Spatiotemporal variation and environmental sensitivity of childhood enteric pathogen infection risk: a Planetary Health approach to predictive modelling and risk mapping

Abstract

BackgroundDiarrhoeal disease is a major cause of childhood illness caused by many ecologically sensitive enteropathogens. The bacterium Shigella causes around 64 000 child deaths annually and thrives in warm, moist conditions. Spatiotemporally varying estimates of disease burden are crucial for targeting interventions like the forthcoming phase 2 trials of the Shigella vaccine. We aimed to model and map paediatric Shigella infection risk across low and middle-income countries (LMICs) using covariates with quasi-global coverage. MethodsNational Aeronautics and Space Administration (NASA), Johns Hopkins University, and University of Virginia have been collaborating to compile diagnostic data from multiple sources into a repository of unparalleled size, scope, and diversity. Data were combined from 20 studies that used PCR to diagnose Shigella in stool samples from children younger than 5 years in LMICs, resulting in a pooled database of more than 66 000 samples from 22 000 people in 23 countries. These were matched spatiotemporally to time-varying hydrometeorological variables extracted from historical daily Earth observation-based and model-based estimates derived from re-analysis, environmental and demographic spatial covariates, and household-level and subject-level data. Variable selection was carried out and a predictive projection method was implemented, making predictions separately for three age groups: 0–11 months, 12–23 months, and 24–59 months. FindingsAir temperature was the most influential variable, with infection risk peaking at temperatures of around 34°C (close to that of the human body). The interaction of precipitation with soil moisture also had a strong effect. Accessibility to cities was an influential non-hydrometeorological covariate. The model predicted wide belts of elevated Shigella risk in tropical sub-Saharan Africa, India, and Brazil, and smaller pockets of high prevalence in, for example, New Guinea, Ethiopia, the Sahel, coastal Central America, and Colombia, and others. InterpretationAdvances in differential pathogen diagnosis, climatological modelling, and geostatistical methods now make it possible to generate quasi-global maps of enteric pathogen transmission risk. These predictions will be made publicly available for use in decision making, such as identifying populations in hotspots of Shigella transmission risk that can be prioritised when vaccines become available. FundingNASA's Group on Earth Observations Work Programme (16-GEO16-0047).