Co-Administration of Influenza and COVID-19 Vaccines: Policy Review and Vaccination Coverage Trends in the European Union, UK, US, and Canada between 2019 and 2023
Bibliographic record
Abstract
Recommending co-administration of influenza and COVID-19 vaccines has emerged as a strategy to enhance vaccination coverage. This study describes the policy on co-administration and uptake of influenza and COVID-19 vaccination in Europe, the United Kingdom, the United States, and Canada between 2019 and 2023. We collected co-administration policy data from governmental websites, national health organizations, and newspapers. Influenza vaccination coverage among persons ≥65 years and COVID-19 vaccination coverage rates among persons ≥60 years or the general population were collected using national databases, the ECDC database, or ourworldindata.org between 2019 and 2023. Descriptive analyses were used. We collected data from 30/32 (94%) countries on vaccination policy in seasons 2021-2022 and 2022-2023, with most countries (25/30 to 30/30) having policies recommending co-administration. For influenza vaccination coverage, we collected data from 29/32 (91%, 2019-2020), 28/32 (88%, 2020-2021), 27/32 (84%, 2021-2022), and 6/32 (19%, 2022-2023) countries. COVID-19 vaccination was collected from 32/32 (2020-2021), 31/32 (97%, 2021-2022), and 24/32 (75%, 2022-2023) countries. Influenza vaccination coverage increased from 2019-2020 to 2021-2022. COVID-19 vaccination coverage was higher among countries with higher influenza vaccination coverage. By 2022-2023, all countries included implemented a policy supporting co-administration. A positive correlation existed between higher influenza vaccination coverage and higher COVID-19 vaccination rates.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".