The Structure of Personality Disorders within a Depressed Sample: Implications for Personalizing Treatment

Relationship Pattern of Personality Disorder Traits in Major Psychiatric Disorders: A Cross-Sectional Study

BACKGROUND

Normal personality development, gone awry due to genetic or environmental factors, results in personality disorders (PD). These often coexist with other psychiatric disorders, affecting their outcome adversely. Considering the heterogeneity of data, more research is warranted.

METHODS

This was a cross-sectional study on personality traits in psychiatric patients of a tertiary hospital, over 1 year. Five hundred and twenty-five subjects, aged 18-45 years, with substance, psychotic, mood, or neurotic disorders were selected by convenience sampling. They were evaluated for illness-related variables using psychiatric pro forma; diagnostic confirmation and severity assessment were done using ICD-10 criteria and suitable scales. Personality assessment was done using the International Personality Disorder Examination after achieving remission.

RESULTS

Prevalence of PD traits and PDs was 56.3% and 4.2%, respectively. While mood disorders were the diagnostic group with the highest prevalence of PD traits, it was neurotic disorders for PDs. Patients with PD traits had a past psychiatric history and upper middle socioeconomic status (SES); patients with PDs were urban and unmarried. Both had a lower age of onset of psychiatric illness. Psychotic patients with PD traits had higher and lower PANSS positive and negative scores, respectively. The severity of personality pathology was highest for mixed cluster and among neurotic patients. Clusterwise prevalence was cluster C > B > mixed > A (47.1%, 25.2%, 16.7%, and 11.4%). Among subtypes, anankastic (18.1%) and mixed (16.7%) had the highest prevalence. Those in the cluster A group were the least educated and with lower SES than others.

CONCLUSIONS

PD traits were present among 56.3% of the patients, and they had many significant sociodemographic and illness-related differences from those without PD traits. Cluster C had the highest prevalence. Among patients with psychotic disorders, those with PD traits had higher severity of psychotic symptoms.

Uncovering social-contextual and individual mental health factors associated with violence via computational inference

The identification of human violence determinants has sparked multiple questions from different academic fields. Innovative methodological assessments of the weight and interaction of multiple determinants are still required. Here, we examine multiple features potentially associated with confessed acts of violence in ex-members of illegal armed groups in Colombia (N = 26,349) through deep learning and feature-derived machine learning. We assessed 162 social-contextual and individual mental health potential predictors of historical data regarding consequentialist, appetitive, retaliative, and reactive domains of violence. Deep learning yields high accuracy using the full set of determinants. Progressive feature elimination revealed that contextual factors were more important than individual factors. Combined social network adversities, membership identification, and normalization of violence were among the more accurate social-contextual factors. To a lesser extent the best individual factors were personality traits (borderline, paranoid, and antisocial) and psychiatric symptoms. The results provide a population-based computational classification regarding historical assessments of violence in vulnerable populations.