Improved Assessment of Schistosoma Community Infection Through Data Resampling Method.

ABSTRACT

Background: The conventional diagnostic for Schistosoma mansoni infection is stool microscopy with the Kato-Katz (KK) technique to detect eggs. Its outcomes are highly variable on a 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 method that leverages data from a large-scale randomized trial to accurately predict community infection. Methods: We developed a resampling method that provides unbiased community estimates of prevalence, intensity and other statistics for S mansoni infection when a community survey is conducted using KK stool microscopy with a single sample per host. It leverages a large-scale data set, collected in the Schistosomiasis Consortium for Operational Research and Evaluation (SCORE) project, and allows linking single-stool specimen community screening to its putative multiday "true statistics." Results: SCORE data analysis reveals the limited sensitivity of KK stool microscopy and systematic bias of single-day community testing versus multiday testing; for prevalence estimate, it can fall up to 50% below the true value. The proposed SCORE cluster method reduces systematic bias and brings the estimated prevalence values within 5%-10% of the true value. This holds for a broad swath of transmission settings, including SCORE communities, and other data sets. Conclusions: Our SCORE cluster method can markedly improve the S mansoni prevalence estimate in settings using stool microscopy.