Analyse cout-efficacite des interventions pour reduire les maladies diarrheiques chez les consommateurs de laitues irriguees avec des eaux usees au Ghana. In FrenchCost-effectiveness analysis of interventions for diarrhoeal disease reduction among consumers of wastewater-irrigated lettuce in Ghana
Bibliographic record
Abstract
Interventions proposed and implemented for the mitigation of diarrhoeal diseases associated with wastewater reuse in agriculture have received little, if any, comparative assessment of their cost-effectiveness. This chapter assesses the costs, outcomes and cost-effectiveness of the so-called ?treatment? and ?non- or post-treatment? interventions as well as a combination of these for wastewater irrigation in urban Ghana using an approach that integrates quantitative microbial risk assessment (QMRA), disability-adjusted life years (DALYs) and cost-effectiveness analysis (CEA). The cost-effectiveness ratios (CERs) for the treatment and non-treatment interventions assessed ranged from US$31/DALY to US$812/DALY averted. Risk-reduction measures targeting farming practices and the basic rehabilitation of local wastewater treatment plants were the most attractive interventions with a CER well below the threshold of US$150/DALY, sometimes considered as the upper limit for a health intervention to be cost-effective in developing countries. All combinations associated with the basic rehabilitation of the treatment plants, with either on-farm or post-harvest interventions or both, resulted in CERs within the range of US$40/DALY to US$57/DALY. However, the CERs for the construction of a new wastewater treatment plant either as an independent intervention or in combination with on-farm and post-harvest interventions were unattractive in view of health-risk reduction for wastewater irrigation. Although attractive, the CERs of non-treatment options are largely dependent on compliance (adoption) by farmers and food vendors. In this regard, the CER increased by almost fivefold when the adoption rate was only 25 per cent by farmers and food vendors; but was attractive as long as adoption rates did not fall below 70 per cent. On the other hand, the success of the treatment option depends on the functionality of the treatment plants which is not without challenges in a country like Ghana. Thus, this chapter stresses the need for a balanced risk-management approach through a combination of treatment and non-treatment interventions to hedge against failures that may affect CERs at any end. While this chapter provides a contribution to the debate on interventions for health-risk mitigation in wastewater irrigation, more case studies would be useful to verify the data presented here.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".