Guidance for evaluating integrated surveillance of antimicrobial use and resistance
Bibliographic record
Abstract
Abstract Antimicrobial resistance (AMR) resulting from antimicrobial use (AMU) is an emerging threat to global health. One of the key elements for a better understanding and management of AMU and AMR is to develop effective and efficient integrated surveillance systems that consider the complex epidemiology of these issues and the impacts of resistance on humans, animals and the environment. Consequently, for this project, an international consortium of experts from multiple fields called CoEvalAMR was formed with the objectives to study user needs, characterise and compare existing tools for the evaluation of integrated AMU and AMR surveillance, apply them to case studies, and elaborate guidance on the purpose-fit selection and the use of the tools. For the comparison of evaluation tools, questions were extracted from existing tools and attributed to themes, to assess the user needs, interviews were conducted with national key stakeholders, and we applied a series of different evaluation tools to understand and document their strengths and weaknesses. The guidance was refined iteratively. From 12 evaluation tools, 1117 questions/indicators were extracted and attributed to seven emerging themes. Twenty-three experts were interviewed, who suggested to increase the ease-of-use, grant open access, provide web-based interfaces and allow results to be automatically generated. Respondents also wished for tools providing the flexibility to conduct a rapid review, or an in-depth analysis of the surveillance system, depending on the evaluation objectives. The case studies emphasised that proper evaluations require adequate resources, typically requiring the involvement of several assessors and/or stakeholders, and can take weeks or months to complete. The resulting web-based guidance comprises six main sections: 1. Introduction to surveillance evaluation, 2. Evaluation of surveillance for AMU and AMR, 3. Evaluation tools, 4. Support for selecting an evaluation tool, 5. Case studies and 6. Directory of existing tools. The audience for the guidance is personnel working in public, private, and non-governmental organisations, from public health, animal health, plant health and environmental health, at local, national and international levels. We conclude that the field is challenged by opposing user needs for reduction and simplicity versus system approaches allowing the synthesis of that knowledge to sufficiently reflect the complexity of AMU and AMR ecology for real-world decisions. The CoEvalAMR web platform allows a better understanding of the different evaluation tools and assists users in the selection of an approach that corresponds to their evaluation needs. The CoEvalAMR consortium continues to address remaining gaps and consolidate evaluation tools and approaches in the future. One Health Impact Statement Antimicrobial resistance (AMR) resulting from antimicrobial use is a threat to human and animal health and may have unknown impacts on the environment. To understand how governance and human behaviour relate to AMR, it is important to implement integrated surveillance across the human, animal and environmental sectors. In this work, we describe the development of guidance on how to evaluate such surveillance, what the available tools cover and what gaps remain. The integration of surveillance is essential to address the interrelated pathways across the system. Consequently, the work with the reporting interdisciplinary consortium revealed that the broad diversity of users and their needs imply that a sustained exchange is needed to address the evolving challenge. This will be important to develop meaningful measures of impact and to avoid fixing one sector’s problems at the expense of another.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.361 | 0.529 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.025 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".