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

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.

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Publication Details

Journal
PLoS ONE
Published
2024-09-16
DOI
https://doi.org/10.1371/journal.pone.0303632
Citations
3
Primary Topic
Migration, Health and Trauma
Type
article
Field-Weighted Citation Impact
1.92

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article

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

David M. Chan, Muna Saleh, Kyeongmo Kim, Elizabeth B. Amona et al.
3 citations
PLoS ONE
Migration, Health and Trauma
1.92
article

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

David M. Chan, Muna Saleh, Kyeongmo Kim, Elizabeth B. Amona, Hannah George, Miriam Kuttikat, Indranil Sahoo, Jennifer Murphy, Marianne B. Lund
article en
3 citations

Abstract

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.

PLoS ONEVol. 19(9)
Virginia Commonwealth University (US), The University of Texas at Arlington (US), George Washington University (US)
Fogarty International Center
Reduced inequalities
Openalex Percentile: Top 14%
Migration, Health and Trauma
1.92
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