Prevalence of Trachoma After Three Rounds of Antibiotic Mass Drug Administration in 13 Woredas of Gambella Region, Ethiopia
Bibliographic record
Abstract
BACKGROUND: Following baseline surveys in 2013 and 2014, trachoma elimination interventions, including three rounds of azithromycin mass drug administration (MDA), were implemented in 13 woredas (administrative districts) of Gambella Regional State, Ethiopia. We conducted impact surveys to determine if elimination thresholds have been met or if additional interventions are required. METHODS: Cross-sectional population-based surveys were conducted in 13 woredas of Gambella Regional State, combined into five evaluation units (EUs), 6─12 months after their last MDA round. A two-stage systematic (first stage) and random (second stage) sampling technique was used. WHO-recommended protocols were implemented with the support of Tropical Data. Household water, sanitation and hygiene (WASH) access was assessed. RESULTS: The age-adjusted prevalence of trachomatous inflammation - follicular (TF) in 1-9-year-olds in the five EUs ranged from 0.3-19.2%, representing a general decline in TF prevalence compared to baseline estimates. The age- and gender-adjusted prevalence of trachomatous trichiasis (TT) unknown to the health system in those aged ≥ 15 years ranged from 0.47-3.08%. Of households surveyed, 44% had access to an improved drinking water source within a 30-minute return journey of the house, but only 3% had access to an improved latrine. CONCLUSION: In two EUs, no further MDA should be delivered, and a surveillance survey should be conducted after two years without MDA. In one EU, one further round of MDA should be conducted followed by another impact survey. In two EUs, three further MDA rounds are required. Surgery, facial cleanliness and environmental improvement interventions are needed throughout the region.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".