Latent classes and determinants of depressive and anxiety symptoms among older adults with chronic diseases in rural China

Abstract

The mental health of older adults with chronic diseases in rural, underdeveloped regions of China poses a critical public health challenge. Comorbid depressive and anxiety symptoms are highly prevalent but exhibit substantial heterogeneity, necessitating person-centered approaches to identify meaningful subgroups and inform targeted interventions. A cross-sectional study was conducted from May to October 2024 among 3,200 older adults (age ≥ 60) with chronic diseases in rural Guizhou, Southwest China. Latent class analysis (LCA) was applied to symptoms assessed via the PHQ-9 and GAD-7 scales. Multinomial logistic regression was used to examine factors associated with class membership. LCA revealed a four-class model as optimal: Class 1 (34.31%) exhibited severe depressive-anxious symptoms, Class 2 (27.03%) moderate depressive-anxious symptoms, Class 3 (22.31%) moderate depressive with low anxiety, and Class 4 (16.34%) minimal symptoms. Based on any symptom endorsement (PHQ-9 ≥ 5 and GAD-7 ≥ 5), the prevalence rates were 66.7% for depressive and 56.0% for anxiety symptoms; using standard clinical cutoffs (PHQ-9 ≥ 10 and GAD-7 ≥ 10), the corresponding rates were 30.5% and 20.8%. Loneliness, impaired activities of daily living, and poor sleep quality were common risk factors across all symptomatic classes, with the effect sizes decreasing as symptom severity decreased. Social support showed a protective effect in the severe and moderate groups but was associated with increased risk in the mild symptom group. Self-efficacy was protective only in the mild symptom class. Demographic factors such as being underweight and having a lower income elevated risks primarily in the moderate and severe groups. Depressive and anxiety symptoms among rural older adults with chronic diseases are highly heterogeneous. A differentiated, class-specific intervention strategy is urgently needed, incorporating both universal risk factor management (targeting loneliness, sleep quality, and functional impairment) and tailored approaches aligned with distinct symptom profiles. These findings underscore the importance of precision public health in addressing mental health disparities in resource-limited settings.