基于机器学习方法的青少年非自杀性自伤风险预测模型的建立与验证——中国江苏省，2023年
Establishment and Validation of a Risk Prediction Model for Non-Suicidal Self-Injury Among Adolescents Based on Machine Learning Methods - Jiangsu Province, China, 2023.
作者信息
Wang Xin, Wang Yan, Tang Jiawen, Wang Yang, Zhang Ran, Zhang Xiyan, Yang Wenyi, Du Wei, Wang Fei, Yang Jie
机构信息
Department of Child and Adolescent Health Promotion, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing City, Jiangsu Province, China.
School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China.
出版信息
China CDC Wkly. 2025 Jul 11;7(28):952-958. doi: 10.46234/ccdcw2025.160.
WHAT IS ALREADY KNOWN ABOUT THIS TOPIC?: Non-suicidal self-injury (NSSI) has become increasingly common among adolescents, posing a significant public health concern that impacts both physical and mental well-being.
WHAT IS ADDED BY THIS REPORT?: A total of 12.72% of adolescents aged 10-18 had engaged in NSSI in Jiangsu Province, China. A well-calibrated risk prediction model [AUC=0.800, 95% confidence interval (): 0.776, 0.823] identified 8 key predictors of NSSI: insomnia, emotional symptoms, cohesion of family environment, history of drinking alcohol, gender, conflict of family environment, conduct problems, and academic level.
WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE?: This study underscores the importance of personalized prevention strategies for NSSI and highlights the necessity of implementing comprehensive behavioral interventions, such as providing mental health support, enhancing sleep quality, and cultivating supportive family environments.
关于该主题已知的信息有哪些？：非自杀性自伤行为（NSSI）在青少年中日益普遍，这引发了重大的公共卫生问题，对身心健康均有影响。
本报告新增了哪些内容？：在中国江苏省，10至18岁的青少年中，共有12.72%曾有过非自杀性自伤行为。一个校准良好的风险预测模型（AUC = 0.800，95%置信区间：0.776，0.823）确定了非自杀性自伤行为的8个关键预测因素：失眠、情绪症状、家庭环境凝聚力、饮酒史、性别、家庭环境冲突、行为问题和学业水平。
对公共卫生实践有何启示？：本研究强调了针对非自杀性自伤行为采取个性化预防策略的重要性，并突出了实施全面行为干预措施的必要性，例如提供心理健康支持、提高睡眠质量以及营造支持性的家庭环境。
Zotero 插件