Sex Differences in the Immunogenicity and Efficacy of Seasonal Influenza Vaccines: A Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Abstract Background Sex impacts individuals’ response to vaccination. However, most vaccine studies do not report these differences disaggregated by sex. The aim of this study was to assess sex differences in the immunogenicity and efficacy of influenza vaccine. Methods We performed a meta-analysis using phase 3 randomized controlled trial data conducted between 2010 and 2018. Using hemagglutination inhibition antibody titers for each strain, differences in geometric mean ratios (GMRs) were calculated by sex. Risk ratios (RRs) comparing seroconversion proportions were pooled for females and males using random-effects models. Vaccine efficacy (VE) was assessed. Data were analyzed by age group (18–64 vs ≥65 years). Results A total of 33 092 healthy adults from 19 studies were included for immunogenicity analysis, and 6740 from 1 study for VE. Whereas no sex differences in immunogenicity were found in adults <65 years old, older females had a significantly greater chance to seroconvert compared to older males for all strains: RRH1N1 = 1.17 [95% confidence interval {CI}, 1.12–1.23]; RRH3N2 = 1.09 [95% CI, 1.05–1.14]; RRVictoria = 1.23 [95% CI, 1.14–1.31]; RRYamagata = 1.22 [95% CI, 1.14–1.30]. GMRs were also higher in older females for all strains compared to older males. VE in preventing laboratory-confirmed influenza was higher in older females compared to older males with VEs of 27.32% (95% CI, 1.15%–46.56%) and 6.06% (95% CI, −37.68% to 35.90%), respectively. Conclusions Our results suggest a higher immunogenicity and VE in females compared to males in older adults. These differences in immunogenicity and VE support the disaggregation of vaccine data by sex in clinical trials and observational studies. Clinical Trials Registration CRD42018112260.
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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 | 0.030 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.055 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".