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

Abstract

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.

Maintaining social capital in offenders with schizophrenia spectrum disorder—An explorative analysis of influential factors

“…Social capital was defined as having at least one of the following social contacts: family ties, spousal relationships, a circle of friends of more than one person, and membership in a club. The dataset has already been evaluated in other studies as part of a larger, ongoing project with the goal of 10.3389/fpsyt.2022.945732 gaining further knowledge about the complex field of offender patients with SSD (7,9,(26)(27)(28)(29)(30)(31)(32)(33)(34)(35). Although the same database provides the basis for several analyses covering a wide range of objectives in this research area, and although there are a few overlapping parameters, it still contains a substantial number of unique variables, thus resulting in different theoretical and practical conclusions and implications.…”

Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD

“…The dataset has already been evaluated in other studies as part of a larger, ongoing project with the goal of providing further knowledge about the complex field of offender patients with SSD [ 6 , 8 , 17 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. Whereas the aforementioned studies by the authors have so far aimed to exploratively examine the complex interplay of various influential factors in different phenomena to provide a better understanding of them and have tested other outcome variables (e.g., self-harm), the present work is a first approach to develop a clinical model for the prediction of certain events during the hospitalization in question.…”

“…The justice system and, with it, the treatment of offender patients is financed through taxation; therefore, decision on the allocation of resources can be politically and socially controversial associety’s need for security is often set against the desire for a risk management that is as cost-efficient as possible [ 15 , 16 ]. Although it seems obvious that patients with SSD and a history of violent or disorderly behavior are at high risk for recurrence of such behavior, the course of inpatient court-mandated therapy is quite heterogeneous within the patient population: Adverse treatment events, such as escape and absconding, physical aggression, self-harm or substance use only occur in a small percentage of all patients [ 6 , 17 , 18 , 19 ]. A scattershot approach in resource allocation is therefore inefficient at best and negligent at worst, because already-scarce resources are spread too thin, and caregivers may not be able to uphold the necessary treatment standards.…”

Self-Harm Among Forensic Psychiatric Inpatients With Schizophrenia Spectrum Disorders: An Explorative Analysis

“…Parts of the following section were published in Kirchebner et al (2021) and are partially adopted here and extended to include the methodology of the current research question. Given the explorative nature of this study, supervised ML appeared to be the optimal method to identify the most important influencing factors of a wide range variables and to determine the model providing the best predictive power.…”

“…In the subfield of forensic psychiatry, its application is still rare. Nevertheless, ML represents a promising opportunity to gain new insights in this field, where basic knowledge is sparse but large datasets may already exist (Kirchebner et al, 2021).…”