Comparison of annual and regional variation in multidrug resistance using various classification metrics for generic Escherichia coli isolated from chicken abattoir surveillance samples in Canada

Studying Factors Affecting Success of Antimicrobial Resistance Interventions through the Lens of Experience: A Thematic Analysis

Antimicrobial resistance (AMR) affects the environment, and animal and human health. Institutions worldwide have applied various measures, some of which have reduced antimicrobial use and AMR. However, little is known about factors influencing the success of AMR interventions. To address this gap, we engaged health professionals, designers, and implementers of AMR interventions in an exploratory study to learn about their experience and factors that challenged or facilitated interventions and the context in which interventions were implemented. Based on participant input, our thematic analysis identified behaviour; institutional governance and management; and sharing and enhancing information as key factors influencing success. Important sub-themes included: correct behaviour reinforcement, financial resources, training, assessment, and awareness of AMR. Overall, interventions were located in high-income countries, the human sector, and were publicly funded and implemented. In these contexts, behaviour patterns strongly influenced success, yet are often underrated or overlooked when designing AMR interventions. Improving our understanding of what contributes to successful interventions would allow for better designs of policies that are tailored to specific contexts. Exploratory approaches can provide encouraging results in complex challenges, as made evident in our study. Remaining challenges include more engagement in this type of study by professionals and characterisation of themes that influence intervention outcomes by context.

Antimicrobial resistance and genomic characterisation of Escherichia coli isolated from caged and non-caged retail table eggs in Western Australia

Foodborne exposure to antimicrobial-resistant bacteria is a growing global health concern. Escherichia coli (E. coli) is well recognised as an indicator of food contamination with faecal materials. In the present study, we investigated the occurrence of E. coli in table eggs sold at retail supermarkets in Western Australia (WA). A total of 2172 visually clean and intact retail eggs were purchased between October 2017 and June 2018. A single carton containing a dozen eggs was considered as a single sample resulting a total of 181 samples. The shells and contents of each sample were separately pooled and tested using standard culture-based methods. Overall, generic E. coli was detected in 36 (19.8%; 95% confidence interval: 14.3; 26.4) of the 181 tested retail egg samples. We characterised 100 of the recovered E. coli isolates for their phenotypic antimicrobial resistance using minimum inhibitory concentration (MIC). A subset of E. coli isolates (n = 14) were selected on the basis of their MIC patterns, and were further characterised using whole genome sequencing (WGS). Fifty-seven (57%) of the recovered generic E. coli isolates (n = 100) were resistant to at least one of the 14 antimicrobials included in the MIC testing panel, of which 22 isolates (22%) showed multi-class resistance. The highest frequencies of non-susceptibility of E. coli isolated from WA retailed eggs were against tetracycline (49%) and ampicillin (36%). WGS revealed that tet(A) and bla_TEM-1B genes were present in most of the isolates exhibiting phenotypic resistance to tetracycline and ampicillin, respectively. The majority (98%) of the characterised E. coli isolates were susceptible to ciprofloxacin and azithromycin, and none were resistant to the cephalosporin antimicrobials included in the MIC panel. Two isolates demonstrated reduced susceptibility to ciprofloxacin, with MICs of 0.125 and 0.25 mg/L, and WGS revealed the presence of plasmid mediated qnrs1 gene in both isolates. This is the first report on detection of non-wild-type resistance to fluoroquinolones in supermarket eggs in Australia; one of the two isolates was from a cage-laid eggs sample while the other was from a barn-laid retail eggs sample. Fluoroquinolones have never been permitted for use in poultry farms in Australia. Thus, the detection of low-level ciprofloxacin-resistant E. coli in the absence of local antimicrobial selection pressure at the Australian layer farms warrants further research on the potential role of the environment or human-related factors in the transmission of antimicrobial resistance. The results of this study add to the local and global understanding of antimicrobial resistance spread in foods of animal origin.

White Paper: Bridging the gap between surveillance data and antimicrobial stewardship in the animal sector-practical guidance from the JPIAMR ARCH and COMBACTE-MAGNET EPI-Net networks

BACKGROUND

The JPIAMR ARCH and COMBACTE-MAGNET EPI-Net networks have joined efforts to formulate a set of target actions to link the surveillance of antimicrobial usage (AMU) and antimicrobial resistance (AMR) with antimicrobial stewardship (AMS) activities in four different settings. This White Paper focuses on the veterinary setting and embraces the One Health approach.

METHODS

A review of the literature was carried out addressing research questions in three areas: AMS leadership and accountability; AMU surveillance and AMS; and AMR surveillance and AMS. Consensus on target actions was reached through a RAND-modified Delphi process involving over 40 experts in infectious diseases, clinical microbiology, AMS, veterinary medicine and public health, from 18 countries.

RESULTS/DISCUSSION

Forty-six target actions were developed and qualified as essential or desirable. Essential actions included the setup of AMS teams in all veterinary settings, building government-supported AMS programmes and following specific requirements on the production, collection and communication of AMU and AMR data. Activities of AMS teams should be tailored to the local situation and capacities, and be linked to local or national surveillance systems and infection control programmes. Several research priorities were also identified, such as the need to develop more clinical breakpoints in veterinary medicine.

CONCLUSIONS

This White Paper offers a practical tool to veterinary practitioners and policy makers to improve AMS in the One Health approach, thanks to surveillance data generated in the veterinary setting. This work may also be useful to medical doctors wishing to better understand the specificities of the veterinary setting and facilitate cross-sectoral collaborations.

Generalizability and comparability of prevalence estimates in the wild bird literature: methodological and epidemiological considerations

Wild birds have been the focus of a great deal of research investigating the epidemiology of zoonotic bacteria and antimicrobial resistance in the environment. While enteric pathogens (e.g. Campylobacter, Salmonella, and E. coli O157:H7) and antimicrobial resistant bacteria of public health importance have been isolated from a wide variety of wild bird species, there is a considerable variation in the measured prevalence of a given microorganism from different studies. This variation may often reflect differences in certain ecological and biological factors such as feeding habits and immune status. Variation in prevalence estimates may also reflect differences in sample collection and processing methods, along with a host of epidemiological inputs related to overall study design. Because the generalizability and comparability of prevalence estimates in the wild bird literature are constrained by their methodological and epidemiological underpinnings, understanding them is crucial to the accurate interpretation of prevalence estimates. The main purpose of this review is to examine methodological and epidemiological inputs to prevalence estimates in the wild bird literature that have a major bearing on their generalizability and comparability. The inputs examined here include sample type, microbiological methods, study design, bias, sample size, definitions of prevalence outcomes and parameters, and control of clustering. The issues raised in this review suggest, among other things, that future prevalence studies of wild birds should avoid opportunistic sampling when possible, as this places significant limitations on the generalizability of prevalence data.

Research Note: Longitudinal monitoring of chicken houses in a commercial layer farm for antimicrobial resistance in Escherichia coli with special reference to plasmid-mediated quinolone resistance

Plasmid-mediated quinolone resistance (PMQR) genes located on conjugative plasmids can be transferred to other bacteria in the absence of antimicrobial selective pressure. To elucidate the prevalence of resistance, including PMQR in an egg-producing commercial layer farm in western Japan where no antimicrobials were used, minimum inhibitory concentrations (MIC) for a total of 375 Escherichia coli isolates obtained from chicken houses in the farm between 2012 and 2017 were determined using the agar dilution methods. Eighty-seven isolates resistant to oxytetracycline (OTC) accounted for 23.0% of the tested isolates, followed by isolates resistant to dihydrostreptomycin (DSM) (18.4%), sulfisoxazole (18.1%), ampicillin (AMP) (14.4%), trimethoprim (TMP) (14.4%), and nalidixic acid (10.1%). The prevalence rate of multidrug-resistant (MDR) isolates—which are resistant to 3 or more antimicrobial classes, including β-lactams, aminoglycosides, quinolones, folate pathway inhibitors, tetracyclines, and phenicols—was inversely related to the age of chickens at the time of bacterial examination. Probably, the prevalence of MDR isolates in layer chickens may have decreased with age owing to the absence of selective pressure. Furthermore, 45 isolates exhibiting enrofloxacin MICs of more than 0.25 μg/mL were examined for PMQR genes. The transfer of PMQR genes was tested by conjugation analysis. Southern blot analysis of genomic DNA revealed that the qnrS1 (5 isolates), qnrS2 (1 isolate), and qnrS13 genes (1 isolate) were located on plasmids with sizes ranging from approximately 60 to 120 kpb. In 1 of the 5 qnrS1-positive isolates and in an isolate with qnrS13, the qnrS genes were transferred to recipient strains. The plasmid harboring the qnrS1 gene was typed as IncF by PCR-based replicon typing. On this plasmid, the bla_TEM, aadA, tetA, and dfrA1 genes responsible for resistance to AMP, DSM, OTC, and TMP, respectively, were detected. The tetA gene was detected in the plasmid harboring the qnrS13 gene, which was typed as IncI1. These results suggest that despite the low prevalence of quinolone resistance in this farm, various PMQR genes, located on diverse plasmids, exist.

The effect of varying multidrug-resistence (MDR) definitions on rates of MDR gram-negative rods

Background

A multitude of definitions determining multidrug resistance (MDR) of Gram-negative organisms exist worldwide. The definitions differ depending on their purpose and on the issueing country or organization. The MDR definitions of the European Centre for Disease Prevention and Control (ECDC) were primarily chosen to harmonize epidemiological surveillance. The German Commission of Hospital Hygiene and Infection Prevention (KRINKO) issued a national guideline which is mainly used to guide infection prevention and control (IPC) measures. The Swiss University Hospital Zurich (UHZ) – in absentia of national guidelines – developed its own definition for IPC purposes. In this study we aimed to determine the effects of different definitions of multidrug-resistance on rates of Gram-negative multidrug-resistant organisms (GN-MDRO).

Methods

MDR definitions of the ECDC, the German KRINKO and the Swiss University Hospital Zurich were applied on a dataset comprising isolates of Escherichia coli, Klebsiella pneumoniae, Enterobacter sp., Pseudomonas aeruginosa, and Acinetobacter baumannii complex. Rates of GN-MDRO were compared and the percentage of patients with a GN-MDRO was calculated.

Results

In total 11′407 isolates from a 35 month period were included. For Enterobacterales and P. aeruginosa, highest MDR-rates resulted from applying the ‘ECDC-MDR’ definition. ‘ECDC-MDR’ rates were up to four times higher compared to ‘KRINKO-3/4MRGN’ rates, and up to six times higher compared to UHZ rates. Lowest rates were observed when applying the ‘KRINKO-4MRGN’ definitions. Comparing the ‘KRINKO-3/4MRGN’ with the UHZ definitions did not show uniform trends, but yielded higher rates for E. coli and lower rates for P. aeruginosa. On the patient level, the percentages of GN-MDRO carriers were 2.1, 5.5, 6.6, and 18.2% when applying the ‘KRINKO-4MRGN’, ‘UHZ-MDR’, ‘KRINKO-3/4MRGN’, and the ‘ECDC-MDR’ definition, respectively.

Conclusions

Different MDR-definitions lead to considerable variation in rates of GN-MDRO. Differences arise from the number of antibiotic categories required to be resistant, the categories and drugs considered relevant, and the antibiotic panel tested. MDR definitions should be chosen carefully depending on their purpose and local resistance rates, as definitions guiding isolation precautions have direct effects on costs and patient care.

Shared Multidrug Resistance Patterns in Chicken-Associated Escherichia coli Identified by Association Rule Mining

Using multiple antimicrobials in food animals may incubate genetically-linked multidrug-resistance (MDR) in enteric bacteria, which can contaminate meat at slaughter. The U.S. National Antimicrobial Resistance Monitoring System tested 14,418 chicken-associated Escherichia coli between 2004 and 2012 for resistance to 15 antimicrobials, resulting in >32,000 possible MDR patterns. We analyzed MDR patterns in this dataset with association rule mining, also called market-basket analysis. The association rules were pruned with four quality measures resulting in a <1% false-discovery rate. MDR rules were more stable across consecutive years than between slaughter and retail. Rules were decomposed into networks with antimicrobials as nodes and rules as edges. A strong subnetwork of beta-lactam resistance existed in each year and the beta-lactam resistances also had strong associations with sulfisoxazole, gentamicin, streptomycin and tetracycline resistances. The association rules concur with previously identified E. coli resistance patterns but provide significant flexibility for studying MDR in large datasets.