Cited by 296Mental Health Research TopicsAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesPersonality Disorders and Psychopathology

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

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Kaiser, Tim, Herzog, Philipp, Voderholzer, Ulrich, Brakemeier, Eva‐Lotta·2021·Depression and Anxiety

Abstract

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.

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Authors
Kaiser, T., Herzog, P., Voderholzer, U., & Brakemeier, E.
Affiliations
  • Universität Greifswald (DE)
  • Schön Klinik Roseneck (DE)
Corresponding author
Kaiser, Tim
Venue
Depression and Anxiety
Publisher
Wiley
Published
2021-01-19
Volume
38
Issue
3
Pages
307-317
ISSN
  • 1091-4269 (ISSN-L)
  • 1520-6394
OpenAlex
W3124170626
Cited by
296
References
60
Citations by year
2026: 65 · 2025: 77 · 2024: 65 · 2023: 47 · 2022: 35 · 2021: 6 · 2020: 1
FWCI
36.93
Type
journal article
Language
en
Open access
Locations
  • Depression and Anxiety (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
  • Social Sciences › Psychology › Clinical Psychology › Personality Disorders and Psychopathology
Keywords
depression, anxiety disorders, comorbidities, bridge symptoms, symptom networks, network centrality, community structure, psychiatric inpatient care, sad mood, psychomotor agitation, concentration difficulties, appetite changes, suicidality, symptom clusters
MeSH terms
Adult, Anxiety, Anxiety Disorders, Depression, Female, Humans, Male, Psychomotor Agitation, Comorbidity

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