Multimorbidity is defined as the co-occurrence of two or more chronic conditions. The prevalence of multimorbidity is closely related to age, and due to population ageing, multimorbidity has become a major burden for healthcare systems. The biggest challenge when modelling multimorbidity is patient heterogeneity since the patients can suffer from a wide variety of disease combinations. Previous work has shown how age, sex, and socioeconomic status are key determinants of multimorbidity prevalence and multimorbidity disease clusters. However, little is known about the order in which patients acquire multiple chronic conditions.
This thesis aims to study the trajectories of disease accumulation that multimorbid patients follow. To address this challenge, we present four models that focus on the different aspects of the problem and apply them to an Electronic Health Record (EHR) dataset. First, we group chronic conditions into concordant clinical clusters and use a Multi-state Markov model to micro-simulate patient cohorts. This approach allows us to estimate how sex, socioeconomic status, and different disease clusters affect the trajectories and Life Expectancy. Second, we adapt a previously proposed method to identify the networks of chronic diseases that condense the most significant trajectories observed in the data. In this model, we avoid grouping or clustering diseases, and the resulting networks describe specific disease accumulation sequences. Third, we use a greedy structure learning algorithm to find the Bayesian Networks that better fit our EHR dataset. The results of this model help better understand the conditional dependencies between chronic conditions. Fourth, we present a Bernoulli mixture model to study multimorbid patient subtypes.
The models presented in this thesis characterize the temporal patterns that multimorbid patients follow from multiple perspectives and could be used to inform where to focus treatment to prevent or delay the multimorbidity progression
