Inter-cluster contamination: a semivariance analysis of community effect ranges of malaria vector control interventions in a four-armed malaria trial in Muleba, Tanzania
Bibliographic record
Abstract
The presence of a community effect in cluster randomized trials of malaria vector control interventions has led to the implementation of “buffer zones” around clusters to limit the potential for contamination between interventions. No consensus has been reached on how large these buffers need to be to encapsulate the effect. Nested within a phase-III cluster randomized malaria vector control trial in Northwest Tanzania, this study aims to determine the presence and spatial range of community effects from long-lasting insecticidal net (LLIN) and indoor residual spraying (IRS) interventions on household-level malaria infection in trial clusters four months post-intervention. Effective spatial range estimates of intervention community effects were compared to the 300m buffer distance implemented to limit intervention spillover between clusters in the trial. Geographically-weighted adjusted odds of malaria infection in children aged 0.5–14 years were determined four months post community-level intervention with a randomized allocation comprising one of two LLIN products (Olyset TM LN: 1000mg/m 2 permethrin or Olyset TM Plus LN: 400 + permethrin 800mg/m 2 ) with either IRS (Actellic ® 300CS: 1000mg/m 2 micro-encapsulated pirimiphos-methyl) or no IRS. Robust semivariances were calculated for each of 48 intervention clusters and fit to semivariogram models by Weighted Least Squares. 6440 children from 2785 households were included in the geographically-weighted logistic regression. Prevalence of Plasmodium falciparum infection was 45.9% in the study population. Twenty (20) clusters had significant residual effect ranges, 13 of which were fit to Sine Hole Effect models, indicating periodicity in the study area. Effective range estimates for the study area had a median value of 1210 m (IQR: 958–1691). Clusters with IRS had a higher median range value: 1535 m (IQR: 976–3398) than those without IRS: 1168m (IQR: 829–1504). Significant semivariogram model range estimates extended beyond the trial buffer sizes by a median average of 868 m in LLIN intervention clusters and 1235 m for IRS clusters. This presents a contamination, or spillover, potential for all trialed intervention types that may reduce the statistical power to detect difference between trial arms. Future studies should consider the ranges of intervention effects and contamination potential between trial arms when designing buffer areas.
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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 | 0.114 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".