Using novel methodologies to support burden of disease estimates

Abstract

Abstract A challenge to estimating burden of diarrheal diseases, particularly in LMICs, where laboratory capacity and surveillance systems are limited, is obtaining valid estimates of etiology proportions of cases. A commonly used method is systematic review of studies reporting pathogen isolation in diarrhea cases. However, studies often differ in design, source population, timeframe, and pathogens included, hampering extrapolation to the target population. In a study co-funded by the Bill and Melinda Gates Foundation and the UK Department for International Development, we explore a novel approach for estimating diarrhea etiology proportions in urban and rural populations in four African countries. We analyse sewage samples using short-read next-generation sequencing (NGS) to determine abundance of genes that can be mapped to specific bacterial genera, providing an estimate of the relative abundance of specific pathogens in each sample. In parallel to collecting sewage samples, a questionnaire-based population survey will estimate diarrheal incidence. By combining results, pathogen-specific incidence will be estimated and compared with incidence estimates from the traditional approach. The application NGS to human sewage has great potential for surveillance of foodborne infections, particularly in resource-poor settings where laboratory capacity for bacterial isolation is limited. First, NGS is a one method takes all approach, as it is based on detection of RNA/DNA, a language common across pathogens. Second, it is culture independent, allowing for real-time data generation and standardized sharing. Finally, few samples are needed to survey large populations for several pathogens at the same time. Thus, surveillance based on NGS of sewage may prove to be an indirect measure of incidence. Although it will not provide an estimate for the true incidence in the population, it will increase our understanding of the burden and as such be a proxy and novel way of ranking diseases.