Establishment and validation of depressive symptom predictive model in middle school students
10.16835/j.cnki.1000-9817.2024219
- VernacularTitle:中学生抑郁症状预测模型的构建和验证
- Author:
TAN Zhenkun, ZHANG Zhuo, ZHANG Ying, PING Junjiao, LUO Jiali, ZHANG Jie, LIU Xinxia
1
Author Information
1. College of Public Health, Guangdong Pharmaceutical University, Guangzhou (510006) , Guangdong Province, China
- Publication Type:Journal Article
- Keywords:
Depression;
Mental health;
Nomograms;
Regression analysis;
Students
- From:
Chinese Journal of School Health
2024;45(7):998-1002
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the influencing factors of depressive symptoms and to construct and verify the prediction model of depressive symptoms in middle school students, so as to provide risk assessment tools for effectively screening depressive symptom.
Methods:Physical examination and questionnaire survey were conducted among middle school students in one city in Guangdong Province from September to October in 2021 ( n =2 376) and from September to October in 2022 ( n =4 344) by a multistage cluster sampling method, and a nomographic prediction model of depressive symptoms in middle school student was constructed. The questionnaire survey was conducted using the student health status and influencing factors questionnaire (secondary school version) and the Center for Epidemiological Studies Depression Scale (CES-D) to measure the lifestyle and depressive symptom of middle school students.
Results:The detection rate of depressive symptoms in 2021 was 23.3%. Multivariate Logistic regression analysis showed that irregular breakfast ( OR =2.64), school bullying ( OR =4.28), being beaten by parents ( OR =2.86), using mobile devices for a long time ( OR =1.08) and sitting for a long time ( OR =1.05) were positively related to depressive symptoms in middle school students ( P <0.05). Long sleep duration ( OR =0.78) and outdoor activity durations of 1-<2, 2-<3 and ≥3 h/d (compared with <1 h/d, OR =0.63, 0.61, 0.49) were negatively related to depressive symptoms in middle school students ( P < 0.05 ). Multivariate Logistic regression analysis showed that 7 statistically signifucant predictive factors constructed a nomogram, and the AUC of the nomogram was 0.77, which had been verified internally and externally with good differentiation and reliability.
Conclusions:The nomogram prediction model of depressive symptoms provides a convenient and effective risk assessment tool for depressive symptoms among middle school students. The life behavior, diet behavior and injury behavior of middle school students play an important role in the formation of depressive symptoms. It should pay attention to the impact of the behavioral factors on the mental health of middle school students.
