Drivers of anemia reduction among women of reproductive age in Pakistan: a mixed-methods country case study
Bibliographic record
Abstract
In Pakistan, anemia prevalence among women of reproductive age (WRA) decreased from 50.5% in 2011 to 42.7% in 2018. The factors associated with this reduction are unclear. We conducted a systematic, in-depth assessment of the quantitative and qualitative determinants of anemia among WRA in Pakistan between 2011 and 2018. Employing standard Exemplars mixed-methods methodology, we conducted quantitative analyses using Pakistan’s National Nutrition Surveys. Qualitative analyses included a systematic literature review, program/policy analysis, key informant interviews, and focus group discussions with stakeholders to identify and understand country-level enablers and barriers to WRA anemia decline in Pakistan. A final Oaxaca-Blinder decomposition analysis (OBDA) evaluated the relative contribution of direct and indirect factors. Among nonpregnant women, mean hemoglobin increased from 11.7 ± 1.8 g/dL in 2011 to 12.1 ± 1.6 g/dL in 2018 ( P < 0.01), corresponding to an 11%-point decline in anemia prevalence during this time (51%–40%). However, inequities by geographical region, household wealth, and urban compared with rural residence persisted. From the policy and qualitative analyses, the Lady Healthcare Worker program was identified as being instrumental in improving women’s health and nutrition, especially for antenatal care, including iron supplementation. However, at the community-level, government corruption was a perceived barrier to effective program implementation, especially the Benazir Income Support Program for women in poverty. OBDA explained 89% of the observed change in mean hemoglobin, with household enrollment in the Benazir Income Support Program (36%), household wealth (17%), and improvement in women’s nutrition [body mass index (in kg/m 2 ): 15%, serum retinol: 12%] emerging as the most critical drivers of anemia decline among nonpregnant women in Pakistan. To protect these gains and continue improvements, anemia prevention efforts should continue to focus on improving healthcare access, women’s economic 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".