A predictive model for depression in Chinese middle-aged and elderly people with arthritis

Abstract Background This study examines the prevalence of depression and its determinants among Chinese middle-aged and elderly arthritis patients, aiming to establish a theoretical foundation for enhancing their mental well-being and to inform the development of targeted prevention and intervention strategies. Methods Data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) were used for this study. We defined depression status in middle-aged and elderly arthritis patients as the dependent variable and included 16 predictor variables. The data were randomly divided into training and validation sets according to 7:3 ratio. LASSO and binary logistic regression analyses were performed on the training set to screen predictor variables and construct the model, which was then internally validated on the validation set. Results This study included 1302 middle-aged and elderly arthritis patients. LASSO and binary logistic regression analysis were used to construct a prediction model for depression applicable to this population in China. The nomogram analysis revealed that female sex, middle age (45–59 years), poor self-rated health, being troubled by body pain, low life satisfaction, low marital satisfaction, low child satisfaction, and difficulties with instrumental activities of daily living (IADL) were risk factors for depression (P < 0.05). The area under the receiver operating characteristic curve(ROC) exceeded 0.70 in both the model training and internal validation phases, demonstrating the model’s high accuracy in predicting depression risk. In addition, decision curve analysis (DCA) and calibration curve analysis further confirmed the model’s practical value and validity. Conclusion In this study, we identified that being female, middle-aged, having poor self-rated health, being troubled by body pain, dissatisfaction with life, marriage, and children, and difficulties with instrumental activities of daily living were risk factors for depression among middle-aged and elderly arthritis patients. We developed a predictive model based on these risk factors to facilitate early identification, intervention, and treatment for high-risk individuals.