Underlying health biases in previously-infected SARS-CoV-2 vaccination recipients: A cohort study is a research paper published in Journal of Infection (2025). On theSindex it has a DataRank of 0.292. It has been cited 6 times, with 3 citing works in its 1-hop citation network.
ObjectivesObservational studies may over- or under-estimate SARS-CoV-2 vaccine effectiveness (VE) depending on whether healthier (i.e. healthy vaccine effect (HVE)) or more ill individuals are preferentially vaccinated. To evaluate this issue, we compared non-COVID-19 mortality in vaccinated versus unvaccinated individuals.MethodsThis is a nationwide retrospective observational study in the entire adult population in Austria with previously documented SARS-CoV-2 infection, with a follow-up from 2021 to 2023. Cox regression analyses were used to calculate hazard ratios (HRs) according to the number of SARS-CoV-2 vaccinations. We also performed matched analyses, where on each day, newly vaccinated individuals were matched with unvaccinated individuals based on age, sex and nursing home residency.ResultsIn 4,324,485 eligible individuals, differences in non-COVID-19 mortality risk between vaccinated and unvaccinated were most prominent in the early periods and decreased thereafter. Matched analyses for the first two weeks after vaccination showed HRs below 0.5 for vaccinated versus unvaccinated individuals, irrespective of vaccination numbers. Similar findings were retrieved for non-COVID-19, all-cause, and cancer deaths. Overall, COVID-19 deaths were significantly reduced in vaccinated individuals (VE of 26 to 53%).ConclusionsHVE for SARS-CoV-2 vaccines was strong early after vaccination and diminished over time. HVE should be considered when estimating VE.
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Base Score Contribution
0.292
From this paper's citation signal
Citation Network Contribution
0
From 0 citing papers with measurable signal
This paper's DataRank is currently driven only by its base citation score. None of the citing papers had measurable citation signal.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.