What works for anemia reduction among women of reproductive age? Synthesized findings from the exemplars in anemia project
Bibliographic record
Abstract
BACKGROUND: Few countries have succeeded to decrease the prevalence of anemia in women of reproductive age (WRA), and where improvements have been observed, contributing factors are not well understood. OBJECTIVES: To synthesize cross-cutting findings from specific exemplar studies in Uganda, Senegal, the Philippines, and Pakistan by reviewing anemia trends, policies, and programs, comparing drivers of change, and proposing strategies to achieve further reductions in WRA anemia. METHODS: A mixed-methods approach was used for exemplar case studies: 1) descriptive analyses of Demographic and Health Surveys and national survey data; 2) review of relevant policies/programs; 3) stakeholder in-depth interviews and focus group discussions with WRA and community members; and 4) Oaxaca-Blinder decomposition to identify determinants of hemoglobin change over time. This cross-country analysis performs triangulation of qualitative and quantitative analyses. RESULTS: Compound annual change rates for anemia from the ∼2005-2018 period were -0.7% in Senegal, -2.4% in Uganda, -3.4% in Pakistan, and -6.2% in the Philippines. Despite these reductions, WRA anemia burden in Senegal and Pakistan continues to be a severe public health problem. Direct and indirect health sector strategies, such as iron-folic acid supplementation in pregnancy, vitamin A supplementation during lactation, malaria control (Uganda and Senegal), investments in family planning, and better access to health services through community-based approaches, contributed to a median of 36.5% (range: 30%-66%) change in hemoglobin. Nonhealth sector strategies, including social protection and poverty alleviation schemes, empowering of girls and women, and improving household conditions, contributed to a 21% (18%-58%) change in hemoglobin. Large-scale food fortification (for example, wheat flour with iron) could have also contributed to improved micronutrient intakes and reduction in iron deficiency anemia. CONCLUSIONS: A context-specific, multisectoral approach is needed to decrease WRA anemia, incorporating direct nutritional interventions and indirect strategies within the health and nonhealth sectors. Lessons from the successes and challenges from exemplar countries could help accelerate global anemia reduction.
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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 | 0.031 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".