Casting wider diagnostic nets for anxiety and depression: Disability-driven cross-diagnostic subtypes in a large population study

Abstract

IntroductionData-driven techniques are frequently applied to identify subtypes of depression and anxiety. Although they are highly comorbid and often grouped under a single internalizing banner, most subtyping studies have focused on either depression or anxiety. Furthermore, most previous subtyping studies have not taken into account experienced disability.ObjectivesTo incorporate disability into a data-driven cross-diagnostic subtyping model.AimsTo capture heterogeneity of depression and anxiety symptomatology and investigate the importance of domain-specific disability-levels to distinguish between homogeneous subtypes.MethodsSixteen symptoms were assessed without skips using the MINI-interview in a population sample (LifeLines; n = 73403). Disability was measured with the RAND-36. To identify the best-fitting subtyping model, different nested latent variable models (latent class analysis, factor analysis and mixed-measurement item response theory [MM-IRT]) with and without disability covariates were compared. External variables were compared between the best model's classes.ResultsA five-class MM-IRT model incorporating disability showed the best fit (Fig. 1). Accounting for disability improved the differentiation between classes reporting isolated non-specific symptoms (“Somatic” [13.0%], and “Worried” [14.0%]) and those reporting more psychopathological symptoms (“Subclinical” [8.8%], and “Clinical” [3.3%]). A “Subclinical” class reported symptomatology at subthreshold levels. No pure depression or anxiety, but only mixed classes were observed.ConclusionsAn overarching subtyping model incorporating both symptoms and disability identified distinct cross-diagnostic subtypes. Diagnostic nets should be cast wider than current phenomenology-based categorical systems.Figure not available.Disclosure of interestThe authors have not supplied their declaration of competing interest.