Survivorship and Multimorbidity Epidemiology
Introduction
Led by Dr Marlous Hall – the Survivorship and Multimorbidity Epidemiology group focusses on developing a deeper understanding of the long term survival trajectories for individuals with chronic disease. In particular, the groups’ research focusses on the discovery of multi-disease clusters and trajectories to drive forward our understanding of the role of concomitant disease and multimorbidity on individual survivorship trajectories. The group has a strong methodological foundation in advanced statistical techniques and health data analytics, and use a range of national electronic health care records and clinical registry data to underpin its research. The research group is based within the Leeds Institute for Data Analytics, and is part of the Clinical and Population Sciences Department in the Leeds Institute of Cardiovascular and Metabolic Medicine.
Dr Marlous Hall joined the University of Leeds as a medical statistician in 2009, building up expertise in applied healthcare research in cancer and cardiovascular disease. She was successfully awarded a Sir Henry Wellcome Fellowship and appointed as University Academic Fellow in Cardiovascular Survivorship and Multimorbidity in 2017. Dr Hall has extensive expertise in the processing and analyses of large scale and complex healthcare data, with a particular focus in the application of advanced methodologies including multistate modelling, multiple imputation, multilevel modelling, survival techniques such as flexible parametric survival modelling and relative survival. Following her work in both cancer and cardiovascular disease epidemiology, Dr Hall developed a particular interest in multi-disease research which relies on the efficient use of increasingly complex data and methodologies. Further resources from our team’s work are available on our GitHub Page.
Select outputs and projects
Multimorbidity clusters for patients with MI – It is now common to survive over a decade after the diagnosis of myocardial infarction (MI). This has resulted in a growing population with multiple chronic diseases. We used latent class analyses of 693,388 patients with myocardial infarction and up to 7 co-morbidities. We discovered a highly multi-morbid cluster of MI patients with concomitant heart failure, peripheral vascular disease and hypertension. Patients were more often female and less likely to receive guideline-recommended care including aspirin and beta-blockers compared with the low multi-morbidity cluster. The multi-morbidity phenotypes had a significant differential impact on survivorship trajectories over and above the impact of age, sex, individual co-morbidities and treatment, such that highly multi-morbid patients were 2.4 times more likely to die and had a reduced life expectancy by up to 3 years. The findings were published in PLOS Medicine (2018) and the associated press release can be viewed here.
Data phenotyping longitudinal multimorbidity trajectories in cardiovascular disease: a statistical machine learning approach using nationwide electronic healthcare record. This programme of work pools expertise in the analyses of large scale electronic healthcare record (EHR) data, clinical cardiovascular epidemiology and statistical machine learning to investigate the longitudinal multimorbidity trajectories following MI and the develop the required methodologies to underpin this. The work is led by Dr Hall at the University of Leeds, in collaboration with Professor Niels Peek (University of Manchester), Professor Ronan Lyons (University of Swansea), Professor Chris Holmes (University of Oxford) and Professor Chis Gale (University of Leeds). The work is funded through the AI for Science and Government fund via the Alan Turing Institute Health Programme. April 2020 – March 2023. £303,744 (PI: M Hall).
Healthcare utilisation and clinical outcomes among survivors of acute myocardial infarction: a national electronic health records cohort study
This personal fellowship supports Dr Marlous Hall’s research into healthcare utilization and clinical outcomes among survivors of acute myocardial infarction – in particular, the work focusses on the role of concomitant disease and multimorbidity on individual survivorship trajectories. The fellowship is funded through a Sir Henry Wellcome Fellowship awarded by the Wellcome Trust. Sep 2017 – March 2022, £270,336 (PI: M Hall).
VICORI – The Virtual cardio-oncology Research Initiative
This study led by Dr David Adlam, University of Leicester and Dr Michael Peake, Public Health England and is a collaborative initiative with the University of Leeds (Hall: Statistical co-lead & CP Gale: work package lead) and national cancer and cardiovascular audit network in order to link national electronic health records for the high resolution investigation of the interplay between cancer and cardiovascular disease. This programme of work is jointly funded by Cancer Research UK and the British Heart Foundation £1.4 million. 2016–2021.
Collaborations
University of Leeds
The group has strong collaborative links with the Cardiovascular Epidemiology Group – from which the Survivorship and Multimorbidity Epidemiology group was founded. The Cardiovascular Epidemiology group, led by Professor Chris Gale, has a mission to better understand and improve the quality of care for patients with cardiovascular disease both in the UK as well as nationally. This work is highly complementary to our focus on data phenotyping and redefining of traditional single disease siloes which aims to better understand the quality of care interacting across multiple chronic diseases and the combined impact on outcomes. Both groups are situated in the Leeds Institute for Data Analytics, and are part of the Clinical and Population Sciences Department within the Leeds Institute of Cardiovascular and Metabolic Medicine.
External
The group has further external collaborations through joint publications and grant income nationally and internationally with partners at the University College London, University of Leicester, Karolinska Institute Sweden, University of Manchester and University of Oxford.