Gender Differences in Psychosocial Pathways to Depression and Anxiety: Cross-Sectional and Bayesian Causal Network Study.

IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Han Zhang, Ye Xia, Peicai Fu, Cun Li, Ke Shi, Yuan Yang
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引用次数: 0

Abstract

Background: Depression and anxiety are widespread disorders with documented gender differences in symptom progression and associated psychosocial factors. However, the complex interrelationships between childhood trauma, self-esteem, social support, emotion regulation, and their gender-specific impacts on the development of depression and anxiety remain unclear.

Objective: The objective of this study was to investigate the network structures of depression, anxiety, and psychosocial factors and to examine the pathways contributing to the development of depression and anxiety, with a focus on gender-specific differences.

Methods: This study included 6105 participants from across China, collecting their sociodemographic characteristics and psychological scale data. Cross-sectional network analysis was used to explore the complex relationships between depression, anxiety, insomnia, somatic symptoms, childhood trauma, self-esteem, social support, and emotional regulation. Subsequently, Bayesian network analysis was used to infer potential causal pathways. Gender differences in the network structures were specifically examined.

Results: Network analysis revealed strong associations among depression, anxiety, insomnia, and somatic symptoms. Network strength centrality exhibited the highest stability across overall networks (CS-C=0.75), with high predictability for depression (R²=72.4%) and anxiety (R²=64%), supporting the robustness of the model. The network structure invariance test between male and female participants was significant (P=.001). Furthermore, the Bayesian network analysis showed gender-specific symptom progression, where anxiety preceded depression in male participants, while depression preceded anxiety in female participants (with edges retained in nearly 100% of bootstrap samples). Self-esteem, social support, and insomnia were central nodes in female participants, whereas emotion regulation was more influential in male participants. Additionally, childhood trauma influenced depression or anxiety indirectly through self-esteem and social support in both male and female participants.

Conclusions: This study presents a novel application of network analyses to delineate distinct gender-specific pathways in the development of depression and anxiety. The findings underscore insomnia, self-esteem, and social support as intervention targets for women and emotion regulation for men. Findings support gender-sensitive mental health strategies and emphasize the need for longitudinal validation.

抑郁和焦虑的社会心理通路的性别差异:横断面和贝叶斯因果网络研究。
背景:抑郁和焦虑是广泛存在的障碍,在症状进展和相关的社会心理因素方面有文献记载的性别差异。然而,童年创伤、自尊、社会支持、情绪调节及其对抑郁和焦虑发展的性别影响之间的复杂相互关系尚不清楚。目的:本研究的目的是研究抑郁、焦虑和心理社会因素的网络结构,并研究导致抑郁和焦虑发展的途径,重点关注性别差异。方法:收集全国6105名被试的社会人口学特征和心理量表数据。采用横断面网络分析探讨抑郁、焦虑、失眠、躯体症状、童年创伤、自尊、社会支持和情绪调节之间的复杂关系。随后,使用贝叶斯网络分析来推断潜在的因果途径。对网络结构中的性别差异进行了专门研究。结果:网络分析显示抑郁、焦虑、失眠和躯体症状之间有很强的联系。网络强度中心性在整个网络中表现出最高的稳定性(CS-C=0.75),对抑郁(R²=72.4%)和焦虑(R²=64%)具有较高的可预测性,支持模型的稳健性。男女参与者的网络结构不变性检验具有显著性(P=.001)。此外,贝叶斯网络分析显示了性别特异性的症状进展,男性参与者的焦虑先于抑郁,而女性参与者的抑郁先于焦虑(几乎100%的bootstrap样本保留了边缘)。自尊、社会支持和失眠是女性参与者的中心节点,而情绪调节对男性参与者的影响更大。此外,童年创伤通过自尊和社会支持间接影响了男性和女性参与者的抑郁或焦虑。结论:本研究提出了一种新的应用网络分析来描绘抑郁和焦虑发展中不同的性别特异性途径。研究结果强调,失眠、自尊和社会支持是女性的干预目标,而男性的情绪调节是干预目标。研究结果支持对性别问题敏感的心理健康战略,并强调需要进行纵向验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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