Network Analysis of Comorbid Anxiety and Insomnia Among Clinicians with Depressive Symptoms During the Late Stage of the COVID-19 Pandemic: A Cross-Sectional Study

• Authors: Hong Cai, University of Macau; Yan-Jie Zhao, University of Macau; Xiaomeng Xing, Capital Medical University; Tengfei Tian, Capital Medical University; Wang Qian, Capital Medical University; Sixiang Liang, Capital Medical University; Zhe Wang, Capital Medical University; Teris Cheung, Hong Kong Polytechnic University; Zhaohui Su, Southeast Univ; Yilang Tang, Emory University; Chee H Ng, University of Melbourne; Sha Sha, Capital Medical University; Yu-Tao Xiang, University of Macau
• Journal Title: NATURE AND SCIENCE OF SLEEP
• Volume: Volume 14
• Publisher: DOVE MEDICAL PRESS LTD | 2022-01-01, Pages 1351-1362
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.2147/NSS.S367974
• Subjects: Health Sciences, Mental Health; Health Sciences, Nursing; Health Sciences, Public Health
• Keywords: Science & Technology; Life Sciences & Biomedicine; Clinical Neurology; Neurosciences; Neurosciences & Neurology; depression; anxiety; sleep; network analysis; health personnel; QUALITY-OF-LIFE; MENTAL-HEALTH; SLEEP; DISORDER; ASSOCIATION; CENTRALITY; OUTBREAK; FATIGUE; WORKERS

Abstract

Background: A high proportion of clinicians experienced common anxiety, insomnia and depression during the COVID-19 pandemic. This study examined the item-level association of comorbid anxiety and insomnia symptoms among clinicians who suffered from depressive symptoms during the late stage of the COVID-19 pandemic using network analysis (NA). Methods: Clinicians with depressive symptoms (with a Patients Health Questionnaire (PHQ-9) total score of 5 and above) were included in this study. Anxiety and insomnia symptoms were measured using the Generalized Anxiety Disorder Scale-7-item (GAD-7) and Insomnia Severity Index (ISI), respectively. Network analysis was conducted to investigate the network structure, central symptoms, bridge symptoms, and network stability of these disturbances. Expected influence (EI) was used to measure the centrality of index. Results: Altogether, 1729 clinicians were included in this study. The mean age was 37.1 [standard deviation (SD)=8.04 years], while the mean PHQ-9 total score was 8.42 (SD=3.33), mean GAD-7 total score was 6.45 (SD=3.13) and mean ISI total score was 8.23 (SD=5.26). Of these clinicians, the prevalence of comorbid anxiety symptoms (GAD-7≥5) was 76.8% (95% CI 74.82–78.80%), while the prevalence of comorbid insomnia symptoms (ISI≥8) was 43.8% (95% CI: 41.50–46.18%). NA revealed that nodes ISI7 (“Interference with daytime functioning”) (EI=1.18), ISI4 (“Sleep dissatisfaction”) (EI=1.08) and ISI5 (“Noticeability of sleep problem by others”) (EI=1.07) were the most central (influential) symptoms in the network model of comorbid anxiety and insomnia symptoms in clinicians. Bridge symptoms included nodes PHQ3 (“Sleep”) (bridge EI=0.55) and PHQ4 (“Fatigue”) (bridge EI=0.49). Gender did not significantly influence the network structure, but “having the experience of caring for COVID-19 patients” significantly influenced the network structure. Conclusion: Central symptoms and key bridge symptoms identified in this NA should be targeted in the treatment and preventive measures for clinicians suffering from comorbid anxiety, insomnia and depressive symptoms during the late stage of the COVID-19 pandemic.