Leading Predictors of COVID-19-Related Poor Mental Health in Adult Asian Indians: An Application of Extreme Gradient Boosting and Shapley Additive Explanations

Mohammad Ikram; Nazneen Fatima Shaikh; Jamboor K Vishwanatha; Usha Sambamoorthi

doi:10.3390/ijerph20010775

Leading Predictors of COVID-19-Related Poor Mental Health in Adult Asian Indians: An Application of Extreme Gradient Boosting and Shapley Additive Explanations

Int J Environ Res Public Health. 2022 Dec 31;20(1):775. doi: 10.3390/ijerph20010775.

Authors

Mohammad Ikram¹, Nazneen Fatima Shaikh¹, Jamboor K Vishwanatha², Usha Sambamoorthi³

Affiliations

¹ Department of Pharmaceutical Systems and Policy, School of Pharmacy, West Virginia University, Robert C. Byrd Health Sciences Center [North], P.O. Box 9510, Morgantown, WV 26506-9510, USA.
² Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX 76107, USA.
³ Department of Pharmacotherapy, University of North Texas Health Science Center, Fort Worth, TX 76107, USA.

Abstract

During the COVID-19 pandemic, an increase in poor mental health among Asian Indians was observed in the United States. However, the leading predictors of poor mental health during the COVID-19 pandemic in Asian Indians remained unknown. A cross-sectional online survey was administered to self-identified Asian Indians aged 18 and older (N = 289). Survey collected information on demographic and socio-economic characteristics and the COVID-19 burden. Two novel machine learning techniques-eXtreme Gradient Boosting and Shapley Additive exPlanations (SHAP) were used to identify the leading predictors and explain their associations with poor mental health. A majority of the study participants were female (65.1%), below 50 years of age (73.3%), and had income ≥ $75,000 (81.0%). The six leading predictors of poor mental health among Asian Indians were sleep disturbance, age, general health, income, wearing a mask, and self-reported discrimination. SHAP plots indicated that higher age, wearing a mask, and maintaining social distancing all the time were negatively associated with poor mental health while having sleep disturbance and imputed income levels were positively associated with poor mental health. The model performance metrics indicated high accuracy (0.77), precision (0.78), F1 score (0.77), recall (0.77), and AUROC (0.87). Nearly one in two adults reported poor mental health, and one in five reported sleep disturbance. Findings from our study suggest a paradoxical relationship between income and poor mental health; further studies are needed to confirm our study findings. Sleep disturbance and perceived discrimination can be targeted through tailored intervention to reduce the risk of poor mental health in Asian Indians.

Keywords: COVID-19; XGBoost; adult Asian Indians; discrimination; economic crisis; interpersonal trust; machine learning; mental health; predictors; sleep disturbance.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Asian People
COVID-19* / epidemiology
Cross-Sectional Studies
Female
Humans
Male
Mental Health
Middle Aged
Pandemics
United States

Grants and funding

This research was, in part, funded by the National Institutes of Health Agreement No. 1OT2OD032581 AIM-AHEAD (Usha Sambamoorthi, Jamboor K Vishwanatha), National Institutes of Health- NIH/1OT2HL158258 Texas CEAL Alliance (Usha Sambamoorthi, Jamboor K Vishwanatha), and the National Institute on Minority Health and Health Disparities through the Texas Center for Health Disparities (NIMHD), 5U54MD006882 (Usha Sambamoorthi, Jamboor K Vishwanatha). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the NIH.