4.6 Article

Network comparison analysis of comorbid depression and anxiety disorder in a large clinical sample before and after treatment

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CURRENT PSYCHOLOGY
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SPRINGER
DOI: 10.1007/s12144-023-05308-3

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Network analysis; Comorbidity; Depression; Anxiety; Longitudinal analysis

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The prevalence of comorbid depression and anxiety has increased significantly over the last three years. Network analysis was used to identify core symptoms and comorbid pathways between the two disorders, and reveal temporal changes in symptoms that traditional assessment tools fail to capture. The results indicate that emptiness is the most common central symptom in the network, and mental agitation has higher strength within the male network.
The prevalence of comorbid depression and anxiety has increased significantly over the last three years. Herein, we used network analysis to identify core symptoms and comorbid pathways between the two disorders, and reveal temporal changes in symptoms that traditional assessment tools fail to capture. Data was collected from 787 clinically comorbid patients (mean age: 31.8 years, 67.1% female) who completed the Zung Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) before and after treatment (T1 -> T2) from 2019 to 2022. Node and bridge strengths were calculated, and the Network Comparison Test (NCT) was used to explore network differences from T1 to T2 and across gender groups. Results indicate that emptiness exhibited the highest strength centrality, particularly among adolescents. The strongest bridge nodes were found in the emotional, physical, and psychological symptoms communities of the two disorders. Although the total score of the assessment tools decreased during treatment, NCT confirmed a significant increase in the overall strength of the network (p = .03). Notably, mental agitation exhibited higher strength within the male network. In conclusion, our findings provide further insights into the mechanisms of comorbid symptomatology and its evolution during treatment, offering valuable targets for intervention. Thus, suggesting the inclusion of network analysis in existing diagnostic, treatment, and follow-up processes facilitates personalized interventions and enhances patient recovery.

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