What is the cost of integration? Evidence from an integrated health and agriculture project to improve nutrition outcomes in Western Kenya
Bibliographic record
Abstract
Integrated nutrition and agricultural interventions have the potential to improve the efficiency and effectiveness of investments in food security and nutrition. This article aimed to estimate the costs of an integrated agriculture and health intervention (Mama SASHA) focused on the promotion of orange-fleshed sweet potato (OFSP) production and consumption in Western Kenya. Programme activities included nutrition education and distribution of vouchers for OFSP vines during antenatal care and postnatal care (PNC) visits. We used expenditures and activity-based costing to estimate the financial costs during programme implementation (2011-13). Cost data were collected from monthly expense reports and interviews with staff members from all implementing organizations. Financial costs totalled US$507 809 for the project period. Recruiting and retaining women over the duration of their pregnancy and postpartum period required significant resources. Mama SASHA reached 3281 pregnant women at a cost of US$155 per beneficiary. Including both pregnant women and infants who attended PNC services with their mothers, the cost was US$110 per beneficiary. Joint planning, co-ordination and training across sectors drove 27% of programme costs. This study found that the average cost per beneficiary to implement an integrated agriculture, health and nutrition programme was substantial. Planning and implementing less intensive integrated interventions may be possible, and economies of scale may reduce overall costs. Empirical estimates of costs by components are critical for future planning and scaling up of integrated programmes.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".