Associate Professor Derrick Bennett
BSc MSc PhD CStat
Derrick has a BSc (Hons) in Mathematics and Statistics, an MSc in Medical Statistics, and a PhD in Epidemiology and Statistics. He has been a Royal Statistical Society accredited Chartered Statistician since 2005.
His research is interdisciplinary, integrative and collaborative and uses large-scale observational studies and randomised trials to generate reliable evidence for the prevention of premature deaths and disability from chronic diseases. His work involves applying statistical, epidemiological, computational, and genetic tools to understand associations of exposures with chronic diseases. His research aims to drive improvements in population health by identifying novel treatment targets and implementing precision strategies for primary and secondary prevention of major disease outcomes such as cardiovascular disease, stroke, diabetes and cancer.
He co-leads the Statistical Group in the China Kadoorie Biobank and oversees a portfolio of research related to aging, cardiovascular, respiratory, and lifestyle factors. He is responsible for ensuring that the study design methodology is robust, appropriate and deliverable as well as for securing grant income as the statistical lead.
Derrick co-leads the Principles of Data Science module of the MSc in Global Health Science and Epidemiology, and leads the curriculum development for data science teaching. He is currently supervising several MSc and DPhil students.
He has also contributed chapters to four textbooks and was named as a highly cited researcher in 2018 for papers that rank in the top 1% in his field of research. In 2022 he was listed among the top 1000 scientists in the UK in the Research.com Medicine rankings.
Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK Biobank
Small SR. et al, (2023)
Genetically Predicted Differences in Systolic Blood Pressure and Risk of Cardiovascular and Noncardiovascular Diseases: A Mendelian Randomization Study in Chinese Adults.
Clarke R. et al, (2023), Hypertension (Dallas, Tex. : 1979)
Validation of the World Health Organization non-laboratory-based cardiovascular disease risk prediction models in ten diverse regions of China
CHEN Z. et al, (2022), Bulletin of the World Health Organization
Mapping development and health effects of cooking with solid fuels in low-income and middle-income countries, 2000–18: a geospatial modelling study
Frostad JJ. et al, (2022), The Lancet Global Health, 10, e1395 - e1411
Systematic review of the utility of the frailty index and frailty phenotype to predict all-cause mortality in older people.
Kim DJ. et al, (2022), Syst Rev, 11