The impact of varicella vaccination on varicella-related hospitalization rates: global data review
Bibliographic record
Abstract
Abstract Objective: To describe the impact of varicella vaccination on varicella-related hospitalization rates in countries that implemented universal vaccination against the disease. Data source: We identified countries that implemented universal vaccination against varicella at the http://apps.who.int/immunization_monitoring/globalsummary/schedules site of the World Health Organization and selected articles in Pubmed describing the changes (pre/post-vaccination) in the varicella-related hospitalization rates in these countries, using the Keywords "varicella", "vaccination/vaccine" and "children" (or) "hospitalization". Publications in English published between January 1995 and May 2015 were included. Data synthesis: 24 countries with universal vaccination against varicella and 28 articles describing the impact of the vaccine on varicella-associated hospitalizations rates in seven countries were identified. The US had 81.4%–99.2% reduction in hospitalization rates in children younger than four years, 6–14 years after the onset of universal vaccination (1995), with vaccination coverage of 90%; Uruguay: 94% decrease (children aged 1–4 years) in six years, vaccination coverage of 90%; Canada: 93% decrease (age 1–4 years) in 10 years, coverage of 93%; Germany: 62.4% decrease (age 1–4 years) in 8 years, coverage of 78.2%; Australia: 76.8% decrease (age 1–4 years) in 5 years, coverage of 90%; Spain: 83.5% decrease (age <5 years) in four years, coverage of 77.2% and Italy 69.7%–73.8% decrease (general population), coverage of 60%–95%. Conclusions: The publications showed variations in the percentage of decrease in varicella-related hospitalization rates after universal vaccination in the assessed countries; the results probably depend on the time since the implementation of universal vaccination, differences in the studied age group, hospital admission criteria, vaccination coverage and strategy, which does not allow direct comparison between data.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".