Latent Profile Analysis of Depressive Symptoms and Their Associated Factors Among Older Adults in Chinese Nursing Homes: Identification of Three Distinct Symptom Profiles.

BACKGROUND

With global population aging, the number of older adults in Chinese nursing homes is rising rapidly, and depression is the most prevalent mental health problem in this population. Most previous studies assessed depression via total scale scores, ignoring individual heterogeneity of depressive symptoms. This study aimed to identify distinct depressive symptom profiles and their associated factors in this population.

METHODS

Data were derived from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), with 353 valid nursing home older adults included. Depressive symptoms, anxiety and functional status were assessed using the CESD-10, GAD-7 and IADL scales. Latent profile analysis (LPA), univariate tests and multinomial logistic regression were performed, with supplementary effect size and sensitivity analyses to verify result robustness.

RESULTS

Three distinct depressive symptom profiles were identified: low level (39%, n = 135), medium level (52%, n = 187) and high level (9%, n = 31). Town residence and anxiety were risk factors for moderate depression, while good self-rated health, regular exercise and social activity participation were protective factors. Good self-rated health protected against severe depression, while occasional television/radio viewing and anxiety were risk factors. Anxiety was the only independent correlate of high-level versus medium-level depression (OR = 1.322, p < 0.001). Supplementary analyses confirmed the robustness of core findings.

LIMITATIONS: The CESD-10, as a screening tool, has limited diagnostic efficacy for clinical depression, and the cross-sectional design cannot confirm causal relationships.

CONCLUSION

Depressive symptoms in Chinese nursing home older adults show significant heterogeneity with three distinct latent profiles. Early screening and targeted stratified interventions should be implemented for this population to improve quality of life and promote healthy aging.