A cross-sectional latent class analysis of depressive and anxiety symptoms in people aged 65 and older: Identifying subgroups and related factors.
TL;DR
Key factors contribute to differences in depression and anxiety symptoms among older adults: gender, body mass index, ethnicity, occupation before retirement, marital status, physical disability status, independence in activities of daily living, number of chronic diseases, smoking and drinking habits, dietary habits, recreational activities, physical activity, and sleep quality.
Abstract
As the older population grows, depression and anxiety have become common mental health problems that significantly undermine the quality of life of older persons. Improving older people's mental health is vital for their quality of life. Before developing targeted interventions, priority must be given to identifying key factors affecting mental health to address the significant individual differences within this population. The aim was to identify subgroups of depressive/anxiety symptoms in the older population and factors influencing their mental health. Secondary analysis of mental health data of elderly people aged 65 years and older in Guizhou Province, China. Latent Class Modeling was developed using Mplus 8.3 software to examine the factors that influence the occurrence of anxiety and depression in different subgroups of older adults. Chi-square test and unordered multivariate logistic regression were used for detailed analysis from 3 perspectives (demographic characteristics, physical condition and family relationships, and lifestyle). A total of 4 potential category subgroups were identified in this study: the severe depressive and anxiety symptoms group (Class 1, N = 406, 1.46%), the moderate depressive and anxiety symptoms group (Class 2, N = 1834, 6.58%), the mild depressive and anxiety symptoms group (Class 3, N = 3789, 13.59%), and the group without depressive or anxiety symptoms (Class 4, N = 21,851, 78.38%). Significant individual differences in symptoms of depression and anxiety exist among people aged 65 and older. The results of the disordered multiple logistic regression analysis revealed that the following factors contribute to differences in depression and anxiety symptoms among older adults: gender, body mass index, ethnicity, occupation before retirement, marital status, physical disability status, independence in activities of daily living, number of chronic diseases, smoking and drinking habits, dietary habits, recreational activities, physical activity, and sleep quality. In the context of global aging, the mental health of older adults is paramount. Given significant heterogeneity in their mental health status, health and policy sectors should develop individualized interventions targeting specific subgroups and their risk factors to effectively reduce anxiety and depression.
