Populations Addressed in Vaccines Approved via the European Medicines Agency
Bibliographic record
Abstract
Therapeutic and prophylactic agents require robust evidence before patient use. Randomized controlled trials are essential for evaluating safety and efficacy but often exclude specific populations that are also targets for the intervention. This study assessed which populations are included in vaccine registration studies and/or addressed in label indications, and if special populations are considered at any point in the regulatory life cycle of vaccines approved in the European Union. We analyzed product labels, pivotal studies, risk management plans, and post-authorization studies for all vaccines centrally approved via the European Medicines Agency between 2012 and 2022. For the 31 vaccines approved, we identified 90 pivotal studies supporting initial marketing authorizations and 46 studies supporting product revisions. At the end of our follow-up, 27 vaccines (87%) were approved for adults, 19 (68%) for pediatric patients, 3 (11%) were recommended for pregnant populations, 4 (14%) for breastfeeding populations, and 7 (23%) for immunocompromised populations. Pregnant, breastfeeding, and immunocompromised individuals were rarely included in studies supporting regulatory actions. We observed a slight increase in the inclusion of special populations in post-marketing studies, yet this had limited impact on product indications or information availability on labels. Pivotal studies supporting vaccine registrations were also highly selective and predominantly conducted in high-income settings. These findings highlight significant variations in how different populations are considered during vaccine development and by regulators. Greater inclusion of special populations in the evidence-generation chain is essential to ensure that vaccines respond to unmet medical needs equitably.
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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 | 0.081 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".