Drivers of anemia reduction among women of reproductive age in Uganda: a country case study
Bibliographic record
Abstract
BACKGROUND: In Uganda, anemia prevalence among women of reproductive age (WRA) decreased from 41% in 2006 to 32% in 2016. The factors associated with this reduction are uncertain. OBJECTIVES: We conducted a systematic in-depth assessment of the quantitative and qualitative determinants of anemia among WRA in Uganda between 2006 and 2016. METHODS: Employing standard Exemplars in Global Health methodology, quantitative analyses were conducted using Uganda's Demographic and Health Surveys. Qualitative analyses included a comprehensive literature review, program analysis, and stakeholder interviews to identify and understand country-level enablers and barriers to WRA anemia decline in Uganda over the past 2 decades. A final Oaxaca-Blinder decomposition analysis (OBDA) evaluated the relative contribution of direct and indirect factors. RESULTS: Among nonpregnant women (NPW), mean hemoglobin (Hb) increased from 12.3 g/dL in 2006 to 12.6 g/dL in 2016 (P < 0.01), corresponding to a 9%-point decline in anemia prevalence during this time (from 40% to 31%). However, inequities by geographical region, household wealth, and women's educational attainment persisted. Key programs over the study period included food fortification, the Uganda Anemia Policy, and the Uganda Nutrition Action Plan (UNAP). Stakeholders also identified malaria control, family planning programs, and continued strengthening of Uganda's health care system as key enablers of anemia decline. Our OBDA explained 89% of the observed change in mean Hb, with family planning (27%), increased access to bednets (26%), household sociodemographics (17%), and improvement in women's overall nutrition (body mass index [BMI]: 15%) emerging as the most critical drivers of anemia decline among NPW in Uganda, corroborating our qualitative and policy analyses. CONCLUSIONS: To protect the hard-fought gains and continue improvements, WRA anemia prevention efforts in Uganda should remain focused on improving health care access especially within antenatal care and malaria control programs. Additionally, multisectoral collaborations and investments to empower women and poverty alleviation strategies need to be enhanced.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".