Citation

Shevlin M, Hyland P, Nolan E, Owczarek M, Ben-Ezra M, Karatzias T. Br. J. Clin. Psychol. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, British Psychological Society)

DOI

10.1111/bjc.12321

PMID

unavailable

Abstract

BACKGROUND: The new International Classification of Diseases was published in 2018 (ICD-11; World Health Organization, 2018) and now includes 'Mixed depressive and anxiety disorder' (6A73: MDAD) designated as a mood disorder. This disorder is defined by symptoms of both anxiety and depression occurring more days than not, for a period of two weeks, and neither set of symptoms considered separately reaches a diagnostic threshold for either disorder. However, to date no study has examined the validity of these guidelines in a general population sample.

METHODS: Using Goldberg et al.'s (2017) guidelines regarding measurement of depression and anxiety, this study used factor mixture modelling (FMM) to examine the validity of the ICD-11 criteria of MDAD. Symptom endorsement rates are provided as well as demographic predictors and somatization outcomes.

RESULTS: Fit indices suggested the two-factor four-class solution was the best balance between model complexity and model fit. The results did not support a class that is subsyndromal to both anxiety and depression. On the contrary, we suggest that there exists a 'Comorbid' class that represents endorsement of both anxiety and depression symptoms at a higher level when compared to both 'anxiety' and 'depression' groups. Demographic predictors, as well as somatization and functional impairment outcomes, provided support for this FMM solution.

CONCLUSIONS: The 'Comorbid' group was the largest symptomatic group and had the highest levels of both anxiety and depression symptoms. Importantly, this group was larger than either the 'anxiety' or 'depression' group and was associated with high levels of functional impairment and somatization.

Language: en

Keywords

factor mixture models; ICD-11 mixed depressive and anxiety disorder; sub-clinical