Lei Tingting, Qiu Huiling, Liu Xueer, Li Xuemei, He Yuqian, Huang Yajie, Yang Boyi, Zhou Xinyu
Department of Psychiatry, Key Laboratory of Major Brain Disease and Aging Research (Ministry of Education), The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
School of Public Health, Sun Yat-Sen University, Guangzhou, China.
J Affect Disord. 2025 Nov 15;389:119678. doi: 10.1016/j.jad.2025.119678. Epub 2025 Jun 14.
Identifying key risk factors for depressive symptoms in children and adolescents is crucial for prevention. However, few studies have explored this topic. This study aimed to examine the prevalence of depressive symptoms in Chinese children and adolescents and rank prominent risk factors.
A total of 198,062 children and adolescents were recruited from Chongqing, China. The Center for Epidemiological Studies Depression Scale for Children was used to assess depressive symptoms. Covariates were collected via single-item questions and scales. Logistic regression and three machine learning (ML) methods (Random Forest, Random Ferns, and Extreme Gradient Boosting) were used to identify and rank risk factors. Subgroup analysis was conducted to examine variations among different demographics.
The prevalence of depressive symptoms among children and adolescents was 29.6%. Risk factors included socio-demographics, family background, lifestyle, academic performance, trauma experiences, relationships, and personality traits (adjusted odds ratio = 1.03-1.88, all p < 0.05). ML analysis highlighted nine key risk factors: psychological resilience, age, sleep satisfaction, self-expectations, gender, relationship with father, academic performance, relationship with classmates, and breakfast frequency. A minimal model was established to identify depressive symptoms, achieving an AUC value of 0.844. Subgroup analysis showed similar factor rankings pattern in the overall sample.
Our study identified psychological resilience, age, sleep satisfaction, self-expectations, gender, relationship with father, academic performance, relationship with classmates, and breakfast frequency may be the prominent factors for Chinese children and adolescents. These factors should be prioritized in the prevention process of depressive symptoms.
识别儿童和青少年抑郁症状的关键风险因素对于预防至关重要。然而，很少有研究探讨这个话题。本研究旨在调查中国儿童和青少年抑郁症状的患病率，并对突出的风险因素进行排名。
从中国重庆招募了总共198,062名儿童和青少年。使用儿童流行病学研究中心抑郁量表来评估抑郁症状。通过单项问题和量表收集协变量。使用逻辑回归和三种机器学习（ML）方法（随机森林、随机蕨类和极端梯度提升）来识别和排名风险因素。进行亚组分析以检查不同人口统计学之间的差异。
儿童和青少年抑郁症状的患病率为29.6%。风险因素包括社会人口统计学、家庭背景、生活方式、学业成绩、创伤经历、人际关系和人格特质（调整后的优势比 = 1.03 - 1.88，所有p < 0.05）。ML分析突出了九个关键风险因素：心理韧性、年龄、睡眠满意度、自我期望、性别、与父亲的关系、学业成绩、与同学的关系和早餐频率。建立了一个最小模型来识别抑郁症状，AUC值为0.844。亚组分析显示总体样本中因素排名模式相似。
我们的研究确定心理韧性、年龄、睡眠满意度、自我期望、性别、与父亲的关系、学业成绩、与同学的关系和早餐频率可能是中国儿童和青少年的突出因素。在抑郁症状的预防过程中应优先考虑这些因素。
