Comparative analysis of methods for identifying multimorbidity patterns: a study of 'real-world' data.

ABSTRACT: OBJECTIVE:The aim was to compare multimorbidity patterns identified with the two most commonly used methods: hierarchical cluster analysis (HCA) and exploratory factor analysis (EFA) in a large primary care database. Specific objectives were: (1) to determine whether choice of method affects the composition of these patterns and (2) to consider the potential application of each method in the clinical setting. DESIGN:Cross-sectional study. Diagnoses were based on the 263 corresponding blocks of the International Classification of Diseases version 10. Multimorbidity patterns were identified using HCA and EFA. Analysis was stratified by sex, and results compared for each method. SETTING AND PARTICIPANTS:Electronic health records for 408 994 patients with multimorbidity aged 45-64 years in 274

SUBMITTER: Roso-Llorach A 

PROVIDER: S-EPMC5875653 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature