• J R Soc Med · Jan 2023

    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.

    • Mehrdad A Mizani, Ashkan Dashtban, Laura Pasea, Alvina G Lai, Johan Thygesen, Chris Tomlinson, Alex Handy, Jil B Mamza, Tamsin Morris, Sara Khalid, Francesco Zaccardi, Mary Joan Macleod, Fatemeh Torabi, Dexter Canoy, Ashley Akbari, Colin Berry, Thomas Bolton, John Nolan, Kamlesh Khunti, Spiros Denaxas, Harry Hemingway, Cathie Sudlow, Amitava Banerjee, and CVD-COVID-UK Consortium.
    • Institute of Health Informatics, University College London, London NW1 2DA, UK.
    • J R Soc Med. 2023 Jan 1; 116 (1): 102010-20.

    ObjectivesTo use national, pre- and post-pandemic electronic health records (EHR) to develop and validate a scenario-based model incorporating baseline mortality risk, infection rate (IR) and relative risk (RR) of death for prediction of excess deaths.DesignAn EHR-based, retrospective cohort study.SettingLinked EHR in Clinical Practice Research Datalink (CPRD); and linked EHR and COVID-19 data in England provided in NHS Digital Trusted Research Environment (TRE).ParticipantsIn the development (CPRD) and validation (TRE) cohorts, we included 3.8 million and 35.1 million individuals aged ≥30 years, respectively.Main Outcome MeasuresOne-year all-cause excess deaths related to COVID-19 from March 2020 to March 2021.ResultsFrom 1 March 2020 to 1 March 2021, there were 127,020 observed excess deaths. Observed RR was 4.34% (95% CI, 4.31-4.38) and IR was 6.27% (95% CI, 6.26-6.28). In the validation cohort, predicted one-year excess deaths were 100,338 compared with the observed 127,020 deaths with a ratio of predicted to observed excess deaths of 0.79.ConclusionsWe show that a simple, parsimonious model incorporating baseline mortality risk, one-year IR and RR of the pandemic can be used for scenario-based prediction of excess deaths in the early stages of a pandemic. Our analyses show that EHR could inform pandemic planning and surveillance, despite limited use in emergency preparedness to date. Although infection dynamics are important in the prediction of mortality, future models should take greater account of underlying conditions.

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