Factors associated with depressive symptoms among the elderly in China: structural equation model

Abstract

Objectives: To establish a structural equation model for exploring the direct and indirect relationships of depressive symptoms and their associated factors among the Chinese elderly population. Design: A cross-sectional research. The 2015 data from the China Health and Retirement Longitudinal Study (CHARLS) were adopted. Setting: CHARLS is an ongoing longitudinal study assessing the social, economic, and health status of nationally representative samples of middle-aged and elderly Chinese residents. Participants: A total of 5791 participants aged 60 years and above were included. Measurements: Depressive symptoms were used as the study outcome. Sociodemographic characteristics, poor health status, unhealthy habits, and sleep duration were used as predictors. Confirmatory factor analysis was first conducted to test the latent variables. Structural equation model was then utilized to examine the associations among latent variables and depressive symptoms. Results: The mean age of the participants was 68.82 ± 6.86 years, with 55.53% being males. The total prevalence of depressive symptoms was 37.52%. The model paths indicated that sociodemographic characteristics, poor health status, unhealthy habits, and sleep duration were directly associated with depressive symptoms, and the effects were 0.281, 0.509, −0.067, and −0.162, respectively. Sociodemographic characteristics, unhealthy habits, and sleep duration were indirectly associated with depressive symptoms, mediating by poor health status. Their effects on poor health status were −0.093, 0.180, and −0.279, respectively. All paths of the model were significant (P < 0.001). The model could explain 40.9% of the variance in the depressive symptoms of the Chinese elderly population. Conclusions: Depressive symptoms were significantly associated with sociodemographic characteristics, poor health status, unhealthy habits, and sleep duration among Chinese elderly population. The dominant predictor of depressive symptoms was poor health status. Targeting these results might be helpful in rationally allocating health resources during screening or other mental health promotion activities for the elderly.

Independent and joint associations of body mass index, waist circumference, waist-height ratio and their changes with risks of hyperuricemia in middle-aged and older Chinese individuals: a population-based nationwide cohort study

“…The CES-D scale exhibited a good consistency in the present study, with Cronbach’s coefficient of 0.80. A total score of ≥ 10 was used to identify depression [ 20 ].…”

Uncovering the Most and the Least Factors Affecting Elderly Health Using Association Mining

“…The study variables included sociodemographic characteristics, poor health status, unhealthy habits, and sleep duration. Correlations and descriptive statistics, such as mean and standard deviation were measured to disclose the significant predictor for depressive symptoms in the elderly [7]. Another study in China occurred in Shanghai.…”

Public health efforts toward reducing depression in older people

“…Xie and colleagues (2020) conducted a study that investigated the relationship between biopsychosocial factors and symptoms of depression among 5791 primarily community-dwelling people older than 60 years in China. The results demonstrated a direct association of depressive symptoms with sociodemographic characteristics, poor health status, unhealthy habits (alcohol use and smoking), and sleep duration with the dominant predictor of depressive symptoms being poor health status.…”

“…A meta-analysis of 15 randomized trials compared antidepressants with placebo in 4756 participants with Major Depressive Disorder, aged 55 years and older (mean age 70 years old) Xie etal. (2020). Results demonstrated antidepressants to be effective in treating depressive symptoms in the geriatric population (Tedeschini etal.…”

“…, 2018). The Xie and colleagues’ (2020) manuscript suggests a number of additional interventions and/or preventive strategies for depression that might be considered. In that study, older people not living with their spouses were more likely to have higher levels of depressive symptoms in older people.…”