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Bias and negative values of COVID-19 vaccine effectiveness estimates from a test-negative design without controlling for prior SARS-CoV-2 infection

Author

Listed:
  • Ryan E. Wiegand

    (Centers for Disease Control and Prevention)

  • Bruce Fireman

    (Kaiser Permanente Northern California Division of Research)

  • Morgan Najdowski

    (Centers for Disease Control and Prevention)

  • Mark W. Tenforde

    (Centers for Disease Control and Prevention)

  • Ruth Link-Gelles

    (Centers for Disease Control and Prevention)

  • Jill M. Ferdinands

    (Centers for Disease Control and Prevention)

Abstract

Test-negative designs (TNDs) are used to assess vaccine effectiveness (VE). Protection from infection-induced immunity may confound the association between case and vaccination status, but collecting reliable infection history can be challenging. If vaccinated individuals have less infection-induced protection than unvaccinated individuals, failure to account for infection history could underestimate VE, though the bias is not well understood. We simulated individual-level SARS-CoV-2 infection and COVID-19 vaccination histories and a TND. VE against symptomatic infection and VE against severe disease estimates unadjusted for infection history underestimated VE compared to estimates adjusted for infection history, and unadjusted estimates were more likely to be below 0%, which could lead to an incorrect interpretation that COVID-19 vaccines are harmful. TNDs assessing VE immediately following vaccine rollout introduced the largest bias and potential for negative VE against symptomatic infection. Despite the potential for bias, VE estimates from TNDs without prior infection information are useful because underestimation is rarely more than 8 percentage points.

Suggested Citation

  • Ryan E. Wiegand & Bruce Fireman & Morgan Najdowski & Mark W. Tenforde & Ruth Link-Gelles & Jill M. Ferdinands, 2024. "Bias and negative values of COVID-19 vaccine effectiveness estimates from a test-negative design without controlling for prior SARS-CoV-2 infection," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54404-w
    DOI: 10.1038/s41467-024-54404-w
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    1. Sophie Graham & Elise Tessier & Julia Stowe & Jamie Lopez Bernal & Edward P. K. Parker & Dorothea Nitsch & Elizabeth Miller & Nick Andrews & Jemma L. Walker & Helen I. McDonald, 2023. "Bias assessment of a test-negative design study of COVID-19 vaccine effectiveness used in national policymaking," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Hiam Chemaitelly & Houssein H. Ayoub & Sawsan AlMukdad & Peter Coyle & Patrick Tang & Hadi M. Yassine & Hebah A. Al-Khatib & Maria K. Smatti & Mohammad R. Hasan & Zaina Al-Kanaani & Einas Al-Kuwari & , 2022. "Duration of mRNA vaccine protection against SARS-CoV-2 Omicron BA.1 and BA.2 subvariants in Qatar," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Jia Wei & Philippa C. Matthews & Nicole Stoesser & Ian Diamond & Ruth Studley & Emma Rourke & Duncan Cook & John I. Bell & John N. Newton & Jeremy Farrar & Alison Howarth & Brian D. Marsden & Sarah Ho, 2022. "SARS-CoV-2 antibody trajectories after a single COVID-19 vaccination with and without prior infection," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
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