Trends in pediatric ambulatory community acquired infections before and during COVID-19 pandemic: A prospective multicentric surveillance study in France
Bibliographic record
Abstract
Covid-19 pandemic control has imposed several non-pharmaceutical interventions (NPIs). Strict application of these measures has had a dramatic reduction on the epidemiology of several infectious diseases. As the pandemic is ongoing for more than 2 years, some of these measures have been removed, mitigated, or less well applied. The aim of this study is to investigate the trends of pediatric ambulatory infectious diseases before and up to two years after the onset of the pandemic. We conducted a prospective surveillance study in France with 107 pediatricians specifically trained in pediatric infectious diseases. From January 2018 to April 2022, the electronic medical records of children with an infectious disease were automatically extracted. The annual number of infectious diseases in 2020 and 2021 was compared to 2018-2019 and their frequency was compared by logistic regression. From 2018 to 2021, 185,368 infectious diseases were recorded. Compared to 2018 (n=47,116) and 2019 (n=51,667), the annual number of cases decreased in 2020 (n=35,432) by about a third. Frequency of scarlet fever, tonsillopharyngitis, enteroviral infections, bronchiolitis, and gastroenteritis decreased with OR varying from 0·6 (CI95% [0·5;0·7]) to 0·9 (CI95% [0·8;0·9]), p<0·001. In 2021, among the 52,153 infectious diagnoses, an off-season rebound was observed with increased frequency of enteroviral infections, bronchiolitis, gastroenteritis and otitis with OR varying from 1·1 (CI95% [1·0;1·1]) to 1·5 (CI95% [1·4;1·5]), p<0·001. While during NPIs strict application, the overall frequency of community-acquired infections was reduced, after relaxation of these measures, a rebound of some of them (enteroviral infections, bronchiolitis, gastroenteritis, otitis) occurred beyond the pre-pandemic level. These findings highlight the need for continuous surveillance of infectious diseases, especially insofar as future epidemics are largely unpredictable. ACTIV, AFPA, GSK, MSD, Pfizer and Sanofi.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".