A case for subnational nutrition financing: The development and use of county-level investment cases in Kenya
Bibliographic record
Abstract
This paper aims to emphasize the significance of creating subnational nutrition action plans in regions with high variation in nutrition challenges and evaluates their projected return on investment in Kenya. Despite steady progress, undernutrition in Kenya remains high, costing the country an estimated US$ 4.2 billion or 7% of its GDP annually. Under Kenya's decentralized government system, numerous counties developed sectoral County Nutrition Action Plans (CNAPs) in 2018 to identify and prioritize essential nutrition actions to target undernutrition at the subnational level. In this paper, the authors present findings from county investment cases (CICs) in five counties - Nandi, Busia, Makueni, Vihiga, and Elgeyo Marakwet-including the costs, health impacts, and benefit to cost ratios of implementing high-impact nutrition interventions. Data was collected on the target coverage and cost of interventions prioritized in each county's CNAPs for the 2018 to 2022 period. A monetized DALY approach, using the value of a statistical life methodology was used for cost-benefit analysis and the Optima Nutrition tool was used for cost-effectiveness analysis. The estimated cumulative impact of the five CNAPs was projected as 1,800 child and 115 maternal deaths averted; preventing and treating 19,000 cases of stunting and 4,700 cases of wasting in children under five and averting 67,000 cases of anaemia in pregnant women and adolescent girls. The county-level benefit-cost ratios range from $5:1 to $14:1 (at a default 3% discount rate). This analysis demonstrates that localized subnational plans can be advantageous for policymaking and prioritization to better address subnational disparities in undernutrition and offer a high return on investment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".