The Rotavirus Surveillance System in Yemen: Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Rotavirus (RV) kills over 185,000 children <5 years every year and is responsible for over one-third of all child diarrheal deaths worldwide. The Rotavirus Surveillance System (RVSS) in Yemen was launched in 2007 at five sentinel sites to monitor the impact of the vaccine on RV morbidity and mortality. OBJECTIVE: This study aimed to determine the usefulness of the RVSS, assess its performance, and identify the strengths and weaknesses of its implementation. METHODS: The Centers for Disease Control and Prevention's updated guidelines on evaluating a public health surveillance system were used to evaluate the RVSS. In this assessment, qualitative indicators, such as usefulness, flexibility, stability, simplicity, and acceptability, were assessed through in-depth interviews with stakeholders at the central level and semistructured questionnaires with the sentinel site coordinators. The indicators for quantitative attributes-sensitivity, positive predictive value (PPV), completeness, and timeliness-were assessed by reviewing the results of laboratory samples and a random sample of case report forms. The scores for the indicators were expressed as poor (<60%), average (60% to <80%), and good (≥80%). RESULTS: The overall usefulness score of the RVSS was 73%, indicating an average rank. The RVSS was rated as having good flexibility (91%) and stability (81%), and average simplicity (77%) and acceptability (76%). In terms of quantitative attributes, the system was poor for sensitivity (16%), average for PPV (73%), and good for completeness (100%) and timeliness (100%). CONCLUSIONS: Although the system attributes were flexible, stable, capable of providing quality data, and performing timely data reporting, some attributes still needed improvements (eg, usefulness, simplicity, acceptability, and PPV). There is a need for a gradual replacement of donor funds with government funds to ensure sustainability. The RVSS in Yemen strongly requires a progressive increase in the number of sites in governorates and sensitivity enhancement.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".