Evidence map›Paper›PMID 34307513›Full record

ArticleFrontiers in veterinary science2021

Assessment of Evaluation Tools for Integrated Surveillance of Antimicrobial Use and Resistance Based on Selected Case Studies.

Marianne Sandberg, Ayla Hesp, Cécile Aenishaenslin, Marion Bordier, Houda Bennani, Ursula Bergwerff, Ilias Chantziaras, Daniele De Meneghi, Johanne Ellis-Iversen, Maria-Eleni Filippizi and 6 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in veterinary science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
1.9field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 42 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 13 institutions in 9 countries.

Marianne SandbergDepartment for Food Safety, Veterinary Issues and Risk Analysis, Danish Agriculture and Food Council, Copenhagen, Denmark.
Ayla HespDepartment of Bacteriology and Epidemiology, Host Pathogen Interaction and Diagnostics Development, Wageningen Bioveterinary Research, Lelystad, Netherlands.
Cécile AenishaenslinGroupe de recherche en épidémiologie des zoonoses et santé publique, Université de Montréal, Saint-Hyacinthe, QC, Canada.
Marion BordierUMR Astre, Cirad, INRAE, University of Montpellier, Montpellier, France.
Houda BennaniVeterinary Epidemiology, Economics and Public Health Group, Department of Pathobiology and Population Sciences, Royal Veterinary College, London, United Kingdom.
Ursula BergwerffDepartment of Farm Animal Health, Utrecht University, Utrecht, Netherlands.
Ilias ChantziarasUnit of Animal Science and Unit of Social Science, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Merelbeke, Belgium.
Daniele De MeneghiDepartment of Veterinary Sciences, University of Turin, Turin, Italy.
Johanne Ellis-IversenNational Food Institute, Technical University of Denmark, Lyngby, Denmark.
Maria-Eleni FilippiziVeterinary Epidemiology Unit, Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
Koen MintiensFood and Agriculture Organization of the United Nations, Rome, Italy.
Liza R NielsenDepartment of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark.
Madelaine NorströmDepartment of Epidemiology, Norwegian Veterinary Institute, Oslo, Norway.
Laura TomassoneDepartment of Veterinary Sciences, University of Turin, Turin, Italy.
Gerdien van SchaikDepartment of Farm Animal Health, Utrecht University, Utrecht, Netherlands.
Lis AlbanDepartment for Food Safety, Veterinary Issues and Risk Analysis, Danish Agriculture and Food Council, Copenhagen, Denmark.
University of Copenhagen · DKUniversity of Turin · ITUtrecht University · NLDanish Agriculture and Food Council · DKFood and Agriculture Organization of the United Nations · ITInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement · FRInstituut voor Landbouw en Visserijonderzoek · BENorwegian Veterinary Institute · NORoyal GD (Netherlands) · NLRoyal Veterinary College · GBSciensano (Belgium) · BETechnical University of Denmark · DKUniversité de Montréal · CA

Funding

Medical Research Council MR/S037721/1
6 · The paper itself

Abstract

Regular evaluation of integrated surveillance for antimicrobial use (AMU) and resistance (AMR) in animals, humans, and the environment is needed to ensure system effectiveness, but the question is how. In this study, six different evaluation tools were assessed after being applied to AMU and AMR surveillance in eight countries: (1) ATLASS: the Assessment Tool for Laboratories and AMR Surveillance Systems developed by the Food and Agriculture Organization (FAO) of the United Nations, (2) ECoSur: Evaluation of Collaboration for Surveillance tool, (3) ISSEP: Integrated Surveillance System Evaluation Project, (4) NEOH: developed by the EU COST Action "Network for Evaluation of One Health," (5) PMP-AMR: The Progressive Management Pathway tool on AMR developed by the FAO, and (6) SURVTOOLS: developed in the FP7-EU project "RISKSUR." Each tool was scored using (i) 11 pre-defined functional aspects (e.g., workability concerning the need for data, time, and people); (ii) a strengths, weaknesses, opportunities, and threats (SWOT)-like approach of user experiences (e.g., things that I liked or that the tool covered well); and (iii) eight predefined content themes related to scope (e.g., development purpose and collaboration). PMP-AMR, ATLASS, ECoSur, and NEOH are evaluation tools that provide a scoring system to obtain semi-quantitative results, whereas ISSEP and SURVTOOLS will result in a plan for how to conduct evaluation(s). ISSEP, ECoSur, NEOH, and SURVTOOLS allow for in-depth analyses and therefore require more complex data, information, and specific training of evaluator(s). PMP-AMR, ATLASS, and ISSEP were developed specifically for AMR-related activities-only ISSEP included production of a direct measure for "integration" and "impact on decision making." NEOH and ISSEP were perceived as the best tools for evaluation of One Health (OH) aspects, and ECoSur as best for evaluation of the quality of collaboration. PMP-AMR and ATLASS seemed to be the most user-friendly tools, particularly designed for risk managers. ATLASS was the only tool focusing specifically on laboratory activities. Our experience is that adequate resources are needed to perform evaluation(s). In most cases, evaluation would require involvement of several assessors and/or stakeholders, taking from weeks to months to complete. This study can help direct future evaluators of integrated AMU and AMR surveillance toward the most adequate tool for their specific evaluation purpose.

Indexed as

AMRevaluationintegrated surveillanceone healthtools

Identifiers

PMID34307513
PMCPMC8298032
OpenAlexW3182316010

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.