Chronic disease profiles of subjective memory complaints: a latent class analysis of older people in a rural Malaysian community
Yap, Kwong Hsia
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Yap, K.H., Warren, N., Allotey, P. and Reidpath, D.D. (2020) ‘Chronic disease profiles of subjective memory complaints: a latent class analysis of older people in a rural Malaysian community’, Aging & Mental Health, 24(5), pp. 709–716. Available at: https://doi.org/10.1080/13607863.2018.1550632.
Background: Subjective memory complaints (SMC) are common in the elderly and have been suggested as the first subtle sign of decline which can predict dementia. Cognitive decline is thought to be related to inflammatory processes similarly found in other chronic diseases and conditions such as stroke, heart disease and arthritis. This study aimed to examine the association of SMC with chronic diseases and the profile of these health conditions reported by a group of older adults. Methods: Data from a cross-sectional survey conducted from August 2013 and March 2014 was drawn from 6179 individuals aged 56 years and above. Multivariable logistic regression analyses were used to examine SMC’s relationship with individual chronic diseases (asthma, kidney disease, heart disease, stroke, arthritis, hypertension and diabetes) and multimorbidity. Latent class analysis (LCA) was used to identify the profile of health conditions. The effect of SMC was estimated in a multinomial logistic regression as part of the latent class model. Results: SMC was statistically significant in its association with asthma, stroke, heart disease, arthritis and multimorbidity in the fully controlled multivariable logistic regression models. Three health profiles were identified: low comorbidity (n = 4136, low rates in all health conditions), arthritis group (n = 860) and diabetes and hypertension group (n = 1183). SMC was associated with arthritis group (OR = 2.04, 95% CI = 1.51–2.75) and diabetes and hypertension group (OR = 1.22, 95% CI = 1.03–1.46). Conclusion: Adapting a combination of analytical approaches allows a better understanding in the assessment of SMC’s relationship with chronic diseases and the patterns of distribution of these health conditions.