Morbidity hotspot surveillance: A novel approach to detect lymphatic filariasis transmission in non-endemic areas of the Tillabéry region of Niger
Bibliographic record
Abstract
The Niger Lymphatic Filariasis (LF) Programme is making good progress towards the elimination goal and scaling up morbidity management and disability prevention (MMDP) activities. Clinical case mapping and the increased availability of services has prompted patients to come forward in both endemic and non-endemic districts. The latter included Filingué, Baleyara and Abala districts of the Tillabéry region, and in 2019, 315 patients were found during a follow-up active case finding activity, suggesting it may have low transmission. The aim of this study was to assess the endemicity status in areas reporting clinical cases, 'morbidity hotspots', in three non-endemic districts of the Tillabéry region. A cross-sectional survey was conducted in 12 villages in June 2021. Filarial antigen was detected using the rapid Filariasis Test Strip (FTS) diagnostic, and information obtained on gender, age, residency length, bed net ownership and usage, and presence of hydrocoele and/or lymphoedema. Data were summarised and mapped using QGIS software. A total of 4058 participants between 5 and 105 years old were surveyed, with 29 (0.7%) participants found to be FTS positive. Baleyara district had significantly higher FTS positive rates than the other districts. No significant differences were found by gender (male 0.8%; female 0.6%), age group (<26 years 0.7%; ≥26 years 0. 7%), and residency length (<5 years 0.7%; ≥5 years 0.7%). Three villages reported no infections; seven villages <1%, one village 1.1% and one village 4.1%, which was on the border of an endemic district. Bed net ownership (99.2%) and usage (92.6%) was very high and there was no significant difference between FTS infection rates. The results indicate that there are low levels of transmission in populations, including children, living in districts previously classified as non-endemic. This has implications for the Niger LF programme in terms of delivering targeted mass drug administration (MDA) in transmission hotspots, and MMDP services, including hydrocoele surgery to patients. The use of morbidity data may be a practical proxy to trigger mapping of ongoing transmission in low endemic areas. Continued efforts to study morbidity hotspots, post-validation transmission, cross-border and cross-district endemicity are needed to meet the WHO NTD 2030 roadmap targets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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".