Respiratory syncytial virus hospitalisation burden in children below 18 years in six European countries (2016-2023) pre- and post-COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVES: Respiratory syncytial virus (RSV) is a substantial cause of hospital admission in young children and leads to seasonal pressure on pediatric emergency units in most countries. This study aims to assemble national or large-scale data on RSV hospitalisations from six European countries with a standardised approach to provide recent burden data for all children and assess changes since SARS-CoV-2's emergence. METHODS: We analysed 2016-2023 hospital records from national registries in Denmark, England, Finland, The Netherlands, and Scotland, and from a hospital surveillance network in Spain-Valencia for children below 18 years. We considered separately RSV-coded and RSV laboratory-confirmed cases, comparing them to respiratory tract infections. We studied the temporal evolution of incidence rates and case reporting practices, comparing pre- and post-COVID-19 periods. RESULTS: Post-COVID-19 observed RSV hospital burden was similar to the pre-COVID-19 one for younger children but higher for the 1-2 years, 3-4 years, and 5-17 years age groups. No change in terms of coding-neither diagnosis nor RSV-coding when RSV was laboratory-confirmed-was detected. CONCLUSIONS: Hospital RSV burden in children is significant but currently not fully monitorable. Further efforts to harmonise coding practices both within and across countries would improve the quality of future analyses. Additional data in future seasons should complement current outcomes to inform decisions regarding RSV prevention.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".