Predicting Suicidal Ideation in College Students with Mental Health Screening Questionnaires

Development and validation of a predictive model for suicidal thoughts and behaviors among freshmen

BACKGROUND

There are fewer studies on prospective predictors of first-time suicidal thoughts and behaviors (STB) among first-year university students and fewer studies prospectively identifying and screening for those at high risk of suicide among college students. This study assessed the impact of prospective baseline variables on the risk of new STB onset among first-year university students over two years and developed a multivariate risk prediction model.

METHODS

4,560 first-year university students (38.4% males, mean age:18.34) from China participated and completed this prospective cohort study over a three-year period from 2018 to 2020. LASSO regression, and logistic regression models under resilient networks, were used for risk predictor variable screening and final prediction model building. Independent validation sets were used for external validation of the models. Independent validation sets were used for external validation of the models. Area Under the Curve (AUC), accuracy, F1 scores, and Hosmer-Lemeshow test metrics were used to evaluate the model performance.

RESULTS

The incidence rates of suicidal thoughts, suicidal behaviors, and STB within two years were 4.89%,1.03%, and 4.96%, respectively. Predictors in the final model included females, always solo activity, bigotry under pressure, socially oriented perfectionism, drinking to relieve stress, autonomy attitude, poorer parental marriage satisfaction, maternal emotional warmth, perceived others social support, and number of lifetime severe traumatic events. The predictive model had an AUC of 0.738 (95% CI: 0.697-0.780) for predictive accuracy in the training dataset as well as 0.710 (95% CI: 0.657-0.763) for predictive accuracy in the validation dataset, which represents a high degree of model discrimination.

CONCLUSION

Based on this predictive model of suicidal thoughts and behaviors, this study may help to assess and screen college students at risk for STB and develop suicide prevention strategies for at-risk populations.

The effect of social media use on suicidal ideation in college students: Mediation by daytime sleepiness and sleep quality

BACKGROUND

Social media use and daytime sleepiness are common among university students and have significant implications for their mental health. The aim of this study was to examine the effects of social media use on suicidal ideation among university students and to analyse the mediating effects of daytime sleepiness and sleep quality.

METHODS

A total of 5899 full-time undergraduate students were included in this study. Questionnaires were distributed and collected using the QuestionStar platform. Logistic regression analysis was used to examine the association between social media use, daytime sleepiness and sleep quality, and suicidal ideation among college students. The mediation model was tested using the bias-corrected percentile bootstrap method.

RESULTS

Among 4835 students, 612 (12.66 %) reported having had suicidal ideation. Regression analysis revealed that social media use (OR = 1.09, 1.05-1.12), daytime sleepiness (OR = 1.09, 1.06-1.11), general and poor sleep quality (OR = 1.89, 1.56-2.28; OR = 4.82, 3.76-6.18) were all significantly and positively associated with suicidal ideation. Furthermore, there was a chain-mediated effect of daytime sleepiness and sleep quality on the relationship between social media use and suicidal ideation.

LIMITATIONS

Causality could not be explored by cross-sectional studies, and future cohort studies are needed.

CONCLUSIONS

There was a chain-mediated effect between daytime sleepiness and sleep quality in the relationship between social media use and suicidal ideation. Therefore, it is recommended that students reduce their use of social media to improve their sleep quality and mental health.

Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review

The youth mental health crisis is exacerbated by limited access to care and resources. Mobile health (mHealth) platforms using predictive artificial intelligence (AI) can improve access and reduce barriers, enabling real-time responses and precision prevention. This systematic review evaluates predictive AI approaches in mHealth platforms for forecasting mental health symptoms among youth (13-25 years). We searched studies from Embase, PubMed, Web of Science, PsycInfo, and CENTRAL, to identify relevant studies. From 11 studies identified, three studies predicted multiple symptoms, with depression being the most common (63%). Most platforms used smartphones and 25% integrated wearables. Key predictors included smartphone usage (N=5), sleep metrics (N=6), and physical activity (N=5). Nuanced predictors like usage locations and sleep stages improved prediction. Logistic regression was most used (N=6), followed by Support Vector Machines (N=3) and ensemble methods (N=4). F-scores for anxiety and depression ranged from 0.73 to 0.84, and AUCs from 0.50 to 0.74. Stress models had AUCs of 0.68 to 0.83. Bayesian model selection and Shapley values enhanced robustness and interpretability. Barriers included small sample sizes, privacy concerns, missing data, and underrepresentation bias. Rigorous evaluation of predictive performance, generalizability, and user engagement is critical before mHealth platforms are integrated into psychiatric care.

Deep graph neural network-based prediction of acute suicidal ideation in young adults

Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffered from low sensitivity. We developed and validated a deep graph neural network model that increased the prediction sensitivity of suicide risk in young adults (n = 17,482 for training; n = 14,238 for testing) using multi-dimensional questionnaires and suicidal ideation within 2 weeks as the prediction target. The best model achieved a sensitivity of 76.3%, specificity of 83.4%, and an area under curve of 0.878 (95% confidence interval, 0.855-0.899). We demonstrated that multi-dimensional deep features covering depression, anxiety, resilience, self-esteem, and clinico-demographic information contribute to the prediction of suicidal ideation. Our model might be useful for the remote evaluation of suicide risk in the general population of young adults for specific situations such as the COVID-19 pandemic.

Sex Difference in Peripheral Inflammatory Biomarkers in Drug-Naïve Patients with Major Depression in Young Adulthood

The number of patients with major depressive disorder (MDD) is increasing worldwide. In particular, the early onset of MDD from adolescence to young adulthood is more problematic than the later onset. The specific and expeditious identification of MDD before the occurrence of severe symptoms is significant for future interventions or therapies; however, there is no accurate diagnostic marker that has sufficient sensitivity and specificity for clinical use. In the present study, to identify the possibility of blood markers for depression, we first measured the baseline inflammatory biomarkers in the peripheral blood of 50 treatment-naïve young adults with MDD and 50 matched healthy controls. We then analyzed the correlation between prospective biomarkers and depressive symptoms using scores from various clinical depression indices. We also identified differential responses between males and females in prospective biomarkers. In young adulthood, men with MDD had increased peripheral interleukin (IL)-17 levels, whereas women with MDD had significantly increased IL-1β, IL-6, and C-reactive protein (CRP) levels compared with healthy controls. However, tumor necrosis factor-α (TNF-α), CCL1, CCL2, adiponectin, and cortisol were not significantly different in young adult individuals with MDD. Higher levels of IL-17 in the male group and of IL-1β, IL-6, and CRP in the female group may have been associated with the clinical symptoms of MDD, including depressive moods, hopelessness, suicidal ideation, low self-esteem, and reduced psychological resilience. Our findings will be useful in developing diagnostic tools or treatments for MDD in young adulthood.

Sleep and suicide: A systematic review and meta-analysis of longitudinal studies

The current review provides a quantitative synthesis of the empirical literature on sleep disturbance as a risk factor for suicidal thoughts and behaviors (STBs). A systematic search of PsycINFO, MEDLINE, and the references of prior reviews resulted in 41 eligible studies included in this meta-analysis. Sleep disturbance, including insomnia, prospectively predicted STBs, yielding small-to-medium to medium effect sizes for these associations. Complicating interpretation of these findings however, is that few studies of suicidal ideation and suicide attempts, as well as none of suicide deaths, assessed short-term risk (i.e., employed follow-up assessments of under a month). Such studies are needed to evaluate current conceptualizations of sleep dysregulation as being involved in acute risk for suicidal behavior. This want of short-term risk studies also suggests that current clinical recommendations to monitor sleep as a potential warning sign of suicide risk has a relatively modest empirical basis, being largely driven by cross-sectional or retrospective research. The current review ends with recommendations for generating future research on short-term risk and greater differentiation between acute and chronic aspects of sleep disturbance, and by providing a model of how sleep disturbance may confer risk for STBs through neuroinflammatory and stress processes and associated impairments in executive control.

Attitudes towards suicide and risk factors for suicide attempts among university students in South Korea

BACKGROUND

In the new global socioeconomic development, suicidal deaths have increased which is progressively gaining more attention in the field of psychiatry, social work, psychology, and public health research. The present study aimed at examining the evidence for the attitudes towards suicide and risk factors for suicide attempts among university students in South Korea and suggested significant recommendations for future prevention programs and research.

METHOD

A study was conducted by reviewing recent publications and significant research journals published since 2000 related to the study objective, themes, and drawn broader perspectives and implications. The recent journal articles from PubMed, Global Health journal database, Google Scholar, MEDLINE, Academic Journals Database, WHO, and PsychInfo data sources were included. The study key terms were "suicide", "attitudes", "risk factors", "university students", & "South Korea".

RESULTS

The negative attitudes towards self, confusion of meaning in life, anxiety and stress about academic achievement, family problems, hopelessness, depression, and bipolar disorder were the main factors.

LIMITATIONS

We mainly included peer review journals in our study, but address the few relevant books and reports also.

CONCLUSIONS

The authors discussed important implications for future actions, including conducting relevant studies and regular collaborative discussions among responsible stakeholders.

Data-driven analysis using multiple self-report questionnaires to identify college students at high risk of depressive disorder

Depression diagnosis is one of the most important issues in psychiatry. Depression is a complicated mental illness that varies in symptoms and requires patient cooperation. In the present study, we demonstrated a novel data-driven attempt to diagnose depressive disorder based on clinical questionnaires. It includes deep learning, multi-modal representation, and interpretability to overcome the limitations of the data-driven approach in clinical application. We implemented a shared representation model between three different questionnaire forms to represent questionnaire responses in the same latent space. Based on this, we proposed two data-driven diagnostic methods; unsupervised and semi-supervised. We compared them with a cut-off screening method, which is a traditional diagnostic method for depression. The unsupervised method considered more items, relative to the screening method, but showed lower performance because it maximized the difference between groups. In contrast, the semi-supervised method adjusted for bias using information from the screening method and showed higher performance. In addition, we provided the interpretation of diagnosis and statistical analysis of information using local interpretable model-agnostic explanations and ordinal logistic regression. The proposed data-driven framework demonstrated the feasibility of analyzing depressed patients with items directly or indirectly related to depression.

The relationship between left-behind experience and obsessive-compulsive symptoms in college students in China: the mediation effect of self-esteem

The number of left-behind children in China is gradually increasing, and college students with left-behind experience (LBE) have more severe mental health problems. The aim of this study was to evaluate the association of LBE and the obsessive-compulsive (OC) symptoms of college students, explore the mediation role of self-esteem in the relationship between them. A total of 4145 college students were recruited in Anhui province, China. The Chinese Obsessive-Compulsive Inventory-Revised (OCI-R) and Rosenberg Self-Esteem Scale (RSES) were used to measure OC symptoms and self-esteem. Bootstrap program was used to test the mediation effect. The results showed that the detection rate of OC symptoms was 24.1%. Multiple linear regression analyses found that LBE was positively associated with OC symptoms (t = 2.928, p = 0.003). High self-esteem scores in college students were significantly associated with a lower probability of OC symptoms (t = -17.023, p < 0.001). Furthermore, the test of Bootstrap showed that the indirect effect of self-esteem between LBE and OC symptoms was significant for 95% CI (LLCI = 0.3586, ULCL = 0.7264) and the mediation effect was 0.5396. The ratio of the indirect effect to the total effect was 0.408. OC symptoms were common mental health problems among college students. LBE had a positive predictive effect for OC symptomsand self-esteem plays a mediating role between them. Improving self-esteem will be beneficial to prevent and control the OC symptoms of college students.