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

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.

Network analysis of anxiety and depressive symptoms among nursing students during the COVID-19 pandemic

“…In addition, Trouble relaxing, and Depressed mood were central symptoms in this study. This finding was partially consistent with the emergence of Depressed mood and Uncontrollable worry as the most central symptoms of depression and anxiety observed in a large German inpatient sample ( Kaiser et al., 2021 ). One hypothesis that follows from this overlap between samples is that depressed mood and uncontrollable worry, in particular, characterize distress experienced by groups with increased risk for direct exposure to the virus and/or its consequences (i.e., medical personnel, students in training to be medical practitioners, hospital in-patients).…”

“…Clinically, bridge symptoms can be viewed as transdiagnostic and targeted interventions may be effective for both disorders ( Kaiser et al., 2021 ). In a meta-analysis involving 66 longitudinal studies with 88,336 persons, Jacobson and Newman ( Jacobson and Newman, 2017 ) investigated prospective relations between anxiety and depression at both symptom and disorder levels; they found that anxiety symptoms could predict depressive symptoms and vice versa, a finding that provides a basis for considering bridge symptoms within anxiety-depressive symptom networks.…”

The network of stress-related states and depression and anxiety symptoms during the COVID-19 lockdown

“…By moving the level of causality to that of observable symptoms rather than unobservable latent entities, the network perspective easily accommodates this possibility by pointing to overlapping clinical features and symptoms (e.g., concentration difficulties, sleep problems, low energy; Cramer et al., 2010 ) as well as bridge symptoms connecting the two disorders. Studies have found the strongest bridges between anhedonia and generalized worry ( Cramer et al., 2010 ), psychomotor problems and restlessness ( Beard et al., 2016 ; Kaiser et al., 2021 ), and depressed mood and anxiety ( Garabiles et al., 2019 ).…”

“…In a community sample of Kenyan youths, depressed mood and worthlessness were most central ( Osborn et al., 2020 ). In Western clinical samples, anhedonia and depressed mood ( Beard et al., 2016 ), anhedonia and low energy ( Fried et al., 2016 ) and depressed mood ( Kaiser et al., 2021 ) have been found to be most central. In specific populations, depressed mood ( Briganti et al.…”

Maximal Clique and Graph Coloring Heuristics for Overlapping Community Detection in Network Psychometrics

“…In the case of weighted networks, cliques may be filtered based on their intensity, which is measured as the geometric mean of their edge weights (Farkas et al, 2007). In recent years, clique percolation has received significant attention in network psychometrics applications (Blanken et al, 2018;Cosgrove et al, 2021;Kaiser et al, 2021;Lange, 2019;Lange & Zickfeld, 2021).…”