long-term hospitalization of schizophrenic offenders referred to a Swiss forensic institution, using machine learning algorithms that are better suited than conventional methods to detect nonlinear dependencies between variables. Methods: In this retrospective file and registry study, multidisciplinary notes of 143 schizophrenic offenders were reviewed using a structured protocol on patients' characteristics, criminal and medical history and course of treatment. Via a forward selection procedure, the most influential factors for length of stay were preselected. Machine learning algorithms then identified the most efficient model for predicting length-of-stay. Results: Two factors have been identified as being particularly influential for a prolonged forensic hospital stay, both of which are related to aspects of the index offense, namely (attempted) homicide and the extent of the victim's injury. The results are discussed in light of previous research on this topic. Conclusions: In this study, length of stay was determined by legal considerations, but not by factors that can be influenced therapeutically. Results emphasize that forensic risk assessments should be based on different evaluation criteria and not merely on legal aspects

Escape and absconding among offenders with schizophrenia spectrum disorder – an explorative analysis of characteristics

Background: Escape and absconding, especially in forensic settings, can have serious consequences for patients, staff and institutions. Several characteristics of affected patients could be identified so far, albeit based on heterogeneous patient populations, a limited number of possible factors and basal statistical analyses. The aim of this study was to determine the most important characteristics among a large number of possible variables and to describe the best statistical model using machine learning in a homogeneous group of offender patients with schizophrenia spectrum disorder. Methods: A database of 370 offender patients suffering from schizophrenia spectrum disorder and 507 possible predictor variables was explored by machine learning. To counteract overfitting, the database was divided into training and validation set and a nested validation procedure was used on the training set. The best model was tested on the validation set and the most important variables were extracted. Results: The final model resulted in a balanced accuracy of 71.1% (95% CI = [58.5, 83.1]) and an AUC of 0.75 (95% CI = [0.63, 0.87]). The variables identified as relevant and related to absconding/ escape listed from most important to least important were: more frequent forbidden intake of drugs during current hospitalization, more index offences, higher neuroleptic medication, more frequent rule breaking behavior during current hospitalization, higher PANSS Score at discharge, lower age at admission, more frequent dissocial behavior during current hospitalization, shorter time spent in current hospitalization and higher PANSS Score at admission. Conclusions: For the first time a detailed statistical model could be built for this topic. The results indicate the presence of a particularly problematic subgroup within the group of offenders with schizophrenic spectrum disorder who also tend to escape or abscond. Early identification and tailored treatment of these patients could be of clinical benefit. Keywords: Absconding; Escape; Forensic psychiatry; Machine learning; Offending; Schizophrenia

Individuals with schizophrenia who act violently towards others profit unequally from inpatient treatment—Identifying subgroups by latent class analysis

Background: People with schizophrenia show a higher risk of committing violent offenses. Previous studies indicate that there are at least three subtypes of offenders with schizophrenia. Objectives: Employing latent class analysis, the goals of this study were to investigate the presence of homogeneous subgroups of offender patients in terms of remission in psychopathology during inpatient treatment and whether or not these are related to subtypes found in previous studies. Results should help identify patient subgroups benefitting insufficiently from forensic inpatient treatment and allow hypotheses on possibly more suitable therapy option for these patients. Methods: A series of latent class analyses was used to explore extensive and detailed psychopathological reports of 370 offender patients with schizophrenia before and after inpatient treatment. Results: A framework developed by Hodgins to identify subgroups of offenders suffering from schizophrenia is useful in predicting remission of psychopathology over psychiatric inpatient treatment. While "early starters" were most likely to experience remission of psychopathology over treatment, "late late starters" and a subgroup including patients from all three of Hodgins' subgroups in equal proportions benefited least. Negative symptoms generally seemed least likely to remit. Conclusion: Psychiatric treatment may have to be more tailored to offender patient subgroups to allow them to benefit more equally