Xin Yiliang, Wang Yan, Zhang Xiyan, Li Peixuan, Yang Wenyi, Wang Bosheng, Yang Jie
Department of Child and Adolescent Health Promotion, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing City, Jiangsu Province, China.
Department of Occupational Disease Prevention and Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing City, Jiangsu Province, China.
Child Adolesc Psychiatry Ment Health. 2025 Aug 31;19(1):100. doi: 10.1186/s13034-025-00959-5.
This study investigates the current mental health status among children and adolescents in Jiangsu Province by analyzing symptoms of depression, anxiety, and stress using standardized psychological scales. Machine learning models were utilized to identify key influencing variables and predict mental health outcomes, aiming to establish a rapid psychological well-being assessment framework for this population.
A cross-sectional survey was conducted via random cluster sampling across 98 counties (cities/districts) in Jiangsu Province, enrolling 141,725 students (47,502 primary, 47,274 junior high, 11,619 vocational high school students, and 35,330 senior high ). The study focused on prevalent mental health disorders and associated risk factors.
Depression, anxiety, and stress scores served as dependent variables, with 57 socio-demographic and behavioral factors as independent variables. Five supervised machine learning models (Decision Tree, Naive Bayes, Random Forest, K-Nearest Neighbors (KNN), and XGBoost) were implemented using R software. Model performance was evaluated using accuracy, precision, recall, F1 Score and Area Under the ROC Curve (AUC). Feature importance analysis was conducted to identify key predictors.
The study revealed significant mental health disparities: depression (14.9%), anxiety (25.5%), and stress (10.9%) prevalences showed clear gender and regional gradients. Females exhibited higher rates across all conditions (p < 0.05), and urban areas had elevated risks compared to suburban regions. Mental health deterioration escalated with educational stages (e.g., depression from 9.2% in primary to 21.2% in senior high; χ² = 2274.55, p < 0.05). The XGBoost model demonstrated optimal predictive performance (AUC: depression = 0.799, anxiety = 0.770, stress = 0.762), outperforming other models. Feature importance analysis consistently identified bullying duration, age, and drinking history as top risk factors across both Gain and SHAP methods, while SHAP values additionally emphasized modifiable lifestyle factors (e.g., breakfast frequency) and demographic variables (e.g., gender).
This study identifies bullying, age, and alcohol consumption history as key mental health risk factors among Jiangsu's children and adolescents. These findings emphasize the need for school-based anti-bullying programs, age-specific mental health counseling, and healthy lifestyle education (including alcohol refusal). Lifestyle behaviors like daily breakfast intake should be integrated into dietary interventions for mental health promotion. Urban-rural and gender disparities necessitate targeted support for urban adolescent females, while educational stage differences highlight the criticality of early prevention.
本研究通过使用标准化心理量表分析抑郁、焦虑和压力症状，调查江苏省儿童和青少年的当前心理健康状况。利用机器学习模型识别关键影响变量并预测心理健康结果，旨在为该人群建立一个快速的心理健康评估框架。
通过随机整群抽样对江苏省98个县（市/区）进行横断面调查，招募141725名学生（47502名小学生、47274名初中生、11619名职业高中生和35330名高中生）。该研究关注普遍存在的心理健康障碍及相关风险因素。
将抑郁、焦虑和压力得分作为因变量，57个社会人口学和行为因素作为自变量。使用R软件实施五个监督机器学习模型（决策树、朴素贝叶斯、随机森林、K近邻（KNN）和XGBoost）。使用准确率、精确率、召回率、F1分数和ROC曲线下面积（AUC）评估模型性能。进行特征重要性分析以识别关键预测因素。
该研究揭示了显著的心理健康差异：抑郁（14.9%）、焦虑（25.5%）和压力（10.9%）的患病率呈现出明显的性别和地区梯度。在所有情况下，女性的患病率更高（p < 0.05），与郊区相比，城市地区的风险更高。心理健康状况随教育阶段而恶化（例如，抑郁从小学的9.2%升至高中的21.2%；χ² = 2274.55，p < 0.05）。XGBoost模型表现出最佳的预测性能（AUC：抑郁 = 0.799，焦虑 = 0.770，压力 = 0.762），优于其他模型。特征重要性分析通过增益法和SHAP法一致确定欺凌持续时间、年龄和饮酒史为主要风险因素，而SHAP值还强调了可改变的生活方式因素（如早餐频率）和人口统计学变量（如性别）。
本研究确定欺凌、年龄和饮酒史是江苏省儿童和青少年心理健康的关键风险因素。这些发现强调了开展基于学校的反欺凌项目、针对特定年龄的心理健康咨询以及健康生活方式教育（包括拒绝饮酒）的必要性。日常早餐摄入等生活方式行为应纳入促进心理健康的饮食干预措施中。城乡和性别差异需要对城市青少年女性提供有针对性的支持，而教育阶段差异凸显了早期预防的重要性。
