Zhang Chao, Li Ruyong, Zhang Wei, Tao Yanqiang, Liu Xiangping, Lv Yichao
School of Education Science, Shanxi Normal University, Taiyuan, China.
Institute of Applied Psychology, Shanxi Normal University, Taiyuan, China.
BMC Psychiatry. 2025 May 6;25(1):457. doi: 10.1186/s12888-025-06931-z.
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.
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.
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.
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.
Not applicable.
在中国动态清零政策正式结束后，医护人员继续承受着繁重的工作量和心理压力。在这一新阶段，与工作和家庭相关的担忧，而非感染，可能已成为医护人员抑郁和焦虑等心理问题的新来源，导致了新的共病模式。然而，很少有研究探讨这些问题。为填补这一空白，本研究采用网络分析方法来研究抑郁和焦虑症状共病的新特征及机制，并模拟针对特定症状的干预措施以确定有效的干预靶点。
共招募了708名中国医护人员（女性占71.2%；年龄：M = 37.55，标准差 = 9.37），他们完成了患者健康问卷-9（PHQ-9）和广泛性焦虑障碍量表-7（GAD-7）。本研究首先计算了焦虑、抑郁及其共病的发生率，然后构建了共病伊辛网络。分别用预期影响（EI）和桥梁EI确定中心症状和桥梁症状。然后使用节点识别算法（NIRA）在网络内模拟干预措施，考察减轻或加重特定症状对网络严重程度的影响。
48.2%的中国医护人员报告有抑郁（19.8%）、焦虑（11.7%）或两者皆有（16.2%）。在焦虑-抑郁网络中，“内疚”和“食欲改变”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，减轻“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靶点。
在动态清零政策后的时代，中国医护人员仍面临着较高的抑郁、焦虑及共病风险。我们的研究结果突出了内疚、食欲改变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，制定针对快感缺失的干预策略，并着重预防内疚感，以改善医护人员的心理健康。
不适用。
