Environmental impacts of mass drug administration programs: exposures, risks, and mitigation of antimicrobial resistance
Bibliographic record
Abstract
Mass drug administration (MDA) of antimicrobials has shown promise in the reduction and potential elimination of a variety of neglected tropical diseases (NTDs). However, with antimicrobial resistance (AMR) becoming a global crisis, the risks posed by widespread antimicrobial use need to be evaluated. As the role of the environment in AMR emergence and dissemination has become increasingly recognized, it is likewise crucial to establish the role of MDA in environmental AMR pollution, along with the potential impacts of such pollution. This review presents the current state of knowledge on the antimicrobial compounds, resistant organisms, and antimicrobial resistance genes in MDA trials, routes of these determinants into the environment, and their persistence and ecological impacts, particularly in low and middle-income countries where these trials are most common. From the few studies directly evaluating AMR outcomes in azithromycin MDA trials, it is becoming apparent that MDA efforts can increase carriage and excretion of resistant pathogens in a lasting way. However, research on these outcomes for other antimicrobials used in MDA trials is sorely needed. Furthermore, while paths of AMR determinants from human waste to the environment and their persistence thereafter are supported by the literature, quantitative information on the scope and likelihood of this is largely absent. We recommend some mitigative approaches that would be valuable to consider in future MDA efforts. This review stands to be a valuable resource for researchers and policymakers seeking to evaluate the impacts of MDA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".