Objective  Constructing a depression risk prediction model for elderly people with chronic diseases living alone, to provide a scientific basis for early prevention and control of depression in the elderly.

Methods  Empirical analysis was conducted using the fifth round of China Health and Retirement Longitudinal Study (CHARLS) survey project. The Center for Epidemiological Studies Depression Scale (CSE-D) was used to evaluate the depressive mood of elderly people living alone with chronic diseases. Non-parametric tests and chi-square tests were used for univariate analysis, Lasso regression was used to screen potential predictive factors, and multiple Logistic regression was used to analyze the influencing factors of depression in elderly people living alone with chronic diseases. A depression risk prediction nomogram model for elderly people living alone with chronic diseases was constructed. The model was divided into a prediction set and a validation set by 7 ∶ 3 random splitting method. Receiver operating charcteristic(ROC) curve and area under cuve(AUC), H-L goodness of fit test, calibration curve graph, clinical decision curve, and clinical impact curve was used to evaluate the model.

Results  522 elderly people with chronic diseases living alone were included in the study. The mean age was (69.54±6.56) years old. The prevalence of depression among elderly people with chronic diseases living alone in China was 50.38%. Self-rated health, life satisfaction, pain and discomfort, and internet use were the main influencing factors for depression in elderly people with chronic diseases living alone (P<0.05). The ROC curve and AUC sizes for the training and validation sets were 0.799[95%CI(0.755, 0.843)] and 0.805[95%CI(0.738, 0.873)], respectively. The H-L tests of training set and validation set were not statistically significant (P>0.05). The goodness of fit is better for both groups, and the result of desicion curve and clinical impact curve showed that the risk prediction model has better calibration and net benefit.

Conclusion  The prevalence of depression is higher among elderly people with chronic diseases living alone, and descendent self-assessed health, decreased life satisfaction, pain, and lack of internet usage significantly increase the risk of depression among elderly people with chronic diseases living alone. This provides a basis for medical staff to conduct early and dynamic screening of depression among elderly people with chronic diseases living alone.

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