Depressive characteristics of adult psychiatric inpatients with a history of multiple versus one or no suicide attempts

Characteristics of Single vs. Multiple Suicide Attempters Among Adult Population: A Systematic Review and Meta-Analysis

PURPOSE OF REVIEW

We aimed to examine the factors that differentiate single and multiple suicide attempters in adult population. We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to conduct this review and meta-analysis. The review protocol was registered in PROSPERO. We carried out a systematic literature search in three databases to identify original studies that explored the differences between single and multiple suicide attempters among adult population.

RECENT FINDINGS

There might be meaningful differences between those individuals that attempt suicide once in their lifespan and those who make multiple attempts in terms of sociodemographic and clinical characteristics. There are no previous meta-analysis addressing this topic in the adult population. A total of 75 studies were included in the review and 69 were included in the meta-analysis. Multiple attempters were more likely to present certain disorders such as mood and psychotic disorders, as well as personality or substance use disorders. Higher suicide ideation and suicide intent scores also characterized this group. Childhood trauma experiences, stressful life events, and higher rates of hopelessness were statistically significant in multiple attempters. Identifying the factors predicting multiple suicide attempts helps to delineate a high-risk suicidal profile that should be taken into account in the clinical and suicide prevention scenario.

Predicting the Risk of Future Multiple Suicide Attempt among First-Time Suicide Attempters: Implications for Suicide Prevention Policy

Suicide is listed in the top ten causes of death in Taiwan. Previous studies have pointed out that psychiatric patients having suicide attempts in their history are more likely to attempt suicide again than non-psychiatric patients. Therefore, how to predict the future multiple suicide attempts of psychiatric patients is an important issue of public health. Different from previous studies, we collect the psychiatric patients who have a suicide diagnosis in the National Health Insurance Research Database (NHIRD) as the study cohort. Study variables include psychiatric patients’ characteristics, medical behavior characteristics, physician characteristics, and hospital characteristics. Three machine learning techniques, including decision tree (DT), support vector machine (SVM), and artificial neural network (ANN), are used to develop models for predicting the risk of future multiple suicide attempts. The Adaboost technique is further used to improve prediction performance in model development. The experimental results show that Adaboost+DT performs the best in predicting the behavior of multiple suicide attempts among psychiatric patients. The findings of this study can help clinical staffs to early identify high-risk patients and improve the effectiveness of suicide prevention.