Performance of Multidrug-Resistant Tuberculosis Surveillance in Yemen: Interview Study
Bibliographic record
Abstract
BACKGROUND: Multidrug-resistant tuberculosis (MDR-TB) is a major challenge to ending TB occurrence by 2035. In Yemen, the 2011 survey showed an MDR-TB prevalence of 1.4% among new cases and 14.4% among previously treated cases. The National Tuberculosis Control Program (NTCP) established four MDR-TB sentinel surveillance sites in 2013 to monitor the MDR-TB situation. In Yemen, the 2011 survey showed an MDR-TB prevalence of 1.4% among new cases and 14.4% among previously treated cases. The NTCP established four MDR-TB sentinel surveillance sites in 2013 to monitor the MDR-TB situation. OBJECTIVE: This study aimed to assess the performance of MDR-TB surveillance and determine its strengths and weaknesses. METHODS: We used the updated Center for Diseases Control and Prevention guidelines for evaluating public health surveillance systems. Interviews were conducted with NTCP managers and Regional MDR-TB centers' staff using a semistructured questionnaire. We used a 5-point Likert scale to assess the usefulness and other attributes (eg, simplicity and flexibility). The mean percentage was calculated for each attribute and used for the final rank of the performance: poor (<60%), average (60%-80%), and good (>80%). RESULTS: The MDR-TB surveillance system achieved good performance in usefulness (87%), acceptability (82%), and data quality (91%); average performance in flexibility (61%) and simplicity (72%); and poor performance in stability (55%). The overall performance score was average (74%). Although strong commitment, good monitoring, and well-trained staff are the main strengths, depending on an external fund is a major weakness along with unavailability of the MDR-TB unit at the governorate level. CONCLUSIONS: Although the MDR-TB surveillance system has achieved an average overall performance, more efforts are required to improve its stability by ensuring constant power supply to enable laboratories to perform necessary diagnostic and follow-up tests. Gradual replacement of donors' funds by the government is recommended. Scaling up of MDR-TB services and removing access barriers are crucial.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".