What do we mean when we say we are clustering multimorbidity?

TL;DR

This paper discusses the variability in methods and purposes of clustering multimorbidity, emphasizing the need for explicit choices and purpose-driven analysis to improve clarity and utility.

Contribution

It highlights the importance of clarifying clustering choices and aligning methods with research goals in multimorbidity studies.

Findings

Clustering approaches vary based on purpose and choices made.

Explicitly stating clustering decisions improves transparency.

Purpose-driven clustering enhances interpretability and utility.

Abstract

Clustering multimorbidity has been a global research priority in recent years. Existing studies usually identify these clusters using one of several popular clustering methods and then explore various characteristics of these clusters, e.g., their genetic underpinning or their sociodemographic drivers, as downstream analysis. These studies make several choices during clustering that are often not explicitly acknowledged in the literature, e.g., whether they are clustering conditions or clustering individuals, and thus, they lead to different clustering solutions. We observe that, in general, clustering multimorbidity might mean different things in different studies, and argue that making these choices more explicit and, more importantly, letting the downstream analysis, or the purpose of identifying multimorbidity clusters, guide these choices, might lead to more transparent and operationalizable multimorbidity clusters. In this study, we discuss various purposes of identifying multimorbidity clusters and build a case for how different purposes can justify the different choices in data and methods.