Evaluation of a Malaria Surveillance System in Hodeidah City, Yemen, 2021
Bibliographic record
Abstract
Background Despite continuing control and elimination efforts, malaria continues to represent a major public health problem. Evaluation of the Malaria Surveillance System (MSS) is critical to obtain credible data that can be used for providing information. Hodeidah City, Yemen, is a worthy region to conduct an evaluation of the MSS because it has the greatest malaria burden. Objective The aim of this study was to determine the usefulness of the MSS and assess its performance in terms of qualitative and quantitative attributes. Methods The updated Centers for Disease Control and Prevention guideline was used to evaluate the MSS in Hodeidah City. After desk reviews and in-depth interviews were conducted, self-administered questionnaires with 5-point Likert scale and yes/no questions were used to collect data from stakeholders at four levels. The indicator’s score percent was interpreted according to the following criteria: excellent, ≥90%; good, 80% to <90%; average, 60% to <80%; poor, 40% to <60%; and very poor, <40%. EPI info version 7.2 was used to enter and analyze the data. Results Thirty-one stakeholders participated; 55% of the respondents were men. The system was found to be useful (88%) to portray the trend of malaria and to guide policy and intervention, with excellent scores (100%) for timeliness and completeness. The overall simplicity, representativeness, acceptability, and stability scores were 78%, 66%, 62%, and 61%, respectively, representing an average rank. However, flexibility scored 40% and sensitivity only scored 5.5%. The overall performance scores for the MSS were average (68%), good (82%), and average (73%) in central, governorate, and district and health facilities, respectively. Conclusions Although the MSS was found to be useful and stable, and the data quality and timeliness were deemed excellent, flexibility and sensitivity were considered to be poor. To ensure sustainability of the MSS, there is a need for gradual replacement of donors’ funds with governmental funds. Furthermore, enhancing laboratory diagnosis and proper training of health workers should be adopted for improving flexibility and sensitivity.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".