Latent profile analysis of depression in elderly patients with cardio- and cerebrovascular diseases in China- based on CLHLS data.

Abstract

Background: This study explored the depressive status of elderly patients with cardio- and cerebrovascular disease, using latent profile analysis to explore different profiles of depression. It also explored the factors influencing different profile of depression in patients with cardio- and cerebrovascular diseases to provide reference to healthcare workers to identify the high-risk group of anxiety and depression symptoms at an early stage.

Methods: Data came from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). In this study, we used latent profile analysis (LPA) to develop a latent profile model of elderly patients with cardio- and cerebrovascular disease combined with depression and to explore its influencing factors.

Results: The 1890 study participants were divided into a low-level group (11%), a medium-level group (52%), and a high-level group (37%). The results of the univariate analysis showed statistically significant differences in the distribution of gender, age, co-residence, self-reported health, main source of financial support, marital status, diabetes, smoke, drank, exercise, level of anxiety, and IADL in the three profiles. Multiple logistic regression showed that good or fair self-reported health and exercise were associated with the low-level of depression; no spouse, and anxiety level were associated with moderately severe depressive conditions; and retirement wages, and local government or community predicted the appearance of low-level of depression compared to medium-level of depression.