Comparing human and animal antimicrobial usage: a critical appraisal of the indicators used is needed.

Comparisons between antimicrobial usage (AMU) in humans and food-producing animals are regularly made. The accuracy of such comparisons depends on the indicators used to quantify AMU. Indicators for AMU quantitatively relate use data (the numerator) to population data (the denominator). The denominator should be a proxy for the population at risk in a certain period when comparing the exposure of different populations to antimicrobials. Denominators based on numbers of animals slaughtered, such as the commonly used population correction unit, do not consider the time at risk of antimicrobial treatment. Production-based indicators underestimate animal AMU. Additionally, production-based indicators are fundamentally different from indicators used to quantify human AMU. Using such indicators to compare human and animal AMU therefore leads to biased results. More caution should be taken in selecting the indicator to quantify AMU when comparing AMU in food-producing animals and humans.

Seasonality of antimicrobial use in Dutch food-producing animals.

Due to globally increasing antimicrobial resistance (AMR), it is pivotal to understand factors contributing to antimicrobial use (AMU) to enable development and implementation of AMR-reducing interventions. Therefore, we explored seasonal variations of systemic AMU in food-producing animals in the Netherlands. Dutch surveillance data from January 2013 to December 2018 from cattle, pig, and broiler farms were used. AMU was expressed as the number of Defined Daily Dosages Animal per month (DDDA/animal-month) per farm by animal sector, antimicrobial line (first, second, and third), antimicrobial class, and farm type. Seasonality of AMU was analyzed using Generalized Additive Models (GAMs) with DDDA/animal-month as outcome variable, and year and month as independent variables. Year and month were modelled as smooth terms represented with penalized regression splines.Significant seasonality of AMU was found in the cattle and pig sectors, but not in broilers. Significant seasonality of AMU was found mainly for first-line antimicrobials. In the cattle sector, a significant increase during winter was found for the use of amphenicols (an increase of 23.8%) and long-acting macrolides (an increase of 3.4%). In the pig sector, seasonality of AMU was found for pleuromutilins (p < 0.001) with an increase of 20% in October-November. The seasonality of pleuromutilins was stronger in sows/piglets (an increase of 47%) than in fattening pigs (16% increase). Only in fattening pigs, the use of amphenicols showed a significant seasonality with an increase of 11% during winter (P < 0.001). AMU in cattle and pig sectors shows seasonal variations likely caused by seasonality of diseases. In broilers, no AMU seasonality was observed, possibly due to the controlled environment in Dutch farms. In the context of the one health concept, future studies are necessary to explore whether this seasonality is present in other populations and whether it has implications for antimicrobial resistance in humans through the food chain.

Use of a new antimicrobial consumption monitoring system (Vet-AMNet): Application to Dutch dairy sector over a 9-year period.

Introduction: The urgency of preventing the increase of antimicrobial resistance has been emphasized by international authorities such as the World Health Organization, European Medicines Agency, and World Organization for Animal Health. Monitoring systems capable of reporting antimicrobial consumption data are regarded as a crucial pillar of this fight. The Vet-AMNet system was developed to collect and analyze national antimicrobial consumption data in Portuguese dairy farms to support the veterinary authority in stewardship actions and to assist both veterinarians and farmers in daily decisions related to antimicrobials. Methods: To evaluate the robustness of the system and other identified critical success factors, it was used to analyze antimicrobial consumption data available from the Dutch dairy cow sector over the period from 2012 to 2020. The data previously used for publications by the Netherlands Veterinary Medicines Institute (SDa) were imported and pre-processed by the Vet-AMNet system according to the SDa's standard operating procedure and the Dutch metrics to measure antimicrobial consumption were calculated. Results: By comparing the outputs with the figures generated by the system established in the Netherlands, the Portuguese system was validated. Antimicrobial consumption data from the Dutch dairy sector during the 9-year period will be presented in unpublished graphs and tables, where each molecule's pharmaceutical formulation, pharmacotherapeutic group and line of choice will be related and discussed, illustrating the evolution of sectorial antimicrobial consumption against a background of a strong national antimicrobial policy initiated by public-private cooperation and supported by legislation.

Comparison of different antimicrobial use indicators and antimicrobial resistance data in food-producing animals.

OBJECTIVES: To explore the effects of using different indicators to quantify antimicrobial usage (AMU) in livestock and compare outcomes with antimicrobial resistance (AMR) data. METHODS: Three indicators were used to quantify AMU, two indicators in which the denominator varied: defined daily doses per average mass of the animals present per year (DDD/AY) and defined daily doses per population correction unit (DDD/PCU) and one in which the numerator varied: milligrams of active ingredient per PCU (mg/PCU). AMU was compared with antimicrobial resistance data from the national monitoring programme from 2013 to 2018 with the proportion of Escherichia coli isolates fully susceptible to a predefined panel of antimicrobials for the broiler, dairy cattle and pig farming livestock sectors in the Netherlands. RESULTS: The ranking of livestock sectors differs between sectors when using different indicators to express AMU. Dairy cattle rank lowest when expressing AMU in DDD/AY, followed by pigs and broilers corresponding to the rankings of the sectors for AMR. When changing the denominator to PCU, the ranking in AMU is reversed: use ranks highest in dairy cattle and lowest broilers. CONCLUSIONS: Using different denominators in AMU indicators has a major impact on measured use. This might result in misinterpretation of effects of interventions on AMU and the associations of AMU with AMR across animal sectors. From an epidemiological perspective, indicators that take into account time at risk of exposure to antimicrobials are to be preferred and reflect the AMR risk most accurately.

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.

Association of antimicrobial usage with faecal abundance of aph(3')-III, ermB, sul2 and tetW resistance genes in veal calves in three European countries.

BACKGROUND: High antimicrobial use (AMU) and antimicrobial resistance (AMR) in veal calves remain a source of concern. As part of the EFFORT project, the association between AMU and the abundance of faecal antimicrobial resistance genes (ARGs) in veal calves in three European countries was determined. METHODS: In 2015, faecal samples of veal calves close to slaughter were collected from farms located in France, Germany and the Netherlands (20 farms in France, 20 farms in the Netherlands and 21 farms in Germany; 25 calves per farm). Standardized questionnaires were used to record AMU and farm characteristics. In total, 405 faecal samples were selected for DNA extraction and quantitative polymerase chain reaction to quantify the abundance (16S normalized concentration) of four ARGs [aph(3')-III, ermB, sul2 and tetW] encoding for resistance to frequently used antimicrobials in veal calves. Multiple linear mixed models with random effects for country and farm were used to relate ARGs to AMU and farm characteristics. RESULTS: A significant positive association was found between the use of trimethoprim/sulfonamides and the concentration of sul2 in faeces from veal calves. A higher weight of calves on arrival at the farm was negatively associated with aph(3')-III and ermB. Lower concentrations of aph(3')-III were found at farms with non-commercial animals present. Furthermore, farms using only water for the cleaning of stables had a significantly lower abundance of faecal ermB and tetW compared with other farms. CONCLUSION: A positive association was found between the use of trimethoprim/sulfonamides and the abundance of sul2 in faeces in veal calves. Additionally, other relevant risk factors associated with ARGs in veal calves were identified, such as weight on arrival at the farm and cleaning practices.

Virus Shedding of Avian Influenza in Poultry: A Systematic Review and Meta-Analysis.

Understanding virus shedding patterns of avian influenza virus (AIV) in poultry is important for understanding host-pathogen interactions and developing effective control strategies. Many AIV strains were studied in challenge experiments in poultry, but no study has combined data from those studies to identify general AIV shedding patterns. These systematic review and meta-analysis were performed to summarize qualitative and quantitative information on virus shedding levels and duration for different AIV strains in experimentally infected poultry species. Methods were designed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Four electronic databases were used to collect literature. A total of 1155 abstract were screened, with 117 studies selected for the qualitative analysis and 71 studies for the meta-analysis. A large heterogeneity in experimental methods was observed and the quantitative analysis showed that experimental variables such as species, virus origin, age, inoculation route and dose, affect virus shedding (mean, peak and duration) for highly pathogenic AIV (HPAIV), low pathogenic AIV (LPAIV) or both. In conclusion, this study highlights the need to standardize experimental procedures, it provides a comprehensive summary of the shedding patterns of AIV strains by infected poultry and identifies the variables that influence the level and duration of AIV shedding.