Predictors of Enteric Pathogens in the Domestic Environment from Human and Animal Sources in Rural Bangladesh.
Erica R FuhrmeisterAyse ErcumenAmy J PickeringKaitlyn M JeanisMahaa AhmedSara BrownBenjamin F ArnoldAlan E HubbardMahfuja AlamDebashis SenSharmin IslamMir Himayet KabirLaura H KwongMahfuza IslamLeanne UnicombMahbubur RahmanAlexandria B BoehmStephen P LubyJohn M ColfordKara L NelsonPublished in: Environmental science & technology (2019)
Fecal indicator organisms are measured to indicate the presence of fecal pollution, yet the association between indicators and pathogens varies by context. The goal of this study was to empirically evaluate the relationships between indicator Escherichia coli, microbial source tracking markers, select enteric pathogen genes, and potential sources of enteric pathogens in 600 rural Bangladeshi households. We measured indicators and pathogen genes in stored drinking water, soil, and on mother and child hands. Additionally, survey and observational data on sanitation and domestic hygiene practices were collected. Log10 concentrations of indicator E. coli were positively associated with the prevalence of pathogenic E. coli genes in all sample types. Given the current need to rely on indicators to assess fecal contamination in the field, it is significant that in this study context indicator E. coli concentrations, measured by IDEXX Colilert-18, provided quantitative information on the presence of pathogenic E. coli in different sample types. There were no significant associations between the human fecal marker (HumM2) and human-specific pathogens in any environmental sample type. There was an increase in the prevalence of Giardia lamblia genes, any E. coli virulence gene, and the specific E. coli virulence genes stx1/2 with every log10 increase in the concentration of the animal fecal marker (BacCow) on mothers' hands. Thus, domestic animals were important contributors to enteric pathogens in these households.
Keyphrases
- escherichia coli
- drinking water
- genome wide
- gram negative
- antimicrobial resistance
- endothelial cells
- genome wide identification
- health risk assessment
- bioinformatics analysis
- health risk
- biofilm formation
- genome wide analysis
- south africa
- risk assessment
- pluripotent stem cells
- induced pluripotent stem cells
- risk factors
- human health
- staphylococcus aureus
- healthcare
- microbial community
- heavy metals
- big data
- candida albicans
- health information
- pseudomonas aeruginosa
- machine learning
- gene expression
- air pollution
- artificial intelligence