Cognitive and clinical characteristics of offenders and non-offenders diagnosed with schizophrenia spectrum disorders: results of the Recoviwel observational study

Barlati, Stefano; Nibbio, Gabriele; Stanga, Valentina; Giovannoli, Giulia; Calzavara‐Pinton, Irene; Necchini, Nicola; Lisoni, Jacopo; Deste, Giacomo; Vita, Antonio

doi:10.1007/s00406-022-01510-9

Cited by 14 publications

(6 citation statements)

References 65 publications

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“…NOP also showed a higher prevalence of global cognitive deficits and scored higher on the PANSS-item "lack of spontaneity and flow of speech", which seems likely to be interlinked. This was surprising, as previous research comparing OP and NOP with SSD found the former to have worse overall cognitive functioning [43]. However, it has been hypothesised that institutionalised OP may participate in cognitive tests with greater engagement to demonstrate motivation, resulting in better scores when compared to their controls [44].…”

Section: Discussionmentioning

confidence: 93%

“…Another striking finding was that, while individual symptom domains such as anxiety and cognitive deficits were of great influence in the model, overall clinical impairment as measured by the total PANSS score was not. The few studies directly comparing offender and non-offender patients with SSD reported the former to be more clinically impaired, which seems logical, as court-mandated treatment is mostly intended for highly severe courses of the disorder [20,43,55]. A potential explanation for this contradictive result could be in the selection of our comparison group: As described in the methodology section, we chose patients within a rehabilitative psychiatric institution due to their similar rate of chronically ill patients.…”

Section: Discussionmentioning

confidence: 99%

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Suicidal Offenders and Non-Offenders with Schizophrenia Spectrum Disorders: A Retrospective Evaluation of Distinguishing Factors Using Machine Learning

et al. 2023

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Patients with schizophrenia spectrum disorders (SSD) have an elevated risk of suicidality. The same has been found for people within the penitentiary system, suggesting a cumulative effect for offender patients suffering from SSD. While there appear to be overlapping characteristics, there is little research on factors distinguishing between offenders and non-offenders with SSD regarding suicidality. Our study therefore aimed at evaluating distinguishing such factors through the application of supervised machine learning (ML) algorithms on a dataset of 232 offenders and 167 non-offender patients with SSD and history of suicidality. With an AUC of 0.81, Naïve Bayes outperformed all other ML algorithms. The following factors emerged as most powerful in their interplay in distinguishing between offender and non-offender patients with a history of suicidality: Prior outpatient psychiatric treatment, regular intake of antipsychotic medication, global cognitive deficit, a prescription of antidepressants during the referenced hospitalisation and higher levels of anxiety and a lack of spontaneity and flow of conversation measured by an adapted positive and negative syndrome scale (PANSS). Interestingly, neither aggression nor overall psychopathology emerged as distinguishers between the two groups. The present findings contribute to a better understanding of suicidality in offender and non-offender patients with SSD and their differing characteristics.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Suicidal Offenders and Non-Offenders with Schizophrenia Spectrum Disorders: A Retrospective Evaluation of Distinguishing Factors Using Machine Learning

et al. 2023

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“…Better understanding of the underlying causes of violent and offending behaviour is imperative for devising effective preventative and therapeutical interventions. Research comparing individuals with SSD who are offenders to those who are not is limited, and is mostly conducted in populations with mixed diagnoses and small case numbers ( 13 , 14 ). In this study we compare patients with SSD and comorbid SUD in a forensic setting (offender patients) and a general psychiatric setting (non-offender patients).…”

Section: Introductionmentioning

confidence: 99%

When do drugs trigger criminal behavior? a machine learning analysis of offenders and non-offenders with schizophrenia and comorbid substance use disorder

Bender,

Machetanz,

von Känel

et al. 2024

Front. Psychiatry

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IntroductionComorbid substance use disorder (SUD) is linked to a higher risk of violence in patients with schizophrenia spectrum disorder (SSD). The objective of this study is to explore the most distinguishing factors between offending and non-offending patients diagnosed with SSD and comorbid SUD using supervised machine learning.MethodsA total of 269 offender patients and 184 non-offender patients, all diagnosed with SSD and SUD, were assessed using supervised machine learning algorithms.ResultsFailures during opening, referring to rule violations during a permitted temporary leave from an inpatient ward or during the opening of an otherwise closed ward, was found to be the most influential distinguishing factor, closely followed by non-compliance with medication (in the psychiatric history). Following in succession were social isolation in the past, no antipsychotics prescribed (in the psychiatric history), and no outpatient psychiatric treatments before the current hospitalization.DiscussionThis research identifies critical factors distinguishing offending patients from non-offending patients with SSD and SUD. Among various risk factors considered in prior research, this study emphasizes treatment-related differences between the groups, indicating the potential for improvement regarding access and maintenance of treatment in this particular population. Further research is warranted to explore the relationship between social isolation and delinquency in this patient population.

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“…Those who did often times had a limited number of cases, thus having an increased risk of being subjected to a type II error ( 25 – 28 ). To date, poorer impulse control; higher levels of excitement, tension, and hostility; and worse overall cognitive functioning appear to draw a dividing line between offenders and non-offenders with SSD ( 20 , 25 , 26 ). This, however, leads to another issue: all of these factors are complex, multifactorial, and oftentimes interdependent and have yet to be comprehensively understood.…”

Section: Introductionmentioning

confidence: 99%

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

Machetanz,

Hofmann,

Möhrke

et al. 2023

Front. Psychiatry

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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.

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Cognitive and clinical characteristics of offenders and non-offenders diagnosed with schizophrenia spectrum disorders: results of the Recoviwel observational study

Cited by 14 publications

References 65 publications

Suicidal Offenders and Non-Offenders with Schizophrenia Spectrum Disorders: A Retrospective Evaluation of Distinguishing Factors Using Machine Learning

Suicidal Offenders and Non-Offenders with Schizophrenia Spectrum Disorders: A Retrospective Evaluation of Distinguishing Factors Using Machine Learning

When do drugs trigger criminal behavior? a machine learning analysis of offenders and non-offenders with schizophrenia and comorbid substance use disorder

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

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