Improved assessment of Schistosoma community infection through data resampling methodology

Abstract

Abstract Introduction The conventional diagnostic for Schistosoma mansoni infection is stool microscopy with Kato-Katz technique to detect eggs. Its outcomes are highly variable on day-to-day basis, and may lead to biased estimates of community infection used to inform public health programs. Our goal is to develop a resampling methodology that leverages data from a large-scale randomized trial to accurately predict community infection. Methods We developed a resampling methodology that provides unbiased community estimates of prevalence, intensity and other statistics for S. mansoni infection when a community survey is conducted using single Kato-Katz stool microscopy per host. It leverages a large-scale dataset, collected in the SCORE project, and allows linking single-stool community screening to its putative multi-day ‘true statistics’. Results SCORE data analysis reveals limited sensitivity of Kato-Katz stool microscopy, and systematic bias of single-day community testing vs. multi-day testing; for prevalence estimate, it can fall up to 50% below true value. The proposed SCORE-cluster methodology reduces systematic bias and brings estimated prevalence values within 5-10% of the true value. This holds for a broad swath of transmission settings, including SCORE communities, and other datasets. Discussion Our SCORE-cluster methodology can markedly improve the S. mansoni prevalence estimate in settings using stool microscopy.