Resistance to extended-spectrum cephalosporins in Escherichia coli and Salmonella enterica isolated from food-producing animals: Ecological study from selected national surveillance programs
Bibliographic record
Abstract
Extended-spectrum cephalosporins (ESC) are categorized by World Health Organization as critically important antimicrobials with limited therapeutic alternatives for the treatment of severe bacterial infections in humans. Preserving the effectiveness of ESC requires continuous monitoring of resistance and comparison of associated data across national surveillance programs in the face of globalization. In this ecological study, we compared ESC resistance in Escherichia coli and Salmonella enterica isolated from food-producing animals from 2003 to 2019 between nine countries (Canada, Denmark, Finland, Japan, Netherlands, Norway, Sweden, United Kingdom, and the United States). Using the beta-regression model, compared to Canada, non-selective ESC-R Salmonella enterica was less likely isolated from food producing animals in other eight countries (Odds ratio range: 0.07–0.76). We observed an interaction between the country and the year with a significantly decreased proportion (P < 0.05) of non-selective ESC-R Escherichia coli from the Netherlands, the United Kingdom, and the United States compared to Canada over the years. There was a linear correlation between non-selective ESC-R Escherichia coli and ESC use from Netherlands (Spearman’s ρ = 0.91, P < 0.0001). For the six European countries, the interaction between the country and year showed a significant decrease in the proportion of selective ESC-R Escherichia coli over the years for the Netherlands compared to Denmark (P = 0.002). While there were variations in the proportion of beta-lactamase genes reported over the years, blaCTX-M and blaCMY-2 genes were commonly detected among the selective ESC-R Escherichia coli. This study reveals variability in the recovery of ESC-resistant bacteria among the countries that seems likely influenced by the individual country policy on the use of critically important antimicrobials and resistance surveillance programs. However, there is a need for harmonization and consistency in food animal sources of bacterial isolates used in surveillance programs within and between the countries for easy comparability.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".