Micronutrient Status among Pregnant Women in Zinder, Niger and Risk Factors Associated with Deficiency
Bibliographic record
Abstract
Anemia and micronutrient (MN) deficiencies in pregnant women are associated with adverse pregnancy outcomes. In Niger, 58.6% of pregnant women are anemic; however, MN statuses are unknown. The study objectives were to estimate the prevalence of MN deficiencies among pregnant women in Zinder, Niger and explore associated risk factors. Pregnant women living in randomly selected rural villages (n = 88) were included. Capillary and venous blood samples (n = 770) were analyzed for hemoglobin (Hb) and plasma ferritin, soluble transferrin receptor (sTfR), zinc (pZn), retinol binding protein (RBP), folate and vitamin B12. C-reactive protein and alpha-1-acid glycoprotein were measured to adjust for inflammation. The prevalence of MN deficiencies in pregnant woman was high, indicative of a severe public health problem. Prevalence of iron deficiency was 20.7% and 35.7%, by ferritin (<15 µg/L) and sTfR (>8.3 mg/L), respectively. In total, 40.7% of women had low pZn (<50 µg/dL), 79.7% had marginal RBP (<1.32 µmol/L), 44.3% of women had low folate (<10 nmol/L) and 34.8% had low B12 concentrations (<148 pmol/L). Common risk factors associated with MN status included gravidity, mid-upper-arm circumference, geophagy, malaria, and result of the woman’s last pregnancy. Interventions to promote the strengthening of antenatal care, and access and adherence to nutrition and health interventions are critical among pregnant women in this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".