The Impact of Household and Community Indoor Residual Spray Coverage with Fludora Fusion in a High Malaria Transmission Setting in Northern Zambia
Bibliographic record
Abstract
Zambia's National Malaria Elimination Program transitioned to Fludora Fusion in 2019 for annual indoor residual spraying (IRS) in Nchelenge District, an area with holoendemic malaria transmission. Previously, IRS was associated with reductions in parasite prevalence during the rainy season only, presumably because of insufficient residual insecticide longevity. This study assessed the impact of transitioning from Actellic 300CS to long-acting Fludora Fusion using active surveillance data from 2014 through 2021. A difference-in-differences analysis estimated changes in rainy season parasite prevalence associated with living in a sprayed house, comparing insecticides. The change in the 2020 to 2021 dry season parasite prevalence associated with living in a house sprayed with Fludora Fusion was also estimated. Indoor residual spraying with Fludora Fusion was not associated with decreased rainy season parasite prevalence compared with IRS with Actellic 300CS (ratio of prevalence ratios [PRs], 1.09; 95% CI, 0.89-1.33). Moreover, living in a house sprayed with either insecticide was not associated with decreased malaria risk (Actellic 300CS: PR, 0.97; 95% CI, 0.86-1.10; Fludora Fusion: rainy season PR, 1.06; 95% CI, 0.89-1.25; dry season PR, 1.21; 95% CI, 0.99-1.48). In contrast, each 10% increase in community IRS coverage was associated with a 4% to 5% reduction in parasite prevalence (rainy season: PR, 0.95; 95% CI, 0.92-0.97; dry season: PR, 0.96; 95% CI, 0.94-0.99), suggesting a community-level protective effect, and corroborating the importance of high-intervention coverage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".