Abstract
BACKGROUND: Many interventions against infectious diseases have geographically diffuse effects. This leads to contamination between arms in cluster-randomized trials (CRTs). Pathogen elimination is the goal of many intervention programs against infectious agents, but contamination means that standard CRT designs and analyses do not provide inferences about the potential of interventions to interrupt pathogen transmission at maximum scale-up. METHODS: A generic model of disease transmission was used to simulate infections in stepped wedge cluster-randomized trials (SWCRTs) of a transmission-reducing intervention, where the intervention has spatially diffuse effects. Simulations of such trials were then used to examine the potential of such designs for providing generalizable causal inferences about the impact of such interventions, including measurements of the contamination effects. The simulations were applied to the geography of Rusinga Island, Lake Victoria, Kenya, the site of the SolarMal trial on the use of odor-baited mosquito traps to eliminate Plasmodium falciparum malaria. These were used to compare variants in the proposed SWCRT designs for the SolarMal trial. RESULTS: Measures of contamination effects were found that could be assessed in the simulated trials. Inspired by analyses of trials of insecticide-treated nets against malaria when applied to the geography of the SolarMal trial, these measures were found to be robust to different variants of SWCRT design. Analyses of the likely extent of contamination effects supported the choice of cluster size for the trial. CONCLUSIONS: The SWCRT is an appropriate design for trials that assess the feasibility of local elimination of a pathogen. The effects of incomplete coverage can be estimated by analyzing the extent of contamination between arms in such trials, and the estimates also support inferences about causality. The SolarMal example illustrates how generic transmission models incorporating spatial smoothing can be used to simulate such trials for a power calculation and optimization of cluster size and randomization strategies. The approach is applicable to a range of infectious diseases transmitted via environmental reservoirs or via arthropod vectors.