Systematic review on evaluation tools applicable to One Health surveillance systems: A call for adapted methodology
Bibliographic record
Abstract
Developing and implementing effective surveillance programs for infectious diseases (ID) and antimicrobial resistance (AMR) requires the integration of information across relevant disciplines and sectors. Yet, establishing and sustaining collaboration at each step of the surveillance process, and modalities to translate integrated surveillance results into actions, are not well understood. This systematic review was designed to map and explore peer-reviewed tools that were either designed or used for evaluation of integrated surveillance systems for ID or AMR, and to identify the limitations of these tools and remaining methodological or knowledge gaps. A systematic search was conducted using keywords related to: "Evaluation", "Surveillance" and "One Health" in four databases (Medline, Embase, Web of Science and CAB abstract) up to the 28th of October 2022. Articles were selected if they presented an evaluation tool for integrated surveillance systems for ID or AMR (methodological study) or an application of such a tool (case study). All selected articles went through a quality check using the MetaQAT tool. Of 25 articles retrieved, 13 presented a methodological study, while 12 described a case study. Three main types of evaluation were identified through 17 tools: theoretical, process and impact evaluations. Both methodological and case study papers predominantly considered organizational and operational aspects in their evaluation. Although costs and/or impacts were discussed in some case studies, only one article reported an economic impact analysis. Evaluation of One Health integration and multisectoral collaboration was included in four methodological and four case study articles. One major challenge identified in this systematic review is the lack of clear guidance and standardized criteria for the comprehensive evaluation of complex integrated surveillance systems. To overcome this, it is essential to develop, validate, and apply methodologies adapted to these evaluation needs.
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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 | 0.563 | 0.767 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.035 | 0.028 |
| Bibliometrics | 0.053 | 0.050 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.026 | 0.035 |
| Open science | 0.012 | 0.015 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".