Yang Yuan, Zhang Shu-Fang, Yang Bing Xiang, Li Wen, Sha Sha, Jia Fu-Jun, Cheung Teris, Zhang De-Xing, Ng Chee H, Xiang Yu-Tao
Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China.
Front Psychiatry. 2022 Mar 17;13:814790. doi: 10.3389/fpsyt.2022.814790. eCollection 2022.
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.
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.
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, = 0.427), network structure-distribution of edge weights (M = 0.12, = 0.541), and individual edge weights between male and female participants.
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.
抑郁症状和疼痛症状常常相互重叠,并且它们会对这两种病症的预后和治疗结果产生负面影响。然而,抑郁与疼痛的共病情况尚未通过网络分析进行研究,尤其是在大流行背景下。因此,我们绘制了新冠疫情后期中国武汉居民抑郁症状与疼痛症状之间的网络连通性。
这项横断面研究于2020年5月25日至2020年6月18日在中国武汉进行。分别使用9项患者健康问卷(PHQ9)和疼痛数字评定量表(NRS)对参与者的抑郁症状和疼痛症状进行评估。进行了网络分析。
共有2598名参与者完成了所有评估。抑郁群落中的PHQ4(疲劳)显示出最高的强度值,其次是PHQ6(无价值感)和PHQ2(抑郁或悲伤情绪)。PHQ4(疲劳)也是连接抑郁和疼痛的最关键的桥梁症状,其次是PHQ3(睡眠困难)。网络全局强度(女性:4.36对男性:4.29;S = 0.075,P = 0.427)、边权重的网络结构分布(M = 0.12,P = 0.541)以及男性和女性参与者之间的个体边权重均无显著差异。
抑郁症状和疼痛症状彼此之间表现出强烈的交叉关联。“疲劳”是网络模型中最强的中心和桥梁症状,而“睡眠困难”是第二强的桥梁症状。针对疲劳和睡眠问题进行治疗可能有助于改善受影响者的抑郁和疼痛症状。