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Long COVID risk and pre-COVID vaccination in an EHR-based cohort study from the RECOVER program

Author

Listed:
  • M. Daniel Brannock

    (RTI International)

  • Robert F. Chew

    (RTI International)

  • Alexander J. Preiss

    (RTI International)

  • Emily C. Hadley

    (RTI International)

  • Signe Redfield

    (Patient-Led Research Collaborative)

  • Julie A. McMurry

    (University of Colorado Anschutz Medical Campus)

  • Peter J. Leese

    (University of North Carolina at Chapel Hill)

  • Andrew T. Girvin

    (Palantir Technologies)

  • Miles Crosskey

    (CoVar Applied Technologies)

  • Andrea G. Zhou

    (iTHRIV, University of Virginia)

  • Richard A. Moffitt

    (Stony Brook University
    Emory University)

  • Michele Jonsson Funk

    (University of North Carolina at Chapel Hill)

  • Emily R. Pfaff

    (University of North Carolina at Chapel Hill)

  • Melissa A. Haendel

    (University of Colorado Anschutz Medical Campus)

  • Christopher G. Chute

    (Johns Hopkins University)

Abstract

Long COVID, or complications arising from COVID-19 weeks after infection, has become a central concern for public health experts. The United States National Institutes of Health founded the RECOVER initiative to better understand long COVID. We used electronic health records available through the National COVID Cohort Collaborative to characterize the association between SARS-CoV-2 vaccination and long COVID diagnosis. Among patients with a COVID-19 infection between August 1, 2021 and January 31, 2022, we defined two cohorts using distinct definitions of long COVID—a clinical diagnosis (n = 47,404) or a previously described computational phenotype (n = 198,514)—to compare unvaccinated individuals to those with a complete vaccine series prior to infection. Evidence of long COVID was monitored through June or July of 2022, depending on patients’ data availability. We found that vaccination was consistently associated with lower odds and rates of long COVID clinical diagnosis and high-confidence computationally derived diagnosis after adjusting for sex, demographics, and medical history.

Suggested Citation

  • M. Daniel Brannock & Robert F. Chew & Alexander J. Preiss & Emily C. Hadley & Signe Redfield & Julie A. McMurry & Peter J. Leese & Andrew T. Girvin & Miles Crosskey & Andrea G. Zhou & Richard A. Moffi, 2023. "Long COVID risk and pre-COVID vaccination in an EHR-based cohort study from the RECOVER program," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38388-7
    DOI: 10.1038/s41467-023-38388-7
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    References listed on IDEAS

    as
    1. Sunhee Park & David J. Hendry, 2015. "Reassessing Schoenfeld Residual Tests of Proportional Hazards in Political Science Event History Analyses," American Journal of Political Science, John Wiley & Sons, vol. 59(4), pages 1072-1087, October.
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    Cited by:

    1. Debbie E. Malden & In-Lu Amy Liu & Lei Qian & Lina S. Sy & Bruno J. Lewin & Dawn T. Asamura & Denison S. Ryan & Cassandra Bezi & Joshua T. B. Williams & Robyn Kaiser & Matthew F. Daley & Jennifer C. N, 2024. "Post-COVID conditions following COVID-19 vaccination: a retrospective matched cohort study of patients with SARS-CoV-2 infection," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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