Trends in childhood vaccination coverage in the European Union, 1980–2024: between long-term increases and recent decreases
Bibliographic record
Abstract
Abstract Background Vaccination is among the most effective public health interventions, yet vaccine hesitancy in the European Union (EU) has driven declining coverage and resurgences of vaccine-preventable diseases. Trend assessments are essential for informing strategies to maintain high coverage levels. Methods We analysed WHO/UNICEF Estimates of National Immunization Coverage data (as of September 2025) for 27 EU countries from 1980 to 2024. Coverage for seven first-year antigens (DTP-3, HEPB-3, HIB-3, POL-3, PCV-3, MCV-1, RCV-1) was evaluated using joinpoint regression for Average Annual Percent Change (AAPC) overall and recent Annual Percent Change (APC) shifts. Findings Of 183 country-vaccine trends, 128 showed positive AAPCs but recent APCs indicated predominant declines (110 negative evolutions). In 2024, only 71 combinations reached ≥95% coverage; Luxembourg achieved this for all antigens. Seventeen countries (e.g., Germany, Romania) had declines in ≥4 vaccines, especially HIB-3, POL-3, and DTP-3. Interpretation Despite long-term gains, recent EU-wide coverage declines signal urgent risks to herd immunity. National and community actions are needed to reverse trends. Funding None.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".