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. 2021 May 31;21(1):279.
doi: 10.1186/s12888-021-03276-1.

Network structure of depression and anxiety symptoms in Chinese female nursing students

Lei Ren #  1 , Yifei Wang #  1 , Lin Wu #  1 , Zihan Wei  2 , Long-Biao Cui  1 , Xinyi Wei  3 , Xinyu Hu  4 , Jiaxi Peng  5 , Yinchuan Jin  1 , Fengzhan Li  1 , Qun Yang  6 , Xufeng Liu  7
Affiliations

Network structure of depression and anxiety symptoms in Chinese female nursing students

Lei Ren et al. BMC Psychiatry. .

Abstract

Background: Comorbidity between depressive and anxiety disorders is common. From network perspective, mental disorders arise from direct interactions between symptoms and comorbidity is due to direct interactions between depression and anxiety symptoms. The current study investigates the network structure of depression and anxiety symptoms in Chinese female nursing students and identifies the central and bridge symptoms as well as how other symptoms in present network are related to depression symptom "thoughts of death".

Methods: To understand the full spectrum of depression and anxiety, we recruited 776 Chinese female nursing students with symptoms of depression and anxiety that span the full range of normal to abnormal. Depression symptoms were measured by Patient Health Questionnaire-9 while anxiety symptoms were measured by Generalized Anxiety Disorder 7-Item Questionnaire. Network analysis was used to construct networks. Specifically, we computed the predictability, expected influence and bridge expected influence for each symptom and showed a flow network of "thoughts of death".

Results: Nine strongest edges existed in network were from the same disorder. Four were between depression symptoms, like "sleep difficulties" and "fatigue", and "anhedonia" and "fatigue". Five were between anxiety symptoms, like "nervousness or anxiety" and "worry too much", and "restlessness" and "afraid something will happen". The symptom "fatigue", "feeling of worthlessness" and "irritable" had the highest expected influence centrality. Results also revealed two bridge symptoms: "depressed or sad mood" and "irritable". As to "thoughts of death", the direct relations between it and "psychomotor agitation/retardation" and "feeling of worthlessness" were the strongest direct relations.

Conclusions: The current study highlighted critical central symptoms "fatigue", "feeling of worthlessness" and "irritable" and critical bridge symptoms "depressed or sad mood" and "irritable". Particularly, "psychomotor agitation/retardation" and "feeling of worthlessness" were identified as key priorities due to their strongest associations with suicide ideation. Implications for clinical prevention and intervention based on these symptoms are discussed.

Keywords: Anxiety; Comorbidity; Depression; Female nursing students; Network analysis; Suicide ideation.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Network structure of depression and anxiety symptoms in Chinese female nursing students. (a) Blue edges represent positive correlations, red edges represent negative correlations. The thickness of the edge reflects the magnitude of the correlation. The circles around nodes depict its predictability. (b) Centrality plot depicting the expected influence of each symptom in the network (z-score)
Fig. 2
Fig. 2
Network structure of depression and anxiety symptoms showing bridge symptoms in Chinese female nursing students. (a) Blue edges represent positive correlations, red edges represent negative correlations. The thickness of the edge reflects the magnitude of the correlation. The circles around nodes depict its predictability. (b) Centrality plot depicting the bridge expected influence of each symptom in the network (z-score)
Fig. 3
Fig. 3
Flow network of suicide thoughts. Blue edges represent positive correlations, red edges represent negative correlations. The thickness of the edge reflects the magnitude of the correlation. The circles around nodes depict its predictability
See this image and copyright information in PMC

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