Turning poop into profit: Cost-effectiveness and soil transmitted helminth infection risk associated with human excreta reuse in Vietnam
Bibliographic record
Abstract
Human excreta is a low cost source of nutrients vital to plant growth, but also a source of pathogens transmissible to people and animals. We investigated the cost-savings and infection risk of soil transmitted helminths (STHs) in four scenarios where farmers used either inorganic fertilizer or fresh/composted human excreta supplemented by inorganic fertilizer to meet the nutrient requirements of rice paddies in the Red River Delta, Vietnam. Our study included two main components: 1) a risk estimate of STH infection for farmers who handle fresh excreta, determined by systematic review and meta-analysis; and 2) a cost estimate of fertilizing rice paddies, determined by nutrient assessment of excreta, a retailer survey of inorganic fertilizer costs, and a literature review to identify region-specific inputs. Our findings suggest that farmers who reuse fresh excreta are 1.24 (95% CI: 1.13–1.37, p-value<0.001) times more likely to be infected with any STH than those who do not handle excreta or who compost appropriately, and that risk varies by STH type (Ascaris lumbricoides RR = 1.17, 95% CI = 0.87–1.58, p-value = 0.29; Hookworm RR = 1.02, 95% CI = 0.50–2.06, p-value = 0.96; Trichuris trichiura RR = 1.38, 95% CI = 0.79–2.42, p-value = 0.26). Average cost-savings were highest for farmers using fresh excreta (847,000 VND) followed by those who composted for 6 months as recommended by the WHO (312,000 VND) and those who composted for a shorter time (5 months) with lime supplementation (37,000 VND/yr); however, this study did not assess healthcare costs of treating acute or chronic STH infections in the target group. Our study provides evidence that farmers in the Red River Delta are able to use a renewable and locally available resource to their economic advantage, while minimizing the risk of STH infection.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".