后动态零冠政策时代中国医护人员抑郁和焦虑共病症状干预目标的模拟网络分析

IF 3.4 2区 医学 Q2 PSYCHIATRY
Chao Zhang, Ruyong Li, Wei Zhang, Yanqiang Tao, Xiangping Liu, Yichao Lv
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引用次数: 0

摘要

背景:在中国“动态零冠”政策正式结束后,医护人员的工作量和心理压力持续加大。在这个新阶段,与工作和家庭有关的担忧,而不是感染,可能已成为保健工作者中抑郁和焦虑等心理问题的新来源,导致新的合并症模式。然而,很少有研究解决这些问题。为了填补这一空白,本研究使用网络分析来研究抑郁和焦虑症状共病的新特征和机制,并模拟症状特异性干预以确定有效的干预目标。方法:共708名中国医护人员(女性71.2%;年龄:M = 37.55, SD = 9.37),完成患者健康问卷-9 (PHQ-9)和广泛性焦虑障碍问卷-7 (GAD-7)。本研究首先计算焦虑、抑郁及其共病发生率,并构建共病Ising网络。中心症状和桥型症状分别被确定为预期影响(EI)和桥型EI。然后使用NodeIdentifyR算法(NIRA)模拟网络内的干预措施,检查减轻或加重特定症状对网络严重程度的影响。结果:48.2%的中国医护人员报告有抑郁(19.8%)、焦虑(11.7%)或两者兼而有之(16.2%)。在焦虑-抑郁网络中,“内疚”和“食欲变化”被确定为中心症状,“内疚”和“过度担忧”被确定为桥梁症状。模拟干预表明,缓解“快感缺失”最能降低网络的整体严重程度,而加重“内疚”最能增加网络的整体严重程度。这两种症状分别被认为是治疗和预防的关键目标。结论:在后动态零冠政策时代,我国医护人员抑郁、焦虑和共病风险仍然较高。我们的研究结果强调了内疚、食欲变化和过度担忧在抑郁和焦虑症状网络中的关键作用。未来的研究应应用模拟干预的结果,制定针对快感缺乏症的干预策略,并将重点放在预防内疚感上,以改善医护人员的心理健康。试验注册:不适用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A simulation-based network analysis of intervention targets for comorbid symptoms of depression and anxiety in Chinese healthcare workers in the post-dynamic zero-COVID policy era.

Background: After the official end of the dynamic zero-COVID policy in China, healthcare workers continued to heavy workloads and psychological stress. In this new phase, concerns related to work and family, rather than infection, may have become new sources of psychological issues such as depression and anxiety among healthcare workers, leading to new patterns of comorbidity. However, few studies have addressed these issues. To fill this gap, this study used network analysis to examine new features and mechanisms of comorbidity between depression and anxiety symptoms, and simulated symptom-specific interventions to identify effective targets for intervention.

Methods: A total of 708 Chinese healthcare workers (71.2% females; Age: M = 37.55, SD = 9.37) were recruited and completed the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7). This study first calculated the incidence rates of anxiety, depression, and their comorbidity, and then constructed the comorbid Ising network. Central and bridge symptoms were identified with expected influence (EI) and bridge EI, respectively. The NodeIdentifyR algorithm (NIRA) was then used to simulate interventions within the network, examining the effects of alleviating or aggravating specific symptoms on the network's severity.

Results: 48.2% of Chinese healthcare workers reported experiencing depression (19.8%), anxiety (11.7%), or both (16.2%). In the anxiety-depression network, "guilt" and "appetite changes" were identified as the central symptoms, and "guilt" and "excessive worry" were identified as the bridge symptoms. Simulated interventions suggested that alleviating "Anhedonia" can the most reduce the overall severity of the network, while aggravating "guilt" can the most increase the overall severity. These two symptoms were considered the key target for treatment and prevention, respectively.

Conclusions: Chinese healthcare workers still face high risk of depression, anxiety, and comorbidity in the post-dynamic zero-COVID policy era. Our findings highlight the key roles of guilt, appetite changes, and excessive worry in the network of depression and anxiety symptoms. Future research should apply the results of the simulated interventions, develop intervention strategies targeting anhedonia, and focus on preventing guilt to improve the healthcare workers' mental health.

Trial registration: Not applicable.

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来源期刊
BMC Psychiatry
BMC Psychiatry 医学-精神病学
CiteScore
5.90
自引率
4.50%
发文量
716
审稿时长
3-6 weeks
期刊介绍: BMC Psychiatry is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of psychiatric disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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