Multimorbidity as a multistage disease process

There is a growing proportion of people with several disease conditions ("multimorbidity"), placing increasing demands on healthcare systems. One hypothesis is that clusters of diseases may arise from shared underlying disease processes (shared "pathogenesis"), whereby the presence of one disease indicates the state of disease progression to several related disease types. This article explains how this hypothesis can be tested using observational data for disease incidence. Specifically, a multistage model is used to test whether two diseases can have a "shared stage" or "step," before either disease can occur, and how the unobserved rate of this step can be determined. The approach offers a simple method for studying multiple diseases and identifying shared underlying causes of multiple conditions and is illustrated with published data and numerical examples. The fundamental mathematical model is analyzed to compare key statistical properties such as the expectation and variance with those of independent diseases. The main results do not need an understanding of the underlying mathematics and can be appreciated by a nonexpert.

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Publication Details

Journal
PNAS Nexus
Published
2025-12-30
DOI
https://doi.org/10.1093/pnasnexus/pgaf410
Primary Topic
Chronic Disease Management Strategies
Type
article
Field-Weighted Citation Impact
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article

Multimorbidity as a multistage disease process

A Webster
PNAS Nexus
Chronic Disease Management Strategies
article

Multimorbidity as a multistage disease process

A Webster
article en

Abstract

There is a growing proportion of people with several disease conditions ("multimorbidity"), placing increasing demands on healthcare systems. One hypothesis is that clusters of diseases may arise from shared underlying disease processes (shared "pathogenesis"), whereby the presence of one disease indicates the state of disease progression to several related disease types. This article explains how this hypothesis can be tested using observational data for disease incidence. Specifically, a multistage model is used to test whether two diseases can have a "shared stage" or "step," before either disease can occur, and how the unobserved rate of this step can be determined. The approach offers a simple method for studying multiple diseases and identifying shared underlying causes of multiple conditions and is illustrated with published data and numerical examples. The fundamental mathematical model is analyzed to compare key statistical properties such as the expectation and variance with those of independent diseases. The main results do not need an understanding of the underlying mathematics and can be appreciated by a nonexpert.

PNAS NexusVol. 5(1)
University of Oxford (GB)
Quality Education
Openalex Percentile: Top 29%
Chronic Disease Management Strategies
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