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Cost‐effectiveness of Mama‐SASHA: a project to improve health and nutrition through an integrated orange‐fleshed sweetpotato production and health service delivery model

2015· article· en· W1553055032 on OpenAlexaff
Julie L. Self, Amy Girard, Deborah A. McFarland, Frederick Grant, Jan W. Low, Donald C. Cole, Carol Levin

Bibliographic record

VenueThe FASEB Journal · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsCentre for Global Health ResearchUniversity of Toronto
Fundersnot available
KeywordsOrange (colour)Production (economics)Service delivery frameworkService (business)Health benefitsBusinessMedicineMarketingFood scienceChemistryTraditional medicineEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.130
GPT teacher head0.321
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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