Cost‐effectiveness of Mama‐SASHA: a project to improve health and nutrition through an integrated orange‐fleshed sweetpotato production and health service delivery model
Bibliographic record
Abstract
The Mama‐SASHA project aims to improve the health and nutrition of pregnant/lactating women and children <2 years through an integrated orange‐fleshed sweetpotato (OFSP) and health service strategy in Western Kenya. We analyzed the cost effectiveness from a societal perspective. We estimated the incremental cost‐effectiveness ratio (ICER) of the intervention, which includes OFSP vouchers provided at antenatal care (ANC) visits, nutrition education, and pregnant women's clubs, compared to status quo ANC services. Effectiveness data from a quasi‐experimental study were used to estimate DALYs associated with changes in vitamin A deficiency, stunting, wasting, anemia, diarrhea, and mortality for children <2 years and their mothers. We used ingredients based micro‐costing to estimate economic costs of agriculture, health and community interventions, including opportunity costs of labor for health workers, community volunteers and participants. Net economic cost over three years was US @445,151. 77 DALYs were averted per year, mostly attributable to improvements in stunting and anemia. The ICER was US @1,919 per DALY averted, which is two times Kenya's GDP per capita (@994 per person) and meets cost‐effectiveness criteria set by WHO. Benefits not convertible into DALY's include improved sweetpotato yield, food security, extension services and nutritional knowledge.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".