小児家庭内機能不全を持つ子供の不安とうつ病のリスク評価モデルの開発と検証
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.
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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