Identifying the subgroups of depression trajectories among the middle-aged and older Chinese individuals with chronic diseases: an 8-year follow-up study based on CHARLS

Abstract

Background: Prior studies have demonstrated a prevalent occurrence of depression among the middle-aged and older Chinese individuals with chronic diseases. Nevertheless, there is limited research on the specific subgroups of depression trajectories within this population and the factors influencing these subgroups.

Objective: To explore the changing trajectory and influencing factors of depression in the middle-aged and older individuals with chronic disease in China, and provide the data reference for the health management of the older adult population in China.

Methods: A longitudinal cohort study was conducted using the data from the China Health and Retirement Longitudinal Study (CHARLS) in 2011, 2013, 2015, 2018, and 2020. A total of 2,178 participants with complete data were included. The level of depression was evaluated using the Center for Epidemiologic Studies Depression Scale (CESD-10). The Latent Class Mixed Models (LCMM) were employed to estimate trajectories of depressive symptoms. The Kruskal-Wallis H test and the Pearson χ ² test were used to determine the significant factors affecting trajectory grouping. Subsequently, the multinomial logistic regression model was utilized to perform a multifactorial analysis of the variables impacting the trajectory subgroup of change in depressive symptoms.

Results: The LCMM-analysis revealed three distinct subgroups of depression trajectories: the "Low stable group" comprising 36.7% of the sample, the "Medium growth group" comprising 34.4% of the sample, and the "High growth group" comprising 28.9% of the sample. Among the baseline characteristics of different depression trajectory subgroups, there were significant differences in gender, residence, education, marital status, social activity participation, number of chronic diseases, smoking status, BMI, midday napping (minutes) and nighttime sleep duration (hours). Through multiple logistic regression analysis, our findings demonstrate that among the middle-aged and older Chinese individuals with chronic diseases, the following individuals should be the key groups for the prevention and treatment of depressive symptoms: Those who are young, female, residing in rural areas, having primary school education and below, being single, not participating in social activities, suffering from multiple chronic diseases, and having shorter naps and sleeping at night.

Conclusion: There is heterogeneity in the subgroups of depression trajectories among the Chinese middle-aged and older individuals with chronic diseases. The focus should be on the distinct characteristics of various trajectories of depression within the realm of health management.

Keywords: aging; chronic disease; depression; latent class mixture modeling; trajectories.

Figure 1

Process of sample population screening.

Figure 2

The mean of CES-D score (mean ± standard error) across 5 data collections during 2011–2020; 12-month prevalence (p 95% CI).

Figure 3

Longitudinal LCMM based depression trajectories using CESD-10 score.

Figure 4

Longitudinal LCMM based depression trajectories using CESD-10 score. Multinomial logistic regression model 3: age, gender, residence, education, marital status, social activity participation, number of chronic diseases, smoking status, BMI, midday napping, nighttime sleep duration, with the above 11 predictors as independent variables and trajectory subgroups as the dependent variable.

Figure 5

Longitudinal LCMM based depression trajectories using CESD-10 score (sensitive analysis).