Dell Nathaniel A, Salas-Wright Christopher P, Vaughn Michael G, Maldonado-Molina Mildred M, Oh Sehun, Bates Melissa, Schwartz Seth J
Washington University School of Medicine in St. Louis, Department of Psychiatry, United States of America.
Boston College School of Social Work, United States of America.
J Affect Disord. 2024 Feb 15;347:77-84. doi: 10.1016/j.jad.2023.11.055. Epub 2023 Nov 20.
Data science approaches have increasingly been used in behavioral health research and may be useful for addressing social factors contributing to disparities in health status. This study evaluated the importance of cultural stress-related factors in classifying depression and post-traumatic stress disorder (PTSD) among adult survivors (N = 319) of Hurricane Maria who migrated from Puerto Rico to the United States mainland.
We evaluated the performance of random forests (RF) and logistic regression (LR) for classifying PTSD and depression. Models included demographic, hurricane exposure, and migration-related cultural stress variables. We inspected area under the receiver operating characteristic curve (AUC), accuracy, balanced accuracy, F1 score, precision, recall, and specificity.
Negative context of reception and language-related stressors were moderately important for accurately classifying depression and PTSD. For classifying depression, RF showed higher accuracy, balanced accuracy, specificity, precision, and F1. For classifying PTSD, RF showed higher accuracy, specificity, precision, and F1.
A more thorough classification model would also include biomarkers (e.g., of allostatic load), family, community, or neighborhood-level attributes. Findings may not generalize to other groups who have experienced crisis-related migration.
Findings underscore the importance of culturally and linguistically appropriate and trauma-informed clinical services for recent migrants. Use of assessments to identify pre-migration and post-migration stressors could inform clinical practice with migrants presenting with behavioral health-related difficulties.
数据科学方法在行为健康研究中的应用日益广泛，可能有助于解决导致健康状况差异的社会因素。本研究评估了与文化压力相关的因素在对从波多黎各移民到美国大陆的成年飓风玛丽亚幸存者（N = 319）中的抑郁症和创伤后应激障碍（PTSD）进行分类时的重要性。
我们评估了随机森林（RF）和逻辑回归（LR）对PTSD和抑郁症进行分类的性能。模型包括人口统计学、飓风暴露以及与移民相关的文化压力变量。我们检查了受试者工作特征曲线下面积（AUC）、准确性、平衡准确性、F1分数、精确率、召回率和特异性。
接受的负面背景和与语言相关的压力源对于准确分类抑郁症和PTSD具有中等重要性。对于抑郁症分类，RF显示出更高的准确性、平衡准确性、特异性、精确率和F1分数。对于PTSD分类，RF显示出更高的准确性、特异性、精确率和F1分数。
一个更全面的分类模型还应包括生物标志物（例如，关于应激负荷的）、家庭、社区或邻里层面的属性。研究结果可能不适用于经历过与危机相关移民的其他群体。
研究结果强调了为近期移民提供文化和语言上合适且考虑创伤因素的临床服务的重要性。使用评估工具来识别移民前和移民后的压力源可为有行为健康相关困难的移民的临床实践提供参考。
