Burden of Respiratory Syncytial Virus in the European Union: estimation of RSV-associated hospitalizations in children under 5 years
Bibliographic record
Abstract
BACKGROUND: No overall estimate of respiratory syncytial virus (RSV)-associated hospitalizations in children aged under 5 years has been published for the European Union (EU). We aimed to estimate the RSV hospitalization burden in children aged under 5 years in EU countries and Norway, by age group. METHODS: We collated national RSV-associated hospitalization estimates calculated using linear regression models via the RESCEU project for Denmark, England, Finland, Norway, the Netherlands, and Scotland, 2006-2018. Additional estimates were obtained from a systematic review. Using multiple imputation and nearest neighbor matching methods, we estimated overall RSV-associated hospitalizations and rates in the EU. RESULTS: Additional estimates for 2 countries (France and Spain) were found in the literature. In the EU, an average of 245 244 (95% confidence interval [CI], 224 688-265 799) yearly hospital admissions with a respiratory infection per year were associated with RSV in children aged under 5 years, with most cases occurring among children aged under 1 year (75%). Infants aged under 2 months represented the most affected group (71.6 per 1000 children; 95% CI, 66.6-76.6). CONCLUSIONS: Our findings will help support decisions regarding prevention efforts and represent an important benchmark to understand changes in the RSV burden following the introduction of RSV immunization programs in Europe.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".