Comparative Performance Evaluation of the Public Health Surveillance Systems in 6 Gulf Cooperation Countries: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Evaluating public health surveillance systems is important to ensure that events of public health importance are appropriately monitored. Evaluation studies based on the Centers for Disease Control and Prevention (CDC) guidelines have been used to appraise surveillance systems globally. Previous evaluation studies undertaken in member countries of the Gulf Cooperation Council (GCC) were limited to specific illnesses within a single nation. OBJECTIVE: We aimed to evaluate public health surveillance systems in GCC countries using CDC guidelines and recommend necessary improvements to enhance these systems. METHODS: The CDC guidelines were used for evaluating the surveillance systems in GCC countries. A total of 6 representatives from GCC countries were asked to rate 43 indicators across the systems' level of usefulness, simplicity, flexibility, acceptability, sensitivity, predictive value positive, representativeness, data quality, stability, and timeliness. Descriptive data analysis and univariate linear regression analysis were performed. RESULTS: All surveillance systems in the GCC covered communicable diseases, and approximately two-thirds (4/6, 67%, 95% CI 29.9%-90.3%) of them covered health care-associated infections. The mean global score was 147 (SD 13.27). The United Arab Emirates scored the highest in the global score with a rating of 167 (83.5%, 95% CI 77.7%-88.0%), and Oman obtained the highest scores for usefulness, simplicity, and flexibility. Strong correlations were observed between the global score and the level of usefulness, flexibility, acceptability, representativeness, and timeliness, and a negative correlation was observed between stability and timeliness score. Disease coverage was the most substantial predictor of the GCC surveillance global score. CONCLUSIONS: GCC surveillance systems are performing optimally and have shown beneficial outcomes. GCC countries must use the lessons learned from the success of the systems of the United Arab Emirates and Oman. To maintain GCC surveillance systems so that they are viable and adaptable to future potential health risks, measures including centralized information exchange, deployment of emerging technologies, and system architecture reform are necessary.
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 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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".