Department of Psychological Medicine, Children's Hospital of Fudan University, Shanghai, China.
Department of General Medicine, Zhoupu Health Service Center, Pudong New Area, Shanghai, China.
Front Public Health. 2023 Jan 9;10:1083144. doi: 10.3389/fpubh.2022.1083144. eCollection 2022.
Our study aimed to identify the latent class of depressive symptoms in the Shanghai population during the city-wide temporary static management period and compare differences in the factors influencing depressive symptoms between medical staff and residents.
An online cross-sectional survey was conducted with 840 participants using questionnaires, including Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Pittsburgh Sleep Quality Index (PSQI), and self-compiled questionnaire (demographic characteristics and internet usage time). Latent class analysis (LCA) was performed based on participants' depressive symptoms. The latent class subgroups were compared using the chi-square test and test. Logistic regression was used in our study to analyze the factors influencing depressive symptoms within the medical staff group and residents group and then compare their differences.
Two distinct subgroups were identified based on the LCA: the group with low-depressive symptoms and the group with high-depressive symptoms. There were significant differences between the two groups ( < 0.05) on age, education level, marital status, internet usage time, identity characteristics (medical staff or residents), family income level, living style, overall quality of sleep, and anxiety levels. Furthermore, logistic regression analysis results showed that compared with the residents group, the participants in the group of medical staff with "increasing internet usage time" and the "daytime dysfunction" would have nearly two times the possibility of getting serious depressive symptoms.
There are differences in the factors influencing depression symptoms between medical staff and residents during the 2022 city-wide temporary static management period to fighting against the COVID-19 pandemic in Shanghai. We should pay special attention to those with increasing internet usage time and daytime dysfunction in medical staff working in a special environment such as the COVID-19 pandemic.
本研究旨在识别上海全域静态管理期间人群的抑郁症状潜类别,并比较医务人员和居民中影响抑郁症状的因素差异。
采用在线横断面问卷调查法,对 840 名参与者进行问卷调查,包括患者健康问卷-9 项(PHQ-9)、广泛性焦虑障碍-7 项(GAD-7)、匹兹堡睡眠质量指数(PSQI)和自编问卷(人口统计学特征和上网时间)。基于参与者的抑郁症状进行潜在类别分析(LCA)。采用卡方检验和检验比较潜在类别亚组间的差异。采用 logistic 回归分析医务人员和居民群体中影响抑郁症状的因素,并比较其差异。
基于 LCA 确定了两个不同的亚组:低抑郁症状组和高抑郁症状组。两组在年龄、教育程度、婚姻状况、上网时间、身份特征(医务人员或居民)、家庭收入水平、生活方式、整体睡眠质量和焦虑水平等方面存在显著差异(<0.05)。此外,logistic 回归分析结果表明,与居民组相比,“上网时间增加”和“日间功能障碍”的医务人员组参与者出现严重抑郁症状的可能性几乎增加了两倍。
在上海抗击 2022 年全市全域静态管理期间的 COVID-19 疫情过程中,医务人员和居民影响抑郁症状的因素存在差异。对于处于 COVID-19 等特殊环境下工作的医务人员,应特别关注上网时间增加和日间功能障碍的人群。