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REVIEW article

Front. Med.
Sec. Regulatory Science
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1474045
This article is part of the Research Topic Collection of Covid-19 Induced Biases in Medical Research View all 6 articles

Biases in COVID-19 Vaccine Effectiveness Studies Using Cohort Design

Provisionally accepted
Suneth Agampodi Suneth Agampodi 1,2*Birkneh Tilahun Tadesse Birkneh Tilahun Tadesse 1Sushant Sahastrabuddhe Sushant Sahastrabuddhe 1Jean-Louis Excler Jean-Louis Excler 1Jerome Kim Jerome Kim 1,3
  • 1 International Vaccine Institute, Seoul, Republic of Korea
  • 2 Department of Internal Medicine, School of Medicine, Yale University, New Haven, Connecticut, United States
  • 3 Seoul National University, Seoul, Republic of Korea

The final, formatted version of the article will be published soon.

    Observational studies on COVID-19 vaccine effectiveness (VE) have provided critical realworld data, informing public health policy globally. These studies, primarily using pre-existing data sources, have been indispensable in assessing VE across diverse populations and developing sustainable vaccination strategies. Cohort design is frequently employed in VE research. The rapid implementation of vaccination campaigns during the COVID-19 pandemic introduced differential vaccination influenced by sociodemographic disparities, public policies, perceived risks, health-promoting behaviors, and health status, potentially resulting in biases such as healthy user bias, healthy vaccinee effect, frailty bias, differential depletion of susceptibility bias, and confounding by indication. The overwhelming burden on healthcare systems has escalated the risk of data inaccuracies, leading to outcome misclassifications. Additionally, the extensive array of diagnostic tests used during the pandemic has also contributed to misclassification biases. The urgency to publish quickly may have further influenced these biases or led to their oversight, affecting the validity of the findings. These biases in studies vary considerably depending on the setting, data sources, and analytical methods and are likely more pronounced in low-and middle-income country (LMIC) settings due to inadequate data infrastructure. Addressing and mitigating these biases is essential for accurate VE estimates, guiding public health strategies, and sustaining public trust in vaccination programs. Transparent communication about these biases and rigorous improvement in the design of future observational studies are essential.

    Keywords: COVID-19, Vaccine effectiveness, Cohort Studies, biases, misclassification bias, healthy user bias, Healthy vaccinee effect, Differential depletion of susceptibility bias No Spacing

    Received: 31 Jul 2024; Accepted: 14 Oct 2024.

    Copyright: © 2024 Agampodi, Tadesse, Sahastrabuddhe, Excler and Kim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Suneth Agampodi, International Vaccine Institute, Seoul, Republic of Korea

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.