CCU037: Improving methods to minimise bias in ethnicity data for more representative and generalisable models, using CVD in COVID-19 as an example

Project lead:
Sara Khalid, University of Oxford

This research project is awarded through a funding call by Health Data Research UK and the Alan Turing Institute as part of the wider Data and Connectivity National Core Study.

Further details on this project are available here.

View this project on the Health Data Research Gateway

Sub-projects

CCU037_01: Implementing a novel approach to improve correctness, completeness, and granularity of ethnicity information using routinely collected data

CCU037_02: Ethnic disparities in health: a population-wide analysis of digital health records for mortality and cardiovascular risk in individuals diagnosed with COVID-19

CCU037_03: Building ethnicity-specific CVD-in-COVID-19 risk prediction models using improved ethnicity data

CCU037_04: A national cohort study to assess whether COVID-19 has resulted in surgical inequality based upon ethnicity

Outputs

Ethnicity data resource in population-wide health records: completeness, coverage and granularity of diversity

  • Scientific Data publication 22/02/24 can be viewed here
  • medRxiv preprint 11/11/22 can be viewed here
  • Code and phenotypes used in this study are available in GitHub here

Ethnic disparities in COVID-19 mortality and cardiovascular disease in England and Wales between 2020-2022

  • Nature Communications publication 02/07/25 can be viewed here
  • Code and phenotypes used in this study are available in GitHub here

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