Management Options for Reducing the Release of Antibiotics and Antibiotic Resistance Genes to the Environment
Bibliographic record
Abstract
BACKGROUND: There is growing concern worldwide about the role of polluted soil and water environments in the development and dissemination of antibiotic resistance. OBJECTIVE: Our aim in this study was to identify management options for reducing the spread of antibiotics and antibiotic-resistance determinants via environmental pathways, with the ultimate goal of extending the useful life span of antibiotics. We also examined incentives and disincentives for action. METHODS: We focused on management options with respect to limiting agricultural sources; treatment of domestic, hospital, and industrial wastewater; and aquaculture. DISCUSSION: We identified several options, such as nutrient management, runoff control, and infrastructure upgrades. Where appropriate, a cross-section of examples from various regions of the world is provided. The importance of monitoring and validating effectiveness of management strategies is also highlighted. Finally, we describe a case study in Sweden that illustrates the critical role of communication to engage stakeholders and promote action. CONCLUSIONS: Environmental releases of antibiotics and antibiotic-resistant bacteria can in many cases be reduced at little or no cost. Some management options are synergistic with existing policies and goals. The anticipated benefit is an extended useful life span for current and future antibiotics. Although risk reductions are often difficult to quantify, the severity of accelerating worldwide morbidity and mortality rates associated with antibiotic resistance strongly indicate the need for action.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".