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| Construction of a prediction model for comorbidity of depressive and anxiety symptoms among middle school students |
| LI Huiyu1, YANG Yunjuan1,2,3
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1. School of Public Health, Kunming Medical University, Kunming, Yunnan 650500, China;
2. Yunnan Provincial Center for Disease Control and Prevention, Kunming, Yunnan 650500, China;
3. School of Public Health, Dali University, Dali, Yunnan 650000, China |
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Abstract Objective To construct a prediction model for the comorbidity of depressive and anxiety symptoms among middle school students, so as to enable early identification of these symptoms in this population. Methods From June to August 2025, a multi-stage stratified random cluster sampling method was used to select students from 8 junior high schools and 8 senior high schools across 4 cities in Yunnan Province. Self-administered questionnaires were used to collect demographic information, including age and gender. The Center for Epidemiologic Studies Depression Scale, the Generalized Anxiety Disorder scale, and the Adolescent Self-Rating Life Events Scale were used to assess depressive symptoms, anxiety symptoms, and life stress caused by negative life events in the past year, respectively. A multinomial logistic regression model was employed to screen predictive factors, followed by the development of a random forest model to predict comorbidity of depressive and anxiety symptoms among middle school students. Model performance was evaluated using receiver operating characteristic (ROC) curves and calibration plots. Results A total of 1 173 middle school students were surveyed, including 569 males (48.51%) and 604 females (51.49%). There were 616 junior high school students (52.51%) and 557 senior high school students (47.49%). The comorbidity of depressive and anxiety symptoms was detected in 238 students, with a detection rate of 20.29%. Multivariable logistic regression analysis revealed that gender (female, OR=1.625, 95%CI: 1.118-2.363), residence (rural area, OR=1.845, 95%CI: 1.202-2.832), left-behind experience (OR=1.665, 95%CI: 1.069-2.595), parenting style (authoritative type, OR=0.301, 95%CI: 0.164-0.554), electronic device usage duration (3-<5 h/d, OR=2.442, 95%CI: 1.242-4.803), sleep deprivation (OR=2.316, 95%CI: 1.574-3.407), and negative life stressors (OR=1.110, 95%CI: 1.093-1.128) were significant risk predictors for comorbidity of depressive and anxiety symptoms among middle school students. The random forest model analysis indicated that the top five variables in terms of importance ranking were negative life stress, residence, left-behind experience, sleep deprivation, and electronic device usage duration. The area under the ROC curve, sensitivity, and specificity were 0.834, 0.731, and 0.792 for the training set, and 0.795, 0.732, and 0.771 for the validation set, respectively. The calibration curves showed good model fit for both the training and validation sets (both P>0.05). Conclusions The comorbidity of depressive and anxiety symptoms among middle school students in this survey is primarily associated with negative life stress, residence, left-behind experience, sleep, and electronic device usage duration. The constructed random forest model demonstrates certain predictive value.
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Received: 19 December 2025
Revised: 31 March 2026
Published: 21 April 2026
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[1] World Health Organization.World mental health report 2022:transforming mental health for all[R].Geneva:WHO,2022.
[2] SOLMI M,RADUA J,OLIVOLA M,et al.Age at onset of mental disorders worldwide:large-scale meta-analysis of 192 epidemiological studies[J].Mol Psychiatry,2022,27(1):281-295.
[3] TARIQ A,GRAY E,M GREGORY A,et al.Emotional Vulnerability in Adolescents(EVA)Longitudinal Study:identifying individual differences in symptoms of adolescent depression and anxiety and their biopsychosocial mechanisms based on demographic and mental health characteristics[J/OL].Wellcome Open Res,2024,9[2026-03-31].https://doi.org/10.12688/wellcomeopenres.22685.2.
[4] KIELING C,BUCHWEITZ C,CAYE A,et al.Worldwide prevalence and disability from mental disorders across childhood and adolescence:evidence from the global burden of disease study[J].JAMA Psychiatry,2024,81(4):347-356.
[5] 於阅,李骏.流动儿童的家庭教养方式与学校适应[J].青年研究,2025(2):59-77,195.
[6] 李霞,熊鹰,杨思维,等.重庆市中学生抑郁焦虑症状共存现状及影响因素分析[J].中国学校卫生,2025,46(10):1443-1448.
[7] 辛秀红,姚树桥.青少年生活事件量表效度与信度的再评价及常模更新[J].中国心理卫生杂志,2015,29(5):355-360.
[8] RADLOFF L S.The CES-D scale:a self-report depression scale for research in the general population[J].Appl Psychol Meas,1977,1(3):385-401.
[9] 梁茵茵,袁瑞,刘光涛,等.湖州市中学生抑郁症状现况调查[J].预防医学,2025,37(6):622-626,631.
[10] SPITZER R L,KROENKE K,WILLIAMS J B,et al.A brief measure for assessing generalized anxiety disorder:the GAD-7[J].Arch Intern Med,2006,166(10):1092-1097.
[11] 高红霞,陈靖,张晓红,等.基于随机森林算法的中学生焦虑、抑郁影响因素分析及预测模型构建[J].现代预防医学,2025,52(2):267-273,368.
[12] 王萌,陶芳标,伍晓艳.儿童青少年焦虑抑郁共病研究进展[J].中华预防医学杂志,2022,56(7):1011-1016.
[13] 郑帅印,李丽丹,陈佩弟,等.2型糖尿病合并非酒精性脂肪肝的预测模型研究[J].预防医学,2024,36(9):741-745,749.
[14] 曾玲,张丽,任悄悄,等.2024年四川省中学生抑郁焦虑症状共患现状及其影响因素分析[J].预防医学情报杂志,2025,41(8):1031-1037.
[15] MOUSIKOU M,KYRIAKOU A,SKORDIS N.Stress and growth in children and adolescents[J].Horm Res Paediatr,2023,96(1):25-33.
[16] 姜敏,刘芳,肖媛媛.农村留守儿童的自伤情况及其与焦虑、抑郁的关系[J].心理月刊,2024,19(5):31-33.
[17] 黄友,舒逍,张敬,等.重庆市中学生抑郁焦虑共患现状及其与睡眠不足和网络成瘾的关系[J].现代预防医学,2025,52(24):4558-4563.
[18] HAMILTON O S,STEPTOE A,AJNAKINA O.Polygenic predisposition,sleep duration,and depression:evidence from a prospective population-based cohort[J/OL].Transl Psychiat,2023,13(1)[2026-03-31].https://doi.org/10.1038/s41398-023-02622-z. |
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