Mizani, Mehrdad ADashtban, AshkanPasea, LauraLai, Alvina GThygesen, JohanTomlinson, ChrisHandy, AlexMamza, Jil BMorris, TamsinKhalid, SaraZaccardi, FrancescoMacleod, Mary JoanTorabi, FatemehCanoy, DexterAkbari, AshleyBerry, ColinBolton, ThomasNolan, JohnKhunti, KamleshDenaxas, SpirosHemingway, HarrySudlow, CathieBanerjee, AmitavaCVD-COVID-UK Consortium2023-10-232023-10-232023-01Mizani, M A, Dashtban, A, Pasea, L, Lai, A G, Thygesen, J, Tomlinson, C, Handy, A, Mamza, J B, Morris, T, Khalid, S, Zaccardi, F, Macleod, M J, Torabi, F, Canoy, D, Akbari, A, Berry, C, Bolton, T, Nolan, J, Khunti, K, Denaxas, S, Hemingway, H, Sudlow, C, Banerjee, A & CVD-COVID-UK Consortium 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', Journal of the Royal Society of Medicine, vol. 116, no. 1, pp. 10-20. https://doi.org/10.1177/014107682211318970141-0768ORCID: /0000-0003-2115-8184/work/123404630https://hdl.handle.net/2164/21983Acknowledgements This work is carried out with the support of the BHF Data Science Centre led by HDR UK (BHF Grant no. SP/19/3/34678) and makes use of de-identified data held in NHS Digital’s TRE for England, made available via the BHF Data Science Centre’s CVD-COVID-UK/COVID-IMPACT consortium. This work uses data provided by patients and collected by the NHS as part of their care and support. We would also like to acknowledge all data providers who make health relevant data available for research. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The British Heart Foundation Data Science Centre (grant no. SP/19/3/34678, awarded to Health Data Research (HDR) UK) funded co-development (with NHS Digital) of the TRE, provision of linked datasets, data access, user software licences, computational usage, and data management and wrangling support, with additional contributions from the HDR UK data and connectivity component of the UK Government Chief Scientific Adviser’s National Core Studies programme to coordinate national COVID-19 priority research. Consortium partner organisations funded the time of contributing data analysts, biostatisticians, epidemiologists and clinicians. AB, MAM, MHD and LP were supported by research funding from AstraZeneca. AB has received funding from the National Institute for Health Research (NIHR), British Medical Association and UK Research and Innovation. AB, SD and HH are part of the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No 116074. KK is supported by the National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC-EM) and NIHR Lifestyle BRC.11670059engSDG 3 - Good Health and Well-beingclinicalepidemiologyhealth informaticsinfectious diseasespublic healthRA0421 Public health. Hygiene. Preventive MedicineBritish Heart FoundationSP/19/3/34678National Institute for Health Research (NIHR)ARC-EMLifestyle BRCUK Research and Innovation (UKRI)Other116074Supplementary Datasj-pdf-1-jrs-10.1177_01410768221131897RA0421Journal article10.1177/01410768221131897https://cronfa.swan.ac.uk/Record/cronfa61933#detailshttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9909113/https://figshare.le.ac.uk/articles/journal_contribution/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_/215906041161