Background: We aimed to identify specific multimorbidity latent classes among multi-ethnic community-dwelling adults aged ≥ 18 years in Malaysia. We further explored the risk factors associated with these patterns and examined the relationships between the multimorbidity patterns and 11-year all-cause mortality risk, as well as health-related quality of life (HRQoL).
Methods: Using data from 18,101 individuals (aged 18-97 years) from the baseline Census 2012, Health Round 2013, and Verbal Autopsies 2012-2023 of the South East Asia Community Observatory (SEACO) health and demographic surveillance system, latent class analysis was performed on 13 chronic health conditions to identify statistically and clinically meaningful groups. Multinomial logistic regression and Cox proportional hazards regression models were conducted to investigate the adjusted association of multimorbidity patterns with the risk factors and mortality, respectively. HRQoL was analyzed by linear contrasts in conjunction with ANCOVA adjusted for baseline confounders.
Results: Four distinct multimorbidity latent classes were identified: (1) relatively healthy (n = 10,640); (2) cardiometabolic diseases (n = 2428); (3) musculoskeletal, mobility and sensory disorders (n = 2391); and (4) complex multimorbidity (a group with more severe multimorbidity with combined profiles of classes 2 and 3) (n = 699). Significant variations in associations between socio-demographic characteristics and multimorbidity patterns were discovered, including age, sex, ethnicity, education level, marital status, household monthly income and employment status. The complex multimorbidity group had the lowest HRQoL across all domains compared to other groups (p < 0.001), including physical health, psychological, social relationships and environment. This group also exhibited the highest mortality risk over 11 years even after adjustment of confounders (age, sex, ethnicity, education and employment status), with a hazard of death of 1.83 (95% CI 1.44-2.33), followed by the cardiometabolic group (HR 1.42, 95% CI 1.18-1.70) and the musculoskeletal, mobility and sensory disorders group (HR 1.29, 95% CI 1.04-1.59).
Conclusions: Our study advances the understanding of the complexity of multimorbidity and its implications for health outcomes and healthcare delivery. The findings suggest the need for integrated healthcare approaches that account for the clusters of multiple conditions and prioritize the complex multimorbidity cohort. Further longitudinal studies are warranted to explore the underlying mechanisms and evolution of multimorbidity patterns.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1186/s12916-024-03796-z | DOI Listing |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11702131 | PMC |
BMC Med
January 2025
Global Public Health, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Subang Jaya, Sunway City, Selangor, Malaysia.
Background: We aimed to identify specific multimorbidity latent classes among multi-ethnic community-dwelling adults aged ≥ 18 years in Malaysia. We further explored the risk factors associated with these patterns and examined the relationships between the multimorbidity patterns and 11-year all-cause mortality risk, as well as health-related quality of life (HRQoL).
Methods: Using data from 18,101 individuals (aged 18-97 years) from the baseline Census 2012, Health Round 2013, and Verbal Autopsies 2012-2023 of the South East Asia Community Observatory (SEACO) health and demographic surveillance system, latent class analysis was performed on 13 chronic health conditions to identify statistically and clinically meaningful groups.
Am J Epidemiol
December 2024
CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru.
Multimorbidity data is typically analysed by tallying disease counts, which overlooks nuanced relationships among conditions. We identified clusters of multimorbidity and subpopulations with varying risks and examined their association with all-cause mortality using a data-driven approach. We analysed 8-year follow-up data of people ≥35 years who were part of the CRONICAS Cohort Study, a multisite cohort from Peru.
View Article and Find Full Text PDFBMC Geriatr
December 2024
Department of Nursing, Daqing Campus, Harbin Medical University, 39 Xinyang Road, Daqing, 163319, China.
Objectives: This study aimed to examine Intrinsic Capacity (IC) subgroups and the association of IC subgroups with IC predictors in Chinese urban empty nesters.
Methods: A convenient sample of 385 older adults aged 60 and above in Community Health Service Center was recruited from Hei Longjiang Province, China, between June 2023 and December 2023. Latent class Analysis (LCA) was conducted to explore IC subgroups using the sensory, cognition, locomotion, psychological, and vitality domains of IC as input variables.
Arch Gerontol Geriatr
December 2024
Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg Center for Health Economics, Hamburg, Germany.
Aim: Our aim was to identify multimorbidity clusters and, in particular, to examine their contribution to well-being outcomes among the oldest old in Germany.
Methods: Data were taken from the large nationally representative D80+ study including community-dwelling and institutionalized individuals aged 80 years and over residing in Germany (n = 8,773). The mean age was 85.
J Am Geriatr Soc
December 2024
Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden.
Background: Individual chronic conditions have been linked to kidney function decline; however, the role of multimorbidity (the presence of ≥2 conditions) and multimorbidity patterns remains unclear.
Methods: A total of 3094 individuals from the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) were followed for 15 years. Multimorbidity was operationalized as the number of chronic conditions and multimorbidity patterns identified using latent class analysis (LCA).
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!