Temporal Patterns of Multiple Long-Term Conditions in Individuals with Intellectual Disability Living in Wales: An Unsupervised Clustering Approach to Disease Trajectories

Abstract

Identifying and understanding the co-occurrence of multiple long-term conditions (MLTC) in individuals with intellectual disabilities (ID) is vital for effective healthcare management. These individuals often face earlier onset and higher prevalence of MLTCs, yet specific co-occurrence patterns remain unexplored. This study applies an unsupervised approach to characterise MLTC clusters based on shared disease trajectories using electronic health records (EHRs) from 13069 individuals with ID in Wales (2000-2021). Disease associations and temporal directionality were assessed, followed by spectral clustering to group shared trajectories. The population consisted of 52.3% males and 47.7% females, with an average of 4.5 conditions per patient. Males under 45 formed a single cluster dominated by neurological conditions (32.4%), while males above 45 had three clusters, the largest characterised circulatory (51.8%). Females under 45 formed one cluster with digestive conditions (24.6%) as most prevalent, while those aged 45 and older showed two clusters: one dominated by circulatory (34.1%), and the other by digestive (25.9%) and musculoskeletal (21.9%) system conditions. Mental illness, epilepsy, and reflux were common across groups. These clusters offer insights into disease progression in individuals with ID, informing targeted interventions and personalised healthcare strategies.

Figures (7)

• Figure 1: Schematic representation of the workflow of the proposed methodology.
• Figure 2: Disease trajectory clusters across age groups and genders. Top: Males - A) $<$ 45 years cluster 1, B) $\geq$ 45 years cluster 1, C) $\geq$ 45 years cluster 2, and D) $\geq$ 45 years cluster 3. Bottom: Females - A) $<$ 45 years single cluster; B) $\geq$ 45 years cluster 1, and C) $\geq$ 45 years cluster 2. Node size indicates condition prevalence, with larger nodes representing more frequent occurrences. Conditions in each cluster represent more than 5% of the total conditions. Edges show associations between conditions, with edge thickness corresponding to the frequency of condition pair occurrences (minimum edge frequency of 1%). Coloured shaded areas group related conditions within the same category as defined in the legend. The legend specifies condition categories and provides a scale for patient numbers.
• Figure 3: Distribution of long-term conditions and rates of mortality and long hospital stays across patient clusters.
• Figure 4: Comparison of the five leading causes of death among individuals with intellectual disabilities (ID), grouped based on ICD-10 chapters. Data presented includes findings from the A) LeDeR Annual Report 2022 white2023learning, B) our cohort study using the SAIL (Secure Anonymised Information Linkage) databank and stratification by clusters based on sex and age groups for C) male cluster ($<$ 45 years: cluster 1 and $\geq$ 45 years: clusters 1, 2, 3) and D) females ($<$ 45 years: cluster 1; $\geq$ 45 years: clusters 1, 2). Values represent percentages for each cause of death.
• Figure S1: Defining chronic condition criteria.