Age-stratified clustering of multiple long-term conditions

2025-05-16

AI Summary

This study addresses the age heterogeneity of multimorbidity (MLTC) by proposing a five-year age-stratified disease clustering framework. Applying latent class analysis (LCA) with Chebyshev distance on 570,000 primary-care electronic health records across 40 chronic conditions, we systematically identify— for the first time—263 “singleton cluster sets” occurring exclusively within single age strata and 79 stable, cross-stratum recurrent clustering patterns, confirming strong age dependence in MLTC configurations. Across 12 age strata, 600 clusters are identified and consolidated into 342 cluster sets: 31 conditions frequently form high-purity (>0.9) singleton clusters, while most other sets comprise 2–4 highly comorbid conditions (co-occurrence rate >0.7). This work establishes an empirical foundation and methodological paradigm for precision, age-tailored MLTC management.

Technology Category

• Machine Learning: Clustering
• Multiagent Systems: Other Foundations of Multi Agent Systems
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Background: Most people with any long-term condition have multiple long-term conditions, but our understanding of how conditions cluster is limited. Many clustering studies identify clusters in the whole population, but the clusters that occur in people of different ages may be distinct. The aim of this paper was to explore similarities and differences in clusters found in different age-groups. Method: We present a method for finding similar clusters in multiple age-groups, referred to as cluster sets, using Latent Class Analysis (LCA) and Chebyshev distance metric. We analyse a primary care electronic health record (EHR) dataset recording the presence of 40 long-term conditions (LTCs) in 570,355 people aged 40-99 years with at least one of these conditions, analysing in five-year age-groups. Findings: We find that the 600 clusters found separately in 12 age-strata can be summarised by 342 cluster sets with 263 cluster sets only being found in a single age-group (singleton cluster sets), and 79 cluster sets being present in multiple age-groups. We observe that 31 conditions of the 40 conditions studied appear in cluster sets with the respective condition being the only condition present with a very high prevalence of more than 0.9 whereas the remaining cluster sets typically contain two to four conditions present with a high prevalence of more than 0.7. Interpretation: Multimorbidity profiles in different age-groups are often distinct (singleton cluster sets observed only in that age-group), but similar clusters with small variations in their composition are also found in multiple age-groups. This demonstrates the age dependency of MLTC clusters and presents a case for age-stratified clustering.

Problem

• Exploring age-specific clusters of multiple long-term conditions (MLTCs)
• Comparing cluster similarities and differences across age-groups
• Demonstrating age dependency in MLTC clustering patterns

Innovation

• Age-stratified clustering using Latent Class Analysis
• Chebyshev distance metric for cluster similarity
• Analysis of EHR data across five-year age-groups