TY - JOUR
T1 - A machine learning approach using migration-related cultural stress to classify depression and post-traumatic stress disorder among hurricane survivors
AU - Dell, Nathaniel A.
AU - Salas-Wright, Christopher P.
AU - Vaughn, Michael G.
AU - Maldonado-Molina, Mildred M.
AU - Oh, Sehun
AU - Bates, Melissa
AU - Schwartz, Seth J.
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2024/2/15
Y1 - 2024/2/15
N2 - Background: 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. Methods: 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. Results: 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. Limitations: 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. Conclusions: 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.
AB - Background: 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. Methods: 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. Results: 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. Limitations: 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. Conclusions: 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.
KW - Data science
KW - Depression
KW - Ethnic discrimination
KW - Post-traumatic stress disorder
UR - http://www.scopus.com/inward/record.url?scp=85177812155&partnerID=8YFLogxK
U2 - 10.1016/j.jad.2023.11.055
DO - 10.1016/j.jad.2023.11.055
M3 - Article
C2 - 37992771
AN - SCOPUS:85177812155
SN - 0165-0327
VL - 347
SP - 77
EP - 84
JO - Journal of affective disorders
JF - Journal of affective disorders
ER -