This workshop will explore the main challenges to statistical approaches in improving health equity. We will host a series of panel discussions, each focusing on a different aspect of healthcare, featuring international experts from across academia, medicine, and public health.

The workshop is open to all with an interest in using data-driven approaches to improve health equity and we greatly welcome participation from a broad range of backgrounds and disciplines.

Workshop aim

The aim of the workshop is to identify the main barriers to

1. Understanding the cascading effects of health and healthcare inequities in data and in the design and development of statistical models
2. Detecting / quantifying unfair biases and the effects of health and healthcare inequity
3. Improving the equity of statistical approaches and mitigating unfair biases
4. Understanding the limitations of statistical approaches to mitigating unfair biases and addressing inequities.

Confirmed panellists

• Prof Joe Coresh (Johns Hopkins)
• Dr Marzyeh Ghassemi (MIT)
• Dr Owen Chinembiri (NHS Race and Health Observatory)
• Dr Sarah Deeny (UK Health Security Agency)
• Prof Ruma Chunara (NYU)
• Prof Elizabeth Stuart (Johns Hopkins)
• Prof Keith Abrams (Warwick)
• Dr Chris Harbron (Roche)

Agenda (tentative)

All times GMT

• 2.00-2.30pm: Introduction to the workshop
• 2.30-3.30pm: Healthcare delivery panel
• 3.30-4.00pm: Open discussion
• 4.00-5.00pm: Public health panel
• 5.00-6.00pm: Pharmaceuticals & medical devices panel

Organisers

The workshop is being run in collaboration between Data Science for Health Equity, the Alan Turing Institute and the Diverse Data initiative at Genomics England. The organising team is:

• Dr Brieuc Lehmann (UCL)
• Prof Chris Holmes (Oxford)
• Prof David Leslie (Alan Turing Institute)
• Prof Nilanjan Chatterjee (Johns Hopkins)

Please direct any enquiries to Brieuc Lehmann at b.lehmann@ucl.ac.uk.