Exploring multiple long-term conditions clusters among older people in Brazil (ELSI-Brazil Study)
1
Faculty of Medicine, Federal University of Goias, Goiânia, Brazil
2
Programa de Pós-Graduação em Saúde Coletiva, Universidade de Brasilia, Brasilia, Brazil
3
Faculty of Nursing, Federal University of Goias, Goiânia, Brazil
4
Faculty of Sciences and Technologies in Health, University of Brasília, Brasilia, Brazil
Popul. Med. 2026;8(Supplement Supplement 1):A2974
ABSTRACT
INTRODUCTION:
Comprehending the multimorbidity clusters and their effects remains a major public health priority, particularly in LMICs. Therefore, the objective of this study was to identify specific multimorbidity clusters using Latent Class Analysis (LCA) in a population of Brazilian older adults.
METHODS:
We performed a cross-sectional analysis with 4,709 participants aged ≥60 years from ELSI-Brazil (2019–2021), with complete information for the chronic condition variables. A total of 16 chronic diseases were included to identify MLTC clusters using LCA. We tested LCA solutions ranging from 2 to 11 classes to determine the optimal number of latent classes. This was defined using the Bayesian Information Criterion (BIC) and the adjusted BIC, with the lowest values for both observed in the 4-class model. Diseases with both exclusivity ≥25% and an O/E ratio ≥2 were considered overexpressed and used to label the clusters.
RESULTS:
We identified four distinct MLTC clusters, named according to their overexpressed conditions: low multimorbidity (47.72%), cardiometabolic (29.13%), musculoskeletal/respiratory (14.74%), and complex multimorbidity (8.41%). Most variables showed statistically significant associations with the clusters, with particularly strong associations observed for poor/very poor self-rated health, polypharmacy, hospitalization ≤12m, living alone, cognitive decline, frailty, falls ≤12m, and functional disability (p < 0.0001).
CONCLUSIONS:
These findings underscore the importance of using clustering analysis to support intervention planning for individuals with multiple long-term conditions. By incorporating these factors, healthcare systems can be better equipped to design tailored interventions that more effectively address the needs of complex patients.
Comprehending the multimorbidity clusters and their effects remains a major public health priority, particularly in LMICs. Therefore, the objective of this study was to identify specific multimorbidity clusters using Latent Class Analysis (LCA) in a population of Brazilian older adults.
METHODS:
We performed a cross-sectional analysis with 4,709 participants aged ≥60 years from ELSI-Brazil (2019–2021), with complete information for the chronic condition variables. A total of 16 chronic diseases were included to identify MLTC clusters using LCA. We tested LCA solutions ranging from 2 to 11 classes to determine the optimal number of latent classes. This was defined using the Bayesian Information Criterion (BIC) and the adjusted BIC, with the lowest values for both observed in the 4-class model. Diseases with both exclusivity ≥25% and an O/E ratio ≥2 were considered overexpressed and used to label the clusters.
RESULTS:
We identified four distinct MLTC clusters, named according to their overexpressed conditions: low multimorbidity (47.72%), cardiometabolic (29.13%), musculoskeletal/respiratory (14.74%), and complex multimorbidity (8.41%). Most variables showed statistically significant associations with the clusters, with particularly strong associations observed for poor/very poor self-rated health, polypharmacy, hospitalization ≤12m, living alone, cognitive decline, frailty, falls ≤12m, and functional disability (p < 0.0001).
CONCLUSIONS:
These findings underscore the importance of using clustering analysis to support intervention planning for individuals with multiple long-term conditions. By incorporating these factors, healthcare systems can be better equipped to design tailored interventions that more effectively address the needs of complex patients.
| eISSN: | 2654-1459 |
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