Effectiveness of Monovalent Rotavirus Vaccine After Programmatic Implementation in Botswana: A Multisite Prospective Case-Control Study
Bibliographic record
Abstract
BACKGROUND: Botswana introduced monovalent G1P rotavirus vaccine (RV1) in July 2012, providing one of the first opportunities to assess the effectiveness of routine RV1 vaccination in a high-burden setting in Africa. We sought to determine the effectiveness of RV1 against rotavirus diarrhea hospitalization using a case-control evaluation. METHODS: Vaccine age-eligible children <5 years of age admitted with diarrhea at 4 hospitals in Botswana were enrolled from June 2013 to April 2015. Card-confirmed vaccine history was compared between case patients (children with laboratory-confirmed rotavirus diarrhea) and nonrotavirus "test-negative" diarrhea controls. Vaccine effectiveness (VE) was computed using unconditional logistic regression models adjusting for age, birth month/year, and hospital. Sequence-based genotyping was performed on antigen-positive samples. RESULTS: Among 242 case patients and 368 controls, 82% (199/242) and 92% (339/368), respectively, had received ≥1 doses of RV1. Effectiveness of a full series (2 doses) of RV1 against rotavirus diarrhea requiring hospitalization was 54% (95% confidence interval [CI], 23%-73%); 1 dose of RV1 was 48% (95% CI, 1%-72%) effective. Effectiveness was 59% (95% CI, 4%-83%) against rotavirus caused by G2P, the most common (37%) circulating genotype. However, the effectiveness of 2 RV1 doses was significantly higher in children with no undernutrition (VE, 75% [95% CI, 41%-89%]), compared to those with moderate or severe undernutrition (VE, -28% [95% CI, -309% to 60%]) (P= .02). CONCLUSIONS: Routine RV1 vaccination in Botswana showed effectiveness similar to that in clinical trials in Africa, including against a serotype fully heterotypic to the vaccine. Undernutrition may in part explain the lower rotavirus VE in low-income settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".