Evaluation of the Meningitis Surveillance System in Meknes, Morocco
Bibliographic record
Abstract
Background: Meknes is a big city of Morocco with 860.972 population in 2016. It includes 47 health centers, 5 hospitals and 2 laboratories which one is implicate in meningitis surveillance. The Meningitis Surveillance System (MSS) was implemented in Meknes in 1995. Objective: The objective is to evaluate the MSS and to identify its strengths and its potential gaps for its improvement. Methods: The evaluation was based on the CDC guidelines, 2001 for evaluating surveillance systems. A questionnaire was structured and tested to evaluate simplicity and acceptability. Twenty health professionals at the prefectural epidemiology unit, the hospitals and the laboratory were interviewed. MSS Data across 2012-2016 were analyzed to evaluate the representativeness, reactivity and quality of the data. Results: 207 cases were reported with an incidence ranged from 4 per 100.000 population in 2012 to 5 per 100.000 in 2016. 172 (183%) of the cases reside in urban areas and 183 (88%) were from the public sector. The Completeness of selected variables was 89% (184/207). The system was simple regarding structure and all interviewed staff considered the system acceptable. Of 32 confirmed meningococcal meningitis cases, 15 had a serotype confirmation result. The epidemiological investigation of the case after declaration was carried out on average on the day of the declaration. Conclusions: The evaluation of the MSS has demonstrated its strengths, namely good data quality, acceptability and responsiveness. However, these weaknesses reside in the low proportion of the serotype of meningitis agents. These results suggest the need to consolidate achievements and reinforce the importance of routine meningococcal serotype according to clinical and laboratory best practices.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".