Table 1.

A summary of the papers that introduced a new method for the study of multimorbidity patterns.

Study	Method	Context	Data
Hernández et al. (2019) (Hernández et al., 2019)	Pairwise correlations	6101 Irish adults aged 50+ years	Self-reported conditions
Aguado et al. (2020) (Aguado et al., 2020)	Pairwise correlations	500 K adults in Spain with Type 2 diabetes mellitus.	EHR
Jin et al. (2018) (Jin et al., 2018)	Pairwise correlations	21,435 adults from Jilin province, China	Self-reported conditions
Khorrami et al. (2020) (Khorrami et al., 2020)	Latent class analysis	10,069 adult Iranian people	Self-reported conditions
Wang et al. (2019) (Wang et al., 2019)	Principal Component Analysis	2713 adults in São Paulo, Brazil	Self-reported conditions
Schiltzet al. (2017) (Schiltz et al., 2017)	Classification/regression trees and random forest	5771 people from US aged 65+ years	Self-reported conditions linked to Medicare claims
Haug et al. (2020) (Haug et al., 2020)	Hierarchical clustering	5M patients in Austria	EHR
Bueno et al. (2018) (Bueno et al., 2018)	Hidden Markov Models	Dutch patients with comorbidities related to atherosclerosis	EHR
Violán et al. (2018) (Violán et al., 2018)	K-means non-hierarchical cluster analysis	400 patients aged 45−64 years from Spain	EHR
Marengoni et al. (2019) (Marengoni et al., 2019)	Fuzzy c-means cluster algorithm	2931 individuals in Sweden aged 60+ years	EHR
Medlock-Brown et al. (2019) (Madlock‐Brown and Reynolds, 2019)	Pairwise correlations	574,172 patients with obesity in the US	EHR