Descriptive Analysis of Malaria Surveillance System Data, Yemen, 2011-2015
Bibliographic record
Abstract
Background: Malaria remains one of the most serious health problems in Yemen where 68% of population is living in malaria risk areas. An Integrated Malaria Surveillance System (IMSS) was introduced in 2009 to improve reporting. Objective: To describe the epidemiology of malaria and identify groups at risk. Methods: Data for 2011-2015 was obtained from the National Malaria Control Program (NMCP). According to the NMCP Guidelines, confirmed malaria case is defined as a case that is positive by microscopy or rapid test. We calculated incidence rate (IR) by age group, sex, type of plasmodium, seasonality and population at risk using projections from the 2014 Central Statistical Organization data. Results: Although the overall malaria IR dropped from 11/1000 in 2011 to 5 in 2015, the IR among < 5 children increased from 8 to 15/1000 and the percentage of confirmed cases increased from 0.64% to 0.83%. Among pregnant women, the IR increased from 4/1000 in 2011 to 6 in 2014 but decreased to 2 in 2015. Two thirds of malaria cases were reported among males and from the coastal governorates. Plasmodium Falciparum accounted for 99% of cases. Conclusions: Despite IR dropped from 2011 to 2015, such drop might not reflect improvement in control and prevention measures, but could reflects underreporting due to political instability, war situation and poor access to health facilities. Proper targeting especially of coastal areas by insecticide treated bed nets and indoor residual spraying is necessary. Strengthening of surveillance system for high-risk groups i.e. <5 children and pregnant women is recommended. A qualitative research should investigate reasons behind the predominance of malaria among males. Further IMSS evaluation is recommended.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".