Antibiotic resistance risk assessment in Mymensingh, Bangladesh: Current scenario from human-animal-environmental interfaces viewpoint
Bibliographic record
Abstract
The imprudent use of antibiotics increases the environmental microflora's resistance to various drugs, particularly antibiotics. Prescription data is crucial for understanding antibiotic usage frequency and dosage. This health-focused study aims to analyze antibiotic prescription patterns in human and veterinary practices to identify emerging trends in environmental antibiotic resistance.For this survey, A total of 6550 prescriptions were randomly collected from hospitals and pharmacies in Mymensingh sadar upazila, Bangladesh, between August and October 2022. Of these, 5123 (78 %) were for human cases and 1427 (22 %) for veterinary purposes. Photos of the prescriptions were taken and analyzed to understand prescribing habits. Additionally, 30 water samples from rivers, ponds, sewage, and households in Mymensingh City Corporation were collected to assess environmental antibiotic levels and resistance patterns of microorganisms.The analysis showed that Cephalosporins were the most prescribed antibiotics, found in 570 (56.27 %) of human prescriptions and 230 (42.99 %) of veterinary prescriptions. Aminoglycosides had the lowest frequency, with 13 (1.2 %) for humans and 46 (8.6 %) for animals. Macrolides (12.24 %), carboxylic acids (1.87 %), and rifamycins (1.28 %) were only found in human prescriptions, while sulfa drugs (10.84 %), tetracyclines (5.42 %), and combinations of antibiotics (14.77 %) were only in animal prescriptions. Quinolones were prescribed 4.06 times more for humans, while aminoglycosides were used 3.54 times more for animals. Environmental samples showed E. coli had the highest resistance (MAR Value: 0.625) against eight antibiotics.This study illuminates the human-animal prescription patterns that are influenced by environmental factors which drive antibiotic stewardship in Bangladesh. It is imperative for practitioners to exercise caution and adhere to guidelines when prescribing antibiotics, both in human and veterinary practices, given the alarming trend of antibiotic resistance. Additionally, measures must be taken to restrict the influx of antibiotics residue into the environment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".