A systematic review of enteric pathogens in solid waste disposal sites and surrounding environments
Bibliographic record
Abstract
• Solid waste disposal sites harbour various enteric pathogens • The most highly reported pathogen in disposal sites is bacteria, followed by parasites and viruses • Disposal sites are potential sources of AMR and ARGs. • Culture methods are more utilized than molecular and microscopy methods • Research was less focused on animals or humans near the solid waste sites • We recommend quantitative microbial risk assessments (QMRA) and a holistic One Health approach in studies Solid waste disposal sites and indiscriminate dumping are favorable breeding grounds for various pathogens, including enteric pathogens. The pathogens include protozoan parasites, bacteria, and viruses. This study aimed to ascertain the prevalence of various enteric pathogens at solid waste disposal sites and surrounding environments. Additionally, it analyzed detection methods, assessed reported antimicrobial resistance, and identified the research gaps in the literature. We searched five databases, targeting peer-reviewed articles from January 2003 to June 2024. Thirty-eight articles were retained for final analysis. The results indicate that at least one enteric pathogen was detected in every study. 71% of the studies reported on bacteria, 13% on parasites, 5.3% on viruses, and the remaining percentage was on multiple pathogens. Evidence indicates the prevalence of antimicrobial resistance (AMR) and antimicrobial resistance genes (ARGs) in solid waste disposal sites. Culture-based enteric pathogen detection methods dominated compared to molecular and microscopic techniques. Our work identified research gaps such as a lack of completeness and underrepresentation of data in all geographic regions, such as low- and middle-income countries. Further, not all enteric pathogens have been extensively studied, leaving a gap in understanding their impacts. Additionally, the studies are missing the pathways for transmitting enteric pathogens and the employment of quantitative microbial risk assessments (QMRA). We recommend more thorough studies for all pathogens, including fungi, and prioritizing research in low and middle-income countries. Additionally, implementing quantitative microbial risk assessments (QMRA) and integrating a holistic One Health approach in studies.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.025 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".