Impact of Rotavirus Vaccination on Hospitalizations and Deaths From Childhood Gastroenteritis in Botswana
Bibliographic record
Abstract
BACKGROUND: A monovalent human rotavirus vaccine (RV1) was introduced in Botswana in July 2012. We assessed the impact of RV1 vaccination on childhood gastroenteritis-related hospitalizations and deaths in 2013 and 2014. METHODS: We obtained data from registers of 4 hospitals in Botswana on hospitalizations and deaths from gastroenteritis, regardless of cause, among children <5 years of age. Gastroenteritis hospitalizations and deaths during the prevaccine period (January 2009-December 2012) were compared to the postvaccine period (January 2013-December 2014). Vaccine coverage was estimated from data collected through a concurrent vaccine effectiveness study at the same hospitals. RESULTS: By December 2014, coverage with ≥1 dose of RV1 was an estimated 90% among infants <1 year of age and 76% among children 12-23 months of age. In the prevaccine period, the annual median number of gastroenteritis-related hospitalizations in children <5 years of age was 1212, and of gastroenteritis-related deaths in children <2 years of age was 77. In the postvaccine period, gastroenteritis-related hospitalizations decreased by 23% (95% confidence interval [CI], 16%-29%) to 937, and gastroenteritis-related deaths decreased by 22% (95% CI, -9% to 44%) to 60. Declines were most prominent during the rotavirus season (May-October) and among infants <1 year of age, with reductions of 43% (95% CI, 34%-51%) in gastroenteritis hospitalizations and 48% (95% CI, 11%-69%) in gastroenteritis deaths. CONCLUSIONS: Following introduction of RV1 into the national immunization program, significant declines in hospitalizations and deaths from gastroenteritis were observed among children in Botswana, suggestive of the beneficial public health impact of rotavirus vaccination.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".