Ben Lacey
MBChB MSc DPhil FFPH
Senior Clinical Research Fellow
- 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 holds an MSc in Global Health Science and a DPhil in Epidemiology (both from the University of Oxford), and is a Fellow of the Faculty of Public Health. He joined the Nuffield Department of Population Health in 2015, and was appointed as an Honorary Consultant Physician in Public Health at Oxford University Hospitals NHS Trust in 2017. His principal interest is in the use of electronic health records and large-scale prospective studies to understand the epidemiology of vascular and other chronic diseases.
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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Cohort Profile: the Cuba Prospective Study.
Journal article
Armas Rojas N. et al, (2019), Int J Epidemiol
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Burden of hypertension and associated risks for cardiovascular mortality in Cuba: a prospective cohort study.
Journal article
Armas Rojas N. et al, (2019), Lancet Public Health, 4, e107 - e115
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Blood Pressure and Risk of Subarachnoid Hemorrhage in China.
Journal article
McGurgan IJ. et al, (2018), Stroke
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