Malaria reduction drives childhood stunting decline in Uganda: a mixed-methods country case study
Bibliographic record
Abstract
BACKGROUND: Uganda has achieved a considerable reduction in childhood stunting over the past 2 decades, although accelerated action will be needed to achieve 2030 targets. OBJECTIVES: This study assessed the national, community, household, and individual-level drivers of stunting decline since 2000, along with direct and indirect nutrition policies and programs that have contributed to nutrition change in Uganda. METHODS: This mixed-methods study used 4 different approaches to determine the drivers of stunting change over time: 1) a scoping literature review; 2) quantitative data analyses, including Oaxaca-Blinder decomposition and difference-in-difference multivariable hierarchical modeling; 3) national- and community-level qualitative data collection and analysis; and 4) analysis of key direct and indirect nutrition policies, programs, and initiatives. RESULTS: Stunting prevalence declined by 14% points from 2000 to 2016, although geographical, wealth, urban/rural, and education-based inequalities persist. Child growth curves demonstrated substantial improvements in child height-for-age z-scores (HAZs) at birth, reflecting improved maternal nutrition and intrauterine growth. The decomposition analysis explained 82% of HAZ change, with increased coverage of insecticide-treated mosquito nets (ITNs; 35%), better maternal nutrition (19%), improved maternal education (14%), and improved maternal and newborn healthcare (11%) being the most critical factors. The qualitative analysis supported these findings, and also pointed to wealth, women's empowerment, cultural norms, water and sanitation, dietary intake/diversity, and reduced childhood illness as important. The 2011 Uganda Nutrition Action Plan was an essential multisectoral strategy that shifted nutrition out of health and mainstreamed it across related sectors. CONCLUSIONS: Uganda's success in stunting reduction was multifactorial, but driven largely through indirect nutrition strategies delivered outside of health. To further improve stunting, it will be critical to prioritize malaria-control strategies, including ITN distribution campaigns and prevention/treatment approaches for mothers and children, and deliberately target the poor, least educated, and rural populations along with high-burden districts.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".