DATA 
SCIENCE
FOR
HEALTH
EQUITY

What if we could spot the warning signs of a mental health crisis before it escalates? What if we could predict when someone is likely to relapse after treatment? And what if we could identify who might benefit most from early intervention programmes? This is the promise of clinical prediction models: tools that use information like age, medical history, and laboratory test results to estimate someone’s risk of developing a condition or needing care in the future.

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 tr

Socioeconomic status (SES) is part of the default demographic toolkit in health research — right up there with age and sex. And much like those, it’s often included reflexively, adjusted for automatically, and measured using whatever data happen to be available. Part 1 lays out the limits of income-based SES measures and introduces alternative ways to capture deprivation, including household-level metrics and geographic proxies.

We were delighted to host  Vincent Jeanselme a postdoctoral researcher at the reAIM Lab, Columbia University, for the latest instalment of our webinar series on statistical methods for fairness and equity in healthcare. Topic:   Advancing AI-Based Risk Prediction for Improved Decision-Making in Healthcare Abstract Accurate prediction of patient outcomes plays a critical role in healthcare decision-making, shaping treatment strategies, clinical guidelines, hospital operations

Socioeconomic status (SES) is part of the default demographic toolkit in health research — right up there with age and sex. And much like those, it’s often included reflexively, adjusted for automatically, and measured using whatever data happen to be available. Part 1 lays out the limits of income-based SES measures and introduces alternative ways to capture deprivation, including household-level metrics and geographic proxies.

We were delighted to host Jake Hightower, data science and global health innovator, for the latest instalment of our webinar series on statistical methods for fairness and equity in healthcare. Topic:   Preserving Health Equity in a Global Pandemic Model Abstract  In an era of increasingly complex global health threats, pandemic modelling must evolve to reflect not only the spread of disease but the lived realities of those most affected. This talk presents a ground-breaking

By asking how inequity flows through the health system, we can build predictive models and interventions that align with the real-world drivers of injustice. Path-specific fairness helps us design systems that treat patients equitably, not just equally. In this blog post, we walk through a real example that shows how surface-level fairness can reinforce inequities. Then we introduce the idea of causal fairness.

We were delighted to host Stephen Pfohl, Senior Research Scientist at Google Research, for the latest instalment of our webinar series on statistical methods for fairness and equity in healthcare. Stephen shared insights from his research into the challenges of evaluating algorithmic fairness in clinical settings, highlighting that fairness assessments are never one-size-fits-all. Drawing on examples from his work, Stephen emphasized that evaluations of fairness must be groun

by Leandra Bräuninger and Brieuc Lehmann Have you ever wondered whether the statistical methods we use actually help—or hurt—health...

We were pleased to welcome Dr. John Ford, Director of the Health Equity Evidence Centre, for a recent webinar on using machine learning...

We were thrilled to host Dr. Briana Stephenson from the Harvard T.H. Chan School of Public Health in a past installment of our webinar...

This month we welcomed Dr. Nathan Blake to present his work at the Statistical Methods for Health Equity webinar series. Nathan has 12...

We are exited to share that this month Irene Chen from the University of California, Berkeley presented on our online webinar. She talked...

For our first webinar of 2024 we were delighted to welcome Professor Rohini Mathur from QMUL to discuss her work on big data insights and...

For our final Statistical Methods webinar of 2023, we welcomed Dr Wiebke Hutiri from Sony AI to discuss her work on design patterns as a...

Recently, we held another of our webinar and were delighted to welcome Dr Karandeep Singh from the University of Michigan to explore...

Recently, we held another of our webinar and were delighted to welcome Nyalleng Moorosi from the Distributed AI Research Institute (DAIR)...

Recently, we held our very first webinar of 2023 with Dr. Joshua Loftus from LSE. Thank you to all who managed to attend the webinar...

In January, we hosted a series of expert panels to explore the main challenges to statistical approaches for understanding and reducing...

In January, we hosted a series of expert panels to explore the main challenges to statistical approaches for improving fairness in...