Antisocial Personality Traits as a Risk Factor of Violence between Individuals with Mental Disorders

Global burden of diseases attributable to intimate partner violence: findings from the Global Burden of Disease Study 2019

PURPOSE

Our study aims to evaluate the global burden of disease attributable to IPV from 1990 to 2019 at global, regional, national, and socio-demographic index (SDI) levels. Our research question is: What is the global burden of disease attributable to intimate partner violence (IPV) from 1990 to 2019, and how does it vary at global, regional, national, and socio-demographic index (SDI) levels?

METHODS

Data parameters for the number of deaths, disability-adjusted life years (DALYs), and age-standardized rate were obtained from the Global Burden of Disease Study 2019. We calculated the percentage change and population attributable fraction with 95% uncertainty intervals.

RESULTS

IPV directly accounted for 0.14% [95% UI 0.09%, 0.21%] and 0.32% [95% UI 0.17%, 0.49%] of global all-cause deaths and DALYs in 2019, respectively. The age-standardized deaths and DALYs rates of IPV increased by 12.83% and 4.00% respectively from 1990 to 2019. Women aged 35-39 and 30-34 had the highest deaths and DALYs rate respectively. The highest age-standardized rates of IPV-related deaths and DALYs were observed in Southern Sub-Saharan. Both of deaths and DALYs were high in low-socio-demographic Index (SDI) quintile in 2019.

CONCLUSIONS

A higher level of deaths and DALYs attributable to IPV were reported in younger women, in the early 2000s, in Southern Sub-Saharan regions and in low SDI regions. Our study provides policymakers with up-to-date and comprehensive information.

The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms

BACKGROUND

Early to identify male schizophrenia patients with violence is important for the performance of targeted measures and closer monitoring, but it is difficult to use conventional risk factors. This study is aimed to employ machine learning (ML) algorithms combined with routine data to predict violent behavior among male schizophrenia patients. Moreover, the identified best model might be utilized to calculate the probability of an individual committing violence.

METHOD

We enrolled a total of 397 male schizophrenia patients and randomly stratified them into the training set and the testing set, in a 7:3 ratio. We used eight ML algorithms to develop the predictive models. The main variables as input features selected by the least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were integrated into prediction models for violence among male schizophrenia patients. In the training set, 10 × 10-fold cross-validation was conducted to adjust the parameters. In the testing set, we evaluated and compared the predictive performance of eight ML algorithms in terms of area under the curve (AUC) for the receiver operating characteristic curve.

RESULT

Our results showed the prevalence of violence among male schizophrenia patients was 36.8%. The LASSO and LR identified main risk factors for violent behavior in patients with schizophrenia integrated into the predictive models, including lower education level [0.556 (0.378-0.816)], having cigarette smoking [2.121 (1.191-3.779)], higher positive syndrome [1.016 (1.002-1.031)] and higher social disability screening schedule (SDSS) [1.081 (1.026-1.139)]. The Neural Net (nnet) with an AUC of 0.6673 (0.5599-0.7748) had better prediction ability than that of other algorithms.

CONCLUSION

ML algorithms are useful in early identifying male schizophrenia patients with violence and helping clinicians take preventive measures.

Overcoming the Use of Mechanical Restraints in Psychiatry: A New Challenge in the Everyday Clinical Practice at the Time of COVID-19

Restraining interventions, which comprise physical (PR) and mechanical restraint (MR), have a long history in mental health services [...].

Oxytocin receptor gene, childhood maltreatment and borderline personality disorder features among male inmates in China

BACKGROUND

Borderline personality disorder (BPD) is caused by a variety of biological and environmental factors. Accumulating evidence suggests that childhood maltreatment is a risk environmental factor in the development of BPD, but research on the genetic pathology of BPD is still in its early stages, and very little is known about the oxytocin receptor (OXTR) gene. The purpose of this study is to further explore the interactive effects between OXTR gene polymorphisms and childhood maltreatment on BPD risk.

METHODS

Among the 1804 Chinese Han male inmates, 765 inmates who had BPD or antisocial personality disorder (ASPD) or highly impulsive or violent crime were considered as high-risk inmates and included in this study. Childhood maltreatment, BPD, antisocial personality disorder (ASPD) and impulsivity were measured by self-reported questionnaires. Peripheral venous blood was collected for the genotype test.

RESULTS

Analyses revealed that the BP group (inmates with BPD features) had higher rs53576 AA genotype frequency and rs237987 AA genotype frequency than the non-BP group, while the statistical significances were lost after Bonferroni correction. Total childhood maltreatment score, emotional abuse and neglect could positively predict BPD risk. Among the high-risk samples, rs53576 GG genotype carriers had higher BPD scores at higher levels of physical abuse and sexual abuse and had lower BPD scores at lower levels of physical abuse and sexual abuse.

CONCLUSIONS

The findings suggest that the interaction between OXTR gene variations and childhood maltreatment is an important mechanism for the development of BPD. The moderating role of the OXTR gene provides evidence for gene plasticity.