REVIEW article
Front. Drug Saf. Regul.
Sec. Vaccine Safety and Regulation
Volume 5 - 2025 | doi: 10.3389/fdsfr.2025.1650992
This article is part of the Research TopicPandemic Preparedness in Vaccine Safety and RegulationView all 9 articles
The use of Observed-to-Expected analyses as a signal detection tool in COVID-19 vaccine safety surveillance: lessons learned from an industry perspective
Provisionally accepted- 1Epidemiology & Benefit Risk, Patient Safety & Pharmacovigilance, Sanofi, Lyon, France
- 2Worldwide Medical and Safety, Pfizer Inc, Milan, Italy
- 3Global Vaccine Safety, Novavax Inc, Gaithersburg, MD, United States
- 4Global Medical Epidemiology, Pfizer Inc, New York NY, United States
- 5Worldwide Medical and Safety, Independant, previously Pfizer Inc, Collegeville, PA, United States
- 6Global Safety Epidemiology, Moderna Inc, Cambridge, MA, United States
- 7Patient Safety & Pharmacovigilance, Sanofi, Lyon, France
- 8Vaccine Safety, Independent, Previously GSK, Wavre, Belgium
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During the pandemic, the accelerated review and authorization of coronavirus disease 19 (COVID-19) vaccines by regulatory authorities elicited the need for rapid and thorough worldwide signal detection and evaluation. To meet this need, the European Medicines Agency and other health authorities expected that, in addition to routine signal detection, COVID-19 vaccine manufacturers should leverage observed-to-expected (O/E) analyses unconventionally as a quantitative method for signal detection of adverse events of special interest (AESIs). The objective of O/E analyses in vaccine signal detection was to determine if AESIs were occurring at a higher-than-expected rate in the vaccinated population in comparison with an unexposed population. The use of O/E was intended to mitigate the challenge of analyzing large volumes of individual case safety reports (ICSRs) received over a very short period following mass vaccination campaigns. The "Beyond COVID-19 Monitoring Excellence" (BeCOME) initiative, a non-competitive voluntary initiative launched in 2022 by COVID-19 vaccine Marketing Authorization Holders (MAHs) and key stakeholders, was established to align systems, enhance processes, and foster innovation in post-marketing vaccine monitoring, building on lessons from the pandemic. A dedicated working group was created to review and share MAHs' experience on O/E analyses used as an additional tool for signal detection during the COVID-19 pandemic. This review presents the industry perspective on using O/E analyses for COVID-19 vaccine signal detection, including challenges and limitations encountered, and proposes best practices for future improvement. Despite the priority and resources devoted to O/E analyses, no de novo signals resulting in the identification of safety concerns were detected using this methodology during the COVID-19 pandemic. O/E analyses are most useful when source data are accurate and there is a high level of confidence in the assumptions and parameters used. In the context of the COVID-19 pandemic, confidence in certain assumptions and parameters was low, limiting the value of O/E analyses in signal detection. Nevertheless, O/E analyses applied for signal refinement, as traditionally used, proved to be useful. Industry experiences support maintaining O/E analyses as a tool for signal refinement and standardizing methodological approaches as much as possible to enhance its future application and comparability across stakeholders.
Keywords: Observed-to-expected, Adverse Event of Special Interest, COVID-19 vaccine, signaldetection, Signal refinement
Received: 20 Jun 2025; Accepted: 21 Oct 2025.
Copyright: © 2025 SERRADELL, FRETTA, NACHBAR, DREYFUS, GAROFALO, LUCINI, MATHER, ESPOSITO, CHABANON, BAUCHAU and SELLERS. 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: LAURENCE SERRADELL, laurence.serradell-vallejo@sanofi.com
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