Multimorbidity and mortality: A data science perspective
Name
26335565221105431.pdf
Description
Published version
Size
1.56 MB
Format
Adobe PDF
Checksum (MD5)
ab3194342ed78214ab12e5a60b3e35ee
Author(s)
Siah, Kien Wei
Wong, Chi Heem
Gupta, Jerry
Lo, Andrew W
Date Issued
January 2022
Journal
Journal of Multimorbidity and Comorbidity
Publisher
SAGE Publications
Citation
Siah, Kien Wei, Wong, Chi Heem, Gupta, Jerry and Lo, Andrew W. 2022. "Multimorbidity and mortality: A data science perspective." Journal of Multimorbidity and Comorbidity, 12.
Version
Final published version
Abstract
Background With multimorbidity becoming the norm rather than the exception, the management of multiple chronic diseases is a major challenge facing healthcare systems worldwide. Methods Using a large, nationally representative database of electronic medical records from the United Kingdom spanning the years 2005–2016 and consisting over 4.5 million patients, we apply statistical methods and network analysis to identify comorbid pairs and triads of diseases and identify clusters of chronic conditions across different demographic groups. Unlike many previous studies, which generally adopt cross-sectional designs based on single snapshots of closed cohorts, we adopt a longitudinal approach to examine temporal changes in the patterns of multimorbidity. In addition, we perform survival analysis to examine the impact of multimorbidity on mortality. Results The proportion of the population with multimorbidity has increased by approximately 2.5 percentage points over the last decade, with more than 17% having at least two chronic morbidities. We find that the prevalence and the severity of multimorbidity, as quantified by the number of co-occurring chronic conditions, increase progressively with age. Stratifying by socioeconomic status, we find that people living in more deprived areas are more likely to be multimorbid compared to those living in more affluent areas at all ages. The same trend holds consistently for all years in our data. In general, hypertension, diabetes, and respiratory-related diseases demonstrate high in-degree centrality and eigencentrality, while cardiac disorders show high out-degree centrality. Conclusions We use data-driven methods to characterize multimorbidity patterns in different demographic groups and their evolution over the past decade. In addition to a number of strongly associated comorbid pairs (e.g., cardiac-vascular and cardiac-metabolic disorders), we identify three principal clusters: a respiratory cluster, a cardiovascular cluster, and a mixed cardiovascular-renal-metabolic cluster. These are supported by established pathophysiological mechanisms and shared risk factors, and largely confirm and expand on the results of existing studies in the medical literature. Our findings contribute to a more quantitative understanding of the epidemiology of multimorbidity, an important pre-requisite for developing more effective medical care and policy for multimorbid patients.
MIT Department
Sloan School of Management. Laboratory for Financial Engineering
Sloan School of Management
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
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DOI of Published Version
https://doi.org/10.1177/26335565221105431
