Overview
Our health data science research is at the forefront of big data and epidemiology, addressing key population health questions that may beneficially impact the health and well-being of individuals. Our multidisciplinary team brings together a rich blend of expertise in mathematics, statistics, computing, and epidemiology. This diverse expertise not only aids in developing innovative methodologies for a broad range of biomedical science challenges but also empowers us to tackle large complex datasets. These include the records of 57M individuals in the NHS England Secure Data Environment via BHF Data Science Center's CVD-COVID-UK/COVID-IMPACT consortium, 24M in OpenSAFELY-TPP, 2.5M in the Emerging Risk Factors Collaboration, and 500K in the UK Biobank.
Through our team science approach, we are committed to scientific excellence, transparency and integrity. We foster a thriving intellectual environment for research and training, where we recognise our most valuable asset is the people who conceive, drive, and deliver research through their vision, imagination and intellectual vigor.
Research
Areas of key contributions:
Population-wide electronic health records Developed and applied analytical methods to the NHS England Secure Data Environment and OpenSAFELY to understand the interplays between COVID-19 infections and vaccinations with cardiovascular diseases (Wood, BMJ 2021; Knight, Circulation 2022; Whiteley, PLoS Medicine 2022)
Translational epidemiology Developed new risk assessment approaches for national, European and global settings (Chung, PLOS One 2023; Pennells, European Heart Journal 2023; Hageman, Eur Heart J 2021; Kaptoge, The Lancet Global Health 2019) and evaluated impact of disease morbidities on life expectancy (Kaptoge, The Lancet Diabetes & Endocrinology 2023)
Blood donor research Employed simulation modelling to evaluate post-donation testing strategies for blood donation (Kim, Transfusion 2023) ; designed and conducted large-scale pragmatic randomised trials addressing blood donation service questions (STRIDES 2019-2023; INTERVAL 2012-2016; McMahon, Trials 2023; Kaptoge, Lancet Haematol 2019; Di Angelantonio, Lancet 2017; Moore, Trials 2014).
Polygenic Risk Scores Developed the Polygenic Score Catalog (Lambert, Nat Genet 2021) and reporting standards (Wand/Lambert, Nature 2021), quantified the added value of polygenic risk scores for disease prevention (Chung, Journal of the American Heart Association 2023) and screening (Kelemen, medrxiv)
Population health policies Provided evidence (i) for cardiovascular risk prevention guidelines for the European Cardiology Society (Visseren, European Heart Journal 2021; Marx, European Heart Journal 2023) and globally (HEARTS: Technical package for cardiovascular disease management in primary health care: Risk-based CVD management ) ; (ii) underpinning the UK screening programme for abdominal aortic aneurysms (Sweeting, Lancet 2018); (iii) informing post-surgical management of abdominal aortic aneurysms (Kim, 2019); (iv) informing new alcohol guidelines (Wood, Lancet 2018; Ricci, BMJ 2018)
Statistical methods and software Authored methodological book chapters (Multiple Imputation and its application, Wiley 2023; Meta-analysis methods (Ch 21), Chapman & Hall 2021) and made software freely available (Programs | Department of Public Health and Primary Care (PHPC)).
Key partners in health data science:
We are located at The Victor Phillip Dahdaleh Heart & Lung Research Institute (VPD-HLRI) and collaborate across several multidisciplinary groups. Cambridge is a research site of Health Data Research UK, a national health data science institute. HDR UK-Cambridge is a partnership of Cambridge University, Wellcome Sanger Institute, and EMBL-EBI. Cambridge University is a founding partner of the Alan Turing Institute for Data Science and Artificial Intelligence and several CEU members are Turing fellows and serve on committees. Our group is also engaged with the Cambridge BHF Centre of Excellence, BHF Data Science Centre, and Italy’s Human Technopole.