Identifying patterns of co-occurring chronic conditions preceding dementia: An unsupervised machine learning approach using health administrative data 

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Laura C. Maclagan
Daniel A. Harris
Xuesong Wang
Mohamed Abdalla
Tomi Odugbemi
Ruth Ann Marrie
Peter C. Austin
Richard H. Swartz
Sandra E. Black
Myuri Ruthirakuhan
Colleen J. Maxwell
Susan E. Bronskill

Abstract

Objectives
Individual risk factors for dementia are well known, but the influence of co-occurring chronic conditions has not been considered. We identified clusters of chronic conditions using an unsupervised machine learning approach and examined associations with incident dementia.


Approach
Using linked population-based administrative databases, we followed all community-dwelling adults aged 40-54 years in Ontario, Canada from April 2002 until March 2019 for incident dementia. We estimated the prevalence of 29 chronic conditions using validated algorithms and/or diagnosis codes. We reduced dataset dimensionality using multiple correspondence analysis and a fuzzy c-means clustering algorithm identified the optimal number of clusters (between 3-6 tested). Associations between clusters and incident dementia were examined using a cause-specific hazard model adjusted for sociodemographic characteristics and accounting for the competing risk of death.


Results
We identified 82,359 eligible individuals (random 3% sample of total eligible individuals; mean age 46.5 years; 50.4% female). Regression analyses were based on 5 comorbidity clusters (fuzzy silhouette index:0.69). Compared to the low comorbidity cluster, persons in the cerebrovascular disease/metabolic (HRadj=3.06, 95%CI[2.42,3.86]) and neuro-related/mental health clusters (HRadj=2.51, 95%CI[2.05,3.07]) had the highest rates of incident dementia, followed by the cardiovascular risk factor cluster (HRadj=1.66,95%CI[1.32,2.09]). Persons in the cancer cluster did not have an increased incidence of dementia (HRadj=0.96,95%CI[0.77,1.20]).


Conclusions
We found significant associations between machine learning-derived clusters of chronic conditions and dementia.


Implications
Unsupervised machine learning approaches to identify clusters of chronic conditions may be a useful tool for considering the impact of multimorbidity on dementia risk.

Article Details

How to Cite
Maclagan, L. C., Harris, D. A., Wang, X., Abdalla, M., Odugbemi, T., Marrie, R. A., Austin, P. C., Swartz, R. H., Black, S. E., Ruthirakuhan, M., Maxwell, C. J. and Bronskill, S. E. (2024) “Identifying patterns of co-occurring chronic conditions preceding dementia: An unsupervised machine learning approach using health administrative data ”, International Journal of Population Data Science, 9(5). doi: 10.23889/ijpds.v9i5.2849.

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