Rotavirus Vaccine Effectiveness Stratified By National-Level Characteristics: An Introduction to the 24-Country MNSSTER-V Project, 2007–2023
Bibliographic record
Abstract
BACKGROUND: Rotavirus vaccines are moderately protective against illness in settings with high compared with low mortality rates. Vaccine effectiveness (VE) evaluations may clarify our understanding of these disparities, but estimates among key subpopulations and against rare outcomes are not available in many analyses due to sample size. We combined 25 data sets from test-negative design case-control evaluations in 24 countries that enrolled children with medically attended diarrhea, laboratory-confirmed rotavirus stool testing, and documented vaccination status. We calculated rotavirus VE stratified by country-level characteristics. METHODS: Children 3-59 months old with birthdates and surveillance hospital arrival dates were included; other variables were standardized as available. Children were considered vaccinated if they received ≥1 dose of rotavirus vaccine >14 days before arrival. We summarized child- and country-level characteristics, including national <5-year-old mortality rate (U5M). Following the manufacturer recommended dose schedule, complete- and partial-series adjusted VE were estimated using logistic regression models. RESULTS: We included 6626 rotavirus-positive children (case patients) and 19 459 rotavirus negative children (controls). Adjusted complete-series VE was significantly higher among children from countries in the low and medium U5M stratum (74% [95% confidence interval, 64%-81%]) compared with all groups within the high U5M stratum (range, 52% [42%-60%]) to 46% [31%-57%]). Partial-series estimates were lower than complete-series estimates. CONCLUSIONS: These findings are consistent with the published literature, though they suggest heterogeneity in vaccine performance within broad child mortality rate levels. Our findings also highlight the importance of complete-series 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 | 0.026 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".