Chronic disease profiles of subjective memory complaints: a latent class analysis of older people in a rural Malaysian community

Abstract

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.

Keywords:

• Health profile
• subjective memory complaints
• latent class analysis
• population-based study

Acknowledgements

Data collection was undertaken at the Monash SEACO HDSS technology research platform. The Authors would like to express their appreciation to the SEACO Field Team and members of the SEACO Scientific Advisory Group from the Malaysian Ministry of Health. SEACO is funded by the Monash University Malaysia Campus; the Office of the Vice Provost Research, Monash University Australia; the office of the Deputy Dean Research, Faculty of Medicine, Nursing and Health Sciences, Monash University Australia; The Faculty of Arts, Monash University Australia, and the Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia. SEACO is a member of the INDEPTH Network.

Disclosure statement

No potential conflict of interest was reported by the authors.

Ethics

Ethical approval for the study was obtained from the Monash University Human Research Ethics Committee (MUHREC CF11/3663 - 2011001930).

Funding

This work was supported by the Australian Research Council (Discovery Project Scheme, project number DP140101995).