Multimorbidity patterns, sociodemographic characteristics, and mortality: Data science insights from low-resource settings

Abstract

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.

Keywords: cluster analysis; low-and-middle-income countries; mortality risk; multimorbidity patterns; sociodemographic characteristics; unsupervised machine learning.