Dataset Information

Addressing Learning Needs on the Use of Metagenomics in Antimicrobial Resistance Surveillance.

ABSTRACT: One Health surveillance of antimicrobial resistance (AMR) depends on a harmonized method for detection of AMR. Metagenomics-based surveillance offers the possibility to compare resistomes within and between different target populations. Its potential to be embedded into policy in the future calls for a timely and integrated knowledge dissemination strategy. We developed a blended training (e-learning and a workshop) on the use of metagenomics in surveillance of pathogens and AMR. The objectives were to highlight the potential of metagenomics in the context of integrated surveillance, to demonstrate its applicability through hands-on training and to raise awareness to bias factors. The target participants included staff of competent authorities responsible for AMR monitoring and academic staff. The training was organized in modules covering the workflow, requirements, benefits and challenges of surveillance by metagenomics. The training had 41 participants. The face-to-face workshop was essential to understand the expectations of the participants about the transition to metagenomics-based surveillance. After revision of the e-learning, we released it as a Massive Open Online Course (MOOC), now available at https://www.coursera.org/learn/metagenomics. This course has run in more than 20 sessions, with more than 3,000 learners enrolled, from more than 120 countries. Blended learning and MOOCs are useful tools to deliver knowledge globally and across disciplines. The released MOOC can be a reference knowledge source for international players in the application of metagenomics in surveillance.

SUBMITTER: Duarte ASR

PROVIDER: S-EPMC7051937 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Addressing Learning Needs on the Use of Metagenomics in Antimicrobial Resistance Surveillance.

Duarte Ana Sofia Ribeiro ASR Stärk Katharina D C KDC Munk Patrick P Leekitcharoenphon Pimlapas P Bossers Alex A Luiken Roosmarijn R Sarrazin Steven S Lukjancenko Oksana O Pamp Sünje Johanna SJ Bortolaia Valeria V Nissen Jakob Nybo JN Kirstahler Philipp P Van Gompel Liese L Poulsen Casper Sahl CS Kaas Rolf Sommer RS Hellmér Maria M Hansen Rasmus Borup RB Gomez Violeta Munoz VM Hald Tine T

Frontiers in public health 20200225

One Health surveillance of antimicrobial resistance (AMR) depends on a harmonized method for detection of AMR. Metagenomics-based surveillance offers the possibility to compare resistomes within and between different target populations. Its potential to be embedded into policy in the future calls for a timely and integrated knowledge dissemination strategy. We developed a blended training (e-learning and a workshop) on the use of metagenomics in surveillance of pathogens and AMR. The objectives ...[more]

PMID: 32158739

Similar Datasets

Project description:Antimicrobial resistance (AMR) surveillance of mycoplasmas of veterinary importance has been held back for years due to lack of harmonized methods for antimicrobial susceptibility testing (AST) and interpretative criteria, resulting in a crucial shortage of data. To address AMR in ruminant mycoplasmas, we mobilized a long-established clinical surveillance network called "Vigimyc." Here we describe our surveillance strategy and detail the results obtained during a 2-year monitoring period. We also assess how far our system complies with current guidelines on AMR surveillance and how it could serve to build epidemiological cut-off values (ECOFFs), as a first attainable criterion to help harmonize monitoring efforts and move forward to clinical breakpoints. Clinical surveillance through Vigimyc enables continuous collection, identification and preservation of Mycoplasma spp. isolates along with metadata. The most frequent pathogens, i.e., M. bovis and species belonging to M. mycoides group, show stable clinicoepidemiological trends and were included for annual AST. In the absence of interpretative criteria for ruminant mycoplasmas, we compared yearly minimum inhibitory concentration (MIC) results against reference datasets. We also ran a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis on the overall service provided by our AMR surveillance strategy. Results of the 2018-2019 surveillance campaign were consistent with the reference datasets, with M. bovis isolates showing high MIC values for all antimicrobial classes except fluoroquinolones, and species of the Mycoides group showing predominantly low MIC values. A few new AMR patterns were detected, such as M. bovis with lower spectinomycin MICs. Our reference dataset partially complied with European Committee on Antimicrobial Susceptibility Testing (EUCAST) requirements, and we were able to propose tentative epidemiological cut-off values (TECOFFs) for M. bovis with tilmicosin and spectinomycin and for M. mycoides group with tilmicosin and lincomycin. These TECOFFs were consistent with other published data and the clinical breakpoints of Pasteurellaceae, which are often used as surrogates for mycoplasmas. SWOT analysis highlighted the benefit of pairing clinical and antimicrobial resistance surveillance despite the AST method-related gaps that remain. The international community should now direct efforts toward AST method harmonization and clinical interpretation.

Project description:Integrated surveillance systems for antimicrobial use (AMU) and antimicrobial resistance (AMR) require regular evaluation to ensure the effectiveness and efficiency of the system. An important step in the evaluation is to choose an appropriate tool for the purpose of the evaluation. The "Integrated Surveillance System Evaluation" (ISSE) framework is a conceptual framework that was developed to evaluate One Health (OH) integration in surveillance system for AMU/AMR. This study aimed to evaluate the performance and value of integrated surveillance system for AMU/AMR in England by applying the ISSE framework, which was used to develop data collection protocols and define the study design. A qualitative study using semi-structured interviews was conducted to collect the data and analyse it thematically. Eighteen stakeholders from human, animal, food and environment sectors that are involved in AMU/AMR surveillance were interviewed. Four main themes emerged from the analysis: (1) Cross-sectoral integration in the surveillance system for AMU/AMR; (2) Production of OH outputs and outcomes; (3) Drivers and barriers to cross-sectoral collaboration; and 4) Need for more cross-sectoral collaboration. The findings showed that there were links between integrated surveillance information, decision making and interventions. However, there were only few OH examples, such as the UK AMR contingency plan, where the potential of cross-sectoral collaboration was fully exploited. A lot of the benefits described were related to the generation of information and increase in knowledge and understanding without links to how the information generated was used. While these intangible benefits have a value on their own, being able to link surveillance information and mitigation measures would help to enhance the value of integrated surveillance. In terms of improvement, the main areas identified were the development of more harmonised methods for data collection and analysis, provision of resources dedicated to cross-sectoral collaboration, improved coordination, and collection of surveillance data from the environment and from companion animals. By identifying links between OH surveillance information produced and various outputs and outcomes; this study helped to understand the wider benefits of integrated surveillance for AMU/AMR in England and provided insights on how the system could be improved and efficiency increased.

Project description:OBJECTIVE: To report the experience gained over 4 years in working with the German SARI project (Surveillance of Antimicrobial Use and Antimicrobial Resistance in Intensive Care Units), and to compare SARI with data from the Swedish STRAMA and the US AUR surveillance system. METHODS: Prospective unit and laboratory based surveillance was carried out in 40 German ICUs from 2001 through 2004. WHO 2004 definitions of defined daily doses (DDD) per 1,000 patient days (pd) were used to express antimicrobial consumption (AD). Apart from the proportion of resistant isolates (RP), the incidence density of resistant isolates (RD) was calculated on the basis of the number of resistant isolates per 1,000 pd. To determine the changes over time, the Wilcoxon signed rank test for paired samples was used. RESULTS: From 1/2001 through 12/2004, 40 ICUs provided data on 53,399 isolates, a total of 789,569 DDD and 597,592 pd. Total AD ranged from 427 to 2,798, with the median being 1,351. There was no statistically significant change in total antimicrobial use, but a statistically significant decrease was observed in the use of aminoglycosides. RD was highest for MRSA with 4.4 resistant isolates/1,000 pd followed by imipenem resistant Pseudomonas aeruginosa with 1.7 resistant isolates/1,000 pd. The corresponding RPs were 21.5% and 23.2%. Over the 4-year period (2001-2004), significant increases were seen in the RDs of third generation cephalosporin and ciprofloxacin resistant Escherichia coli. In 2004, the mean RD reached 0.28 and 1.41, respectively. In comparison, the RP of selected pathogens was highest in the US ICUs and lowest in Swedish ICUs, with the exception of imipenem resistant P. aeruginosa. CONCLUSION: Antibiotic consumption remained stable over a period of 4 years, (the mean being 1,321 DDD/1,000 pd). The same applied to the situation regarding resistance in Staphylococcus aureus, enterococci and P. aeruginosa. For most pathogens the RP was higher in SARI ICUs than in Swedish ICUs, but lower than in US ICUs.

Dataset Information

Addressing Learning Needs on the Use of Metagenomics in Antimicrobial Resistance Surveillance.

Publications

Addressing Learning Needs on the Use of Metagenomics in Antimicrobial Resistance Surveillance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets