Evidence for Public Health Risks of Wastewater and Excreta Management Practices in Southeast Asia: A Scoping Review
Bibliographic record
Abstract
The use of wastewater and excreta in agriculture is a common practice in Southeast Asia; however, concerns remain about the potential public health risks of this practice. We undertook a scoping review to examine the extent, range, and nature of literature, as well as synthesize the evidence for associations between wastewater and excreta management practices and public health risks in Southeast Asia. Three electronic databases (PubMed, CAB Direct, and Web of Science) were searched and a total of 27 relevant studies were included and evaluated. The available evidence suggested that possible occupational health risks of wastewater and excreta management practices include diarrhea, skin infection, parasitic infection, bacterial infection, and epilepsy. Community members can be at risk for adverse health outcomes through consuming contaminated fish, vegetables, or fruits. Results suggested that practices including handling, treatment, and use of waste may be harmful to human health, particularly farmer's health. Many studies in this review, however, had limitations including lack of gender analyses, exposure assessment, and longitudinal study designs. These findings suggest that more studies on identifying, quantitatively assessing, and mitigating health risks are needed if sustainable benefits are to be obtained from wastewater and excreta reuse in agriculture in Southeast Asia.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".