1. Latent class analyses of multimorbidity and all-cause mortality: A prospective study in Chilean adults.
- Author
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Nazar G, Díaz-Toro F, Concha-Cisternas Y, Leiva-Ordoñez AM, Troncoso-Pantoja C, Celis-Morales C, and Petermann-Rocha F
- Subjects
- Adult, Humans, Prospective Studies, Latent Class Analysis, Multimorbidity, Chile epidemiology, Chronic Disease, Hypertension epidemiology, Cardiovascular Diseases epidemiology
- Abstract
Multimorbidity patterns can lead to differential risks for all-cause mortality. Within the Chilean context, research on morbidity and mortality predominantly emphasizes individual diseases or combinations thereof, rather than specific disease clusters. This study aimed to identify multimorbidity patterns, along with their associations with mortality, within a representative sample of the Chilean population. 3,701 participants aged ≥18 from the Chilean National Health Survey 2009-2010 were included in this prospective study. Multimorbidity patterns were identified from 16 chronic conditions and then classified using latent class analyses. All-cause mortality data were extracted from the Chilean Civil Registry. The association of classes with all-cause mortality was carried out using Cox proportional regression models, adjusting by sociodemographic and lifestyle variables. Three classes were identified: a) Class 1, the healthiest (72.1%); b) Class 2, the depression/cardiovascular disease/cancer class (17.5%); and c) Class 3, hypertension/chronic kidney disease class (10.4%). Classes 2 and 3 showed higher mortality risk than the healthiest class. After adjusting, Class 2 showed 45% higher mortality risk, and Class 3 98% higher mortality risk, compared with the healthiest class. Hypertension appeared to be a critical underlying factor of all-cause morbidity. Particular combinations of chronic diseases have a higher excess risk of mortality than others., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Nazar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
- Published
- 2023
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