Using National Electronic Health Records for Pandemic Preparedness: Validation of a Parsimonious Model for Predicting Excess Deaths Among Those With COVID-19

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