Hospitalisation for lower respiratory tract infection in children in the province of Quebec, Canada, before and during the pneumococcal conjugate vaccine era
Bibliographic record
Abstract
Streptococcus pneumoniae is an important cause of community-acquired pneumonia and pneumococcal conjugate vaccines (PCVs) may reduce this burden. This study's goal was to analyse trends in lower respiratory tract infections (LRTI) hospitalisations before and during a routine vaccination programme targeting all newborns with PCV was started in the province of Quebec, Canada in December 2004. The study population included hospital admissions with a main diagnosis of LRTI among 6-59 month-old Quebec residents from April 2000 to December 2014. Trends in proportions and rates were analysed using Cochran-Armitage tests and Poisson regression models. We observed a general downward trend in all LTRI hospitalisations rate: from 11·55/1000 person-years in 2000-2001 to 9·59/1000 in 2013-2014, a 17·0% reduction, which started before the introduction of PCV vaccination. Downward trends in hospitalisation rates were more pronounced for all-cause of pneumonia (minus 17·8%) than for bronchiolitis (minus 15·4%). There was also a decrease in the mean duration of hospital stay. There was little evidence that all-cause pneumonia decreased over the study period due mainly to the introduction of PCVs. Trends may be related to changes in clinical practice. This study casts doubt on the interpretation of ecological analyses of the implementation of PCV vaccination programmes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".