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Latent profile analysis of depression in elderly patients with cardio- and cerebrovascular diseases in China- based
Man Meng1, Chen Zheng2, Qi Hu1
1First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China.
Insights
This study identified three depression profiles in elderly patients with cardiovascular and cerebrovascular diseases. Factors like self-reported health and exercise influenced depression levels, aiding early identification of at-risk individuals.
Area of Science:
- Gerontology
- Cardiology
- Neurology
- Psychiatry
Background:
- Elderly patients with cardio- and cerebrovascular diseases often experience depressive symptoms.
- Understanding depression profiles is crucial for targeted interventions in this population.
Purpose of the Study:
- To explore depression profiles in elderly patients with cardio- and cerebrovascular disease.
- To identify factors influencing these depression profiles for early risk identification.
Main Methods:
- Utilized latent profile analysis (LPA) on data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS).
- Analyzed 1890 participants to identify distinct depression profiles and their associated factors.
Main Results:
- Identified three depression profiles: low (11%), medium (52%), and high (37%).
- Factors like gender, age, health status, social support, and lifestyle (smoking, drinking, exercise) differed across profiles.
- Good self-reported health and exercise were linked to lower depression; anxiety and marital status were associated with moderate-to-severe depression.
Conclusions:
- Latent profile analysis effectively categorizes depression in elderly patients with cardio- and cerebrovascular disease.
- Specific demographic, health, and lifestyle factors are significantly associated with different depression profiles.
- Findings provide a basis for healthcare professionals to identify high-risk individuals for anxiety and depression early.
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.
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