ArticleEuro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin2024
Bias in vaccine effectiveness studies of clinically severe outcomes that are measured with low specificity: the example of COVID-19-related hospitalisation.
Article in Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- Effectiveness of maternal COVID-19 vaccination varies by gestational timing: results from a claims-based cohort study, 2020-2022.Research square · 2026Article
- Effectiveness of the XBB.1.5 COVID-19 Vaccines Against SARS-CoV-2 Hospitalisation Among Adults Aged ≥ 65 Years During the BA.2.86/JN.1 Predominant Period, VEBIS Hospital Study, Europe, November 2023 to May 2024.Influenza and other respiratory viruses · 2025Article
- Early COVID-19 XBB.1.5 Vaccine Effectiveness Against Hospitalisation Among Adults Targeted for Vaccination, VEBIS Hospital Network, Europe, October 2023-January 2024.Influenza and other respiratory viruses · 2024Article
- Biases in COVID-19 vaccine effectiveness studies using cohort design.Frontiers in medicine · 2024Review
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Abstract
Many vaccine effectiveness (VE) analyses of severe disease outcomes such as hospitalisation and death include 'false' cases that are not actually caused by the infection or disease under study. While the inclusion of such false cases inflate outcome rates in both vaccinated and unvaccinated populations, it is less obvious how they affect estimates of VE. Illustrating the main points through simple examples, this article shows how VE is underestimated when false cases are included as outcomes. Depending how the outcome indicator is defined, estimates of VE against severe disease outcomes, whose definition allows for the inclusion of false cases, will be biased downwards and may in certain circumstances approximate the same level as the VE against infection. The bias is particularly pronounced for vaccines that offer high levels of protection against severe disease outcomes but poor protection against infection. Analysing outcomes that are measured with low sensitivity generally does not cause bias in VE studies; defining outcome indicators that minimise the number of false cases rather than the number of missed cases is preferable in VE studies.
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Registered trials
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