Ben Lacey
MBChB MSc DPhil FFPH
Senior Clinical Research Fellow
- Diverse Populations Group, Clinical Trial Service Unit & Epidemiological Studies Unit (‘CTSU’)
Ben Lacey studied medicine at the University of Edinburgh, with postgraduate training in epidemiology and public health in Oxford. He is a Fellow of the Faculty of Public Health and an Honorary Consultant Physician in Public Health at Oxford University Hospitals NHS Trust. His interests are in the measurement of population health using of electronic health records, and large-scale prospective studies of the lifestyle and genetic determinants of vascular disease.
Recent publications
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Body Fat Distribution and Systolic Blood Pressure in 10,000 Adults with Whole-Body Imaging: UK Biobank and Oxford BioBank.
Journal article
Malden D. et al, (2019), Obesity (Silver Spring)
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Global, regional, and national burden of neurological disorders, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.
Journal article
GBD 2016 Neurology Collaborators None., (2019), Lancet Neurol, 18, 459 - 480
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Blood Pressure and Risk of Subarachnoid Hemorrhage in China.
Journal article
McGurgan IJ. et al, (2018), Stroke
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Body-mass index, blood pressure, and cause-specific mortality in India: a prospective cohort study of 500 810 adults.
Journal article
Gajalakshmi V. et al, (2018), Lancet Glob Health, 6, e787 - e794
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Age-specific association between blood pressure and vascular and non-vascular chronic diseases in 0·5 million adults in China: a prospective cohort study.
Journal article
Lacey B. et al, (2018), Lancet Glob Health, 6, e641 - e649
Research groups
- Accurately estimating the burden of vascular disease using electronic health records
- Adiposity, body fat distribution and risk of chronic disease in Asian and European populations
- Analysing big data from electronic health records to understand the determinants of cardiovascular disease
- Deep phenotyping of vascular events in large-scale epidemiological studies using electronic health records
- Understanding and modelling the geographical variation in relative risks for smoking and other major risk factors for burden of disease analyses