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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".