Intimate Partner Violence Among Patients Diagnosed With Severe Mental Disorder

Prevalence and risk of sexual violence victimization among mental health service users: a systematic review and meta-analyses

PURPOSE

People with mental disorders are more likely to experience sexual violence than the general population, but little is known about the prevalence of sexual violence in people who use psychiatric services. This paper aims to estimate the prevalence and odds of sexual violence victimisation within mental health services by gender and mental health setting (i.e. inpatient, outpatient and mixed settings).

METHODS

This study is a systematic review and meta-analysis (PROSPERO registration number: CRD4201810019). Three databases (Medline, Embase, PsychINFO) were searched and citation tracking, and reference screening of included studies was conducted. Studies were included if the prevalence and/or risk of sexual violence in psychiatric service users were reported or calculable across the past year or adult lifetime. The methodological quality of included studies was assessed. A random effects meta-analyses was conducted to estimate odds ratios and pooled prevalence estimates of sexual violence in different mental health settings.

RESULTS

Twenty-six studies were included encompassing 197,194 participants. The meta-analyses revealed high pooled prevalence estimates and increased odds of sexual violence victimisation in male and female psychiatric service users compared to non-psychiatric service users.

CONCLUSIONS

Mental health practitioners should be trained to respond effectively to disclosures of sexual violence, particularly from these vulnerable groups. Future sexual violence interventions should consider mental health as a treatment outcome.

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

Background

Schizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.

Methods

We performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.

Results

We included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.

Conclusion

ML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field.

[Prevalence of adverse and victimization experiences in adult people with severe mental illness in Barcelona]

OBJECTIVE

To describe the extension of the adverse experiences, including victimization, lived in childhood and adulthood in a sample of people with severe mental illness residing in Barcelona, Spain.

METHOD

Descriptive, quantitative and cross-sectional study. Adverse and victimization experiences were assessed using 26 items from the Adverse Childhood Experiences Questionnaire (ACE-IQ). Items were included obtain information regarding victimization during adulthood. Seventy-four people with severe mental illness were interviewed (median age: 42.03 years; standard deviation: 9.60).

RESULTS

All participants reported at least two victimization experiences throughout their lifetime. The most frequent experiences were conventional crimes (87.8%) and victimization by caregivers (86.5%). Before age 18, half of the participants reported having experienced physical violence by their caregivers (52.7%) and almost one out of three reported to have been victim of sexual abuse (32.4%). Women were almost 13 times more likely to experience sexual victimization than men throughout their lifetime (odds ratio: 12.75; 95% confidence interval: 4.19-38.71).

CONCLUSIONS

Victimization experienced by people with severe mental illness is a widespread problem that has received little attention. The results of this study are consistent with those obtained in previous investigations and underscore the need to translate this knowledge into medical practice into medical practice, considering them in order to develop treatments with a comprehensive approach to mental health.