Integrating Case Detection of Visceral Leishmaniasis and Other Febrile Illness with Vector Control in the Post-Elimination Phase in Nepal
Bibliographic record
Abstract
Nepal has completed the attack phase of visceral leishmaniasis (VL) elimination and now needs active case detection (ACD) and vector control methods that are suitable to the consolidation and maintenance phases. We evaluated different ACD approaches and vector control methods in Saptari district. We assessed 1) mobile teams deployed in villages with VL cases in 2015 to conduct combined camps for fever and skin lesions to detect VL/PKDL (post-kala-azar dermal leishmaniasis) and other infections; 2) an incentive approach by trained female community health volunteers (FCHVs) in villages with no VL cases in 2015. Both were followed by house-to-house visits. For vector control, four villages were randomly allocated to insecticide impregnation of bednets, insecticide wall painting, indoor residual spraying (IRS), and control. Sandfly density (by CDC light traps, The John W. Hock Company, USA) and mortality (World Health Organization cone bioassay) were assessed in randomly selected households. One VL, three tuberculosis, one leprosy, and one malaria cases were identified among 395 febrile cases attending the camps. Post-camp house-to-house screening involving 7,211 households identified 679 chronic fever and 461 skin lesion cases but no additional VL/PKDL. No VL/PKDL case was found by FCHVs. The point prevalence of chronic fever in camp and FCHV villages was 242 and 2 per 10,000 populations, respectively. Indoor residual spraying and bednet impregnation were effective for 1 month versus 12 months with insecticidal wall paint. Twelve-month sandfly mortality was 23%, 26%, and 80%, respectively, on IRS, bednet impregnation, and insecticide wall painting. In Nepal, fever camps and insecticidal wall paint prove to be alternative, sustainable strategies in the VL post-elimination program.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".