SSM ECRs featuring…

SSM ECRs featuring… Leo Westbury!

Leo Westbury is a Senior Research Fellow / Medical Statistician at the University of Southampton.

Leo Westbury University of Southampton Senior Research Fellow / Medical Statistician SSM member since: 2016 SSM participation: Chair elect of the ECR subcommittee Topics of interest:  Musculoskeletal epidemiology; Ageing; Statistics

What inspired you to pursue a career in epidemiology and population health?
Whilst studying Statistics at the University of Warwick, I enjoyed learning about how statistical methods can be used to solve real-world problems and I particularly enjoyed a module about medical statistics. Therefore, I applied for a Research Fellow / Statistician post at the MRC Lifecourse Epidemiology Centre (University of Southampton) during the end of my MSc at Warwick.

What excites you about working in research now?
It’s an exciting time to be involved in data analysis due to the explosion of artificial intelligence (AI) and machine learning in research which may have huge benefits such as improving diagnostics and identifying novel cures for diseases. Also, I think the COVID-19 pandemic has led to greater awareness of the importance of epidemiology.

What area of epidemiology/population health are you interested in?
My research focus since 2013 has been on the lifecourse epidemiology of musculoskeletal ageing. Part of this role involves implementing statistical methods to explore determinants and examine health-related consequences of poor or declining musculoskeletal health in older age. These insights may ultimately be used to develop interventions to reduce the burden of musculoskeletal conditions in the population.

Can you tell us a bit about a project you’re working on now?
One project is part of a collaboration which involves using machine learning techniques to extract information from medical images of the tibia and radius. The aim is to explore whether these methods improve fracture prediction compared to methods currently in use. 

What do you hope this will lead to?
The initial findings are promising and suggest that fracture prediction is improved through this application of machine learning compared to only using bone mineral density and other established clinical risk factors to predict fracture. This could ultimately result in better identification of individuals at high risk of fracture.

Bonus question from ECR sub-committee: What are you most excited for during your first year on the SSM ECR sub-committee as Chair Elect?
I’m looking forward to helping in the development of activities and initiatives to benefit ECRs across the Society for Social Medicine.

To keep up to date with Leo’s work, you can get in touch via email.

SSM ECR features is a blog series that celebrates early career researchers. Each month we meet a member we admire, learn more about their work and find out what and who inspires them. To find out more visit socsocmed.org.uk/blog or email ecr.ssm@gmail.com.