Effect of COVID-19 Pandemic on Influenza Vaccination Intention: A Meta-Analysis and Systematic Review
Bibliographic record
Abstract
Poorer outcomes have been reported with COVID-19 and influenza coinfections. As the COVID-19 pandemic rages on, protection against influenza by vaccination is becoming increasingly important. This study examines how COVID-19 has influenced influenza vaccination intentions from a global perspective. A literature search was conducted on Embase, PubMed, and CNKI from 1 January 2019 to 31 December 2021 for articles reporting rates of influenza vaccination pre-COVID-19 (19/20 season), and intention and/or uptake of influenza vaccination post-COVID-19 (20/21 season). The changes in vaccination intention and reasons for changes were reported. Subgroup analyses were performed by region, gender, age, and occupation. Newcastle Ottawa Scale was used for quality assessment of the articles. Twenty-seven studies with 39,193 participants were included. Among 22 studies reporting intention to vaccinate in 20/21, there was increased intention to vaccinate (RR 1.50, 95% CI 1.32−1.69, p < 0.001) regardless of age, gender, and occupation. The remaining five studies reporting vaccination intention and uptake in 20/21 showed a similar increase (RR 1.68, 95%CI 1.20−2.36). Important determinants include historical vaccine acceptance, and perception of influenza severity and vaccine safety. The COVID-19 pandemic has increased intention to vaccinate against influenza internationally. The pandemic could be a window of opportunity to promote influenza vaccination and decrease vaccine hesitancy.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".