Maternal anaemia and risk of neonatal and infant mortality in low- and middle-income countries: a secondary analysis of 45 national datasets
Bibliographic record
Abstract
BACKGROUND: Anaemia in pregnancy has been recognised worldwide as a growing public health concern and an important cause of adverse neonatal outcomes. However, only a limited number of studies have been done in low-income settings, which have the highest prevalence of anaemia. We aimed to investigate the association between maternal anaemia and neonatal and infant mortality in low- and middle-income countries. METHODS: Secondary analysis of pooled data from 45 national demographic and health surveys (2010-2020). We included all women between 15 and 49 years old, who had singleton live birth within 1 year preceding the survey, with a valid maternal measurement of haemoglobin. We used logistic regression models to estimate the crude and adjusted OR (aOR) with 95% CIs of the association between maternal anaemia (measured at the time of the survey) and the risk of neonatal and infant mortality. RESULTS: Among 106 143 women included in our analysis, there were 53 348 (50.5%) women with no anaemia, 24 670 (23.2%) with mild anaemia, 25 937 (24.3%) with moderate anaemia and 2188 (2.0%) with severe anaemia. Overall, there were 2668 (2.5%) neonatal and 3756 (3.5%) infant deaths. Moderate (aOR 1.20; 95% CI 1.06 to 1.35) and severe (aOR 1.89; 95% CI 1.46 to 2.44) maternal anaemia were associated with increased odds of neonatal mortality, respectively. Similar estimates were observed for moderate and severe anaemia and infant mortality. No increased risk was noted for mild anaemia. INTERPRETATION: Moderate and severe maternal anaemia in low- and middle-income settings are associated with increased risks of neonatal and infant mortality. Future research should examine how targeted interventions for prepregnancy and antenatal treatment of anaemia in reproductive-age women can enhance maternal and child health in low- and middle-income settings.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".