Influenza Vaccination Coverage and Determinants of New Vaccinations During the COVID-19 Pandemic in Spain (ENE-COVID): Nationwide Population-Based Study
Bibliographic record
Abstract
Background: Influenza vaccination coverage is commonly suboptimal. However, the COVID-19 pandemic and consequent high exposure to health information may have changed population attitudes toward this vaccination. Objective: The aim of this study is to describe influenza vaccine uptake in Spain during the first influenza season following the start of the COVID-19 pandemic compared to the previous one and identify characteristics associated with vaccination among those previously unvaccinated. Methods: This was a population-based study of 28,987 adults included in influenza vaccination target groups (≥65 years old, with risk conditions, living with someone with risk conditions, health care workers, security or emergency workers) who were participants in the nationwide Seroepidemiological Survey of SARS-CoV-2 Infection in Spain (ENE-COVID) study. Information on vaccination and sociodemographic, health, and COVID-19-related factors was collected by interview. Coverage change from 2019 to 2020 and standardized prevalences of vaccination in 2020 among the population unvaccinated in 2019 were estimated using logistic model-based methods. Results: Coverage rose from 31.4% (95% CI 30.5%-32.2%) to 46.8% (95% CI 45.8%-47.8%). People ≥65 years old showed the highest uptake in both periods (58.3%, 95% CI 56.8%-59.8% and 74.8%, 95% CI 73.5%-76.1%), while health care workers had the greatest increase (22%, 95% CI 17.8%-26.2%). Among people unvaccinated in 2019, factors associated with vaccination in 2020 were age, female sex, higher education, Spanish nationality, multimorbidity, being a former smoker, obesity, contact with COVID-19 cases, living with older adults, living in provinces with low COVID-19 incidence, wearing a face mask during family meetings, and using surgical/FFP2 masks. Conclusions: This study provides nationwide representative estimates of influenza vaccination coverage, which clearly increased between 2019 and 2020 in the 5 target groups. However, coverage goals were attained only in the ≥65 year old group, highlighting the importance of reinforcing influenza vaccination. Our detailed results on determinants of vaccination provide some clues to tailor vaccination strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".