🎥 Statistical Methods for Health Equity Webinar: Claire Coffey (University of Cambridge)🎥
- Info DSxHE
- Dec 9
- 2 min read
We were delighted to host Claire Coffey - University of Cambridge, for the latest instalment of our webinar series on statistical methods for fairness and equity in healthcare.
Topic: Are polygenic risk scores fair for cardiovascular disease risk prediction?
Abstract:
Polygenic risk scores (PRS) are increasingly proposed to enhance cardiovascular disease (CVD) risk prediction, particularly for individuals whose clinical risk estimates fall in intermediate ranges where treatment decisions are uncertain. However, concerns persist that PRS may widen health inequities due to their variable performance across demographic and ancestry groups.
This talk presents an evaluation of PRS performance and fairness using UK Biobank data, applying four group fairness metrics (accuracy equality, equal opportunity, conditional use accuracy equality, and treatment equality) across age, sex, ethnicity, and deprivation groups. The analysis first assesses PRS as a standalone predictor relative to established clinical risk factors on matched populations. It then examines the impact of incorporating PRS as a “risk-enhancing factor” within a two-stage risk-stratification framework based on the QRISK3 model.
Across analyses, PRS showed fairness levels comparable to or better than conventional predictors, and integrating PRS into the two-stage model improved sensitivity (identifying more true future CVD cases) with minimal changes in fairness metrics. More broadly, this work demonstrates how a fairness evaluation framework can be applied to clinical prediction models to support equitable deployment of emerging tools, including (but not limited to) PRS.
Bio:
Claire Coffey is a Researcher specialising in machine learning, clinical prediction, and algorithmic fairness. She recently completed a Postdoctoral Research Scientist position at Helmholtz Munich, and is currently working with Charité - Universitätsmedizin Berlin. She holds a PhD in Health Data Science from the University of Cambridge, where her research focused on fairness evaluation and clinical prediction models, including polygenic risk scores and cardiovascular risk algorithms. She remains a visiting researcher at Cambridge.
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