ArticleMicroorganisms2024
A Critical Analysis of All-Cause Deaths during COVID-19 Vaccination in an Italian Province.
Article in Microorganisms, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Beyond correlation and causation: epistemic caution, anomalous findings, and COVID-19 vaccine safety.Philosophy, ethics, and humanities in medicine : PEHM · 2026Review
- Review
- Reply to the letter to the editor by Manzoli, Acuti Martelucci and Flacco (doi: 10.17179/excli2025-9176).EXCLI journal · 2026Article
- Updated profiling of COVID-19 vaccine adverse events using VAERS case reports.Frontiers in pharmacology · 2026Article
- Second reply to the second letter to the editor by Alessandria, Berrino, Malatesta and Donzelli (doi: 10.17179/excli2026-9442).EXCLI journal · 2026Article
- Risk stratification for future cardiac arrest after COVID-19 vaccination.World journal of cardiology · 2025Review
- What Lessons can Be Learned From the Management of the COVID-19 Pandemic?International journal of public health · 2025Review
- Reply to the Letter to the editor by Malatesta, Alessandria, Berrino and Donzelli.EXCLI journal · 2025Article
- Comment to "COVID-19 vaccination, all-cause mortality, and hospitalization for cancer: 30-month cohort study in an Italian province".EXCLI journal · 2025Article
- The WHO Algorithm for Causality Assessment of Adverse Effects Following Immunization with Genetic-Based Anti-COVID-19 Vaccines: Pitfalls and Suggestions for Improvement.Journal of clinical medicine · 2024Article
- Biases in COVID-19 vaccine effectiveness studies using cohort design.Frontiers in medicine · 2024Review
- All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data.F1000Research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immortal time bias (ITB) is common in cohort studies and distorts the association estimates between the treated and untreated. We used data from an Italian study on COVID-19 vaccine effectiveness, with a large cohort, long follow-up, and adjustment for confounding factors, affected by ITB, with the aim to verify the real impact of the vaccination campaign by comparing the risk of all-cause death between the vaccinated population and the unvaccinated population. We aligned all subjects on a single index date and considered the "all-cause deaths" outcome to compare the survival distributions of the unvaccinated group versus various vaccination statuses. The all-cause-death hazard ratios in univariate analysis for vaccinated people with 1, 2, and 3/4 doses versus unvaccinated people were 0.88, 1.23, and 1.21, respectively. The multivariate values were 2.40, 1.98, and 0.99. Possible explanations of this trend of the hazard ratios as vaccinations increase could be a harvesting effect; a calendar-time bias, accounting for seasonality and pandemic waves; a case-counting window bias; a healthy-vaccinee bias; or some combination of these factors. With 2 and even with 3/4 doses, the calculated Restricted Mean Survival Time and Restricted Mean Time Lost have shown a small but significant downside for the vaccinated populations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.