Unraveling the comorbidity of depression and anxiety in a large inpatient sample: Network analysis to examine bridge symptoms

• Published in: Depression and anxiety Vol. 38; no. 3; pp. 307 - 317
• Main Authors: Kaiser, Tim, Herzog, Philipp, Voderholzer, Ulrich, Brakemeier, Eva‐Lotta
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.03.2021
• Subjects: Adult; Anxiety; Anxiety disorders; Anxiety Disorders - epidemiology; Appetite; assessment/diagnosis; Comorbidity; Depression; Female; Humans; Intellectual disabilities; International; Male; measurement/psychometrics; Medical diagnosis; Mental depression; Mental disorders; Mood; phobia/phobic disorders; Psychomotor Agitation; depression; measurement/psychometrics; International; assessment/diagnosis; phobia/phobic disorders
• ISSN: 1091-4269; 1520-6394; 1520-6394
• DOI: 10.1002/da.23136

Background Comorbidities in mental disorders are often understood by assuming a common cause. The network theory of mental disorders offers an alternative to this assumption by understanding comorbidities as mutually reinforced problems. In this study, we used network analysis to examine bridge symptoms between anxiety and depression in a large sample. Method Using data from a sample of patients diagnosed with both depression and an anxiety disorder before and after inpatient treatment (N = 5,614, mean age: 42.24, 63.59% female, average treatment duration: 48.12 days), network models of depression and anxiety symptoms are estimated. Topology, the centrality of nodes, stability, and changes in network structure are analyzed. Symptoms that drive comorbidity are determined by bridge node analysis. As an alternative to network communities based on categorical diagnosis, we performed a community analysis and propose empirically derived symptom subsets. Results The obtained network models are highly stable. Sad mood and the inability to control worry are the most central. Psychomotor agitation or retardation is the strongest bridge node between anxiety and depression, followed by concentration problems and restlessness. Changes in appetite and suicidality were unique to depression. Community analysis revealed four symptom groups. Conclusion The estimated network structure of depression and anxiety symptoms proves to be highly accurate. Results indicate that some symptoms are considerably more influential than others and that only a small number of predominantly physical symptoms are strong candidates for explaining comorbidity. Future studies should include physiological measures in network models to provide a more accurate understanding.