The Impact of the COVID-19 Pandemic on the Uptake of Influenza Vaccine: UK-Wide Observational Study
Bibliographic record
Abstract
ABSTRACT Objectives The objective of this study was to measure the impact of the Covid-19 pandemic on acceptance of flu vaccination in the 2020-21 season, including for those newly eligible for the UK National Health Service (NHS) free vaccination programme, extended this year to include an estimated 32.4 million (48.8%) of the UK population. Knowing intended uptake is essential to inform supply and steer public health messaging to maximise vaccination given the combined threats of both flu and Covid-19 — the unknown impact of which on both attitudes and the need for mass uptake yet again create the threat of ill-informed planning resulting in failure to meet necessary public health demand. Methods An online questionnaire posing question items on influenza vaccination was administered to registrants of the Care Information Exchange (CIE), the NHS’s largest patient electronic personal health record. This was part of a longitudinal study initiated during the Covid-19 pandemic lockdown. This analysis was limited to those who, in line with established NHS criteria, were previously or newly eligible but had not routinely received seasonal influenza vaccination in the past. Groups were stratified by response (yes/no) to intending to receive flu vaccination in 2020-21: Group 1.) Previously eligible now responding ‘yes’, 2.) Previously eligible still responding ‘no’, 3.) Newly eligible responding ‘yes’, and 4.) Newly eligible responding ‘no’. Within these groups, response by health worker status and each group’s inclination to vaccinate school age children was also measured. Summary statistics were reported alongside univariate and multivariable regression. Lastly, a network analysis visualised the frequency and co-occurrence of reasons qualifying response for or against influenza vaccination in 2020/21. Findings Among 6,641 respondents, 4,040 (61.1%) had previously routinely received the flu vaccination. 1,624 (24.5%) had been either previously eligible but not vaccinated (945, 58.2%) or newly eligible (679, 41.8%). Among the previously eligible participants who had not routinely received influenza vaccination 536 (56.7%) responded they would in 2020-21, increasing the vaccination rate in the entire previously eligible cohort from 79.6% to 91.2%, and 466 (68.6%) in the newly eligible. Multivariable logistic regression resulted in few substantial changes to effect estimates, with the exception of age, for which all estimates showed a stronger association with intention to receive the flu vaccine. In those who became newly eligible to receive the flu vaccine, there was an association between intention to receive the flu vaccine and increased age (OR = 1.07, 95% CI 1.03 to 1.12), IMD quintile, and considering oneself at high risk from Covid-19 (OR = 1.80, 95% CI 1.22 to 2.70). Network analysis showed the most frequent themes for previously eligible unvaccinated and newly eligible participants accepting vaccination in 2020/21 were: ‘precaution for myself’ (41.2% and 46.1%) and ‘Covid-19’ (27.4% and 27.1%), where the former was qualified by the latter in 36% and 29.1% of responses. Among the previously and newly eligible not intending to receive vaccination in 2020/21, misinformed themes of ‘makes me unwell’, ‘gives me flu’ and ‘vaccine doesn’t work’ were present across 37.4% and 21.9% of responses, respectively. Among participants with school age children, of those previously eligible who now intend to be vaccinated themselves, 82.5% also intend to vaccinate their children in 2020/21 compared to 25.8% of those who would not accept the influenza vaccine for themselves. Among the newly eligible respondents this was 82.1% and 43.5%, respectively. 49.9% of the previously unvaccinated healthcare workers would continue to decline the vaccine in 2020/21. Interpretation In this UK-wide observational study, Covid-19 has increased acceptance of flu vaccination in 2020/21 from 79.6% to 91.2% in those previously eligible, and for the 69% of those newly eligible. This high anticipated vaccination rate (to 26 million (80%) of the UK population) requires appropriate planning, but can be further increased with effective messaging campaigns to address negative misconceptions about flu vaccination, which may also help in preparation for future Covid-19 vaccination. It remains of concern that 50% of healthcare professionals who refused it previously still do not intend to have the flu vaccine.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".