Drivers of anemia reduction among women of reproductive age in the Philippines: a country case study
Bibliographic record
Abstract
BACKGROUND: Anemia prevalence among women of reproductive age (WRA) in the Philippines was 25% in 2000, decreasing to 13% in 2018. To date, an in-depth assessment of the determinants associated with this decline has not been conducted. OBJECTIVES: This study aimed to conduct a systematic in-depth assessment of the quantitative and qualitative determinants of anemia among WRA in the Philippines between 2008 and 2018. METHODS: Using standard Exemplars methodology, we conducted quantitative analyses using the Philippines' National Nutrition Survey, the Expanded National Nutrition Survey, and the Philippines National Demographic and Health Surveys. Qualitative analyses included a comprehensive literature review, program/policy analysis, and interviews with stakeholders to understand country-level enablers and barriers to WRA anemia decline in the Philippines. A final Oaxaca-Blinder decomposition analysis evaluated the relative contribution of direct and indirect factors. RESULTS: Among nonpregnant women (NPW), mean hemoglobin (Hb) increased from 12.7 g/dL in 2008 to 13.1 g/dL in 2018 (P < 0.01), corresponding to an 11%-point decline in anemia prevalence (from 23% to 12%). Inequities by geographical region, household wealth, and women's educational attainment narrowed considerably during this time. Important direct and indirect nutrition programs were introduced during our study period, including universal health care and food fortification. Country experts interviewed credited programs focused on alleviating micronutrient deficiencies and poverty, and improvements in women's health and well-being, for the country's extraordinary success. Oaxaca-Blinder decomposition analysis explained ∼50% of the observed change in mean Hb among NPW, with family planning (35%), household sociodemographics (29%), and improvement in women's nutrition (23%) emerging as critical drivers of anemia decline, corroborating our qualitative and policy analyses. CONCLUSIONS: To protect these gains, WRA anemia prevention efforts in the Philippines should continue to focus on universal health care access, women's empowerment, and poverty alleviation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".