The Statistical Methods for Health Equity Series is a monthly online series co-hosted by the Data Science for Health Equity community, the Alan Turing Institute Health Equity Interest Group, and the Department of Statistical Science at University College London.

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We are excited to announce the next instalment of our webinar series with speaker John Ford who is an academic public health doctor and Director of the Health Equity Evidence Centre.

Specific details on the topic are as below:

Topic: 

Developing Living Evidence Maps of What Works to Address Health and Care Inequalities

Abstract:

Navigating the rapidly growing body of published literature has become increasingly challenging over the past decade. Although more systematic reviews are being published than ever before, they are time-consuming to produce and quickly become outdated. Machine learning technology offers a way to efficiently manage and possibly synthesise this expanding evidence base. At the Health Equity Evidence Centre, we have collaborated with the EPPI Centre at UCL to create living evidence maps, driven by machine learning, which map the evidence of interventions that address health and care inequalities and are kept up-to-date with minimal effort. This presentation will describe our approach, the role of machine learning, and how these tools are being used to inform policy and practice.

Speaker Biography:

John is an academic public health doctor and Director of the Health Equity Evidence Centre. He leads a research programme focused on generating the evidence base for what works to address health and care inequalities. He holds an NIHR Advanced Fellowship and is employed as a Senior Clinical Lecturer in Health Equity at Queen Mary University of London. John is also an Honorary Consultant in Public Health within the national NHS England public health team. His previous roles include positions at Cambridgeshire and Peterborough CCG, the East of England NHS England regional team, and the Universities of Cambridge and East Anglia. 

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Please direct any questions about the webinar series to info@datascienceforhealthequity.com.