Psychology research and behavior management · 2026
Ecological Correlates of Depressive Symptoms among Chinese Adolescents: A Random Forest and Nomogram Analysis of CFPS Data.
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Mei Songli, Su Yaning, Yue Jingyi, Liu Kai, Yuan Tongshuang, Zheng Chengbin, Pan Liru, Zhang Liqiang, Cui Yujie, Zhang Chaofan, Ding Fangfang, Chen Wanyue, Tian Qiannan, Wu Xusheng
Аннотация
BACKGROUND: Adolescence represents a critical developmental stage characterized by complex stressors, including academic pressure and interpersonal challenges, which significantly elevate the risk of depressive symptoms. While conventional research predominantly relies on linear methodologies focusing on isolated environmental determinants, the multi-level interactive effects of ecological environments on adolescent depressive symptoms remain under-explored.
METHODS: Utilizing longitudinal data from the 2020 and 2022 China Family Panel Studies (CFPS), this study included a sample of 971 adolescents (aged 10-15). Baseline ecological variables and covariates were measured in 2020, while the outcome-depressive symptoms-was assessed in 2022 using the 8-item Center for Epidemiologic Studies Depression Scale (CES-D8). We integrated a machine learning framework (Random Forest, RF) to predict continuous CES-D8 scores and a nomogram model to predict binary depression risk (CES-D8 ≥ 9). The nomogram was internally validated using 1000 bootstrap resamples.
RESULTS: Ecological environments demonstrated significant associations with adolescent depressive symptoms. In the RF model (Out-of-Bag Mean Squared Error = 12.72), the predictor s ranked in descending order of their contribution were social trust, interpersonal relationships, parent-child conflict, teacher satisfaction, and age. The developed visual nomogram demonstrated modest discrimination (AUC = 0.614) but excellent calibration (Mean Absolute Error = 0.011) for quantifying individualized risk probabilities.
CONCLUSION: These findings highlight specific environmental correlates and underscore the necessity of transitioning from single-factor management to integrated, multi-systemic approaches. While the predictive model requires further external validation before clinical application, it offers a preliminary epidemiological tool to identify potential intervention targets and facilitate coordinated family, school, community, and governmental interventions.
METHODS: Utilizing longitudinal data from the 2020 and 2022 China Family Panel Studies (CFPS), this study included a sample of 971 adolescents (aged 10-15). Baseline ecological variables and covariates were measured in 2020, while the outcome-depressive symptoms-was assessed in 2022 using the 8-item Center for Epidemiologic Studies Depression Scale (CES-D8). We integrated a machine learning framework (Random Forest, RF) to predict continuous CES-D8 scores and a nomogram model to predict binary depression risk (CES-D8 ≥ 9). The nomogram was internally validated using 1000 bootstrap resamples.
RESULTS: Ecological environments demonstrated significant associations with adolescent depressive symptoms. In the RF model (Out-of-Bag Mean Squared Error = 12.72), the predictor s ranked in descending order of their contribution were social trust, interpersonal relationships, parent-child conflict, teacher satisfaction, and age. The developed visual nomogram demonstrated modest discrimination (AUC = 0.614) but excellent calibration (Mean Absolute Error = 0.011) for quantifying individualized risk probabilities.
CONCLUSION: These findings highlight specific environmental correlates and underscore the necessity of transitioning from single-factor management to integrated, multi-systemic approaches. While the predictive model requires further external validation before clinical application, it offers a preliminary epidemiological tool to identify potential intervention targets and facilitate coordinated family, school, community, and governmental interventions.