Latent class analysis of symptoms of depression and anxiety among older women

Abstract

Objectives

This cross-sectional study aims to consider the potential classification of depression and anxiety symptoms among older women, and identify the influencing factors of this classification.

Methods

This study examines Chinese women aged 65 years and older. Latent class analysis was used to explore the mental health subgroups of older women, and multivariate logistic regression was employed to examine the influencing factors based on the health ecological model among these subgroups.

Results

The results helped classify this population under three subgroups: the coexistence of depression and anxiety group, dominated depression group, and the low symptoms group. Moreover, class differences in terms of age, residence, education, income, assessment of current life and health status, sleep duration, and health behaviors, such as alcohol use and exercise were noted.

Conclusions

These findings explain the heterogeneity among older women, and help illuminate their unique aspects of mental health. Accordingly, they are significant for scholars and policymakers to understand depression and anxiety among older women.

Keywords:

• Anxiety
• depression
• older women

Acknowledgment

The authors thank the CHARLS team for their hard work and generosity with survey data.

Disclosure statement

The author declares no conflicts of interest.

Data availability statement

The data that support the findings of this study are openly available in the Peking University Open Research Data Platform (http://opendata.pku.edu.cn/file.xhtml?fileId=1060&version=1.0)