Latent profile analysis of depressive symptoms among Chinese older adults with disabilities and associated factors

Background With population aging in China, the number of older adults with disabilities is increasing, and mental health problems, especially depressive symptoms, have become increasingly prominent. Examining latent profiles of depressive symptoms and their associated factors in this population may improve understanding of symptom heterogeneity and inform future screening and supportive care research. Methods This study employed data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). Latent profile analysis (LPA) was used to identify latent profiles of depressive symptoms among older adults with disabilities. Subsequent analyses, including univariate and multinomial logistic regression, were conducted to examine factors associated with depression profile membership. Results The 1, 346 older adults with disabilities were classified into three latent depressive symptom profiles: low symptom burden (24.4%), moderate symptom burden (55.5%), and high symptom burden (20.1%). In univariate analyses, all variables except serious illness differed significantly across profiles. In multinomial logistic regression analyses, compared with the low-level profile, membership in the moderate-level profile was associated with MMSE, anxiety, marital status, education level, co-residence status, residential area, drinking, smoking, exercise, self-rated health, and activity limitations. Membership in the high-level profile was associated with MMSE, anxiety, gender, age, marital status, education level, co-residence of the interviewee, drinking, and self-rated health. Compared with the moderate-level profile, membership in the high-level profile was associated with MMSE, gender, marital status, residential area, drinking, and activity limitations. Conclusions Distinct depressive symptom profiles were identified among older adults with disabilities, and several demographics, health-related, and psychosocial factors were associated with profile membership. These findings may inform future mental health screening and provide a basis for further longitudinal and intervention research.