preprint

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

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } 📺 Overview by Dr Sara Khalid Press release

Healthcare Utilisation of 282,080 Individuals with Long COVID Over Two Years: A Multiple Matched Control Cohort Analysis

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A nationwide study of 331 rare diseases among 58 million individuals: prevalence, demographics, and COVID-19 outcomes

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } 📺 BHF Data Science Centre seminar by Dr Honghan Wu

Towards mitigating health inequity via machine learning: a nationwide cohort study to develop and validate ethnicity-specific models for prediction of cardiovascular disease risk in COVID-19 patients

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Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } Twitter thread by Prof Ami Banerjee summarising key results

Digital ethnicity data in population-wide electronic health records in England: a description of completeness, coverage, and granularity of diversity

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } 📺 BHF Data Science Centre seminar by Dr Sara Khalid

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } Twitter thread summarising key results

Evaluation of antithrombotic use and COVID-19 outcomes in a nationwide atrial fibrillation cohort

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } Twitter thread

Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } ❗ Now published in Journal of the Royal Society of Medicine

The adverse impact of COVID-19 pandemic on cardiovascular disease prevention and management in England, Scotland and Wales: A population-scale descriptive analysis of trends in medication data

❗ Now published in Nature Medicine Caroline E Dale, Rohan Takhar, Raymond Carragher, Michalis Katsoulis, Fatemeh Torabi, Stephen Duffield, Seamus Kent, Tanja Mueller, Amanj Kurdi, Stuart McTaggart, Hoda Abbasizanjani, Sam Hollings, Andrew Scourfield, Ronan Lyons, Rowena Griffiths, Jane Lyons, Gareth Davies, Daniel Harris, Alex Handy, Mehrdad Alizadeh Mizani, Chris Tomlinson, Johan Thygesen, Mark Ashworth, Spiros Denaxas, Amitava Banerjee, Jonathan Sterne, Paul Brown, Ian Bullard, Rouven Priedon, Mamas A Mamas, Ann Slee, Paula Lorgelly, Munir Pirmohamed, Kamlesh Khunti, Andrew Morris, Cathie Sudlow, Ashley Akbari, Marion Bennie, Naveed Sattar, Reecha Sofat, CVD-COVID-UK Consortium (2023).