Lin Wang1, Li Chen2, Feiyong Jia3, Xinmiao Shi1, Yiwen Zhang4, Fei Li5, Yan Hao6, Yan Hou7, Hongzhu Deng8, Jie Zhang9, Linjuan Huang10, Xianghui Xie11, Shuanfeng Fang12, Qi Xu1, Liang Xu13, Hongyan Guan14, Weijie Wang15, Jianna Shen16, Feng Li17, Ying Qian18, Xi Wang1, Ling Shan4, Chuanxue Tan9, Jianhong Wang1, Yabin Yu1, Xiaoyan Wang1, Lili Zhang1, Xiaomeng Li1, Xiaoyan Ke19, Tingyu Li20. 1. Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing, China. 2. National Clinical Research Center for Child Health and Disorder, Children's Hospital of Chongqing Medical University, Chongqing, China. 3. Department of Developmental-Behavioral Pediatrics, the First Hospital of Jilin University, Changchun, Jilin, China. 4. Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center Affiliated to Medical School of Shanghai Jiaotong University, Shanghai, China. 5. Department of Developmental-Behavioral Pediatrics, Xinhua Hospital Affiliated to Medical School of Shanghai Jiaotong University, Shanghai, China. 6. Department of Child Health Care, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China. 7. Department of Biostatistics, Peking University, Beijing, China. 8. Child Developmental & Behavioral Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China. 9. Department of Child Health Care, Xi'an Children's Hospital, Xi'an, Shaanxi, China. 10. Health Management Center, Fuzhou Children's Hospital of Fujian Medical University, Fuzhou, Fujian, China. 11. Children's Hospital, Capital Institute of Pediatrics, Beijing, China. 12. Department of Child Health Care, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, Henan, China. 13. WHO Collaborating Center for Research and Training in Child Mental health, Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China. 14. Department of Early Childhood Development, Capital Institute of Pediatrics, Beijing, China. 15. Shanghai Pudong Institute of Education Development, Shanghai, China. 16. Chongqing educational science research academy, Chongqing, China. 17. Jiangjin centre hospital, chongqing, China. 18. Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health (Peking University), Beijing, China. 19. WHO Collaborating Center for Research and Training in Child Mental health, Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China. Electronic address: kexynj@hotmail.com. 20. National Clinical Research Center for Child Health and Disorder, Children's Hospital of Chongqing Medical University, Chongqing, China. Electronic address: tyli@vip.sina.com.
Abstract
BACKGROUND: We aimed to explore the risk profiles attributable to psychosocial and behavioural problems during the coronavirus disease 2019 pandemic. To this end, we created a risk-prediction nomogram model. METHODS: A national multicentre study was conducted through an online questionnaire involving 12,186 children (6-11 years old) and adolescents (12-16 years old). Respondents' psychosocial and behavioural functioning were assessed using the Achenbach Child Behaviour Checklist (CBCL). Data were analysed using STATA software and R-language. RESULTS: The positive detection rate of psychological problems within Wuhan was greater than that outside Wuhan for schizoid (P = 0.005), and depression (P = 0.030) in children, and for somatic complaints (P = 0.048), immaturity (P = 0.023), and delinquent behaviour (P = 0.046) in adolescents. After graded multivariable adjustment, seven factors associated with psychological problems in children and adolescents outside Wuhan were parent-child conflict (odds ratio (OR): 4.94, 95% confidence interval (95% CI): 4.27-5.72), sleep problems (OR: 4.05, 95% CI: 3.77-4.36), online study time (OR: 0.41, 95% CI: 0.37-0.47), physical activity time (OR: 0.510, 95% CI: 0.44-0.59), number of close friends (OR: 0.51, 95% CI: 0.44-0.6), time spent playing videogames (OR: 2.26, 95% CI: 1.90-2.69) and eating disorders (OR: 2.71, 95% CI: 2.35-3.11) (all P < 0.001). Contrastingly, within Wuhan, only the first four factors, namely, parent-child conflict (5.95, 2.82-12.57), sleep problems (4.47, 3.06-6.54), online study time (0.37, 0.22-0.64), and physical activity time (0.42, 0.22-0.80) were identified (all P < 0.01). Accordingly, nomogram models were created with significant attributes and had decent prediction performance with C-indexes over 80%. LIMITATION: A cross-sectional study and self-reported measures. CONCLUSIONS: Besides the four significant risk factors within and outside Wuhan, the three additional factors outside Wuhan deserve special attention. The prediction nomogram models constructed in this study have important clinical and public health implications for psychosocial and behavioural assessment.
BACKGROUND: We aimed to explore the risk profiles attributable to psychosocial and behavioural problems during the coronavirus disease 2019 pandemic. To this end, we created a risk-prediction nomogram model. METHODS: A national multicentre study was conducted through an online questionnaire involving 12,186 children (6-11 years old) and adolescents (12-16 years old). Respondents' psychosocial and behavioural functioning were assessed using the Achenbach Child Behaviour Checklist (CBCL). Data were analysed using STATA software and R-language. RESULTS: The positive detection rate of psychological problems within Wuhan was greater than that outside Wuhan for schizoid (P = 0.005), and depression (P = 0.030) in children, and for somatic complaints (P = 0.048), immaturity (P = 0.023), and delinquent behaviour (P = 0.046) in adolescents. After graded multivariable adjustment, seven factors associated with psychological problems in children and adolescents outside Wuhan were parent-child conflict (odds ratio (OR): 4.94, 95% confidence interval (95% CI): 4.27-5.72), sleep problems (OR: 4.05, 95% CI: 3.77-4.36), online study time (OR: 0.41, 95% CI: 0.37-0.47), physical activity time (OR: 0.510, 95% CI: 0.44-0.59), number of close friends (OR: 0.51, 95% CI: 0.44-0.6), time spent playing videogames (OR: 2.26, 95% CI: 1.90-2.69) and eating disorders (OR: 2.71, 95% CI: 2.35-3.11) (all P < 0.001). Contrastingly, within Wuhan, only the first four factors, namely, parent-child conflict (5.95, 2.82-12.57), sleep problems (4.47, 3.06-6.54), online study time (0.37, 0.22-0.64), and physical activity time (0.42, 0.22-0.80) were identified (all P < 0.01). Accordingly, nomogram models were created with significant attributes and had decent prediction performance with C-indexes over 80%. LIMITATION: A cross-sectional study and self-reported measures. CONCLUSIONS: Besides the four significant risk factors within and outside Wuhan, the three additional factors outside Wuhan deserve special attention. The prediction nomogram models constructed in this study have important clinical and public health implications for psychosocial and behavioural assessment.