Establishment and Validation of a Risk Prediction Model for Non-Suicidal Self-Injury Among Adolescents Based on Machine Learning Methods – Jiangsu Province, China, 2023
Establishment and Validation of a Risk Prediction Model for Non-Suicidal Self-Injury Among Adolescents Based on Machine Learning Methods – Jiangsu Province, China, 2023

Establishment and Validation of a Risk Prediction Model for Non-Suicidal Self-Injury Among Adolescents Based on Machine Learning Methods – Jiangsu Province, China, 2023

China CDC Wkly. 2025 Jul 11;7(28):952-958. doi: 10.46234/ccdcw2025.160.

ABSTRACT

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 (CI): 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.

PMID:40671703 | PMC:PMC12259475 | DOI:10.46234/ccdcw2025.160