Integrated surveillance systems for antibiotic resistance in a One Health context: a scoping review
Bibliographic record
Abstract
BACKGROUND: Antibiotic resistance (ABR) has emerged as a major threat to health. Properly informed decisions to mitigate this threat require surveillance systems that integrate information on resistant bacteria and antibiotic use in humans, animals, and the environment, in line with the One Health concept. Despite a strong call for the implementation of such integrated surveillance systems, we still lack a comprehensive overview of existing organizational models for integrated surveillance of ABR. To address this gap, we conducted a scoping review to characterize existing integrated surveillance systems for ABR. METHODS: The literature review was conducted using the PRISMA guidelines. The selected integrated surveillance systems were assessed according to 39 variables related to their organization and functioning, the socio-economic and political characteristics of their implementation context, and the levels of integration reached, together with their related outcomes. We conducted two distinct, complementary analyses on the data extracted: a descriptive analysis to summarize the characteristics of the integrated surveillance systems, and a multiple-correspondence analysis (MCA) followed by a hierarchical cluster analysis (HCA) to identify potential typology for surveillance systems. RESULTS: The literature search identified a total of 1330 records. After the screening phase, 59 references were kept from which 14 integrated surveillance systems were identified. They all operate in high-income countries and vary in terms of integration, both at informational and structural levels. The different systems combine information from a wide range of populations and commodities -in the human, animal and environmental domains, collection points, drug-bacterium pairs, and rely on various diagnostic and surveillance strategies. A variable level of collaboration was found for the governance and/or operation of the surveillance activities. The outcomes of integration are poorly described and evidenced. The 14 surveillance systems can be grouped into four distinct clusters, characterized by integration level in the two dimensions. The level of resources and regulatory framework in place appeared to play a major role in the establishment and organization of integrated surveillance. CONCLUSIONS: This study suggests that operationalization of integrated surveillance for ABR is still not well established at a global scale, especially in low and middle-income countries and that the surveillance scope is not broad enough to obtain a comprehensive understanding of the complex dynamics of ABR to appropriately inform mitigation measures. Further studies are needed to better characterize the various integration models for surveillance with regard to their implementation context and evaluate the outcome of these models.
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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 | 0.032 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.036 | 0.038 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".