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Development and Validation of Risk Assessment Models for Anxiety and Depression in Chinese Children with Childhood
Ping Mao1, Jing Xia2, Jia Yang2
1Nursing Department, The Third Xiangya Hospital, Central South University, Changsha, 410013, China; Xiangya Nursing School, Central South University, Changsha, 410013, China; Key Laboratory of Medical Information Research (Central South University), College of Hunan Province, Changsha, 410013, China; Clinical Research Center for Cardiovascular Intelligent Healthcare in Hunan Province, Changsha, 410013, China.
Insights
This study developed two risk assessment models to identify children with childhood household dysfunction (CHD) at high risk for anxiety and depression, enabling early detection and intervention.
Area of Science:
- Child Psychology
- Mental Health Assessment
- Epidemiology
Background:
- Children exposed to childhood household dysfunction (CHD) face elevated risks for anxiety and depression.
- Early identification tools for at-risk children are currently limited.
- This research addresses the need for effective early detection methods.
Purpose of the Study:
- To develop and validate risk assessment models for anxiety and depression in children with CHD.
- To enable timely intervention by identifying at-risk youth.
- To provide tools for early detection in clinical settings.
Main Methods:
- A cross-sectional study involving 1276 children with CHD in Changsha, Hunan.
- Data split into training and validation sets (7:3 ratio).
- Bidirectional stepwise multivariate logistic regressions used to build predictive models; performance evaluated using ROC curves, AUC, sensitivity, specificity, and accuracy.
Main Results:
- High prevalence of risk: 57.8% for depression and 70.0% for anxiety.
- Anxiety Risk Model (7 variables) achieved AUC of 0.875 (training) and 0.864 (validation).
- Depression Risk Model (5 variables) achieved AUC of 0.895 (training) and 0.869 (validation).
Conclusions:
- Developed risk assessment models show promise for early identification of anxiety and depression in children with CHD.
- Models demonstrated strong predictive performance in the studied urban adolescent population.
- Further validation with longitudinal designs in diverse populations is recommended for clinical application.
Background:
Children exposed to childhood household dysfunction (CHD) are at high risk for anxiety and depression. However, few tools are available for early identification. This study developed two risk assessment models for anxiety and depression in children with CHD to enable early detection and intervention.
Methods:
A cross-sectional study recruited 1276 children with CHD from Changsha, Hunan, who were randomly divided into training and validation sets (7:3). Bidirectional stepwise multivariate logistic regressions were used to develop models based on socio-demographic, individual, family, and societal levels. Model performance was assessed using receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, specificity, and accuracy (ACC).
Results:
Among the participants, 736 (57.8 %) had depression risk, and 892 (70.0 %) had anxiety risk. The Anxiety Risk Model included seven variables: gender, grade, rumination, adolescent life events, number of childhood trauma experiences, positive coping style, and sleep quality. It demonstrated strong performance with an AUC of 0.875 (95 % CI: 0.852-0.897) in the training set and 0.864 (95 % CI: 0.826-0.902) in the validation set, with a sensitivity of 74.3 %, specificity of 79.6 %, and an accuracy of 75.1 %. The Depression Risk Model included five variables: grade, rumination, adolescent life events, positive coping style, and sleep quality, with an AUC of 0.895 (95 % CI: 0.874-0.916) in the training set and 0.869 (95 % CI: 0.829-0.908) in the validation set, with sensitivity of 75.1 %, specificity of 80.9 %, and accuracy of 76.8 %.
Conclusion:
The two risk assessment models offer valuable insights into the early identification of children at risk of depression and anxiety within similar urban adolescent populations. These findings necessitate further validation using longitudinal designs within larger and more diverse populations to confirm the models' generalizability and robustness before recommendations for wider clinical application can be made.
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