Effects of Influenza Vaccination on the Response to BNT162b2 Messenger RNA COVID-19 Vaccine in Healthcare Workers
Bibliographic record
Abstract
BACKGROUND: Vaccine-induced immunity is at present the main strategy to stop the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recent evidences suggested a protective effect of influenza vaccination against coronavirus disease 2019 (COVID-19) severity, while impact on the immune response to BNT162b2 messenger RNA (mRNA) vaccine is under investigation. METHODS: We aimed to evaluate this aspect in a cohort of 297 healthcare workers (108 males, 189 females) after seasonal influenza vaccination compared to no-flu-vaccination. VAX+ (165 individuals; 63 males and 102 females) had tetravalent influenza vaccine, and VAX- (132 individuals; 45 males and 87 females) had no flu vaccination. Anti-spike-receptor binding domain (RBD) level was tested 15 - 70 days after BNT162b2 second inoculum. RESULTS: Increased antibody response was observed in total VAX+ compared to VAX- (2,047.4 vs. 1,494.2 binding antibody unit (BAU)/mL, P = 0.0039), independently from gender and body mass index (BMI). Younger total individuals (< 35 years) showed significant increase of the level of binding antibodies (2,184.8 vs. 1,590.9 BAU/mL, P = 0.0038) compared to ≥ 35 years; young/old difference was lost restricting to VAX+ subgroup. Flu vaccinations appear associated to better antibody response in older individuals (P = 0.027, ≥ 35 years VAX+ vs. VAX-). A decreasing trend during time was observed for both VAX+ and VAX-, except for < 35 years VAX- individuals. Early response was higher in VAX+ compared to VAX-; however a more rapid waning was observed in VAX+ subjects. CONCLUSIONS: Our data showed better antibody response to SARS-CoV-2 vaccine in subjects already vaccinated against seasonal influenza; this may represent one of the mechanisms underlying the cross-protective effects of influenza vaccination against heterologous infections reported in recent epidemiological studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".