Cross-national presence and sociodemographic correlates of the suicide crisis syndrome.

BACKGROUND: The Suicide Crisis Syndrome (SCS) has been proposed as an acute, pre-suicidal mental state that precedes imminent suicidal behavior; however, its cross-national applicability and sociodemographic correlates have not yet been determined. The present study assessed the presence and severity of the SCS in ten countries and examined several potential sociodemographic correlates (i.e., age, gender, marital status, race/ethnicity) of the SCS. METHODS: 5528 community-based adults across 10 participating countries provided information on their SCS symptoms and sociodemographic characteristics in an anonymous online survey obtained via convenience sampling during the first year of the COVID-19 pandemic. RESULTS: The SCS occurred cross-nationally, with rates ranging from 3.6% (Israel) to 16.2% (Poland). Those in the United States, South Korea, Poland, and Turkey had the highest severity of symptoms. Participants who were older, identified as cisgender men, and married tended to have lower rates of the SCS than their respective counterparts. There were minimal differences in the SCS by race/ethnicity. LIMITATIONS: These data were both cross-sectional and collected via convenience sampling, limiting generalizability of findings and information about the SCS's predictive utility. CONCLUSIONS: These findings support the cross-national presence of the SCS during the COVID-19 pandemic. Sociodemographic correlates aligned with those of suicidal behavior more generally, providing additional evidence for the concurrent/predictive validity of the SCS.

The role of suicide ideation in assessing near-term suicide risk: A machine learning approach.

BACKGROUND: The majority of suicide attempters do not disclose suicide ideation (SI) prior to making an attempt. When reported, SI is nevertheless associated with increased risk of suicide. This paper implemented machine learning (ML) approaches to assess the degree to which current and lifetime SI affect the predictive validity of the Suicide Crisis Syndrome (SCS), an acute condition indicative of imminent risk, for near-term suicidal behaviors (SB ). METHODS: In a sample of 591 high-risk inpatient participants, SCS and SI were respectively assessed using the Suicide Crisis Inventory (SCI) and the Columbia Suicide Severity Rating Scale (C-SSRS). Two ML predictive algorithms, Random Forest and XGBoost, were implemented and framed using optimism adjusted bootstrapping. Metrics collected included AUPRC, AUROC, classification accuracy, balanced accuracy, precision, recall, and brier score. AUROC metrics were compared by computing a z-score. RESULTS: The combination of current SI and SCI showed slightly higher predictive validity for near-term SB as evidenced by AUROC metrics than the SCI alone, but the difference was not significant (p<0.05). Current SI scored the highest amongst a chi square distribution in regards to predictors of near-term SB. CONCLUSION: The addition of SI to the SCS does not materially improve the model's predictive validity for near-term SB, suggesting that patient self-reported SI should not be a requirement for the diagnosis of SCS.