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Temporal patterns of multiple long-term conditions in individuals with intellectual disability living in Wales: an unsupervised clustering approach to disease trajectories
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Francesco Zaccardi,
Gyuchan Thomas Jun,
Reza Kiani,
Satheesh Gangadharan
Frontiers in Digital Health, Volume: 7, Start page: 1528882
Swansea University Author:
Ashley Akbari
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© 2025 Kousovista, Cosma, Abakasanga, Akbari, Zaccardi, Jun, Kiani and Gangadharan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
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DOI (Published version): 10.3389/fdgth.2025.1528882
Abstract
Introduction: Identifying and understanding the co-occurrence of multiple long-term conditions (MLTCs) in individuals with intellectual disability (ID) is crucial for effective healthcare management. Individuals with ID often experience earlier onset and higher prevalence of MLTCs compared to the ge...
| Published in: | Frontiers in Digital Health |
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| ISSN: | 2673-253X |
| Published: |
Frontiers Media S.A.
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa69277 |
| Abstract: |
Introduction: Identifying and understanding the co-occurrence of multiple long-term conditions (MLTCs) in individuals with intellectual disability (ID) is crucial for effective healthcare management. Individuals with ID often experience earlier onset and higher prevalence of MLTCs compared to the general population, however, the specific patterns of co-occurrence and temporal progression of these conditions remain largely unexplored. This study presents an innovative unsupervised approach for examining and characterising clusters of MLTC in individuals with ID, based on their shared disease trajectories. Methods: Using a dataset of electronic health records (EHRs) from 13,069 individuals with ID, encompassing primary and secondary care data in Wales from 2000 to 2021, this study analysed the time sequences of disease diagnoses. Significant pairwise disease associations were identified, and their temporal directionality assessed. Subsequently, an unsupervised clustering algorithm—spectral clustering—was applied to the shared disease trajectories, grouping them based on common temporal patterns. Results: The study population comprised 52.3% males and 47.7% females, with a mean of 4.5 ± 3 long-term conditions (LTCs) per patient. Distinct MLTC clusters were identified in both males and females, stratified by age groups (<45 and ≥45 years). For males under 45, a single cluster dominated by neurological conditions (32.4%), while three clusters were identified for older males, with the largest characterised by circulatory (51.8%). In females under 45, one cluster was found with digestive system conditions (24.6%) being most prevalent. For females ≥ 45 years, two clusters were identified: the first cluster was predominantly defined by circulatory (34.1%), while the second cluster by digestive (25.9%) and musculoskeletal (21.9%) system conditions. Mental illness, epilepsy, and reflux disorders were prevalent across all groups. Discussion: This study reveals complex multimorbidity patterns in individuals with ID, highlighting age and sex differences. The identified clusters provide new insights into disease progression and co-occurrence in this population. These findings can inform the development of targeted interventions and risk stratification strategies, potentially improving personalised healthcare for individuals with ID and MLTCs with the aim of improving health outcome for this vulnerable group of patients i.e. reducing frequency and length of hospital admissions and premature mortality. |
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| Keywords: |
disease trajectories, chronic disease, co-morbidity, clustering, intellectual disability, multimorbidity |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
The work was funded by National Institute for Health and Care Research. The project is entitled “DECODE: Data-driven machinE-learning aided stratification and management of multiple long-term COnditions in adults with intellectual disabilitiEs.” Grant no. NIHR203981. |
| Start Page: |
1528882 |