Spatiotemporal variation in risk of Shigella infection in childhood: a global risk mapping and prediction model using individual participant data

Abstract

Background: Diarrheal disease remains a leading cause of childhood illness and mortality and Shigella is a major etiological contributor for which a vaccine may soon be available. This study aimed to model the spatiotemporal variation in pediatric Shigella infection and map its predicted prevalence across low- and middle-income countries (LMICs). Methods: Independent participant data on Shigella positivity in stool samples collected from children aged [≥]59 months were sourced from multiple LMIC-based studies. Covariates included household- and subject-level factors ascertained by study investigators and environmental and hydrometeorological variables extracted from various data products at georeferenced child locations. Multivariate models were fitted, and prevalence predictions obtained by syndrome and age stratum. Findings: 20 studies from 23 countries contributed 66,563 sample results. Age, symptom status, and study design contributed most to model performance followed by temperature, wind speed, relative humidity, and soil moisture. Shigella probability exceeded 20% when both precipitation and soil moisture were above average and had a 43% peak in uncomplicated diarrhea cases at 33{degrees}C temperatures, above which it decreased. Improved sanitation and open defecation decreased Shigella odds by 19% and 18% respectively compared to unimproved sanitation. Interpretation: The distribution of Shigella is more sensitive to climatological factors like temperature than previously recognized. Conditions in much of sub-Saharan Africa are particularly propitious for Shigella transmission, though hotspots also occur in South and Central America, the Ganges-Brahmaputra Delta, and New Guinea. These findings can inform prioritization of populations for future vaccine trials and campaigns.