Child mental health predictors among camp Tamil refugees: Utilizing linear and XGBOOST models

• Published in: PloS one 2024-09, Vol.19 (9), p.e0303632
• Main Authors: Saleh, Muna, Amona, Elizabeth, Kuttikat, Miriam, Sahoo, Indranil, Chan, David, Murphy, Jennifer, Kim, Kyeongmo, George, Hannah, Lund, Marianne
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Algorithms; Analysis; Biology and Life Sciences; Care and treatment; Child; Child & adolescent mental health; Child mental health; Children; Data mining; Depression - epidemiology; Depression - psychology; Displaced persons; Emotional behavior; Families & family life; Family; Female; Health aspects; Human settlements; Humans; India; Linear Models; Living conditions; Machine Learning; Male; Medical care; Medicine and Health Sciences; Mental depression; Mental disorders; Mental Health; Middle Aged; Migration; Multivariate analysis; Parent and child; Parental behavior; Parenting; Parents; Parents & parenting; Parents - psychology; People and Places; Population studies; Psychological aspects; Psychopathology; Refugee Camps; Refugees; Refugees - psychology; Social Sciences; Statistical analysis; Surveys; Utilization
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0303632

While the association between migration and deteriorated refugee mental health is well-documented, existing research overwhelmingly centers on adult populations, leaving a discernible gap in our understanding of the factors influencing mental health for forcibly displaced children. This focus is particularly noteworthy considering the estimated 43.3 million children who are forcibly displaced globally. Little is known regarding the association between family processes, parental and child wellbeing for this population. This study addresses these gaps by examining the relationship between parental mental health and child mental health among refugees experiencing transmigration. We conducted in-person structured survey interviews with 120 parent-adolescent dyads living in the Trichy refugee camp in Tamil Nadu, India. Descriptive, multivariate analysis (hierarchical regression), and Machine Learning Algorithm (XGBOOST) were conducted to determine the best predictors and their importance for child depressive symptoms. The results confirm parental mental health and child behavioral and emotional factors are significant predictors of child depressive symptoms. While our linear model did not reveal a statistically significant association between child mental health and family functioning, results from XGBOOST highlight the substantial importance of family functioning in contributing to child depressive symptoms. The study's findings amplify the critical need for mental health resources for both parents and children, as well as parenting interventions inside refugee camps.