Despite the significant public health burden of common mental disorders (CMDs) among refugee populations, their underlying symptom structures remain underexplored. This study uses Gaussian graphical modeling to examine the symptom network of post-traumatic stress disorder (PTSD), depression, anxiety, and somatic distress among Eritrean refugees in the Greater Washington, DC area. Given the small sample size (n) and high-dimensional symptom space (p), we propose a novel extension of the standard graphical LASSO by incorporating adaptive penalization, which improves sparsity selection and network estimation stability under n < p conditions. To evaluate the reliability of the network, we apply bootstrap resampling and use centrality measures to identify the most influential symptoms. Our analysis identifies six distinct symptom clusters, with somatic-anxiety symptoms forming the most interconnected group. Notably, symptoms such as nausea and reliving past experiences emerge as central symptoms linking PTSD, anxiety, depression, and somatic distress. Additionally, we identify symptoms like feeling fearful, sleep problems, and loss of interest in activities as key symptoms, either being closely positioned to many others or acting as important bridges that help maintain the overall network connectivity, thereby highlighting their potential importance as possible intervention targets.

翻译：尽管常见精神障碍（CMDs）在难民群体中造成了重大的公共卫生负担，但其潜在的症状结构仍未得到充分探索。本研究采用高斯图模型，考察了华盛顿特区大都会区厄立特里亚难民中创伤后应激障碍（PTSD）、抑郁、焦虑和躯体困扰的症状网络。鉴于样本量（n）较小且症状空间（p）维度较高，我们提出了一种标准图套索（graphical LASSO）的新扩展方法，通过引入自适应惩罚，改进了在 n < p 条件下的稀疏性选择和网络估计稳定性。为评估网络的可靠性，我们应用了自助重采样方法，并利用中心性度量来识别最具影响力的症状。我们的分析识别出六个不同的症状簇，其中躯体-焦虑症状构成了连接最紧密的组群。值得注意的是，恶心和重现过去经历等症状成为连接PTSD、焦虑、抑郁和躯体困扰的中心症状。此外，我们识别出如感到恐惧、睡眠问题和丧失活动兴趣等症状为关键症状，它们要么与许多其他症状紧密相连，要么充当维持整体网络连通性的重要桥梁，从而凸显了它们作为潜在干预目标的重要性。