Interpersonal Well-Being and Suicidal Outcomes in a Nationally Representative Study of Adolescents: A Translational Study.

Abstract

Adolescent suicide continues to rise despite burgeoning research on interpersonal risk for suicide. This may reflect challenges in applying developmental psychopathology research into clinical settings. In response, the present study used a translational analytic plan to examine indices of social well-being most accurate and statistically fair for indexing adolescent suicide. Data from the National Comorbidity Survey Replication Adolescent Supplement were used. Adolescents aged 13-17 (N = 9,900) completed surveys on traumatic events, current relationships, and suicidal thoughts and attempts. Both frequentist (e.g., receiver operating characteristics) and Bayesian (e.g., Diagnostic Likelihood Ratios; DLRs) techniques provided insight into classification, calibration, and statistical fairness. Final algorithms were compared to a machine learning-informed algorithm. Overall, parental care and family cohesion best classified suicidal ideation, while these indices and school engagement best classified attempts. Multi-indicator algorithms suggested adolescents at high risk across these indices were approximately 3-times more likely to engage in ideation (DLR = 3.26) and 5-times more likely to engage in attempts (DLR = 4.53). Although equitable for attempts, models for ideation underperformed in non-White adolescents. Supplemental, machine learning-informed algorithms performed similarly, suggesting non-linear and interactive effects did not improve model performance. Future directions for interpersonal theories for suicide are discussed and clinical implications for suicide screening are demonstrated.