Antimicrobial resistance in dairy slurry tanks: A critical point for measurement and control

From Herd Health to Public Health: Digital Tools for Combating Antibiotic Resistance in Dairy Farms

The emergence of antimicrobial resistance (AMR) is a significant threat to global food security, human health, and the future of livestock production. Higher rates of antimicrobial use in dairy farming and the sheer lack of new antimicrobials available for use focused attention on the question of how the dairy production sector contributed to the development of AMR and paved the path toward taking action to curtail it on the targeted type of farms. This paper aims to provide an introduction to a phenomenon that has gained considerable attention in the recent past due to its ever-increasing impact, the use of antimicrobial drugs, the emergence of antimicrobial resistance (AMR) on dairy farms, and seeks to discuss the possibilities of approaches such as digital health monitoring and precision livestock farming. Using sensors, data, knowledge, automation, etc., digital health monitoring, as well as Precision Livestock Farming (PLF), is expected to enhance health control and minimize disease and antimicrobial usage. The work presents a literature review on the current status and trends of AMR in dairy farms, an understanding of the concept of digital health monitoring and PLF, and the presentation and usefulness of digital health monitoring and PLF in preventing AMR. The study also analyses the strengths and weaknesses of adopting and incorporating digital technologies and artificial intelligence for dairy farming and presents areas for further study and level of use.

Reduction in antimicrobial resistance in a watershed after closure of livestock farms

Natural environments play a crucial role in transmission of antimicrobial resistance (AMR). Development of methods to manage antibiotic resistance genes (ARGs) in natural environments are usually limited to the laboratory or field scale, partially due to the complex dynamics of transmission between different environmental compartments. Here, we conducted a nine-year longitudinal profiling of ARGs at a watershed scale, and provide evidence that restrictions on livestock farms near water bodies significantly reduced riverine ARG abundance. Substantial reductions were revealed in the relative abundance of genes conferring resistance to aminoglycosides (42%), MLSB (36%), multidrug (55%), tetracyclines (53%), and other gene categories (59%). Additionally, improvements in water quality were observed, with distinct changes in concentrations of dissolved reactive phosphorus, ammonium, nitrite, pH, and dissolved oxygen. Antibiotic residues and other pharmaceuticals and personal care products (PPCPs) maintain at a similarly low level. Microbial source tracking demonstrates a significant decrease in swine fecal indicators, while human fecal pollution remains unchanged. These results suggest that the reduction in ARGs was due to a substantial reduction in input of antibiotic resistant bacteria and genes from animal excreta. Our findings highlight the watershed as a living laboratory for understanding the dynamics of AMR, and for evaluating the efficacy of environmental regulations, with implications for reducing environmental risks associated with AMR on a global scale.

Multidrug-resistance and extended-spectrum beta-lactamase-producing lactose-fermenting enterobacteriaceae in the human-dairy interface in northwest Ethiopia

BACKGROUND

Antimicrobial resistance (AMR) is among the top public health concerns in the globe. Estimating the prevalence of multidrug resistance (MDR), MDR index (MDR-I) and extended-spectrum beta-lactamase (ESBL)-producing lactose fermenting Enterobacteriaceae (LFE) is important in designing strategies to combat AMR. Thus, this study was designed to determine the status of MDR, MDR-I and ESBL-producing LFE isolated from the human-dairy interface in the northwestern part of Ethiopia, where such information is lacking.

METHODOLOGY

A cross-sectional study was conducted from June 2022 to August 2023 by analyzing 362 samples consisting of raw pooled milk (58), milk container swabs (58), milker's hand swabs (58), farm sewage (57), milker's stool (47), and cow's feces (84). The samples were analyzed using standard bacteriological methods. The antimicrobial susceptibility patterns and ESBL production ability of the LFE isolates were screened using the Kirby-Bauer disk diffusion method, and candidate isolates passing the screening criteria were phenotypically confirmed by using cefotaxime (30 μg) and cefotaxime /clavulanic acid (30 μg/10 μg) combined-disk diffusion test. The isolates were further characterized genotypically using multiplex polymerase chain reaction targeting the three ESBL-encoding- genes namely blaTEM, blaSHV, and blaCTX-M.

RESULTS

A total of 375 bacterial isolates were identified and the proportion of MDR and ESBL-producing bacterial isolates were 70.7 and 21.3%, respectively. The MDR-I varied from 0.0 to 0.81 with an average of 0.30. The ESBL production was detected in all sample types. Genotypically, the majority of the isolates (97.5%), which were positive on the phenotypic test, were carrying one or more of the three genes.

CONCLUSION

A high proportion of the bacterial isolates were MDR; had high MDR-I and were positive for ESBL production. The findings provide evidence that the human-dairy interface is one of the important reservoirs of AMR traits. Therefore, the implementation of AMR mitigation strategies is highly needed in the area.

Modelling the impact of wastewater flows and management practices on antimicrobial resistance in dairy farms

Dairy slurry is a major source of environmental contamination with antimicrobial resistant genes and bacteria. We developed mathematical models and conducted on-farm research to explore the impact of wastewater flows and management practices on antimicrobial resistance (AMR) in slurry. Temporal fluctuations in cephalosporin-resistant Escherichia coli were observed and attributed to farm activities, specifically the disposal of spent copper and zinc footbath into the slurry system. Our model revealed that resistance should be more frequently observed with relevant determinants encoded chromosomally rather than on plasmids, which was supported by reanalysis of sequenced genomes from the farm. Additionally, lower resistance levels were predicted in conditions with lower growth and higher death rates. The use of muck heap effluent for washing dirty channels did not explain the fluctuations in cephalosporin resistance. These results highlight farm-specific opportunities to reduce AMR pollution, beyond antibiotic use reduction, including careful disposal or recycling of waste antimicrobial metals.

The need for One Health systems-thinking approaches to understand multiscale dissemination of antimicrobial resistance

Although the effects of antimicrobial resistance (AMR) are most obvious at clinical treatment failure, AMR evolution, transmission, and dispersal happen largely in environmental settings, for example within farms, waterways, livestock, and wildlife. We argue that systems-thinking, One Health approaches are crucial for tackling AMR, by understanding and predicting how anthropogenic activities interact within environmental subsystems, to drive AMR emergence and transmission. Innovative computational methods integrating big data streams (eg, from clinical, agricultural, and environmental monitoring) will accelerate our understanding of AMR, supporting decision making. There are challenges to accessing, integrating, synthesising, and interpreting such complex, multidimensional, heterogeneous datasets, including the lack of specific metrics to quantify anthropogenic AMR. Moreover, data confidentiality, geopolitical and cultural variation, surveillance gaps, and science funding cause biases, uncertainty, and gaps in AMR data and metadata. Combining systems-thinking with modelling will allow exploration, scaling-up, and extrapolation of existing data. This combination will provide vital understanding of the dynamic movement and transmission of AMR within and among environmental subsystems, and its effects across the greater system. Consequently, strategies for slowing down AMR dissemination can be modelled and compared for efficacy and cost-effectiveness.

Impact of contrasting poultry exposures on human, poultry, and wastewater antibiotic resistomes in Bangladesh

IMPORTANCE

Through the use of DNA sequencing, our study shows that there is no significant difference in the antibiotic resistance genes found in stool samples taken from individuals with high exposure to poultry routinely fed antibiotics and those without such exposure. This finding is significant as it suggests limited transmission of antibiotic resistance genes between poultry and humans in these circumstances. However, our research also demonstrates that commercially reared poultry are more likely to possess resistance genes to antibiotics commonly administered on medium-sized farms. Additionally, our study highlights the under-explored potential of wastewater as a source of various antibiotic resistance genes, some of which are clinically relevant.