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Comorbidity: A network perspective

机译：合并症：网络视角

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The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (I.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy.
机译：合并症研究的关键问题在于其基于的心理学计量基础，即潜变量理论，在该理论中，精神障碍被视为导致症状群的潜变量。从这个角度来看，合并症是多个潜在变量之间的双向关系。我们认为，这种潜在的可变观点在合并症研究中遇到了严重的问题，并且在网络方法方面提供了根本不同的概念，其中网络合并症被认为是由多种疾病症状之间的直接关系引起的。我们提出了一种可视化合并症网络的方法，并基于严重抑郁和广泛性焦虑的经验网络，我们认为这种方法产生了关于合并症，重叠症状和诊断界限的现实假设，而潜伏变量自然无法适应这些假设模型：通过症状空间达到合并症的某些途径比其他途径更可能；这些途径通常具有相同的方向（即从一种疾病的症状转变为另一种疾病的症状）;症状重叠在合并症中起重要作用；诊断类别之间的界限必然是模糊的。

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