Lessons Learnt from Cerebrospinal Meningitis Outbreak Surveillance Data-A Case for Public Health Action
Bibliographic record
Abstract
BACKGROUND: Outbreak of cerebrospinal meningitis (CSM) remains a major public health concern in Nigeria, particularly in northern Nigeria. The paper evaluates the effect of mass vaccination against cerebrospinal meningitis outbreak in 2013 on the incidence rate in 2014 and 2015, and to document lessons learnt from field experiences of the meningitis epidemic surveillance in Kebbi State, North-west, Nigeria. METHODS: The authors analysed cerebrospinal meningitis surveillance data generated from the routine integrated disease surveillance and response (IDSR) programme executed by the Ministry of Health with support from the World Health Organisation (WHO). Cerebrospinal fluid (CSF) samples via lumbar puncture procedures from a small proportion of all suspected cases that met the standard case definitions were collected, and then tested using the rapid agglutination test kits (Pastorex) at the state public health laboratory. The WHO supported the trained Local Government Areas Disease Surveillance and Notification Officers to ensure data quality. RESULTS: A total of 544 and 1,992 cases were analysed in 2014 and 2015 respectively. In 2014, 14% CSF samples were taken and 55.1% tested positive to Neisseria meningitidis type C. Of all the cases in 2014, 14% were reported dead. Further, in 2015, 4% CSF samples were tested and 83% were positive to Neisseria meningitidis type C. Of the total 1,992 cases in 2015, 4% were reported dead. Gender and CSF sample testing significantly predicts survival in 2014 (p<0.05). CONCLUSION: Desired political will and comprehensive epidemic prevention and control strategies are needed for effective control of seasonal outbreaks of CSM and other epidemic-prone diseases. Need for infrastructural and capacity development of hospital and state public health laboratories for adequate surveillance, testing of samples collected and effective case management cannot be over-emphasized.
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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 | 0.129 | 0.327 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".