Evaluation of the Leishmania Surveillance System, Yemen, 2021
Bibliographic record
Abstract
Background Control of preventive chemotherapy-targeted neglected tropical diseases (PC-NTDs) depends on strengthened health systems. Efficient health information systems provide a stimulus to reaching the sustainable development goal aimed at ending PC-NTD epidemics. However, there is limited assessment of surveillance system functions linked to PC-NTDs that are hinged on the optimal performance of surveillance system attributes. Objective The aim of this study was to assess the usefulness and performance of the National Leishmania Control Program (NLCP), and to estimate the strength and weakness points of the system. Methods We followed the updated six steps of Centers for Diseases Control and Prevention (CDC) guidelines for evaluating public health surveillance systems. Data were collected using in-depth interviews with relevant stakeholders at the central level and semistructured questionnaires at the peripheral level. We used questions (yes, no) to assess the usefulness and a 5-point Likert scale to measure the attributes. The final score was interpreted as poor (<60), average (60-80), and good (>80). Results The NLCP seemed to be useful (86%) and some of its objectives were met. The system has average performance in flexibility (78%), simplicity (64%), acceptability (80%), and data quality (65%). Poor performance was indicated for stability (33%) and timeliness (8%). The overall performance of the NLCP was deemed to be poor (55%). Continuation of the system was the strongest point, whereas the lack of governmental and agency funds was the weakest point. Conclusions The NLCP was found to be useful regarding the attributes assessed; simplicity, flexibility, acceptability, and data quality were deemed to be average, whereas stability and timeliness were considered to be poor. Governmental financial support to the program is highly recommended. In addition, creating a database for staff at the peripheral level and expanding the number of health facilities that serve as Leishmania units are required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".