Does mass drug administration for community-based scabies control works? The experience in Ethiopia
Bibliographic record
Abstract
INTRODUCTION: After a scabies outbreak in Amhara Region, Ethiopia in 2015/2016, the Regional Health Bureau performed an extensive Mass Drug Administration (MDA). In May 2017, we collected data to assess the impact of the treatment on the scabies control. METHODOLOGY: We retrieved baseline data from the 2015/16 burden assessment: campaign organization and administration information. We did a community based cross-sectional study using a structured questionnaire on disease and treatment history plus the presence or absence of active scabies in three Zones. We selected households using stratified random sampling deployed 7581 questionnaires and performed key informant interviews. RESULTS: 46.3% had a previous scabies diagnosis in the last 2 years of which 86.1% received treatment, and the cure rate was 90.6%. Fifteen months after intervention the scabies prevalence was 21.0 % (67.3% new cases and 32.7% recurrences). The highest burden of new cases (93.1%) was found in the North Gondar zone. The likelihood of treatment failure was higher for treatments offered in clinics (12.2%) as opposed to via the campaign (7.9%). Failure to follow the guidelines, shortage of medicine and lack of leadership prioritization were identified as reasons for resurgence of the disease. CONCLUSIONS: We demonstrated that community engagement is essential in the success of scabies MDA, alongside strong political commitment, and guideline adherence. Effectiveness and sustainability of the MDA was compromised by the failing of proper contact treatment, surveillance and case management.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".