Zhao Na, Li Wen, Zhang Shu-Fang, Yang Bing Xiang, Sha Sha, Cheung Teris, Jackson Todd, Zang Yu-Feng, Xiang Yu-Tao
Unit of Psychiatry, Department of Public Health and Medicinal Administration, and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao, SAR China.
Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, China.
Front Psychiatry. 2021 Sep 30;12:735973. doi: 10.3389/fpsyt.2021.735973. eCollection 2021.
Depression has been a common mental health problem during the COVID-19 epidemic. From a network perspective, depression can be conceptualized as the result of mutual interactions among individual symptoms, an approach that may elucidate the structure and mechanisms underlying this disorder. This study aimed to examine the structure of depression among residents in Wuhan, the epicenter of the COVID-19 outbreak in China, in the later stage of the COVID-19 pandemic. A total of 2,515 participants were recruited from the community via snowball sampling. The Patient Health Questionnaire was used to assess self-reported depressive symptoms with the QuestionnaireStar program. The network structure and relevant centrality indices of depression were examined in this sample. Network analysis revealed Fatigue, Sad mood, Guilt and Motor disturbances as the most central symptoms, while Suicide and Sleep problems had the lowest centrality. No significant differences were found between women and men regarding network structure (maximum difference = 0.11, = 0.44) and global strength (global strength difference = 0.04; female vs. male: 3.78 vs. 3.83, = 0.51), a finding that suggests there are no gender differences in the structure or centrality of depressive symptoms. Due to the cross-sectional study design, causal relationships between these depressive symptoms or dynamic changes in networks over time could not be established. Fatigue, Sad mood, Guilt, and Motor disturbances should be prioritized as targets in interventions and prevention efforts to reduce depression among residents in Wuhan, in the later stage of the COVID-19 pandemic.
在新冠疫情期间，抑郁症一直是一个常见的心理健康问题。从网络角度来看，抑郁症可被概念化为个体症状之间相互作用的结果，这种方法可能有助于阐明该疾病的结构和机制。本研究旨在考察中国新冠疫情爆发中心武汉的居民在疫情后期抑郁症的结构。通过滚雪球抽样从社区招募了总共2515名参与者。使用患者健康问卷通过问卷星程序评估自我报告的抑郁症状。在这个样本中考察了抑郁症的网络结构和相关中心性指标。网络分析显示，疲劳、悲伤情绪、内疚和运动障碍是最核心的症状，而自杀和睡眠问题的中心性最低。在网络结构（最大差异=0.11，P=0.44）和全局强度方面（全局强度差异=0.04；女性与男性：3.78对3.83，P=0.51），女性和男性之间未发现显著差异，这一发现表明抑郁症状的结构或中心性不存在性别差异。由于采用横断面研究设计，无法确定这些抑郁症状之间的因果关系或网络随时间的动态变化。在新冠疫情后期，疲劳、悲伤情绪、内疚和运动障碍应被优先作为干预和预防措施的目标，以减少武汉居民的抑郁症。
