Intervention Packages for Early Visceral Leishmaniasis Case Detection and Sandfly Control in Bangladesh: A Comparative Analysis
Bibliographic record
Abstract
We compared the efficacy of three intervention packages for active case detection (ACD) of visceral leishmaniasis (VL)/post–kala-azar dermal leishmaniasis (PKDL) combined with sandfly control around an index case. The packages were 1) no kala-azar transmission activity involving indoor residual spraying (IRS) with deltamethrin, peri-domestic deployment of larvicide with temephos, and house-to-house search for cases; 2) fever camp (FC) plus durable wall lining (DWL) with deltamethrin; and 3) FC plus insecticide (deltamethrin) impregnated bed-nets (ITN) around an index case. Fever camp includes 1-day campaign at the village level to screen and diagnose VL, PKDL, leprosy, malaria, and tuberculosis among residents with chronic fever or skin disease. Efficacy was measured through yield of new cases, vector density reduction, and mortality at 1, 3, 6, 9, and 12 months following intervention. Fever camp + DWL was the most efficacious intervention package with 0.5 case detected per intervention, 79% reduction in vector density (incidence rate ratio [IRR] = 0.21, P = 0.010), and 95.1% (95% confidence interval: 93.4%, 96.8%) sandfly mortality at 12 months. No kala-azar transmission activity was efficacious for vector control (74% vector reduction, IRR = 0.26, P < 0.0001 at 9 months; and 84% sandfly mortality at 3 months), but not for case detection (0 case per intervention). Fever camp + ITN was efficacious in detection of VL/PKDL cases (0.43 case per intervention), but its efficacy for vector control was inconsistent. We recommend index case–based FC for ACD combined with DWL or IRS plus larvicide for sandfly control during the consolidation and maintenance phases of the VL elimination program of the Indian subcontinent.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".