Kim Hyunsik, Eaton Nicholas R
Department of Psychology, Stony Brook University.
J Abnorm Psychol. 2015 Nov;124(4):1064-78. doi: 10.1037/abn0000113.
Studies of mental disorder comorbidity have produced an unsynthesized literature with multiple competing transdiagnostic models. The current study attempted to (a) integrate these models into an overarching comorbidity hierarchy, (b) link the resulting transdiagnostic factors to the bifactor model of psychopathology, and (c) investigate predictive validity of transdiagnostic factors for important future outcomes. A series of exploratory structural equation models (ESEMs) was conducted on 12 common mental disorders from a large, 2-wave nationally representative sample, using the bass-ackwards method to explore the hierarchical structure of transdiagnostic comorbidity factors. These Wave 1 factors were then linked with the bifactor model and with mental disorders at Wave 2. Results indicated that common mental disorder comorbidity was structured into an interpretable hierarchy. Connections between the hierarchy's general factor of psychopathology (denoted p), internalizing, and distress were very strong; these factors also linked strongly with the bifactor model's p factor. Predictive validity analyses prospectively predicting subsequent diagnoses indicated that, overall: (a) transdiagnostic factors outperformed disorder-specific variance; (b) within hierarchy levels, transdiagnostic factors where disorders optimally loaded outperformed other transdiagnostic factors, but this differed by disorder type; and (c) between hierarchy levels, transdiagnostic factors where disorders optimally loaded showed similar predictive validity. We discuss implications for hierarchical structure modeling, the integration of multiple competing comorbidity models, and benefits of transdiagnostic factors for understanding the continuity of mental disorders over time.
对精神障碍共病的研究产生了大量相互竞争的跨诊断模型且缺乏综合的文献。当前研究试图：（a）将这些模型整合到一个总体共病层次结构中；（b）将由此产生的跨诊断因素与精神病理学的双因素模型相联系；（c）研究跨诊断因素对重要未来结果的预测效度。对来自一个具有全国代表性的两波大型样本中的12种常见精神障碍进行了一系列探索性结构方程模型（ESEM）分析，采用反向法来探索跨诊断共病因素的层次结构。然后将这些第一波因素与双因素模型以及第二波时的精神障碍相联系。结果表明，常见精神障碍共病构成了一个可解释的层次结构。该层次结构中精神病理学的一般因素（表示为p）、内化和痛苦之间的联系非常紧密；这些因素也与双因素模型的p因素紧密相连。前瞻性预测后续诊断的预测效度分析表明，总体而言：（a）跨诊断因素优于特定障碍的方差；（b）在层次结构水平内，障碍最优负荷的跨诊断因素优于其他跨诊断因素，但这因障碍类型而异；（c）在层次结构水平之间，障碍最优负荷的跨诊断因素显示出相似的预测效度。我们讨论了对层次结构建模的影响、多个相互竞争的共病模型的整合以及跨诊断因素对理解精神障碍随时间连续性的益处。
