Comparison of two methods for the estimation of COVID-19 vaccine effectiveness of the autumnal booster within the VEBIS-EHR network in 2022/23

Within an infrastructure to monitor vaccine effectiveness (VE) against hospitalization due to COVID-19 and COVID-19 related deaths from November 2022 to July 2023 in seven countries in real-world conditions (VEBIS network), we compared two approaches: (a) estimating VE of the first, second or third COVID-19 booster doses administered during the autumn of 2022, and (b) estimating VE of the autumn vaccination dose regardless of the number of prior doses (autumnal booster approach). Retrospective cohorts were constructed using Electronic Health Records at each participating site. Cox regressions with time-changing vaccination status were fit and site-specific estimates were combined using random-effects meta-analysis. VE estimates with both approaches were mostly similar, particularly shortly after the start of the vaccination campaign, and showed a similar timing of VE waning. However, autumnal booster estimates were more precise and showed a clearer trend, particularly compared to third booster estimates, as calendar time increased after the vaccination campaign and during periods of lower SARS-CoV-2 activity. Moreover, the decrease in protection by increasing calendar time was more clear and precise than when comparing protection by number of doses. Therefore, estimating VE under an autumnal booster framework emerges as a preferred method for future monitoring of COVID-19 vaccination campaigns.

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Publication Details

Journal
Epidemiology and Infection
Published
2025-01-01
DOI
https://doi.org/10.1017/s0950268825000317
Citations
2
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
2.72
Controls
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article

Comparison of two methods for the estimation of COVID-19 vaccine effectiveness of the autumnal booster within the VEBIS-EHR network in 2022/23

Hanne‐Dorthe Emborg, James Humphreys, Toon Braeye, Jesús Castilla et al.
2 citations
Epidemiology and Infection
COVID-19 diagnosis using AI
2.72
article

Comparison of two methods for the estimation of COVID-19 vaccine effectiveness of the autumnal booster within the VEBIS-EHR network in 2022/23

Hanne‐Dorthe Emborg, James Humphreys, Toon Braeye, Jesús Castilla, Anja Bråthen Kristoffersen, Susan Hahné, Esther Kissling, Susana Monge, Brechje de Gier, Hinta Meijerink, Mario Fontán‐Vela, Izaak Van Evercooren, Massimo Fabiani, Ausenda Machado, Nathalie Nicolay, Chiara Sacco, Christian Holm Hansen, Anthony Nardone, Patrícia Soares, Iván Martínez‐Baz, Baltazar Nunes
article en
2 citations

Abstract

Within an infrastructure to monitor vaccine effectiveness (VE) against hospitalization due to COVID-19 and COVID-19 related deaths from November 2022 to July 2023 in seven countries in real-world conditions (VEBIS network), we compared two approaches: (a) estimating VE of the first, second or third COVID-19 booster doses administered during the autumn of 2022, and (b) estimating VE of the autumn vaccination dose regardless of the number of prior doses (autumnal booster approach). Retrospective cohorts were constructed using Electronic Health Records at each participating site. Cox regressions with time-changing vaccination status were fit and site-specific estimates were combined using random-effects meta-analysis. VE estimates with both approaches were mostly similar, particularly shortly after the start of the vaccination campaign, and showed a similar timing of VE waning. However, autumnal booster estimates were more precise and showed a clearer trend, particularly compared to third booster estimates, as calendar time increased after the vaccination campaign and during periods of lower SARS-CoV-2 activity. Moreover, the decrease in protection by increasing calendar time was more clear and precise than when comparing protection by number of doses. Therefore, estimating VE under an autumnal booster framework emerges as a preferred method for future monitoring of COVID-19 vaccination campaigns.

Epidemiology and InfectionVol. 153
Statens Serum Institut (DK), Norwegian Institute of Public Health (NO), Universidad de Alcalá (ES), Istituto Superiore di Sanità (IT), Instituto de Salud Carlos III (ES), Ricardo (United Kingdom) (GB), Instituto de Salud Pública de Navarra (ES), National Institute for Public Health and the Environment (NL), Centre for Biomedical Network Research on Rare Diseases (ES), Instituto Nacional de Saúde (MZ), Sciensano (Belgium) (BE), Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (ES), European Centre for Disease Prevention and Control (SE)
Zero hunger
Openalex Percentile: Top 12%
COVID-19 diagnosis using AI
2.72
DOIOpen Access PDFOpenAlex
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