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. (January 2023)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Journal of the Royal Society of Medicine. Volume 116:Number 1(2023)
• Page Start: 10
• Page End: 20
• Publication Date: 2023-01
• Subjects: Clinical -- epidemiology -- health informatics -- infectious diseases -- public health; Medicine -- Periodicals; Medicine -- Great Britain -- Periodicals; Periodicals; 610.5
• DOI: 10.1177/01410768221131897
• Languages: English
• ISSNs: 0410-0768

Abstract

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.