Determinants of anaemia among women of reproductive age in South Africa: A Healthy Life Trajectories Initiative (HeLTI)
Bibliographic record
Abstract
Anaemia continues to be a persistent concern among South African women of reproductive age (WRA), yet population specific information on its determinants remains sparse. We used baseline data from the Healthy Lives Trajectory Initiative a randomised trial (n = 480) to quantify factors associated with anaemia in Soweto, South Africa aged 18-25 years. We used multivariable logistic regression to describe associations with anaemia and used structural equation modelling to assess a theoretical model, which tested three categories socioeconomic status (household asset score, education level), nutritional factors (food security, leafy green vegetable and chicken and beef consumption, iron status and vitamin A status) and biodemographic factors (parity, age at start of menarche, HIV status, contraception use, anthropometry, and inflammation status). 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. Iron deficiency was the main anaemia risk factor in this low resource setting. However, anaemia of inflammation is present. Therefore, we suggest that in our setting, WRA anaemia control programs that include interventions to reduce ID and inflammation should be tested.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".