Prevalence of Antibiotic-Resistant Bacteria and Antibiotic-Resistant Genes and the Quantification of Antibiotics in Drinking Water Treatment Plants of Malaysia: Protocol for a Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance is a known global public health threat. In addition, it brings serious economic consequences to agriculture. Antibiotic resistance in humans, animals, and environment is interconnected, as proposed in the tricycle surveillance by the World Health Organization. In Malaysia, research and surveillance of antimicrobial resistance are mainly performed in clinical samples, agricultural settings, and surface waters, but no surveillance of the drinking water systems has been performed yet. Hence, this policy-driven study is a combined effort of microbiologists and engineers to provide baseline data on the magnitude of antimicrobial resistance in the drinking water systems of Malaysia. OBJECTIVE: The aim of this study was to study the baseline level of antibiotic-resistant bacteria in the drinking water distribution systems of Malaysia by collecting samples from the pretreatment and posttreatment outlets of water treatment plants in a selected state of Malaysia. We aimed to determine the prevalence of antibiotic-resistant bacteria, the occurrence of antibiotic-resistant genes, and the level of antibiotics present in the drinking water systems. METHODS: This is a laboratory-based, cross-sectional study in a selected state of Malaysia. Water samples from 6 drinking water treatment plants were collected. Samples were collected at 3 sampling points, that is, the intake sampling station, service reservoir outlet station, and the distribution system sampling station. These were tested against 7 types of antibiotics in triplicates. Samples were screened for antibiotic-resistant bacteria and antibiotic-resistant genes and quantified for the level of antibiotics present in the drinking water treatment plants. RESULTS: We will show the descriptive statistics of the number of bacterial colonies harvested from water samples grown on Reasoner's 2A agar with or without antibiotics, the occurrence of antibiotic-resistant genes, and the level of antibiotics detected in the water samples. The sampling frame was scheduled to start from November 2021 and continue until December 2022. Data analysis is expected to be completed by early 2023, and the results are expected to be published in mid-2023. CONCLUSIONS: This study provides baseline information on the status of the antimicrobial-resistant bacteria, the presence of resistance genes as contaminants, and the level of antibiotics present in the drinking water systems of Malaysia, with the aim of demonstrating to policymakers the need to consider antimicrobial resistance as a parameter in drinking water surveillance. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37663.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".