Non-typhoidal human salmonellosis in Rio Grande do Sul, Brazil: A combined source attribution study of microbial subtyping and outbreak data.

Salmonella spp. remains the most significant foodborne pathogen in south Brazil, but its epidemiology tends to change over time. Using official and surrogate data, a microbial subtyping model attributed different Salmonella serovars to laying hens, pigs, broilers, and turkeys from 2005 to 2015 in Rio Grande do Sul (RS). Additional to the subtyping model, three sub-analyses of outbreak data attributed Salmonella spp. in humans to animal and non-animal food. Laying hens/eggs was the most important source of human salmonellosis in RS, with almost 40% (159 cases; 95% credibility interval, 43-247) attribution proportion, followed by pigs reared in Santa Catarina, a neighbor state (34.5%). The Salmonella serovars Enteritidis and Typhimurium were the most common serovars involved. Source-related parameters had wide credibility intervals but showed a higher risk of illness from contaminated eggs than from the other three animal-food sources. Analysis of the outbreak data corroborated the findings and indicated signs of decreasing importance for eggs and increasing importance for pork consumption.

Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage.

Antimicrobial resistance (AMR) is a serious threat to global public health, but obtaining representative data on AMR for healthy human populations is difficult. Here, we use metagenomic analysis of untreated sewage to characterize the bacterial resistome from 79 sites in 60 countries. We find systematic differences in abundance and diversity of AMR genes between Europe/North-America/Oceania and Africa/Asia/South-America. Antimicrobial use data and bacterial taxonomy only explains a minor part of the AMR variation that we observe. We find no evidence for cross-selection between antimicrobial classes, or for effect of air travel between sites. However, AMR gene abundance strongly correlates with socio-economic, health and environmental factors, which we use to predict AMR gene abundances in all countries in the world. Our findings suggest that global AMR gene diversity and abundance vary by region, and that improving sanitation and health could potentially limit the global burden of AMR. We propose metagenomic analysis of sewage as an ethically acceptable and economically feasible approach for continuous global surveillance and prediction of AMR.