Comparing Multimorbidity Patterns Among Discharged Middle-Aged and Older Inpatients Between Hong Kong and Zurich: A Hierarchical Agglomerative Clustering Analysis of Routine Hospital Records

Abstract

Background: Multimorbidity, defined as the co-occurrence of ≥2 chronic conditions, is clinically diverse. Such complexity hinders the development of integrated/collaborative care for multimorbid patients. In addition, the universality of multimorbidity patterns is unclear given scarce research comparing multimorbidity profiles across populations. This study aims to derive and compare multimorbidity profiles in Hong Kong (HK, PRC) and Zurich (ZH, Switzerland).Methods: Stratified by sites, hierarchical agglomerative clustering analysis (dissimilarity measured by Jaccard index) was conducted with the objective of grouping inpatients into clinically meaningful clusters based on age, sex, and 30 chronic conditions among 20,000 randomly selected discharged multimorbid inpatients (10,000 from each site) aged ≥ 45 years. The elbow point method based on average within-cluster dissimilarity, complemented with a qualitative clinical examination of disease prevalence, was used to determine the number of clusters.Results: Nine clusters were derived for each site. Both similarities and dissimilarities of multimorbidity patterns were observed. There was one stroke-oriented cluster (3.9% in HK; 6.5% in ZH) and one chronic kidney disease-oriented cluster (13.1% in HK; 11.5% ZH) in each site. Examples of site-specific multimorbidity patterns, on the other hand, included a myocardial infarction-oriented cluster in ZH (2.3%) and several clusters in HK with high prevalence of heart failure (>65%) and chronic pain (>20%).Conclusion: This is the first study using hierarchical agglomerative clustering analysis to profile multimorbid inpatients from two different populations to identify universalities and differences of multimorbidity patterns. Our findings may inform the coordination of integrated/collaborative healthcare services.

Booster vaccination with inactivated whole-virus or mRNA vaccines and COVID-19–related deaths among people with multimorbidity: a cohort study

“…31, 2022, of the same SARS-CoV-2 vaccine they had received for the first 2 doses (BNT162b2 or CoronaVac). 14,15 Both vaccines were made freely available to all residents of Hong Kong during the study period. Dozens of community vaccination centres that provided either vaccine were set up in geographically convenient public facilities for a wider reach to the community.…”

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

“…Euclidean distance in d-dimensional space), the closest clusters are merged and continues until all subjects have been merged into either the pre-specified number of clusters (k), or one cluster [ 186 ] BIRCH: Speed, scalability; CURE: Arbitrary shapes. Spectral: Performs dimensionality reduction before clustering based on the similarity matrix which describes the similarity between each pair of data points Hierarchical: Comparison of multimorbidity patterns in Hong Kong and Zurich using hierarchical agglomerative clustering [ 196 ] BIRCH: Ability to detect outlier clusters of depressed patients and polypharmacy patients not detectable using regression methods [ 197 ] CURE: CURE-SMOTE – a hybrid algorithm for feature selection, parameter optimization and synthetic minority oversampling technique (SMOTE) based on random forests [ 198 ] STING: Useful for mining of geospatial data [ 199 ] Spectral: Clustering high-dimensional data via feature selection [ 200 ] Affinity propagation: Parallel clustering algorithm for large-scale biological data sets [ 201 ] Model-based Algorithms: Gaussian Mixture Model, (GMM), Expectation–Maximisation, (EM), Dirichlet Mixture Model, (DMM), CLARANS, Self Organisng Map (SOM), Adaptive Resonance Theory, (ART) Specific features: Integrates background knowledge into gene expression, interactomes, and sequences. Models are an oversimplification since assumptions may be false and then results are inaccurate GMM, EM, DMM, CLARANS, DBSCAN: Clustering compositional data using Dirichlet mixture model [ 185 ] Density-based Algorithms: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [ 202 ], Ordering Points To Identify Clustering Structure, (OPTICS), Mean-shift Specific features: DBSCAN regards clusters as dense regions of objects in space that are separated by regions of low density.…”

The interplay between kidney function and multimorbidity: A scoping review

“…Twelve studies examined the coexistence and interactions of CKD with other chronic diseases, nine of which conducted cluster analysis, and three used network analysis [16][17][18][19][20][21][22][23][24] . The studies were from Europe (k = 7), North America (k = 3), and Asia (k = 1).…”