Influenza vaccination and COVID-19 infection risk and disease severity: A systematic review and multilevel meta-analysis of prospective studies
Bibliographic record
Abstract
BACKGROUND: In light of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic, the influence of influenza vaccination on the risk and severity of Coronavirus Disease 19 (COVID-19) has been a subject of debate. This systematic review and meta-analysis of prospective studies aim to assess the association between influenza immunization and the risk of SARS-CoV-2 infection and subsequent COVID-19 disease severity. METHODS: A comprehensive search of PubMed and Embase databases was performed to identify prospective studies published before March 2024. We focused on evaluating the effect of influenza vaccination on SARS-CoV-2 infection risk and severe COVID-19 outcomes, such as hospitalization and mortality. The analysis employed a multilevel random effects meta-analysis approach. The risk of bias assessment was conducted using the Newcastle-Ottawa Scale. RESULTS: From an initial pool of 5,863 records, 14 studies were selected for inclusion. The aggregated data yielded a summary relative risk (SRR) that showed no significant protective correlation between influenza vaccination and SARS-CoV-2 infection risk (SRR 0.95, 95% confidence interval [CI] 0.81-1.11), COVID-19-associated hospitalization (SRR 0.90, 95% CI 0.68-1.19), or COVID-19-related mortality (SRR 0.83, 95% CI 0.56-1.23). CONCLUSIONS: This systematic review and meta-analysis, based exclusively on prospective studies, demonstrates the lack of a proven protective effect of influenza vaccination against COVID-19 and related outcomes. Our results do not support a significant protective effect of influenza vaccination against the risk or severe outcomes of COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.044 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".