Drivers of stunting reduction in Senegal: a country case study
Bibliographic record
Abstract
BACKGROUND: Senegal has been an exemplar country in the West African region, reducing child stunting prevalence by 17.9% from 1992 to 2017. OBJECTIVES: In this study, we aimed to conduct a systematic in-depth assessment of factors at the national, community, household, and individual levels to determine the key enablers of Senegal's success in reducing stunting in children <5 y old between 1992/93 and 2017. METHODS: A mixed methods approach was implemented, comprising quantitative data analysis, a systematic literature review, creation of a timeline of nutrition-related programs, and qualitative interviews with national and regional stakeholders and mothers in communities. Demographic and Health Surveys and Multiple Indicator Cluster Surveys were used to explore stunting inequalities and factors related to the change in height-for-age z-score (HAZ) using difference-in-difference linear regression and the Oaxaca-Blinder decomposition method. RESULTS: Population-wide gains in average child HAZ and stunting prevalence have occurred from 1992/93 to 2017. Stunting prevalence reduction varied by geographical region and prevalence gaps were reduced slightly between wealth quintiles, maternal education groups, and urban compared with rural residence. Statistical determinants of change included improvements in maternal and newborn health (27.8%), economic improvement (19.5%), increases in parental education (14.9%), and better piped water access (8.1%). Key effective nutrition programs used a community-based approach, including the Community Nutrition Program and the Nutrition Enhancement Program. Stakeholders felt sustained political will and multisectoral collaboration along with improvements in poverty, women's education, hygiene practices, and accessibility to health services at the community level reduced the burden of stunting. CONCLUSIONS: Senegal's success in the stunting decline is largely attributed to the country's political stability, the government's prioritization of nutrition and execution of nutrition efforts using a multisectoral approach, improvements in the availability of health services and maternal education, access to piped water and sanitation facilities, and poverty reduction. Further efforts in the health, water and sanitation, and agriculture sectors will support continued success.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".