Performance of the Neonatal Tetanus Surveillance System (NTSS) in Sana'a, Yemen: Evaluation Study
Bibliographic record
Abstract
BACKGROUND: The Neonatal Tetanus Surveillance System (NTSS) in Yemen was established in 2009 to identify high-risk areas, determine trends, and evaluate elimination activities. Since its launch, the NTSS had never been evaluated. OBJECTIVE: This study aimed to assess the performance of NTSS and determine its strengths and weaknesses to recommend improvements. METHODS: The US Centers for Disease Control and Prevention (CDC) guidelines were used for evaluating the NTSS. Stakeholders at the central, district, and facility levels were interviewed to rate the attributes of the NTSS. The percentage scores for attributes were ranked as poor (<60%), average (≥60% to <80%) and good (≥80%). RESULTS: The overall usefulness score percentage was 38%, which indicates a poor performance. The performance of the NTSS was rated as average on flexibility (score percent: 68%) and acceptability (score percent: 64%) attributes and poor on stability (score percentage: 33%), simplicity (score percentage: 57%), and representativeness (score percentage: 39%) attributes. About 65% of investigation forms were completed within 48 hours of notification date. Data quality was poor, as 41% of the core variables were missing. CONCLUSIONS: The overall performance of the NTSS was poor. Most of the system attributes require improvement, including stability, simplicity, quality of data, and completeness of investigation. To improve the performance of NTSS, the following are recommended: capacity building of staff (focal points), strengthening NTSS through technical support and government funding to ensure its sustainability, establishing electronic investigation forms for improving the system data quality, and expansion of NTSS coverage to include all private health care facilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".