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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

19 Pages Posted: 8 Mar 2022

See all articles by Mehrdad A. Mizani

Mehrdad A. Mizani

University College London - Institute of Health Informatics

Muhammad Dashtban

University College London - Institute of Health Informatics

Laura Pasea

University College London - Institute of Health Informatics

Alvina Lai

University College London - Institute of Health Informatics

Johan Hilge Thygesen

University College London - Institute of Health Informatics

Christopher Tomlinson

University College London - Institute of Health Informatics

Alex Handy

University College London - Institute of Health Informatics

Jil Billy Mamza

BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs

Tamsin Morris

BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs

Sara Khalid

University of Oxford - Rheumatology and Musculoskeletal Sciences

Francesco Zaccardi

University of Leicester - Leicester Diabetes Centre

Mary J. Macleod

University of Aberdeen - Medical Sciences and Nutrition

Fatemeh Torabi

Swansea University - Population Data Science

Dexter Canoy

University of Oxford - Nuffield Department of Women’s and Reproductive Health

Ashley Akbari

Swansea University - Population Data Science

Colin Berry

University of Glasgow - Institute of Cardiovascular and Medical Sciences

Thomas Bolton

Health Data Research UK - BHF Data Science Centre

John Nolan

Health Data Research UK - BHF Data Science Centre

Kamlesh Khunti

University of Leicester - Leicester Diabetes Centre

Spiros Denaxas

University College London - Institute of Health Informatics

Harry Hemingway

University College London - Institute of Health Informatics

Cathie Sudlow

Health Data Research UK - BHF Data Science Centre

Amitava Banerjee

University College London - Institute of Health Informatics

CVD-COVID-UK Consortium

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Multiple version iconThere are 2 versions of this paper

Abstract

Background: Throughout the pandemic, research, public health, and policy emphasised prediction and surveillance of excess deaths, which have mostly occurred in older individuals with underlying conditions, highlighting importance of baseline mortality risk, infection rate (IR) and pandemic-related relative risk (RR). We now use national, pre- and post-pandemic electronic health records (EHR) to develop and validate a model incorporating these factors for prediction of excess deaths.

Methods: In development (Clinical Practice Research Datalink) and validation (NHS Digital Trusted Research Environment) cohorts in primary and secondary care EHR in England, we included 3·8 million and 35·1 million individuals aged ≥30 years, respectively. For model development, we predicted excess deaths using baseline one-year all-cause mortality risk and assumed RR=3 and IR=10%. For model validation, we observed number of excess deaths from March 2020 to March 2021. We used baseline mortality risk, IR and RR (assumed and observed) to predict excess deaths related to COVID-19.   

Findings: Among individuals with at least one high-risk condition, baseline (pre-pandemic) 1-year mortality risk at one year was 4·46% (95% CI 4·41–4·51) and 3.55% (3.54-3.57) in development and validation cohorts, respectively. In our original published model, we predicted 73,498 COVID-19 deaths over 1 year for the population of England. From 1st March 2020 to 1st March 2021, there were 127,020 observed excess deaths. Observed RR was 4·34 (4·31-4·38, 95% CI) and IR was 6·27% (6·26-6·28, 95%CI). In the validation cohort, predicted excess deaths over one year were 100,338 compared with the observed 127,020 deaths with a ratio of predicted to observed excess deaths of 0.79. We found that vaccination had a negligible effect on overall RR or IR between 1st December 2020 and 1st March 2021, compared to the likely effect of under-reported COVID-19 cases from the pre-vaccination period.

Interpretation: We show that a simple, parsimonious model incorporating baseline mortality risk, one-year infection rate and relative risk of the pandemic can be used to predict excess deaths. 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 prediction of morbidity and mortality, future models should take greater account of underlying conditions and their associated risks.

Funding Information: 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 trusted research environment, 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. K.K. is supported by the National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and NIHR Lifestyle BRC.

Declaration of Interests: JBM and TM are employees of AstraZeneca. KK is chair of the ethnicity subgroup of the Independent Scientific Advisory Group for Emergencies (SAGE) and director of the University of Leicester Centre for Black Minority Ethnic Health. KK and AB are trustees of the South Asian Health Foundation (SAHF). CS is Director of the BHF Data Science Centre. All other authors report no competing interests.

Ethics Approval Statement: Approval for the study in CPRD was granted by the Independent Scientific Advisory Committee (20_074R) of the Medicines and Healthcare products Regulatory Agency in the UK in accordance with the Declaration of Helsinki. The North East-Newcastle and North Tyneside 2 research ethics committee provided ethical approval for the CVD- COVID-UK research programme (REC No 20/NE/0161).

Keywords: coronavirus, excess mortality, risk prediction, health policy, pandemic preparedness

Suggested Citation

Mizani, Mehrdad A. and Dashtban, Muhammad and Pasea, Laura and Lai, Alvina and Thygesen, Johan Hilge and Tomlinson, Christopher and Handy, Alex and Mamza, Jil Billy and Morris, Tamsin and Khalid, Sara and Zaccardi, Francesco and Macleod, Mary J. and Torabi, Fatemeh and Canoy, Dexter and Akbari, Ashley and Berry, Colin and Bolton, Thomas and Nolan, John and Khunti, Kamlesh and Denaxas, Spiros and Hemingway, Harry and Sudlow, Cathie and Banerjee, Amitava and Consortium, CVD-COVID-UK, Using National Electronic Health Records for Pandemic Preparedness: Validation of a Parsimonious Model for Predicting Excess Deaths Among Those With COVID-19. Available at SSRN: https://ssrn.com/abstract=4052647 or http://dx.doi.org/10.2139/ssrn.4052647

Mehrdad A. Mizani

University College London - Institute of Health Informatics ( email )

United Kingdom

Muhammad Dashtban

University College London - Institute of Health Informatics ( email )

United Kingdom

Laura Pasea

University College London - Institute of Health Informatics ( email )

United Kingdom

Alvina Lai

University College London - Institute of Health Informatics ( email )

United Kingdom

Johan Hilge Thygesen

University College London - Institute of Health Informatics ( email )

United Kingdom

Christopher Tomlinson

University College London - Institute of Health Informatics ( email )

United Kingdom

Alex Handy

University College London - Institute of Health Informatics ( email )

United Kingdom

Jil Billy Mamza

BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs ( email )

Cambridge
United Kingdom

Tamsin Morris

BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs ( email )

Cambridge
United Kingdom

Sara Khalid

University of Oxford - Rheumatology and Musculoskeletal Sciences ( email )

Oxford
United Kingdom

Francesco Zaccardi

University of Leicester - Leicester Diabetes Centre ( email )

Leicester
United Kingdom

Mary J. Macleod

University of Aberdeen - Medical Sciences and Nutrition ( email )

Aberdeen
United Kingdom

Fatemeh Torabi

Swansea University - Population Data Science ( email )

Dexter Canoy

University of Oxford - Nuffield Department of Women’s and Reproductive Health ( email )

Oxford
United Kingdom

Ashley Akbari

Swansea University - Population Data Science ( email )

Colin Berry

University of Glasgow - Institute of Cardiovascular and Medical Sciences ( email )

Glasgow
United Kingdom

Thomas Bolton

Health Data Research UK - BHF Data Science Centre ( email )

London
United Kingdom

John Nolan

Health Data Research UK - BHF Data Science Centre ( email )

London
United Kingdom

Kamlesh Khunti

University of Leicester - Leicester Diabetes Centre ( email )

Leicester
United Kingdom

Spiros Denaxas

University College London - Institute of Health Informatics ( email )

United Kingdom

Harry Hemingway

University College London - Institute of Health Informatics ( email )

United Kingdom

Cathie Sudlow

Health Data Research UK - BHF Data Science Centre ( email )

London
United Kingdom

Amitava Banerjee (Contact Author)

University College London - Institute of Health Informatics

United Kingdom

No contact information is available for CVD-COVID-UK Consortium

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