background
In search of empirical classifications of depression and anxiety, most subtyping studies focus solely on symptoms and do so within a single disorder. This study aimed to identify and validate cross-diagnostic subtypes by simultaneously considering symptoms of depression and anxiety, and disability measures.
Method
A large cohort of adults (Lifelines, n = 73 403) had a full assessment of 16 symptoms of mood and anxiety disorders, and measurement of physical, social and occupational disability. The best-fitting subtyping model was identified by comparing different hybrid mixture models with and without disability covariates on fit criteria in an independent test sample. The best model's classes were compared across a range of external variables.
Results
The best-fitting Mixed Measurement Item Response Theory model with disability covariates identified five classes. Accounting for disability improved differentiation between people reporting isolated non-specific symptoms ['Somatic' (13.0%), and 'Worried' (14.0%)] and psychopathological symptoms ['Subclinical' (8.8%), and 'Clinical' (3.3%)]. Classes showed distinct associations with clinically relevant external variables [e.g. somatization: odds ratio (OR) 8.1-12.3, and chronic stress: OR 3.7-4.4]. The Subclinical class reported symptomatology at subthreshold levels while experiencing disability. No pure depression or anxiety, but only mixed classes were found.
Conclusions
An empirical classification model, incorporating both symptoms and disability identified clearly distinct cross-diagnostic subtypes, indicating that diagnostic nets should be cast wider than current phenomenology-based categorical systems.
Keywords
Anxiety; depression; disability; latent variable mixture modelling; subtypes