Mental Health

A cross-sectional latent class analysis of depressive and anxiety symptoms in people aged 65 and older: Identifying subgroups and related factors.

TL;DR

Four distinct subgroups of depressive and anxiety symptoms were identified among older adults aged 65 and older in Guizhou Province, China, with 78.38% being symptom-free and 1.46% experiencing severe symptoms, with gender, BMI, ethnicity, marital status, chronic diseases, and lifestyle factors contributing to differences across subgroups.

Key Findings

Four latent class subgroups of depressive and anxiety symptoms were identified in older adults aged 65 and older.

  • Class 1 (severe depressive and anxiety symptoms): N = 406, representing 1.46% of the sample
  • Class 2 (moderate depressive and anxiety symptoms): N = 1834, representing 6.58% of the sample
  • Class 3 (mild depressive and anxiety symptoms): N = 3789, representing 13.59% of the sample
  • Class 4 (no depressive or anxiety symptoms): N = 21,851, representing 78.38% of the sample
  • Latent Class Modeling was developed using Mplus 8.3 software

The majority of older adults in Guizhou Province were free from depressive or anxiety symptoms, while a small but meaningful minority experienced moderate to severe symptoms.

  • 78.38% of the sample (N = 21,851) fell into the no symptoms class
  • Approximately 21.62% of the sample experienced some level of depressive or anxiety symptoms
  • The total sample included 27,880 individuals aged 65 years and older in Guizhou Province, China
  • The study was a secondary analysis of mental health data, conducted as a cross-sectional design

Demographic characteristics including gender, ethnicity, occupation before retirement, and marital status were identified as factors contributing to differences in depression and anxiety symptoms among older adults.

  • These factors were identified through disordered multiple logistic regression analysis
  • Chi-square tests and unordered multivariate logistic regression were used for analysis
  • Demographic characteristics were analyzed as one of three perspectives alongside physical condition/family relationships and lifestyle
  • Significant individual differences in symptoms of depression and anxiety were found among people aged 65 and older

Physical health factors including body mass index, physical disability status, independence in activities of daily living, and number of chronic diseases were associated with differences in depression and anxiety symptom subgroup membership.

  • Physical disability status was identified as a contributing factor in the disordered multiple logistic regression analysis
  • Independence in activities of daily living was a significant factor differentiating subgroups
  • Number of chronic diseases contributed to differences in depression and anxiety symptoms
  • BMI was included as a physical condition factor in the analysis

Lifestyle factors including smoking and drinking habits, dietary habits, recreational activities, physical activity, and sleep quality were identified as factors influencing depression and anxiety symptom subgroups.

  • Sleep quality was among the lifestyle factors contributing to subgroup differences
  • Physical activity was identified as a contributing lifestyle factor
  • Dietary habits and recreational activities were also associated with symptom subgroup membership
  • Lifestyle factors were analyzed as a third perspective alongside demographic and physical condition factors

Significant individual differences (heterogeneity) in symptoms of depression and anxiety exist among people aged 65 and older, supporting the need for subgroup-specific interventions.

  • The latent class analysis approach was used specifically to capture heterogeneity within the older adult population
  • The four identified subgroups ranged from 1.46% to 78.38% in size, illustrating substantial variation
  • The authors conclude that 'health and policy sectors should develop individualized interventions targeting specific subgroups and their risk factors'
  • The study was conducted in the context of global aging, with the sample drawn from Guizhou Province, China

What This Means

This research suggests that older adults (age 65 and above) in Guizhou Province, China do not form a uniform group when it comes to depression and anxiety. By analyzing mental health data from nearly 28,000 older individuals, researchers identified four distinct groups: a large majority (about 78%) with no significant depressive or anxiety symptoms, a group with mild symptoms (about 14%), a group with moderate symptoms (about 7%), and a small but important group with severe symptoms (about 1.5%). This approach, called latent class analysis, helps reveal that there is considerable variation in mental health among older people rather than a single uniform experience. The research also found that which group a person fell into was associated with a wide range of factors across three broad categories. Personal and social factors such as gender, ethnicity, marital status, and type of occupation before retirement played a role, as did physical health factors including BMI, disability status, ability to perform daily activities independently, and the number of chronic diseases a person had. Lifestyle factors such as smoking, drinking, diet quality, recreational activities, physical activity, and sleep quality were also linked to differences in mental health symptom severity across the groups. This research matters because it highlights that mental health in older age is not one-size-fits-all. Different subgroups of older adults face different combinations of risk factors for depression and anxiety. The findings suggest that public health programs and policies aimed at improving older adults' mental health would be more effective if they were tailored to specific subgroups rather than applying a blanket approach, particularly targeting those with identifiable risk factors such as poor sleep, physical disability, multiple chronic conditions, or social isolation.

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Citation

Zhang J, Li F, Yao Y, Huang Y, Zhang Y, Yu Y, et al.. (2026). A cross-sectional latent class analysis of depressive and anxiety symptoms in people aged 65 and older: Identifying subgroups and related factors.. Medicine. https://doi.org/10.1097/MD.0000000000050328