title: A two-way clustering framework to identify disparities in multimorbidity patterns of mental and physical health conditions among Australians.
abstract: Multimorbidity is present in more than one quarter of the population in Australia, and its prevalence increases with age. Greater multimorbidity burden among individuals is always associated with poor health-related outcomes, including quality of life, health service utilization and mortality, among others. It is thus significant to identify the heterogeneity in multimorbidity patterns in the community and determine the impact of multimorbidity on individual health outcomes. In this paper, I propose a two-way clustering framework to identify clusters of most significant non-random comorbid health conditions and disparities in multimorbidity patterns among individuals. This framework can establish a clustering-based approach to determine the association between multimorbidity patterns and health-related outcomes and to calculate a multimorbidity score for each individual. The proposed method is illustrated using simulated data and a national survey data set of mental health and wellbeing in Australia.
authors.affiliation: School of Medicine and Menzies Health Institute Queensland, Griffith University, Meadowbrook, 4131, QLD, Australia.
authors.forename: S K
authors.initials: SK
authors.lastname: Ng
mesh_terms: D000368:Aged; D001315:Australia / Q000453:epidemiology; D016000:Cluster Analysis; D015897:Comorbidity; D003198:Computer Simulation; D005260:Female; D006296:Health Services / Q000706:statistics & numerical data*; D006304:Health Status; D006801:Humans; D008297:Male; D008603:Mental Health; D015995:Prevalence; D011788:Quality of Life
keywords: EM algorithm; cluster analysis; mixture models; multimorbidity; multivariate generalized Bernoulli distribution; national survey data