Multimorbidity patterns, sociodemographic characteristics, and mortality: Data science insights from low-resource settings
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. First, we used Partitioning Around Medoids and multidimensional scaling to identify multimorbidity clusters. We then estimated the association between multimorbidity clusters and all-cause mortality. Second, we identified subpopulations using finite mixture modelling. Our analysis revealed three clusters of chronic conditions: respiratory (cluster 1: bronchitis, COPD and asthma), lifestyle, hypertension, depression and diabetes (cluster 2), and circulatory (cluster 3: heart disease, stroke and peripheral artery disease). While only the cluster comprising circulatory diseases showed a significant association with all-cause mortality in the overall population, we identified two latent subpopulations (named I and II) exhibiting differential mortality risks associated with specific multimorbidity clusters. These findings underscore the importance of considering multimorbidity clusters and sociodemographic characteristics in understanding mortality risks. They also highlight the need for tailored interventions to address the unique needs of different subpopulations living with multimorbidity to reduce mortality risks effectively.
Authors
- Antonio Bernabé‐Ortiz (ORCID: https://orcid.org/0000-0002-6834-1376)
- J. Jaime Miranda (ORCID: https://orcid.org/0000-0002-4738-5468)
- William Checkley (ORCID: https://orcid.org/0000-0003-1106-8812)
- Liam Smeeth (ORCID: https://orcid.org/0000-0002-9168-6022)
- Robert H. Gilman (ORCID: https://orcid.org/0000-0002-9037-0712)
- Rodrigo M. Carrillo‐Larco (ORCID: https://orcid.org/0000-0002-2090-1856)
- Juan Carlos Bazo‐Alvarez (ORCID: https://orcid.org/0000-0002-6169-8049)
- Darwin Del Castillo (ORCID: https://orcid.org/0000-0002-8609-0312)
- Luis A. Piza
Institutions
- University of London (GB)
- The University of Sydney (AU)
- Johns Hopkins University (US)
- Emory University (US)
- Universidad Peruana Cayetano Heredia (PE)
- University of Washington (US)
- Johns Hopkins Medicine (US)
- London School of Hygiene & Tropical Medicine (GB)
- University College London (GB)
Publication Details
- Journal
- American Journal of Epidemiology
- Published
- 2024-12-17
- DOI
- https://doi.org/10.1093/aje/kwae466
- Citations
- 2
- Primary Topic
- Chronic Disease Management Strategies
- Type
- article
- Field-Weighted Citation Impact
- 0.77
Funders
- National Science Foundation
- World Health Organization
- UK Research and Innovation
- Bloomberg Philanthropies
- International Development Research Centre
- National Institute for Health and Care Research
- Department of Health and Social Care
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Grand Challenges Canada
- Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica
- Inter-American Institute for Global Change Research
- World Diabetes Foundation
- Alliance for Health Policy and Systems Research
- University of North Carolina at Chapel Hill
- Medical Research Council
- Engineering and Physical Sciences Research Council
- Biotechnology and Biological Sciences Research Council
- National Health and Medical Research Council
- Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica
- National Institute on Aging
- National Heart, Lung, and Blood Institute
- National Institute of Mental Health
- National Cancer Institute
- Fogarty International Center
- National Institute of Diabetes and Digestive and Kidney Diseases