Development and Validation of a Depression Risk Prediction Model for Home-Dwelling Middle-Aged and Older Adults in China Based on CHARLS Data

There is a lack of research on risk prediction models for depressive symptoms among home-dwelling middle-aged and elderly adults in China. This study aimed to develop a risk prediction model and identify the risk factors for depression among home-dwelling middle-aged and elderly adults in China, with the goal of informing evidence-based prevention strategies and public health policy. Using the latest nationally representative CHARLS 2020 wave (n = 14,466), we developed the first parsimonious nomogram to predict depressive symptoms (CES-D-10 ≥ 10) in home-living middle-aged and older Chinese. After chi-square and multivariable logistic screens, LASSO with 10-fold cross-validation identified the five most influential predictors. Model discrimination (AUC), calibration (Hosmer-Lemeshow, calibration plot), and clinical utility (decision-curve analysis) were assessed in training–test splits (7:3). Among the 14,466 home-dwelling middle-aged and elderly adults, 5,488 (37.1%) had depressive symptoms, and 8,978 (62.9%) did not meet the criteria for depressive symptoms. The final nomogram comprised five variables: female sex, rural hukou, primary or less education, unmarried status, and age over 60. The tool achieved AUCs of 0.766 (95% CI 0.752-0.780) in the training set and 0.664 (0.637-0.691) in the test set, with excellent calibration (P = 0.28) and positive net benefit between 30% and 50% risk thresholds. This model can predict the risk of depression in Chinese home-dwelling middle-aged and elderly adults and can serve as a convenient screening tool for early identification and risk management of depression in this population.