Evidence map›Paper›PMID 37035822›Full record

ArticleFrontiers in veterinary science2023

Capturing systematically users' experience of evaluation tools for integrated AMU and AMR surveillance.

Lis Alban, Marion Bordier, Barbara Häsler, Lucie Collineau, Laura Tomassone, Houda Bennani, Cécile Aenishaenslin, Madelaine Norström, Maurizio Aragrande, Maria Eleni Filippitzi and 2 more

Abstract read
In one paragraph

Article in Frontiers in veterinary science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
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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

12 authors.

Lis AlbanDepartment for Food Safety, Veterinary Issues and Risk Analysis, Danish Agriculture and Food Council, Copenhagen, Denmark.
Marion BordierASTRE, Université de Montpellier, CIRAD, INRAE, Montpellier, France.
Barbara HäslerVeterinary Epidemiology Economics and Public Health Group, Department of Pathobiology and Population Sciences, Royal Veterinary College, London, United Kingdom.
Lucie CollineauUniversity of Lyon, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Laboratory of Lyon, Epidemiology and Surveillance Support Unit, Lyon, France.
Laura TomassoneDepartment of Veterinary Sciences, University of Turin, Grugliasco (Turin), Italy.
Houda BennaniVeterinary Epidemiology Economics and Public Health Group, Department of Pathobiology and Population Sciences, Royal Veterinary College, London, United Kingdom.
Cécile AenishaenslinDépartement de pathologie et microbiologie, Université de Montréal, Saint-Hyacinthe, QC, Canada.
Madelaine NorströmDepartment of Animal Health, Welfare and Food Safety, Norwegian Veterinary Institute, Ås, Norway.
Maurizio AragrandeDepartment of Agricultural and Food Sciences, University of Bologna, Bologna, Italy.
Maria Eleni FilippitziLaboratory of Animal Health Economics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Pedro MouraNational Food Institute, Technical University of Denmark, Lyngby, Denmark.
Marianne SandbergNational Food Institute, Technical University of Denmark, Lyngby, Denmark.

Funding

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

Abstract

Tackling antimicrobial resistance (AMR) is a goal for many countries. Integrated surveillance of antimicrobial use (AMU) and resistance is a prerequisite for effective risk mitigation. Regular evaluation of any surveillance is needed to ensure its effectiveness and efficiency. The question is how to evaluate specifically integrated surveillance for AMU and AMR. In an international network called CoEvalAMR, we have developed guidelines for selection of the most appropriate tools for such an evaluation. Moreover, we have assessed different evaluation tools as examples using a country case format and a methodology with a focus on the user's experience. This paper describes the updated methodology, which consists of a brief introduction to the case and to the tool separately. Moreover, there are 12 functional aspects and nine content themes which should be scored using a 4-tiered scale. Additionally, four Strengths, Weaknesses, Opportunities, Threats (SWOT) questions should be addressed. Results are illustrated using radar diagrams. An example of application of the updated methodology is given using the ECoSur evaluation tool. No tool can cover all evaluation aspects comprehensively in a user-friendly manner, so the choice of tool must be based upon the specific evaluation purpose. Moreover, adequate resources, time and training are needed to obtain useful outputs from the evaluation. Our updated methodology can be used by tool users to share their experience with available tools, and hereby assist other users in identifying the most suited tool for their evaluation purpose. Additionally, tool developers can get valuable information for further improvements of their tool.

Indexed as

antimicrobial resistanceantimicrobial useevaluationintegrated surveillanceOne Health assessment

Identifiers

PMID37035822
PMCPMC10081675

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.