One-year incidence, predictors, and accuracy of prediction of suicidal thoughts and behaviors from the first to second year of university

Hippocampus, amygdala, and insula activation in response to romantic relationship dissolution stimuli: A case-case-control fMRI study on emerging adult students

BACKGROUND

Romantic relationship dissolutions (RRDs) are associated with posttraumatic stress symptoms (PTSS). Functional magnetic resonance imaging in RRD studies indicate overlapping neural activation similar to posttraumatic stress disorder. These studies combine real and hypothetical rejection, and lack contextual information and control and/or comparison groups exposed to non-RRD or DSM-5 defined traumatic events.

AIM

We investigated blood oxygen level dependent (BOLD) activation in the hippocampus, amygdala, and insula of participants with RRDs compared with other traumatic or non-trauma stressors.

METHODS

Emerging adults (mean age = 21.54 years; female = 74.7 %) who experienced an RRD (n = 36), DSM-5 defined trauma (physical and/or sexual assault: n = 15), or a non-RRD or DSM-5 stressor (n = 28) completed PTSS, depression, childhood trauma, lifetime trauma exposure, and attachment measures. We used a general and customised version of the International Affective Picture System to investigate responses to index-trauma-related stimuli. We used mixed linear models to assess between-group differences, and ANOVAs and Spearman's correlations to analyse factors associated with BOLD activation.

RESULTS

BOLD activity increased between index-trauma stimuli as compared to neutral stimuli in the hippocampus and amygdala, with no significant difference between the DSM-5 Trauma and RRD groups. Childhood adversity, sexual orientation, and attachment style were associated with BOLD activation changes. Breakup characteristics (e.g., initiator status) were associated with increased BOLD activation in the hippocampus and amygdala, in the RRD group.

CONCLUSION

RRDs should be considered as potentially traumatic events. Breakup characteristics are risk factors for experiencing RRDs as traumatic.

LIMITATION

Future studies should consider more diverse representation across sex, ethnicity, and sexual orientation.

Internet Gaming Disorder and the Incidence of Suicide-Related Ideation and Behaviors in College Students

OBJECTIVES

The longitudinal associations between DSM-5 Internet Gaming Disorder (IGD) and suicide-related ideation and behaviors have not been explored. In this study, we therefore seek to examine the association between baseline IGD and incident suicide ideation, plans, and attempts.

METHODS

This is a prospective cohort study of 2586 Mexican college students followed up from September 2018 to June 2022. We estimated hazards ratios modeling incidence of suicide ideation, plans, and attempts by fitting proportional hazards Cox models with person-time scaled in years.

RESULTS

Among 2140 students without suicide ideation at baseline, there were 467 incident cases in 3987.6 person-years; ideation incidence rates were 179 cases per 1000 person-years among students with IGD and 114 cases per 1000 person-years among those without IGD. Incidence rates for suicide plans were 67 and 39 per 1000 among IGD and non-IGD students, and 15 and 10 per 1000, respectively for attempts. After controlling for age, sex, and mood, anxiety, and substance use disorders, IGD was associated with an 83% increased risk of suicide ideation. Although incidence rate estimates for plans and attempts were higher among students with IGD, results were not statistically significant.

CONCLUSIONS

This study helps to raise awareness of the increased risk of at least suicidal ideation in people experiencing IGD. Clinicians treating patients with IGD may encounter complaints of suicide ideation over time, and even reports of suicidal behavior that should not be disregarded. Identifying these patients and treating/referring them for underlying suicidality should form part of IGD treatment.

Predictive model of suicide risk in Colombian university students: quantitative analysis of associated factors

Introduction

The risk of suicide and completed suicides among young university students presents critical challenges to mental and public health in Colombia and worldwide. Employing a quantifiable approach to comprehend the factors associated with these challenges can aid in visualizing the path towards anticipating and controlling this phenomenon.

Objective

Develop a predictive model for suicidal behavior in university students, utilizing predictive analytics.

Method

We conducted an observational, retrospective, cross-sectional, and analytical research study at the University of Manizales, with a focus on predictive applicability. Data from 2,436 undergraduate students were obtained from the research initiative "Building the Future: World Mental Health Surveys International College Students."

Results

The top ten predictor variables that generated the highest scores (ranking coefficients) for the sum of factors were as follows: history of sexual abuse (13.21), family history of suicide (11.68), medication (8.39), type of student (7.4), origin other than Manizales (5.86), exposure to cannabis (4.27), exposure to alcohol (4.42), history of physical abuse (3.53), religiosity (2.9), and having someone in the family who makes you feel important (3.09).

Discussion

Suicide involves complex factors within psychiatric, medical, and societal contexts. Integrated detection and intervention systems involving individuals, families, and governments are crucial for addressing these factors. Universities also play a role in promoting coping strategies and raising awareness of risks. The predictive accuracy of over 80% in identifying suicide risk underscores its significance.

Conclusion

The risk factors related to suicidal behavior align with the findings in specialized literature and research in the field. Identifying variables with higher predictive value enables us to take appropriate actions for detecting cases and designing and implementing prevention strategies.

Developing a risk prediction model for death at first suicide attempt-Identifying risk factors from Thailand's national suicide surveillance system data

More than 60% of suicides globally are estimated to take place in low- and middle-income nations. Prior research on suicide has indicated that over 50% of those who die by suicide do so on their first attempt. Nevertheless, there is a dearth of knowledge on the attributes of individuals who die on their first attempt and the factors that can predict mortality on the first attempt in these regions. The objective of this study was to create an individual-level risk-prediction model for mortality on the first suicide attempt. We analyzed records of individuals' first suicide attempts that occurred between May 1, 2017, and April 30, 2018, from the national suicide surveillance system, which includes all of the provinces of Thailand. Subsequently, a risk-prediction model for mortality on the first suicide attempt was constructed utilizing multivariable logistic regression and presented through a web-based application. The model's performance was assessed by calculating the area under the receiver operating curve (AUC), as well as measuring its sensitivity, specificity, and accuracy. Out of the 3,324 individuals who made their first suicide attempt, 50.5% of them died as a result of that effort. Nine out of the 21 potential predictors demonstrated the greatest predictive capability. These included male sex, age over 50 years old, unemployment, having a depressive disorder, having a psychotic illness, experiencing interpersonal problems such as being aggressively criticized or desiring plentiful attention, having suicidal intent, and displaying suicidal warning signals. The model demonstrated a good predictive capability, with an AUC of 0.902, a sensitivity of 84.65%, a specificity of 82.66%, and an accuracy of 83.63%. The implementation of this predictive model can assist physicians in conducting comprehensive evaluations of suicide risk in clinical settings and devising treatment plans for preventive intervention.

Suicide Risk among University Students in Spain: Implications for Social Work

Suicide is a phenomenon that affects university students all over the world, and although vulnerability has been revealed in universities, there are still few studies that include large populations, a large number of universities and students pursuing different degrees. The study presented here aims to detect the risk of suicide in Spanish university students pursuing different areas of study. A total of 2,025 students from 16 Spanish universities and 17 degree programs completed an online questionnaire assessing support and suicide risk factors. The results indicate that 29.2 percent of the university students had experienced suicidal ideation in their lifetime. Logistic regression analysis showed that this risk was associated with depressive symptomatology and having suffered sexual violence. In contrast, self-esteem, life satisfaction, and perceived support were shown to be protective factors. Suicide risk affects one in three students. The present study includes particular implications for decision makers in the university community and other related governmental bodies, as well as for social work.

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

Psychotic disorder diseases (PDD) or mental illnesses are group of illnesses that affect the minds and impair the cognitive ability, retard emotional ability and obstruct the process of communication and relationship with others and are characterized by delusions, hallucinations and disoriented or disordered pattern of thinking. Prognosis of PDD is not sufficient because of the nature of the diseases and as such adequate form of diagnosis is required to detect, manage and treat the illness. This paper applied the single-label classification (SLC) machine learning approach in mining of electronic health records of people with PDD in Nigeria using eleven independent (demographic) variables and five PDD as target variables. The five PDDs are Insomnia, Schizophrenia, Minimal Brain dysfunction (MBD), which is also known as Attention-Deficit/Hyperactivity Disorder (ADHD), Vascular Dementia (VD) and Bipolar Disorder (BD). The aim of using SLC is that it would be easier to detect some PDDs that are related to each other without the loss of information, which is a plus over multi-label classification (MLC). ReliefF algorithm was used at each experiment to precipitate the order of importance of the independent variables and redundant variables were excluded from the analysis. The order of the variables in feature selection was matched with feature importance after the classifications and quantified using the Spearman rank correlation coefficient. The data was divided into: 70% for training and 30% for testing. Four new performance metrics adapted from the root mean square (RMSE) were proposed and used to measure the differences between the performance results of the 10 Machine learning models in terms of the training and testing and secondly, feature and without feature selection. The new metrics are close to zero which is an indication that the use of feature selection and cross validation may not greatly affects the accuracy of the SLC. When the PDDs are included as predictors for classifying others, there was a tremendous improvement as revealed by the four new metrics for classification accuracy (CA), precision and recall. Analysis of variance showed the four different metrics differs significantly for classification accuracy (CA) and precision. However, there were no significant difference between the CA and precision when the duo are compared together across the four evaluation metrics at p value less than 0.05.