Interpersonal Well-Being and Suicidal Outcomes in a Nationally Representative Study of Adolescents: A Translational Study.
Interpersonal Well-Being and Suicidal Outcomes in a Nationally Representative Study of Adolescents: A Translational Study.
复制标题
全国青少年代表性研究中的人际福祉和自杀结果：一项转化研究。
DOI：
10.1007/s10802-023-01068-7
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发表时间：
2023
影响因子：
2.5
通讯作者：
Stutts,Morgan
中科院分区：
文献类型：
--
作者：
Cohen,JosephR;Stutts,Morgan
尽管关于人际自杀风险的研究不断涌现，但青少年自杀率仍在持续上升。这可能反映了将发展精神病理学研究应用于临床环境的挑战。作为回应，本研究使用转化分析计划来检查社会福祉指数，这些指数对于青少年自杀指数来说是最准确且统计上公平的。使用来自国家合并症调查复制青少年补充品的数据。 13-17 岁青少年 (N= 9,900) 完成了有关创伤事件、当前关系以及自杀想法和企图的调查。频率论（例如，接收器操作特性）和贝叶斯（例如，诊断似然比；DLR）技术都提供了对分类、校准和统计公平性的深入了解。最终算法与机器学习算法进行了比较。总体而言，父母照顾和家庭凝聚力是自杀意念的最佳分类，而这些指数和学校参与是自杀企图的最佳分类。多指标算法表明，这些指数处于高风险的青少年参与构思的可能性大约高出 3 倍（DLR = 3.26），参与尝试的可能性高出 5 倍（DLR = 4.53）。尽管尝试是公平的，但观念模型在非白人青少年中表现不佳。补充的机器学习算法表现类似，表明非线性和交互效应并没有提高模型性能。讨论了自杀人际理论的未来方向，并论证了自杀筛查的临床意义。
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.