Abstract
A strategy that prioritizes individuals for SARS-CoV-2 vaccination according to their risk of SARS-CoV-2-related mortality would help minimize deaths during vaccine rollout. To develop a model that estimates the risk of SARS-CoV-2-related mortality among all enrollees of the US Department of Veterans Affairs (VA) health care system. This prognostic study used data from 7 635 064 individuals enrolled in the VA health care system as of May 21, 2020, to develop and internally validate a logistic regression model (COVIDVax) that predicted SARS-CoV-2-related death (n = 2422) during the observation period (May 21 to November 2, 2020) using baseline characteristics known to be associated with SARS-CoV-2-related mortality, extracted from the VA electronic health records (EHRs). The cohort was split into a training period (May 21 to September 30) and testing period (October 1 to November 2). SARS-CoV-2-related death, defined as death within 30 days of testing positive for SARS-CoV-2. VA EHR data streams were imported on a data integration platform to demonstrate that the model could be executed in real-time to produce dashboards with risk scores for all current VA enrollees. Of 7 635 064 individuals, the mean (SD) age was 66.2 (13.8) years, and most were men (7 051 912 [92.4%]) and White individuals (4 887 338 [64.0%]), with 1 116 435 (14.6%) Black individuals and 399 634 (5.2%) Hispanic individuals. From a starting pool of 16 potential predictors, 10 were included in the final COVIDVax model, as follows: sex, age, race, ethnicity, body mass index, Charlson Comorbidity Index, diabetes, chronic kidney disease, congestive heart failure, and Care Assessment Need score. The model exhibited excellent discrimination with area under the receiver operating characteristic curve (AUROC) of 85.3% (95% CI, 84.6%-86.1%), superior to the AUROC of using age alone to stratify risk (72.6%; 95% CI, 71.6%-73.6%). Assuming vaccination is 90% effective at preventing SARS-CoV-2-related death, using this model to prioritize vaccination was estimated to prevent 63.5% of deaths that would occur by the time 50% of VA enrollees are vaccinated, significantly higher than the estimate for prioritizing vaccination based on age (45.6%) or the US Centers for Disease Control and Prevention phases of vaccine allocation (41.1%). In this prognostic study of all VA enrollees, prioritizing vaccination based on the COVIDVax model was estimated to prevent a large proportion of deaths expected to occur during vaccine rollout before sufficient herd immunity is achieved.
Highlights
Efficacious vaccines against SARS-CoV-2 have received emergency use authorization from the US Food and Drug Administration (FDA).[1-3]
Assuming vaccination is 90% effective at preventing SARS-CoV-2–related death, using this model to prioritize vaccination was estimated to prevent 63.5% of deaths that would occur by the time 50% of Veterans Affairs (VA) enrollees are vaccinated, significantly higher than the estimate for prioritizing vaccination based on age (45.6%) or the US Centers for Disease Control and Prevention phases of vaccine allocation (41.1%)
In this prognostic study of all VA enrollees, prioritizing vaccination based on the COVIDVax model was estimated to prevent a large proportion of deaths expected to occur during vaccine rollout before sufficient herd immunity is achieved
Summary
Efficacious vaccines against SARS-CoV-2 have received emergency use authorization from the US Food and Drug Administration (FDA).[1-3]. The US Centers for Disease Control and Prevention (CDC) Advisory Committee on Immunization Practices (ACIP) outlined ethical principles that should guide allocation given limited supply and recommended a phased approach to vaccine allocation: phase 1a, health care personnel and long-term care facility (LTCF) residents; phase 1b, frontline essential workers and persons aged 75 years or older; phase 1c, essential workers, persons aged 65 to 74 years, and persons aged 16 to 64 years with high-risk medical conditions; and phase 2, which includes the remaining population.[4-6]. We aimed to develop a model that estimates the risk of SARS-CoV-2–related death in the general population (the COVIDVax model) and to estimate the number of SARS-CoV-2–related deaths prevented by prioritizing vaccination based on our model vs an approach based on age alone (ie, oldest first) or based on the ACIP-recommended phases of vaccination
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