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Background <p>Even after the COVID-19 outbreak, mental health challenges are both serious and widespread among medical staff in infectious disease departments, with nurses being particularly affected. There has been limited research on the relationships between common conditions such as depression, anxiety, and emotional exhaustion. Network analysis (NA) is a novel method for quantifying the relationships between mental variables using mathematical approaches. The purpose of this study was to examine the relationship between depression, anxiety and emotional exhaustion symptoms through NA among nurses working in infectious disease departments.</p> Method <p>Employing a cross-sectional design, we utilized validated scales to assess depression, anxiety and emotional exhaustion in a sample of 1,849 infectious disease nurses. We assessed depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), anxiety symptoms with the Generalized anxiety disorder-7 (GAD-7), and burnout symptoms with the Chinese version of the Maslach Burnout Inventory-Human Service Survey (MBI-HSS). Interactions between depression, anxiety and emotional exhaustion were constructed using NA. In the network model, central and bridge symptoms were identified using the R package qgraph. The network structure’s stability was assessed with the help of the bootnet R package.</p> Results <p>This study revealed a prevalence of clinically significant depressive symptoms (24.61%) and anxiety (9.30%). The average score of emotional exhaustion was 17.59 ± 10.98. Within the depression-anxiety-emotional exhaustion network, stronger connections were observed between PHQ8(Psychomotor problems)-GAD5(Restlessness), as well as between PHQ9(Suicidal ideation)-GAD5(Restlessness). EE5 (I feel burned out from my work) was anticipated to have the greatest impact on central symptoms, with GAD4 (Trouble relaxing) and GAD5 (Restlessness) following. The highest weights for bridge symptoms were given to GAD5 (Restlessness), PHQ8 (Psychomotor problems) and GAD6 (Irritability). The correlation stability coefficients of 0.75 for both central and bridge symptoms indicate a high level of stability in the network structure.</p> Conclusions <p>The GAD5 (Restlessness) symptom was the key factor connecting depression, anxiety, and emotional exhaustion. Focusing on this symptom can help identify mental disorders and alleviate mental health syndromes among infectious disease nurses in the post-COVID-19 <i>era</i>.</p>

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Relationship between depression, anxiety and emotional exhaustion among infectious disease nurses: a network analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月27日

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  • Yalan Huang,
  • Xiaoxiao Luo,
  • Ruisi Xu,
  • Xueling Xia,
  • Yongguang Li,
  • Kangping Li,
  • Jing Yi,
  • Qian Li,
  • Xu Ge,
  • Zhihan Zhao,
  • Li Liu,
  • Mengting Chen

摘要

Background

Even after the COVID-19 outbreak, mental health challenges are both serious and widespread among medical staff in infectious disease departments, with nurses being particularly affected. There has been limited research on the relationships between common conditions such as depression, anxiety, and emotional exhaustion. Network analysis (NA) is a novel method for quantifying the relationships between mental variables using mathematical approaches. The purpose of this study was to examine the relationship between depression, anxiety and emotional exhaustion symptoms through NA among nurses working in infectious disease departments.

Method

Employing a cross-sectional design, we utilized validated scales to assess depression, anxiety and emotional exhaustion in a sample of 1,849 infectious disease nurses. We assessed depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), anxiety symptoms with the Generalized anxiety disorder-7 (GAD-7), and burnout symptoms with the Chinese version of the Maslach Burnout Inventory-Human Service Survey (MBI-HSS). Interactions between depression, anxiety and emotional exhaustion were constructed using NA. In the network model, central and bridge symptoms were identified using the R package qgraph. The network structure’s stability was assessed with the help of the bootnet R package.

Results

This study revealed a prevalence of clinically significant depressive symptoms (24.61%) and anxiety (9.30%). The average score of emotional exhaustion was 17.59 ± 10.98. Within the depression-anxiety-emotional exhaustion network, stronger connections were observed between PHQ8(Psychomotor problems)-GAD5(Restlessness), as well as between PHQ9(Suicidal ideation)-GAD5(Restlessness). EE5 (I feel burned out from my work) was anticipated to have the greatest impact on central symptoms, with GAD4 (Trouble relaxing) and GAD5 (Restlessness) following. The highest weights for bridge symptoms were given to GAD5 (Restlessness), PHQ8 (Psychomotor problems) and GAD6 (Irritability). The correlation stability coefficients of 0.75 for both central and bridge symptoms indicate a high level of stability in the network structure.

Conclusions

The GAD5 (Restlessness) symptom was the key factor connecting depression, anxiety, and emotional exhaustion. Focusing on this symptom can help identify mental disorders and alleviate mental health syndromes among infectious disease nurses in the post-COVID-19 era.