Ranking factors across multiple domains in predicting adolescent mental health: a Bayesian machine learning approach.

Background: The prevalence of mental health problems among adolescents is on the rise globally, and is a pressing public health concern in many developing countries, including China. While a growing body of epidemiological research has identified potential factors affecting adolescent mental health, few have considered both risk and protective factors across multiple domains or utilized machine learning approaches to identify and rank these factors.

Methods: This is a cross-sectional study based on data from 3,526 adolescent participants aged 11-15 years in the Qu County Study in China, and aims to identify and rank factors across five domains-including sociodemographic factors, academic functioning, extracurricular activities, life experiences, and resilience factors-in predicting adolescent mental health outcomes. A Bayesian machine learning approach is used to identify and rank important factors in predicting adolescent mental health outcomes, including depressive symptoms, anxiety symptoms, and sleep quality.

Results: The machine learning models showed satisfactory predictive performance across outcomes (pseudo-R² = 0.24-0.61; RMSE = 0.65-3.60). Experiences of life stress, benevolent events, environmental sensitivity, and shift-and-persist coping strategies were common top predictors in predicting depressive symptoms, anxiety symptoms, and sleep quality. Stress mindset and expressive suppression strategies were unique predictors of sleep quality and depressive symptoms, respectively.

Conclusions: Our results revealed the importance of life experience and resilience factors in predicting adolescent mental health. Future studies should investigate the causal relationship between these understudied factors and adolescent mental health.