新冠疫情期间精神科从业者共病失眠和抑郁症状的网络分析。
Network analysis of comorbid insomnia and depressive symptoms among psychiatric practitioners during the COVID-19 pandemic.
机构信息
Unit of Psychiatry, Department of Public Health and Medicinal Administration, and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao Special Administrative Region (SAR), China.
Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.
出版信息
J Clin Sleep Med. 2023 Jul 1;19(7):1271-1279. doi: 10.5664/jcsm.10586.
STUDY OBJECTIVES
Insomnia and depression are common mental health problems reported by mental health professionals during the COVID-19 pandemic. Network analysis is a fine-grained approach used to examine associations between psychiatric syndromes at a symptom level. This study was designed to elucidate central symptoms and bridge symptoms of a depression-insomnia network among psychiatric practitioners in China. The identification of particularly important symptoms via network analysis provides an empirical foundation for targeting specific symptoms when developing treatments for comorbid insomnia and depression within this population.
METHODS
A total of 10,516 psychiatric practitioners were included in this study. The Insomnia Severity Index (ISI) and 9-item Patient Health Questionnaire (PHQ-9) were used to estimate prevalence rates of insomnia and depressive symptoms, respectively. Analyses also generated a network model of insomnia and depression symptoms in the sample.
RESULTS
Prevalence rates of insomnia (ISI total score ≥8), depression (PHQ-9 total score ≥5) and comorbid insomnia and depression were 22.2% (95% confidence interval: 21.4-22.9%), 28.5% (95% confidence interval: 27.6-29.4%), and 16.0% (95% confidence interval: 15.3-16.7%), respectively. Network analysis revealed that "Distress caused by sleep difficulties" (ISI7) and "Sleep maintenance" (ISI2) had the highest strength centrality, followed by "Motor dysfunction" (PHQ8) and "Sad mood" (PHQ2). Furthermore, the nodes "Sleep dissatisfaction" (ISI4), "Fatigue" (PHQ4), and "Motor dysfunction" (PHQ8) had the highest bridge strengths in linking depression and insomnia communities.
CONCLUSIONS
Both central and bridge symptoms (ie, Distress caused by sleep difficulties, Sleep maintenance, Motor dysfunction, Sad mood, Sleep dissatisfaction, and Fatigue) should be prioritized when testing preventive measures and specific treatments to address comorbid insomnia and depression among psychiatric practitioners during the COVID-19 pandemic.
CITATION
Zhao N, Zhao Y-J, An F, et al. Network analysis of comorbid insomnia and depressive symptoms among psychiatric practitioners during the COVID-19 pandemic. . 2023;19(7):1271-1279.
研究目的
失眠和抑郁是心理健康专业人员在 COVID-19 大流行期间报告的常见心理健康问题。网络分析是一种用于在症状层面检查精神综合征之间关联的精细方法。本研究旨在阐明中国精神科医生中抑郁-失眠网络的核心症状和桥梁症状。通过网络分析确定特别重要的症状为针对该人群中合并失眠和抑郁的特定症状开发治疗方法提供了经验基础。
方法
本研究共纳入 10516 名精神科医生。使用失眠严重度指数（ISI）和 9 项患者健康问卷（PHQ-9）分别估计失眠和抑郁症状的患病率。分析还生成了样本中失眠和抑郁症状的网络模型。
结果
失眠（ISI 总分≥8）、抑郁（PHQ-9 总分≥5）和合并失眠和抑郁的患病率分别为 22.2%（95%置信区间：21.4-22.9%）、28.5%（95%置信区间：27.6-29.4%）和 16.0%（95%置信区间：15.3-16.7%）。网络分析显示，“由睡眠困难引起的痛苦”（ISI7）和“睡眠维持”（ISI2）具有最高的中心度强度，其次是“运动功能障碍”（PHQ8）和“悲伤情绪”（PHQ2）。此外，“睡眠不满”（ISI4）、“疲劳”（PHQ4）和“运动功能障碍”（PHQ8）这三个节点在连接抑郁和失眠两个社区方面具有最高的桥梁强度。
结论
在 COVID-19 大流行期间，针对精神科医生中合并失眠和抑郁的预防性措施和特定治疗，应优先考虑核心症状和桥梁症状（即由睡眠困难引起的痛苦、睡眠维持、运动功能障碍、悲伤情绪、睡眠不满和疲劳）。
引文
Zhao N, Zhao Y-J, An F, et al. Network analysis of comorbid insomnia and depressive symptoms among psychiatric practitioners during the COVID-19 pandemic.. 2023;19(7):1271-1279.
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