| アブストラクト | OBJECTIVES: To 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. DESIGN: An EHR-based, retrospective cohort study. SETTING: Linked EHR in Clinical Practice Research Datalink (CPRD); and linked EHR and COVID-19 data in England provided in NHS Digital Trusted Research Environment (TRE). PARTICIPANTS: In the development (CPRD) and validation (TRE) cohorts, we included 3.8 million and 35.1 million individuals aged >/=30 years, respectively. MAIN OUTCOME MEASURES: One-year all-cause excess deaths related to COVID-19 from March 2020 to March 2021. RESULTS: From 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. CONCLUSIONS: We 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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| ジャーナル名 | Journal of the Royal Society of Medicine |
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| Pubmed追加日 | 2022/11/15 |
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| 投稿者 | Mizani, Mehrdad A; Dashtban, Ashkan; Pasea, Laura; Lai, Alvina G; Thygesen, Johan; Tomlinson, Chris; Handy, Alex; Mamza, Jil B; Morris, Tamsin; Khalid, Sara; Zaccardi, Francesco; Macleod, Mary Joan; Torabi, Fatemeh; Canoy, Dexter; Akbari, Ashley; Berry, Colin; Bolton, Thomas; Nolan, John; Khunti, Kamlesh; Denaxas, Spiros; Hemingway, Harry; Sudlow, Cathie; Banerjee, Amitava |
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| 組織名 | Institute of Health Informatics, University College London, London NW1 2DA, UK.;BHF Data Science Centre, Health Data Research UK, London, NW1 2BE, UK.;Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca,;Cambridge, CB2 0AA, UK.;Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences,;University of Oxford, Oxford, OX3 7HE, UK.;Leicester Diabetes Centre, University of Leicester, Leicester, LE5 4PW, UK.;School of Medicine, Medical Sciences and Nutrition, University of Aberdeen,;Aberdeen, AB24 3FX, UK.;Faculty of Medicine, Health and Life Science, Swansea University, Swansea, SA2;8QA, UK.;Nuffield Department of Women's and Reproductive Health, University of Oxford,;Oxford, OX3 9DU, UK.;Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow,;G12 8TA, UK. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/36374585/ |
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