Anemia and Micronutrient Status of Women of Childbearing Age and Children 6–59 Months in the Democratic Republic of the Congo
Bibliographic record
Abstract
Little is known about the micronutrient status of women and children in the Democratic Republic of the Congo, which is critical for the design of effective nutrition interventions. We recruited 744 mother-child pairs from South Kivu (SK) and Kongo Central (KC). We determined hemoglobin (Hb), serum zinc, vitamin B12, folate, ferritin, soluble transferrin receptor (sTfR), retinol binding protein (RBP), C-reactive protein, and α-1 acid glycoprotein concentrations. Anemia prevalence was determined using Hb adjusted for altitude alone and Hb adjusted for both altitude and ethnicity. Anemia prevalence was lower after Hb adjustment for altitude and ethnicity, compared to only altitude, among women (6% vs. 17% in SK; 10% vs. 32% in KC), children 6-23 months (26% vs. 59% in SK; 25% vs. 42% in KC), and children 24-59 months (14% vs. 35% in SK; 23% vs. 44% in KC), respectively. Iron deficiency was seemingly higher with sTfR as compared to inflammation-adjusted ferritin among women (18% vs. 4% in SK; 21% vs. 5% in KC), children 6-23 months (51% vs. 14% in SK; 74% vs. 10% in KC), and children 24-59 months (23% vs. 4% in SK; 58% vs. 1% in KC). Regardless of indicator, iron deficiency anemia (IDA) never exceeded 3% in women. In children, IDA reached almost 20% when sTfR was used but was only 10% with ferritin. Folate, B12, and vitamin A (RBP) deficiencies were all very low (<5%); RBP was 10% in children. The prevalence of anemia was unexpectedly low. Inflammation-adjusted zinc deficiency was high among women (52% in SK; 58% in KC), children 6-23 months (23% in SK; 20% in KC), and children 24-59 months (25% in SK; 27% in KC). The rate of biochemical zinc deficiency among Congolese women and children requires attention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".