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Schistosoma mansoni IgG antibody response and Kato-Katz infection among pre-school aged children in Mbita, Western Kenya 2012-2014

Citation

Arnold, Benjamin et al. (2020), Schistosoma mansoni IgG antibody response and Kato-Katz infection among pre-school aged children in Mbita, Western Kenya 2012-2014, Dryad, Dataset, https://doi.org/10.7272/Q6DZ06J3

Abstract

We compared serological measures of transmission based on antibody response to S. mansoni soluble egg antigen (SEA) with stool-based measures of infection among 3,663 preschool-age children in an area endemic for S. mansoni in western Kenya. We estimated force of infection among children using the seroconversion rate examined how it varied geographically and by age.

Methods

The analysis included 3,663 children ages 2 months to 5.5 years who were originally enrolled from 30 communities that participated in a cluster randomized controlled trial to measure the effect of community-wide versus school-based mass distribution of praziquantel to reduce S. mansoni infection in Mbita, western Kenya. Villages were selected within 5 km of Lake Victoria from among those with ≥ 25% S. mansoni prevalence that had not received mass treatment with praziquantel. Preschool age children ≤5 years were assessed annually from 2012-2014 in repeated cross-sectional surveys that collected blood and stool specimens, and samples were tested for the presence of infection (in stool) and for antibodies to S. mansoni soluble egg antigen (SEA).

Usage Notes

This repository includes data and replication files to reproduce the analyses in the manuscript entitled Fine-scale heterogeneity in Schistosoma mansoni force of infection measured through antibody response

The materials in this repository are cross-linked with the Open Science Framework (https://osf.io/rnme8/) and with GitHub (https://github.com/ben-arnold/mbita-schisto).

The data subdirectory includes the datasets for the analysis. The R subdirectory includes all computational notebooks, organized by display item. To re-run the analyses, clone the GitHub directory (above), and create a new output subdirectory alongside data and R to store the output files.

Each dataset includes a codebook. We have not included lon/lat village coordinates under guidance from our IRB, so the notebook that creates Figure 1, Figure S2 and Figure S3 cannot be run on the publicly available data (though the source code and HTML output are in the repo)

If you have any questions about these files, please contact Ben Arnold at UCSF (ben.arnold@ucsf.edu).

Funding

National Institute of Allergy and Infectious Diseases, Award: K01 AI119180

Bill and Melinda Gates Foundation, Award: OPP1022543