Determinants of Anaemia Among Women of Reproductive Age In South Africa: A Healthy Life Trajectories Initiative (HeLTI)
Bibliographic record
Abstract
Abstract Background – Anaemia continues to be a major public health problem among women of reproductive age (WRA). A thorough understanding of anaemia risk factors is necessary to design better interventions. This paper examines the determinants of anaemia among WRA in South Africa.Methods- We included baseline data from 480 women participating in the pilot-phase of a randomized controlled trial (HeLTI). We measured haemoglobin (Hb) status using the Hemocue. Plasma iron status markers (ferritin and soluble transferrin receptor (sTfR)), markers of inflammation (C-reactive protein (CRP)) and alpha-1-acid glycoprotein (AGP)) and retinol binding protein (RBP) were assessed using the multiplex method. We used multivariate logistic regression to describe associations with anaemia and structural equation modelling (SEM) to characterise direct and indirect pathways influencing haemoglobin concentrations.Results- The prevalence of anaemia, iron deficiency (ID), and iron deficiency anaemia (IDA) was 39.4%, 38.1% and 21.6% respectively. The multiple logistic regression showed that ID (OR: 2.62, 95% CI: 1.72, 3.98), iron deficiency erythropoiesis (IDE) (OR: 1.62, 95% CI: 1.07, 2.46), and elevated CRP (OR: 1.69, 95% CI: 1.04, 2.76), increased the odds of being anaemic. SEM analysis revealed Hb was directly and positively associated with adjusted ferritin (0.0031 per mg/dl; p≤0.001), and CRP (0.015 per mg/dl; p≤0.05), and directly and negatively associated with soluble transferrin receptor sTfR (-0.042 per mg/dl; p≤0.001). While contraception use had both a direct (0.34; p≤0.05) and indirect (0.11; p≤0.01) positive association with Hb. Additionally, chicken and beef consumption had a positive indirect association with Hb concentrations (0.15; p≤0.05) through adjusted ferritin.Conclusion-A key driver of anaemia in our setting is ID, however the presence of inflammation also increases the risk of anaemia. To address anaemia, interventions should aim to improve the diet quality of women, in particular access to iron rich foods. We recommend the use of multi-micronutrient supplements with a lower dose of iron and other micronutrients which would ensure that women receive the same benefits as with iron folic acid, while alleviating anaemia of inflammation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".