BioMaths Colloquium Series – 2023/24
06 March 2024 – 1pm
Online (register here for Zoom link)
Prof. Rhiannon Owen
(Swansea University Medical School)
Modelling trajectories of disease in multimorbidity using population-level linked electronic health records
Professor Rhiannon Owen is Professor of Statistics at Swansea University Medical School. Her main research interests include the development and application of Bayesian methods in Health Technology Assessment, Population Health, and Health Service Evaluation. In particular, her research interests include evidence synthesis methods, analysis of large scale linked electronic health records, simulation-based methods, clinical trial evaluation, economic decision modelling, and value of information. This work has been and is supported by the Academy of Medical Sciences (AMS), Health Data Research UK (HDR UK), the Medical Research Council (MRC), the National Institute for Health and Care Research (NIHR), and the Wellcome Trust.
Rhiannon is a member of the UK National Institute for Health and Care Excellence (NICE) Technology Appraisal Committee, member of the NICE Decision Support Unit, and Associate Member of the NICE Technical Support Unit. She has extensive experience of cross-sector collaboration including as a consultant, providing methodological and strategic advice to the pharmaceutical and healthcare industry.
Abstract:
People are increasingly living with multiple long-term conditions, also known as multimorbidity. Multimorbidity can encompass many different combinations of long-term conditions, and these combinations can cluster in a concordant or discordant manner. The epidemiology of multimorbidity has to date concentrated on the static clustering of diseases with respect to time rather than their dynamic evolution or trajectory over the life course. This talk will discuss the use of multistate models to examine how common clusters of disease develop over time using a population-level cohort study. The models are applied to the Wales Multi-morbidity e-Cohort (WMC) in The Secure Anonymised Information Linkage (SAIL) databank. The WMC includes population scale, individual-level, anonymised, linked, multi-sourced data, including demographic, administrative and electronic health records for 2.9 million individuals. Using a case study in psychosis, diabetes, and congestive heart failure, this presentation will discuss the impact of different trajectories of disease on mortality, identify potential screening and therapeutic targets, and assess potential risk factors in physical-mental health multimorbidity. The talk will conclude with a discussion around modelling extensions and their application to other settings.