Latent profile analysis of depression in elderly patients with cardio- and cerebrovascular diseases in China– based on CLHLS data DOI Creative Commons

Man Meng,

Zheng Chen, Qi Hu

et al.

Frontiers in Psychiatry, Journal Year: 2025, Volume and Issue: 16

Published: March 21, 2025

Background This study explored the depressive status of elderly patients with cardio- and cerebrovascular disease, using latent profile analysis to explore different profiles depression. It also factors influencing depression in diseases provide reference healthcare workers identify high-risk group anxiety symptoms at an early stage. Methods Data came from Chinese Longitudinal Healthy Longevity Survey (CLHLS). In this study, we used (LPA) develop a model disease combined its factors. Results The 1890 participants were divided into low-level (11%), medium-level (52%), high-level (37%). results univariate showed statistically significant differences distribution gender, age, co-residence, self-reported health, main source financial support, marital status, diabetes, smoke, drank, exercise, level anxiety, IADL three profiles. Multiple logistic regression that good or fair health exercise associated depression; no spouse, moderately severe conditions; retirement wages, local government community predicted appearance compared

Language: Английский

Latent profile analysis of depression in elderly patients with cardio- and cerebrovascular diseases in China– based on CLHLS data DOI Creative Commons

Man Meng,

Zheng Chen, Qi Hu

et al.

Frontiers in Psychiatry, Journal Year: 2025, Volume and Issue: 16

Published: March 21, 2025

Background This study explored the depressive status of elderly patients with cardio- and cerebrovascular disease, using latent profile analysis to explore different profiles depression. It also factors influencing depression in diseases provide reference healthcare workers identify high-risk group anxiety symptoms at an early stage. Methods Data came from Chinese Longitudinal Healthy Longevity Survey (CLHLS). In this study, we used (LPA) develop a model disease combined its factors. Results The 1890 participants were divided into low-level (11%), medium-level (52%), high-level (37%). results univariate showed statistically significant differences distribution gender, age, co-residence, self-reported health, main source financial support, marital status, diabetes, smoke, drank, exercise, level anxiety, IADL three profiles. Multiple logistic regression that good or fair health exercise associated depression; no spouse, moderately severe conditions; retirement wages, local government community predicted appearance compared

Language: Английский

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