Respective roles of non-pharmaceutical interventions in bronchiolitis outbreaks: an interrupted time-series analysis based on a multinational surveillance system
Bibliographic record
Abstract
BACKGROUND: Bronchiolitis is a major source of morbimortality among young children worldwide. Non-pharmaceutical interventions (NPIs) implemented to reduce the spread of severe acute respiratory syndrome coronavirus 2 may have had an important impact on bronchiolitis outbreaks, as well as major societal consequences. Discriminating between their respective impacts would help define optimal public health strategies against bronchiolitis. We aimed to assess the respective impact of each NPI on bronchiolitis outbreaks in 14 European countries. METHODS: We conducted a quasi-experimental interrupted time-series analysis based on a multicentre international study. All children diagnosed with bronchiolitis presenting to the paediatric emergency department of one of 27 centres from January 2018 to March 2021 were included. We assessed the association between each NPI and change in the bronchiolitis trend over time by seasonally adjusted multivariable quasi-Poisson regression modelling. RESULTS: In total, 42 916 children were included. We observed an overall cumulative 78% (95% CI -100- -54%; p<0.0001) reduction in bronchiolitis cases following NPI implementation. The decrease varied between countries from -97% (95% CI -100- -47%; p=0.0005) to -36% (95% CI -79-7%; p=0.105). Full lockdown (incidence rate ratio (IRR) 0.21 (95% CI 0.14-0.30); p<0.001), secondary school closure (IRR 0.33 (95% CI 0.20-0.52); p<0.0001), wearing a mask indoors (IRR 0.49 (95% CI 0.25-0.94); p=0.034) and teleworking (IRR 0.55 (95% CI 0.31-0.97); p=0.038) were independently associated with reducing bronchiolitis. CONCLUSIONS: Several NPIs were associated with a reduction of bronchiolitis outbreaks, including full lockdown, school closure, teleworking and facial masking. Some of these public health interventions may be considered to further reduce the global burden of bronchiolitis.
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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 | 0.033 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".