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Babu Rajendran N, Arieti F, Mena-Benítez CA, Galia L, Tebon M, Alvarez J, Gladstone BP, Collineau L, De Angelis G, Duro R, Gaze W, Göpel S, Kanj SS, Käsbohrer A, Limmathurotsakul D, Lopez de Abechuco E, Mazzolini E, Mutters NT, Pezzani MD, Presterl E, Renk H, Rodríguez-Baño J, Săndulescu O, Scali F, Skov R, Velavan TP, Vuong C, Tacconelli E, Avery L, Bonten M, Cassini A, Chauvin C, Compri M, Damborg P, De Greeff S, Del Toro MD, Filter M, Franklin A, Gonzalez-Zorn B, Grave K, Hocquet D, Hoelzle LE, Kalanxhi E, Laxminarayan R, Leibovici L, Malhotra-Kumar S, Mendelson M, Paul M, Muñoz Madero C, Murri R, Piddock LJ, Ruesen C, Sanguinetti M, Schilling T, Schrijver R, Schwaber MJ, Scudeller L, Torumkuney D, Van Boeckel T, Vanderhaeghen W, Voss A, Wozniak T. EPI-Net One Health reporting guideline for antimicrobial consumption and resistance surveillance data: a Delphi approach. Lancet Reg Health Eur 2023; 26:100563. [PMID: 36895445 PMCID: PMC9989632 DOI: 10.1016/j.lanepe.2022.100563] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 11/22/2022] [Accepted: 11/24/2022] [Indexed: 12/24/2022]
Abstract
Strategic and standardised approaches to analysis and reporting of surveillance data are essential to inform antimicrobial resistance (AMR) mitigation measures, including antibiotic policies. Targeted guidance on linking full-scale AMR and antimicrobial consumption (AMC)/antimicrobial residues (AR) surveillance data from the human, animal, and environmental sectors is currently needed. This paper describes the initiative whereby a multidisciplinary panel of experts (56 from 20 countries-52 high income, 4 upper middle or lower income), representing all three sectors, elaborated proposals for structuring and reporting full-scale AMR and AMC/AR surveillance data across the three sectors. An evidence-supported, modified Delphi approach was adopted to reach consensus among the experts for dissemination frequency, language, and overall structure of reporting; core elements and metrics for AMC/AR data; core elements and metrics for AMR data. The recommendations can support multisectoral national and regional plans on antimicrobials policy to reduce resistance rates applying a One Health approach.
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Affiliation(s)
- Nithya Babu Rajendran
- Infectious Diseases, Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
| | - Fabiana Arieti
- Infectious Diseases Section, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | | | - Liliana Galia
- Infectious Diseases Section, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Maela Tebon
- Infectious Diseases Section, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Julio Alvarez
- VISAVET Health Surveillance Center and Department of Animal Health, Faculty of Veterinary Medicine, Complutense University, Madrid, Spain
| | - Beryl Primrose Gladstone
- Infectious Diseases, Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany.,German Centre for Infection Research (DZIF) Clinical Research Unit for Healthcare Associated and Antibiotic Resistant Bacterial Infections, Tübingen, Germany
| | - Lucie Collineau
- French Agency for Food, Environmental and Occupational Health and Safety, ANSES, Maisons-Alfort, France
| | - Giulia De Angelis
- Dipartimento di Scienze Biotecnologiche di base, Cliniche Intensivologiche e Perioperatorie, Universita Cattolica del Sacro Cuore, Rome, Italy
| | - Raquel Duro
- Unit for the Prevention and Control of Infection and Antimicrobial Resistance, Centro Hospitalar do Tâmega e Sousa, Penafiel, Porto, Portugal
| | - William Gaze
- The European Centre for Environment and Human Health, University of Exeter Medical School, University of Exeter, Penryn, Cornwall, UK
| | - Siri Göpel
- Infectious Diseases, Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany.,German Centre for Infection Research (DZIF) Clinical Research Unit for Healthcare Associated and Antibiotic Resistant Bacterial Infections, Tübingen, Germany
| | - Souha S Kanj
- Department of Internal Medicine, Division of Infectious Diseases, Infection Control Program, Antimicrobial Stewardship Program, American University of Beirut Medical Center, Beirut, Lebanon
| | - Annemarie Käsbohrer
- German Federal Institute for Risk Assessment (BfR), Department 4 - Biological Safety, Berlin, Germany
| | - Direk Limmathurotsakul
- Mahidol Oxford Tropical Medicine Research Unit and Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Thailand.,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, UK
| | | | - Elena Mazzolini
- Department of Epidemiology, Istituto Zooprofilattico Sperimentale delle Venezie, Udine-Padova, Padua, Italy
| | - Nico T Mutters
- Institute for Hygiene and Public Health, Bonn University Hospital, Bonn, Germany.,European Committee on Infection Control, Basel, Switzerland
| | - Maria Diletta Pezzani
- Infectious Diseases Section, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Elisabeth Presterl
- European Committee on Infection Control, Basel, Switzerland.,Department of Infection Control and Hospital Epidemiology, Medical University of Vienna, Vienna, Austria.,ESCMID Study Group for Nosocomial Infections, Basel, Switzerland
| | - Hanna Renk
- Department of Paediatric Cardiology, Pulmology and Intensive Care Medicine, University Children's Hospital Tübingen, Tübingen, Germany
| | - Jesús Rodríguez-Baño
- Infectious Diseases and Microbiology Division, Hospital Universitario Virgen Macarena/Department of Medicine, School of Medicine, University of Seville/Biomedicine Institute of Seville (IBiS)/CSIC, Seville, Spain.,CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
| | - Oana Săndulescu
- Department of Infectious Diseases I, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.,National Institute for Infectious Diseases "Prof. Dr. Matei Balș", Bucharest, Romania
| | - Federico Scali
- Istituto Zooprofilattico Sperimentale della Lombardia e Dell'Emilia Romagna, Brescia, Italy
| | - Robert Skov
- Epidemiological Infectious Disease Preparedness, Statens Serum Institut, Copenhagen, Denmark
| | - Thirumalaisamy P Velavan
- Institute of Tropical Medicine, Universitätsklinikum Tübingen, Tübingen, Germany.,Vietnamese - German Center for Medical Research, Hanoi, Vietnam
| | - Cuong Vuong
- AiCuris Anti-infective Cures GmbH, Wuppertal, Germany.,Jansen Pharmaceuticals, Beerse, Belgium
| | - Evelina Tacconelli
- Infectious Diseases Section, Department of Diagnostics and Public Health, University of Verona, Verona, Italy.,European Committee on Infection Control, Basel, Switzerland
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Lopez de Abechuco E, Dórea F, Buschhardt T, Scaccia N, Günther T, Foddai A, Dups-Bergmann J, Filter M. One Health Consensus Report Annotation Checklist (OH-CRAC): A cross-sector checklist to support harmonized annotation of surveillance data in reports. Zoonoses Public Health 2022; 69:606-614. [PMID: 35733287 DOI: 10.1111/zph.12947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 02/11/2022] [Accepted: 04/03/2022] [Indexed: 11/29/2022]
Abstract
To facilitate cross-sector integration of surveillance data it is necessary to improve and harmonize the meta-information provided in surveillance data reports. Cross-sector integration of surveillance results in sector-specific reports is frequently difficult as reports with a focus on a single sector often lack aspects of the relevant meta-information necessary to clarify the surveillance context. Such reporting deficiencies reduce the value of surveillance reports to the One Health community. The One Health Consensus Report Annotation Checklist (OH-CRAC), described in this paper along with potential application scenarios, was developed to improve the current practice of annotating data presented in surveillance data reports. It aims to provide guidance to researchers and reporting officers on what meta-information should be collected and provided to improve the completeness and transparency of surveillance data reports. The OH-CRAC can be adopted by all One Health-related sectors and due to its cross-sector design, it supports the mutual mapping of surveillance meta-information from sector-specific surveillance reports on federal, national and international levels. To facilitate the checklist completion, OH-CRAC is also available as an online resource that allows the collection of surveillance meta-information in an easy and user-friendly manner. Completed OH-CRAC checklists can be attached as annexes to the corresponding surveillance data reports or even to individual data files regardless of the data source. In this way, reports and data become better interpretable, usable and comparable to information from other sectors, improving their value for all surveillance actors and providing a better foundation for advice to risk managers.
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Affiliation(s)
| | - Fernanda Dórea
- Department of Disease Control and Epidemiology, National Veterinary Institute Sweden, Berlin, Germany
| | - Tasja Buschhardt
- Department 4-Biological Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Nazareno Scaccia
- Department 4-Biological Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Taras Günther
- Department 4-Biological Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Alessandro Foddai
- National Food Institute, Danish Technical University (DTU-Food) Denmark, Greifswald, Germany
| | - Johanna Dups-Bergmann
- Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Institute of Epidemiology, Berlin, Germany
| | - Matthias Filter
- Department 4-Biological Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
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Scaccia N, Günther T, Lopez de Abechuco E, Filter M. The Glossaryfication Web Service: an automated glossary creation tool to support the One Health community. RIO 2021. [DOI: 10.3897/rio.7.e70183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In many interdisciplinary research domains, the creation of a shared understanding of relevant terms is considered the foundation for efficient cross-sector communication and interpretation of data and information. This is also true for the domain of One Health (OH) where many One Health Surveillance (OHS) documents rarely contain glossaries with a list of terms for which their specific meaning in the context of the given document is defined (Cornelia et al. 2018, Buschhardt et al. 2021). The absence of glossaries within these documents may lead to misinterpretation of surveillance results due to the wrong interpretation of terminology specifically when term definitions differ across OH sectors. Under the One Health EJP project ORION, the OHEJP Glossary was recently created. The OHEJP Glossary is a tool to improve communication and collaboration amongst OH sectors by providing an easy-to-use online resource that lists relevant OH terms and sector-specific definitions. To improve the accessibility of content from the OHEJP Glossary and support the creation of integrative glossaries in future OHS-related documents, the OHEJP Glossaryfication Web Service was created. This service can support the practical use of the OHEJP Glossary and other relevant online glossaries by OH professionals.
The Glossaryfication Web Service (GWS) is an application that automatically identifies terms in any uploaded text-based document and creates a document-specific list of matching definitions in selected online glossaries. This auto-generated document-specific glossary can easily be adjusted by the user, for example, by selecting the desired definition in case multiple definitions were found for a specific term. The document-specific glossary could then be downloaded, manually adjusted and finally included into the original document where it supports the correct interpretation of terminology used. Especially in sector-specific reports, such as from animal health or public health authorities, this can be beneficial to ensure the correct interpretation by other OH sectors in the future. The GWS was developed with the open-source desktop software KNIME Analytics Platform and runs as a web service on a KNIME Web Server infrastructure. The core data processing functionality in the GWS is based on KNIME’s Text Processing extension. KNIME's JavaScript nodes provided the basis for an interactive user interface where users can easily upload their files and select between different reference glossaries, such as the OHEJP Glossary, the CDC Glossary, the WHO Glossary or the EFSA Glossary. After retrieval of the user input settings, the GWS tags words within the provided document and maps these tagged words with matching entries in the selected glossaries. As the main output, the user receives a downloadable list of matching terms with their corresponding definitions, sectorial assignments and references, which can then be added by the user to the original document. The GWS is freely accessible via this link as well as the underlying KNIME workflow.
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