Exploring a risk predictive model for depression in self-care elderly people: a national cross-sectional study from CLHLS

Background In order to cope with the problem of population aging, China has put forward the strategy of healthy aging. Strengthening the health management of the elderly is an important measure to achieve healthy aging, and the mental health of the elderly needs special attention. The aim of the present study is to develop a risk predictive model for depression among self-care elderly individuals, so as to efficiently identify high-risk groups for depression and implement targeted screening and mental health management. Methods From the data of China Longitudinal Healthy Longevity Survey (CLHLS) in 2018, a total of 5,592 valid samples of self-care elderly aged over 60 were selected. Depression was measured using the CES−10 depression scale, with a score of 10 or higher being considered to have depression. A binary forward stepwise regression analysis was used to construct a risk prediction model of depression among self-care elderly people, and the c-index was used to assess the predictive power. Results Among the 5,592 self-care elderly people, 9.8% suffered from depression. The risk predictive model for depression included four risk factors, namely, gender, marital status, self-rated quality of life, and self-rated health. Compared with the males, females were more likely to suffer from depression(OR = 1.481, P = 0.023); compared with those current married and living with spouse, the widowed were more likely to suffer from depression(OR = 1.513, P < 0.001); those with poorer self-rated quality of life (OR = 2.916, P < 0.001), poorer self-rated health status (OR = 3.080, P < 0.001) were more likely to suffer from depression. The Hosmer-Lemeshow test showed that P > 0.05, and the ROC index was 0.774; which meant that the model has good fitting degree and good prediction effect. Conclusions The development of a risk prediction model of depression among self-care elderly is instrumental in accurately identifying high - risk groups of depression, which facilitate targeted depression screening and comprehensive mental health management.