Monitoring of Farm-Level Antimicrobial Use to Guide Stewardship: Overview of Existing Systems and Analysis of Key Components and Processes

The acknowledgment of antimicrobial resistance (AMR) as a major health challenge in humans, animals and plants, has led to increased efforts to reduce antimicrobial use (AMU). To better understand factors influencing AMR and implement and evaluate stewardship measures for reducing AMU, it is important to have sufficiently detailed information on the quantity of AMU, preferably at the level of the user (farmer, veterinarian) and/or prescriber or provider (veterinarian, feed mill). Recently, several countries have established or are developing systems for monitoring AMU in animals. The aim of this publication is to provide an overview of known systems for monitoring AMU at farm-level, with a descriptive analysis of their key components and processes. As of March 2020, 38 active farm-level AMU monitoring systems from 16 countries were identified. These systems differ in many ways, including which data are collected, the type of analyses conducted and their respective output. At the same time, they share key components (data collection, analysis, benchmarking, and reporting), resulting in similar challenges to be faced with similar decisions to be made. Suggestions are provided with respect to the different components and important aspects of various data types and methods are discussed. This overview should provide support for establishing or working with such a system and could lead to a better implementation of stewardship actions and a more uniform communication about and understanding of AMU data at farm-level. Harmonization of methods and processes could lead to an improved comparability of outcomes and less confusion when interpreting results across systems. However, it is important to note that the development of systems also depends on specific local needs, resources and aims

Associations between antimicrobial resistance in fecal Escherichia coli isolates and antimicrobial use in Canadian turkey flocks

Antimicrobial resistance (AMR) in enteric bacteria continues to be detected in turkey flocks and retail products worldwide, including in Canada. However, studies assessing linkages between on-farm antimicrobial use (AMU) and the development of AMR are lacking. This study aims to identify AMU characteristics that impact the development of AMR in the indicator bacteria Escherichia coli in turkey flocks, building on the Canadian Integrated Program for Antimicrobial Resistance Surveillance methodology for farm-level AMU and AMR data integration. Two analytic approaches were used: (1) multivariable mixed-effects logistic regression models examined associations between AMU (any route, route-specific, and route-disease-specific indication) summarized as the number of defined daily doses in animals using Canadian standards ([nDDDvetCA]/1,000 kg-animal-days at risk) and AMR and (2) multivariable mixed-effects Poisson regression models studied the linkages between AMU and the number of classes to which an E. coli isolate was resistant (nCRE. coli). A total of 1,317 E. coli isolates from a network of 16 veterinarians and 334 turkey producers across the five major turkey-producing provinces in Canada between 2016 and 2019 were used. Analysis indicated that AMR emerged with the use of related antimicrobials (e.g., tetracycline use-tetracycline resistance), however, the use of unrelated antimicrobial classes was also impacting AMR (e.g., aminoglycosides/streptogramins use-tetracycline resistance). As for studying AMU-nCRE. coli linkages, the most robust association was between the parenteral aminoglycosides use and nCRE. coli, though in-feed uses of four unrelated classes (bacitracin, folate pathway inhibitors, streptogramins, and tetracyclines) appear to be important, indicating that ongoing uses of these classes may slow down the succession from multidrug-resistant to a more susceptible E. coli populations. The analysis of AMU (route and disease-specific)-AMR linkages complemented the above findings, suggesting that treatment of certain diseases (enteric, late-stage septicemic conditions, and colibacillosis) are influential in the development of resistance to certain antimicrobial classes. The highest variances were at the flock level indicating that stewardship actions should focus on flock-level infection prevention practices. This study added new insights to our understanding of AMU-AMR linkages in turkeys and is useful in informing AMU stewardship in the turkey sector. Enhanced surveillance using sequencing technologies are warranted to explain molecular-level determinants of AMR

Antimicrobial Use and Antimicrobial Resistance Indicators—Integration of Farm-Level Surveillance Data From Broiler Chickens and Turkeys in British Columbia, Canada

Using data from the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS), we aimed to describe trends in antimicrobial use (AMU) in broiler chickens and turkeys, to compare AMU across species, to compare with trends in antimicrobial resistance (AMR), and to assess the effects of various AMU/AMR units of measurement (metrics and indicators) on data integration. Data on AMU and AMR in enteric bacteria, collected from 2013 to 2017 from broiler chickens (n = 143 flocks) and turkeys (n = 145) were used. In broiler chickens, the total AMU in milligrams/population correction unit (mg/PCUBr) decreased by 6%, the number (n) of defined daily doses for animals using Canadian standards (nDDDvetCA) per 1,000 broiler chicken-days decreased by 12%, and nDDDvetCA/PCU decreased by 6%. In turkeys, the mg/PCUTk decreased by 1%, whereas the nDDDvetCA/1,000 turkey-days and the nDDDvetCA/PCU increased by 1 and 5%, respectively. The types of antimicrobial classes used in both species were similar. Using the frequency of flocks reporting use (i.e., number of flocks reporting use/number of flocks participating) as a measurement, the use of certain antimicrobials changed over time (e.g., Broilers, decreased cephalosporin use, virginiamycin use, emerging use of lincomycin-spectinomycin, and avilamycin; Turkeys: increased trimethoprim-sulfonamides and macrolide use). The trends in resistance to specific antimicrobials paralleled the frequency and quantity of use (e.g., ceftriaxone use decreased—ceftriaxone resistance decreased, and gentamicin use increased—gentamicin resistance increased) in some situations, but not others (decreased fluoroquinolone use—increased ciprofloxacin resistance). AMR data were summarized using the AMR indicator index (AMR Ix). The most notable AMR Ix trend was the decrease in ceftriaxone AMR Ix among Escherichia coli (0.19 to 0.07); indicative of the success of the poultry industry action to eliminate the preventive use of third generation cephalosporins. Other trends observed were the increase in ciprofloxacin AMR Ix among Campylobacter from 0.23 to 0.41 and gentamicin AMR Ix among E. coli from 0.11 to 0.22, suggestive of the persistence/emergence of resistance related to previous and current AMU not captured in our surveillance timeframe. These data highlight the necessity of multiple AMU and AMR indicators for monitoring the impact of stewardship activities and interventions

Monitoring of Farm-Level Antimicrobial Use to Guide Stewardship: Overview of Existing Systems and Analysis of Key Components and Processes

peer-reviewedThe acknowledgment of antimicrobial resistance (AMR) as a major health challenge in humans, animals and plants, has led to increased efforts to reduce antimicrobial use (AMU). To better understand factors influencing AMR and implement and evaluate stewardship measures for reducing AMU, it is important to have sufficiently detailed information on the quantity of AMU, preferably at the level of the user (farmer, veterinarian) and/or prescriber or provider (veterinarian, feed mill). Recently, several countries have established or are developing systems for monitoring AMU in animals. The aim of this publication is to provide an overview of known systems for monitoring AMU at farm-level, with a descriptive analysis of their key components and processes. As of March 2020, 38 active farm-level AMU monitoring systems from 16 countries were identified. These systems differ in many ways, including which data are collected, the type of analyses conducted and their respective output. At the same time, they share key components (data collection, analysis, benchmarking, and reporting), resulting in similar challenges to be faced with similar decisions to be made. Suggestions are provided with respect to the different components and important aspects of various data types and methods are discussed. This overview should provide support for establishing or working with such a system and could lead to a better implementation of stewardship actions and a more uniform communication about and understanding of AMU data at farm-level. Harmonization of methods and processes could lead to an improved comparability of outcomes and less confusion when interpreting results across systems. However, it is important to note that the development of systems also depends on specific local needs, resources and aims

Informing Stewardship Measures in Canadian Food Animal Species through Integrated Reporting of Antimicrobial Use and Antimicrobial Resistance Surveillance Data—Part II, Application

Using the methodology developed for integrated analysis and reporting of antimicrobial use (AMU) and antimicrobial resistance (AMR) data, farm-level surveillance data were synthesized and integrated to assess trends and explore potential AMU and AMR associations. Data from broiler chicken flocks (n = 656), grower–finisher pig herds (n = 462) and turkey flocks (n = 339) surveyed by the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) at the farm-level (2015–2019) were used. The analyses showed a reduction in mean flock/herd level number of defined daily doses using Canadian standards (nDDDvetCA) adjusted for kg animal biomass that coincided with the decline in % resistance in the three species. This was noted in most AMU-AMR pairs studied except for ciprofloxacin resistant Campylobacter where resistance continued to be detected (moderate to high levels) despite limited fluoroquinolone use. Noteworthy was the significantly negative association between the nDDDvetCA/kg animal biomass and susceptible Escherichia coli (multispecies data), an early indication that AMU stewardship actions are having an impact. However, an increase in the reporting of diseases in recent years was observed. This study highlighted the value of collecting high-resolution AMU surveillance data with animal health context at the farm-level to understand AMR trends, enable data integration and measure the impact of AMU stewardship actions

Comparison of antimicrobial resistance among Salmonella enterica serovars isolated from Canadian turkey flocks, 2013 to 2021

ABSTRACT: The emergence of antimicrobial resistance (AMR) in Salmonella from turkeys has raised a food safety concern in Canada as certain serovars have been implicated in human salmonellosis outbreaks in recent years. While several studies evaluated AMR in broiler chickens in Canada, there are limited studies that assess AMR in turkey flocks. This study analyzed data collected between 2013 and 2021 by the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) farm turkey surveillance program to determine the prevalence of AMR and differences in resistance patterns among Salmonella serovars recovered from turkey flocks. Salmonella isolates were tested for susceptibility to 14 antimicrobials using a microbroth dilution method. Hierarchical clustering dendrograms were constructed to compare the individual AMR status of Salmonella serovars. Differences in the probability of resistance between Salmonella serovars were determined using generalized estimating equation logistic regression models to account for farm-level clustering. Of the 1,367 Salmonella isolates detected, 55.3% were resistant to at least one antimicrobial and 25.3% were multidrug resistant (MDR) (resistant to ≥3 antimicrobial classes). The Salmonella isolates exhibited high resistance to tetracycline (43.3%), streptomycin (47.2%), and sulfisoxazole (29.1%). The 3 most frequently occurring serovars were S. Uganda (22.9%), S. Hadar (13.5%), and S. Reading (12.0%). Streptomycin-sulfisoxazole-tetracycline (n = 204) was the most frequent MDR pattern identified. Heatmaps showed that S. Reading exhibited coresistance to the quinolone class antimicrobials, ciprofloxacin, and nalidixic acid; S. Heidelberg to gentamicin and sulfisoxazole; and S. Agona to ampicillin and ceftriaxone. Salmonella Hadar isolates had higher odds of resistance to tetracycline (OR: 152.1, 95% CI: 70.6–327.4) while the probability of being resistant to gentamicin and ampicillin was significantly higher in S. Senftenberg than in all the other serovars. Moreover, S. Uganda had the highest odds of being MDR (OR: 4.7, 95% CI: 3.7–6.1). The high resistance observed warrants a reassessment of the drivers for AMR, including AMU strategies and other production factors. Differences in AMR patterns highlight the need to implement serovar-specific mitigation strategies

A Systematic Review Characterizing On-Farm Sources of <i>Campylobacter</i> spp. for Broiler Chickens

<div><p><i>Campylobacter</i> and antimicrobial-resistant <i>Campylobacter</i> are frequently isolated from broiler chickens worldwide. In Canada, campylobacteriosis is the third leading cause of enteric disease and the regional emergence of ciprofloxacin-resistant <i>Campylobacter</i> in broiler chickens has raised a public health concern. This study aimed to identify, critically appraise, and synthesize literature on sources of <i>Campylobacter</i> in broilers at the farm level using systematic review methodology. Literature searches were conducted in January 2012 and included electronic searches in four bibliographic databases. Relevant studies in French or English (n = 95) conducted worldwide in any year and all study designs were included. Risk of Bias and GRADE criteria endorsed by the Cochrane collaboration was used to assess the internal validity of the study and overall confidence in the meta-analysis. The categories for on-farm sources were: broiler breeders/vertical transfer (number of studies = 32), animals (n = 57), humans (n = 26), environment (n = 54), and water (n = 63). Only three studies examined the antimicrobial resistance profiles of <i>Campylobacter</i> from these on-farm sources. Subgroups of data by source and outcome were analyzed using random effect meta-analysis. The highest risk for contaminating a new flock appears to be a contaminated barn environment due to insufficient cleaning and disinfection, insufficient downtime, and the presence of an adjacent broiler flock. Effective biosecurity enhancements from physical barriers to restricting human movement on the farm are recommended for consideration to enhance local on-farm food safety programs. Improved sampling procedures and standardized laboratory testing are needed for comparability across studies. Knowledge gaps that should be addressed include farm-level drug use and antimicrobial resistance information, further evaluation of the potential for vertical transfer, and improved genotyping methods to strengthen our understanding of <i>Campylobacter</i> epidemiology in broilers at the farm-level. This systematic review emphasizes the importance of improved industry-level and on-farm risk management strategies to reduce pre-harvest <i>Campylobacter</i> in broilers.</p></div

Summary of meta-analysis, genotyping results and GRADE for hygiene barriers, pests, wildlife and humans.

<p>Outcomes include prevalence of <i>Campylobacter</i> ssp. in the sample, association measures (Odds Ratio based on sampling and Odds Ratio based on questionnaires to establish the presence or absence of risk factors.).</p>¥<p>GRADE, Adapted Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104905#s2" target="_blank">methods</a> for details.</p>§<p>all 37 studies that reported molecular epidemiologic data, number tested include all that obtained samples (positive and negative culture).</p><p>MA- meta-analysis, 95% CI – 95% confidence interval, I²- measure of heterogeneity in the meta-analysis, NA- Not applicable or not isolated or no matching genotype found or not reported.</p><p>Summary of meta-analysis, genotyping results and GRADE for hygiene barriers, pests, wildlife and humans.</p

Summary of meta-analysis, genotyping results and GRADE for drinking water and water treatment.

<p>Outcomes include prevalence of <i>Campylobacter</i> ssp. in the sample, association measures (Odds Ratio based on sampling and Odds Ratio based on questionnaires to establish the presence or absence of risk factors.).</p>¥<p>GRADE, Adapted Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104905#s2" target="_blank">methods</a> for details.</p>§<p>all 37 studies that reported molecular epidemiologic data, number tested include all that obtained samples (positive and negative culture).</p><p>MA- meta-analysis, 95% CI – 95% confidence interval, I²- measure of heterogeneity in the meta-analysis.</p><p>Summary of meta-analysis, genotyping results and GRADE for drinking water and water treatment.</p

Sources of antimicrobial-resistant <i>Campylobacter</i>.

<p>Same broiler integrator that supplied the breeder flock in the study.</p><p>Ery – erythromycin, Tet – Tetracycline, Cip – ciprofloxacin, Nal – nalidixic acid.</p><p>Sources of antimicrobial-resistant <i>Campylobacter</i>.</p