¹Division of Gastroenterology, Veterans Affairs Puget Sound Healthcare System, University of Washington, Seattle
²Research and Development, Veterans Affairs Puget Sound Health Care System, Seattle, Washington
³Division of Pulmonary, Critical Care, and Sleep, Veterans Affairs Puget Sound Healthcare System, University of Washington, Seattle
⁴Division of Nephrology, Veterans Affairs Puget Sound Healthcare System, University of Washington, Seattle
⁵Department of Veterans Affairs, Population Health Services, Palo Alto Healthcare System, Palo Alto, California
⁶Division of Allergy and Infectious Diseases, Veterans Affairs Puget Sound Healthcare System, University of Washington, Seattle
⁷Veterans Affairs Palo Alto Healthcare System, Palo Alto, California
⁸Department of Radiology, Stanford University School of Medicine, Stanford, California
⁹Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle

Read article on JAMA Network

Read Article Claim CME

Key Points

Question How can the risk of SARS-CoV-2–related death be estimated in the general population to be used for vaccination prioritization?

Findings In this prognostic study of more than 7.6 million individuals enrolled in the Veterans Affairs health care system, a logistic regression model (COVIDVax) was developed to estimate risk of SARS-CoV-2–related death using the following 10 characteristics: sex, age, race, ethnicity, body mass index, Charlson Comorbidity Index, diabetes, chronic kidney disease, congestive heart failure, and the Care Assessment Need score. The model was estimated to save more lives than prioritizing vaccination based on age or on the US Centers for Disease Control and Prevention vaccination allocation.

Meaning These findings suggest that prioritizing vaccination based on the model developed in this study could prevent a substantial number of SARS-CoV-2–related deaths during vaccine rollout.

Abstract

Importance 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.

Objective 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.

Design, Setting, and Participants 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).

Main Outcomes and Measures 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.

Results 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%).

Conclusions and Relevance 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.

Claim CME

Buy This Activity

Article Information

Accepted for Publication: February 11, 2021.

Published: April 6, 2021. doi:10.1001/jamanetworkopen.2021.4347

Corresponding Author: George N. Ioannou, BMBCh, MS, Research and Development, Veterans Affairs Puget Sound Healthcare System, 1660 S Columbian Way, Seattle, WA 98108 (georgei@medicine.washington.edu).

Author Contributions: Dr Ioannou had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: G. Ioannou, Green, Fan, Dominitz, Backus, Locke, Eastment, Osborne, N. Ioannou, Berry.

Acquisition, analysis, or interpretation of data: G. Ioannou, Green, Fan, Dominitz, O’Hare, Backus, Osborne, N. Ioannou, Berry.

Drafting of the manuscript: G. Ioannou.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: G. Ioannou, Green, N. Ioannou, Berry.

Obtained funding: G. Ioannou.

Administrative, technical, or material support: Locke.

Supervision: G. Ioannou.

Conflict of Interest Disclosures: Dr O’Hare reported receiving grants from the National Institutes of Health, the US Centers for Disease Control and Prevention, and Veterans Affairs Health Services Research and Development; receiving travel and honoraria from Chugai Pharmaceuticals, the Japanese Society of Dialysis Therapy, the American Society of Nephrology, the Devenir Foundation, Hammersmith Hospital, NYU Lagone, and Kaiser Permanente Southern California; receiving honorarium from UpToDate; and receiving travel reimbursement from the New York Society of Nephrology, Columbia University, Albert Einstein College of Medicine, and Health and Aging Policy Fellows Program outside the submitted work. No other disclosures were reported.

Funding/Support: The study was supported using data from the Veterans Affairs COVID-19 Shared Data Resource provided by the VA Informatics and Computing Infrastructure. The study was supported in part by grant COVID19-8900-11 from the Department of Veterans Affairs, Clinical Sciences Research and Development to Dr Ioannou.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The contents do not represent the views of the US Department of Veterans Affairs or the US government.

References

Jackson LA , Anderson EJ , Rouphael NG , et al; mRNA-1273 Study Group. An mRNA vaccine against SARS-CoV-2—preliminary report. N Engl J Med. 2020;383(20):1920-1931. doi:10.1056/NEJMoa2022483 PubMed Google Scholar Crossref

Baden LR , El Sahly HM , Essink B , et al; COVE Study Group. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. N Engl J Med. 2021;384(5):403-416.PubMed Google Scholar Crossref

Polack FP , Thomas SJ , Kitchin N , et al; C4591001 Clinical Trial Group. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N Engl J Med. 2020;383(27):2603-2615. doi:10.1056/NEJMoa2034577 PubMed Google Scholar Crossref

McClung N , Chamberland M , Kinlaw K , et al. The Advisory Committee on Immunization Practices’ ethical principles for allocating initial supplies of COVID-19 vaccine—United States, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(47):1782-1786. doi:10.15585/mmwr.mm6947e3 PubMed Google Scholar Crossref

The National Academies of Sciences, Engineering and Medicine. A framework for equitable allocation of vaccine for the novel coronavirus. Accessed November 27, 2020. https://www.nationalacademies.org/our-work/a-framework-for-equitable-allocation-of-vaccine-for-the-novel-coronavirus

US Centers for Disease Control and Prevention. Meeting of the CDC Advisory Committee on Immunization Practices (ACIP). Published December 1, 2020. Accessed March 8, 2021. https://www.cdc.gov/vaccines/acip/meetings/downloads/min-archive/summary-2020-12-508.pdf

Omer SB , Yildirim I , Forman HP . Herd immunity and implications for SARS-CoV-2 control. JAMA. 2020;324(20):2095-2096. doi:10.1001/jama.2020.20892 PubMed Google Scholar Crossref

King JT Jr , Yoon JS , Rentsch CT , et al. Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: the Veterans Health Administration COVID-19 (VACO) Index. PLoS One. 2020;15(11):e0241825. doi:10.1371/journal.pone.0241825 PubMed Google Scholar

Ioannou GN , Locke E , Green P , et al. Risk factors for hospitalization, mechanical ventilation, or death among 10 131 US veterans with SARS-CoV-2 infection. JAMA Netw Open. 2020;3(9):e2022310. doi:10.1001/jamanetworkopen.2020.22310 PubMed Google Scholar

10.

Sohn MW , Arnold N , Maynard C , Hynes DM . Accuracy and completeness of mortality data in the Department of Veterans Affairs. Popul Health Metr. 2006;4:2. doi:10.1186/1478-7954-4-2 PubMed Google Scholar Crossref

11.

Williamson EJ , Walker AJ , Bhaskaran K , et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584(7821):430-436. doi:10.1038/s41586-020-2521-4 PubMed Google Scholar Crossref

12.

Rentsch CT , Kidwai-Khan F , Tate JP , et al. Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: a nationwide cohort study. PLoS Med. 2020;17(9):e1003379. doi:10.1371/journal.pmed.1003379 PubMed Google Scholar

13.

Fan VS , Dominitz JA , Eastment MC , et al. Risk factors for testing positive for SARS-CoV-2 in a national US healthcare system. Clin Infect Dis. 2020;ciaa1624. doi:10.1093/cid/ciaa1624 PubMed Google Scholar

14.

Deyo RA , Cherkin DC , Ciol MA . Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613-619. doi:10.1016/0895-4356(92)90133-8 PubMed Google Scholar Crossref

15.

Charlson ME , Pompei P , Ales KL , MacKenzie CR . A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383. doi:10.1016/0021-9681(87)90171-8 PubMed Google Scholar Crossref

16.

US Centers for Disease Control and Prevention. How CDC is making COVID-19 vaccine recommendations. Updated March 3, 2021. Accessed December 12, 2020. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations-process.html#groups-considered

17.

Wang L , Porter B , Maynard C , et al. Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration. Med Care. 2013;51(4):368-373. doi:10.1097/MLR.0b013e31827da95a PubMed Google Scholar Crossref

18.

Osborne TF , Veigulis ZP , Arreola DM , Röösli E , Curtin CM . Automated EHR score to predict COVID-19 outcomes at US Department of Veterans Affairs. PLoS One. 2020;15(7):e0236554. doi:10.1371/journal.pone.0236554 PubMed Google Scholar

19.

Palantir. Accessed December 25, 2020. http://www.palantir.com

20.

COVID-19 vaccine (COVIDVax) model. Accessed January 1, 2021. https://covidvax.xyz

21.

Worldometer. Coronavirus. Accessed December 25, 2020. https://www.worldometers.info/coronavirus/country/us/

22.

Rosenthal E . Some said the vaccine rollout would be a “nightmare.” They were right. The New York Times. Published December 23, 2020. Accessed January 3, 2021. https://www.nytimes.com/2020/12/23/opinion/vaccine-distribution.html

Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2–Related Death Among 7.6 Million US Veterans for Use in Vaccination Prioritization

Who Would Benefit Most From COVID-19 Vaccination?

See More About

If you are not a JN Learning subscriber, you can either:

A subscription to JN Learning (CME from JAMA Network) provides:

Sign in to access

If you are not a JN Learning subscriber, you can either:

Sign in to customize your interests

With a personal account, you can:

Name Your Search

Sign in to save your search

With a personal account, you can:

Lookup An Activity

My Saved Searches

Sign in to customize your interests

With a personal account, you can:

Purchase access