Paediatric infection hospital admissions in England before, during and after the COVID-19 pandemic
Bibliographic record
Abstract
Summary The COVID-19 pandemic and associated non-pharmaceutical interventions (NPIs: containment measures/”lockdown”) reduced transmission of other infections. We quantified changes in hospital admission rates for respiratory and gastrointestinal infections amongst young children in England during and after implementation of NPIs, compared to pre-pandemic, and variations by sociodemographic and clinical characteristics. Children aged <5 years at any time between 1 January 2017 and 31 January 2022 were followed from birth or 1 January 2017, until their 5 th birthday, death or 31 January 2022, in a birth cohort based on Hospital Episode Statistics data. Quarterly emergency admission rates for respiratory and gastrointestinal infections from April-June 2020 onwards were compared to corresponding quarters in 2017-19 using Poisson regression models, with and without interaction terms for time period and sociodemographic/clinical characteristics. Admission rates for respiratory and gastrointestinal infections were lower in April-June 2020 compared to this quarter pre-pandemic (incidence rate ratio (99% CI) 0.17 (0.17-0.18) for respiratory; 0.29 (0.28-0.31) for gastrointestinal). Rates remained below pre-pandemic levels until April-June 2021 (respiratory infections) and July-September 2021 (gastrointestinal infections), subsequently increasing above the corresponding pre-pandemic quarters. Changes in rates did not differ by sociodemographic/clinical characteristics. These results can inform public health planning for future pandemics and their aftermath.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".