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

Self-harming behaviors and forensic system-related factors: an analysis of the Ontario review board database

BACKGROUND

In Canada, ensuring public safety, and the safety and well-being of accused individuals under the jurisdiction of the provincial review board are very important. While previous studies have reported a significant risk of self-harming behaviors (non-suicidal self-injury and suicide attempt) in forensic psychiatric settings, no large population study has assessed any relationship between forensic system-related factors and self-harming behaviors. A better understanding of these factors can help clinicians implement protective measures to mitigate self-harming behaviors or actions.

METHODS

Using the Ontario Review Board (ORB) database covering 2014-2015 period (n = 1211, mean age = 42.5 ± 13.37 years, males = 86.1%), we analyzed the prevalence and factors associated with self-harming behaviors, emphasizing the characterization of the forensic system-related factors (ORB status, legal status, type of offense, previous criminal history, and victim relationship). The relationships between the forensic system-related factors and self-harming behaviors were explored using five separate logistic regression models, controlling for clinical and sociodemographic characteristics.

RESULTS

Approximately 4% of the individuals in the forensic system over the study period engaged in self-harming behaviors Among the studied patients, individuals determined to be unfit to stand trial and inpatients were significantly more likely to have self-harming behaviors. There was no significant relationship between the type of offence, victim relationship, and previous criminal history with self-harming behavior.

CONCLUSION

Forensic psychiatry inpatients should have close observation, screening, monitoring, and individual tailored management strategies for self-harming behaviors. The findings of this study indicate that forensic system-related factors, especially those that pertain to the status of individuals in the forensic system (i.e., unfit to stand trial and being an inpatient) are more responsible for self-harming behaviors among forensic patients in Ontario.

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

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.

Self-mutilation in people with psychiatric diagnosis - literature review

Introduction: Self-mutilation is of an auto-aggressive nature. They take many forms. Usually they do not occur as an isolated phenomenon. Self-injuring persons often have a diagnosed mental disorder, and a psychiatric diagnosis is one of the risk factors for the occurrence of self-harm. Material and method: In this study, articles were analyzed that contained information on the occurrence of various mental disorders. These articles searched for information on the occurrence of self-mutilation, its type, origin and causes.  Discussion: Self-mutilation is common in patients with a psychiatric diagnosis. The frequency of such behaviors can range from 33% to 50% in people with autism, up to 22%, and even 49% in people diagnosed with psychotic disorders. Acts of selfharm may affect 50-78% of people diagnosed with border-line personality disorder. In adolescents with eating disorders, 41% of patients report self-harm. In the group of people diagnosed with bipolar disorder, it may be as high as 77.3%. People with depression are also a large self-injuring group. It is reported that it may be up to 71.2% of patients with unipolar disorder. Conclusions: Self-mutilation is a phenomenon that is more and more topical and common not only in the adolescent population. This is a multi-factorial issue. One of the risk factors are mental disorders. Self-mutineers choose various methods, and this choice depends, among others, on from recognition. The size of the phenomenon and its evolution make it necessary to study it in order to increase the level of knowledge, update information and, consequently, the ability to help people at risk of NSSI.  Keywords: self-mutilation, autism spectrum disorder, schizophrenia, bipolar disorder, personality disorders

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

Today’s extensive availability of medical data enables the development of predictive models, but this requires suitable statistical methods, such as machine learning (ML). Especially in forensic psychiatry, a complex and cost-intensive field with risk assessments and predictions of treatment outcomes as central tasks, there is a need for such predictive tools, for example, to anticipate complex treatment courses and to be able to offer appropriate therapy on an individualized basis. This study aimed to develop a first basic model for the anticipation of adverse treatment courses based on prior compulsory admission and/or conviction as simple and easily objectifiable parameters in offender patients with a schizophrenia spectrum disorder (SSD). With a balanced accuracy of 67% and an AUC of 0.72, gradient boosting proved to be the optimal ML algorithm. Antisocial behavior, physical violence against staff, rule breaking, hyperactivity, delusions of grandeur, fewer feelings of guilt, the need for compulsory isolation, cannabis abuse/dependence, a higher dose of antipsychotics (measured by the olanzapine half-life) and an unfavorable legal prognosis emerged as the ten most influential variables out of a dataset with 209 parameters. Our findings could demonstrate an example of the use of ML in the development of an easy-to-use predictive model based on few objectifiable factors.