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. 2022 Mar 17:13:814790.
doi: 10.3389/fpsyt.2022.814790. eCollection 2022.

Mapping Network Connectivity Among Symptoms of Depression and Pain in Wuhan Residents During the Late-Stage of the COVID-19 Pandemic

Yuan Yang  1 , Shu-Fang Zhang  2   3 , Bing Xiang Yang  4 , Wen Li  5 , Sha Sha  6 , Fu-Jun Jia  1 , Teris Cheung  7 , De-Xing Zhang  8 , Chee H Ng  9 , Yu-Tao Xiang  10   11   12
Affiliations

Mapping Network Connectivity Among Symptoms of Depression and Pain in Wuhan Residents During the Late-Stage of the COVID-19 Pandemic

Yuan Yang et al. Front Psychiatry. .

Abstract

Background: Symptoms of depression and pain often overlap, and they negatively influence the prognosis and treatment outcome of both conditions. However, the comorbidity of depression and pain has not been examined using network analysis, especially in the context of a pandemic. Thus, we mapped out the network connectivity among the symptoms of depression and pain in Wuhan residents in China during the late stage of the COVID-19 pandemic.

Methods: This cross-sectional study was conducted from May 25, 2020 to June 18, 2020 in Wuhan, China. Participants' depressive and pain symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ9) and a pain numeric rating scale (NRS), respectively. Network analyses were performed.

Results: In total, 2,598 participants completed all assessments. PHQ4 (fatigue) in the depression community showed the highest strength value, followed by PHQ6 (worthlessness) and PHQ2 (depressed or sad mood). PHQ4 (fatigue) was also the most key bridge symptom liking depression and pain, followed by PHQ3 (sleep difficulties). There were no significant differences in network global strength (females: 4.36 vs. males: 4.29; S = 0.075, P = 0.427), network structure-distribution of edge weights (M = 0.12, P = 0.541), and individual edge weights between male and female participants.

Conclusion: Depressive and pain symptoms showed strong cross-association with each other. "Fatigue" was the strongest central and bridge symptom in the network model, while "sleep difficulties" was the second strongest bridge symptom. Targeting treatment of both fatigue and sleep problems may help improve depressive and pain symptoms in those affected.

Keywords: COVID-19; Chinese; depression; network analysis; pain.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Network of symptoms of depression and pain. In the diagram, pink node represents pain, and light blue nodes represent 9 depressive symptoms. Nodes with stronger correlations are closer to each other. The thickness of an edge indicates the strength of the correlation. Q = question from the Patient Health Questionnaire; Green lines = positive associations.
Figure 2
Figure 2
Centrality indices of network. Q = question from the Patient Health Questionnaire.
Figure 3
Figure 3
Bridge centrality indices of network. Q = question from the Patient Health Questionnaire.
Figure 4
Figure 4
Stability of network structure by case dropping subset bootstrap. The X-axis represents the percentage of cases of original sample used at each step. The Y-axis represents the average of correlations between the centrality indices from the original network and the centrality indices from the networks that were re-estimated after dropping increasing percentages of cases. Colored areas indicate 95% confidence interval.
See this image and copyright information in PMC

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