Maternal exposure to fine particulate matter and preterm birth and low birth weight in Africa
Bibliographic record
Abstract
Fine particulate matter (PM2.5) exposure has been reported to adversely affect birth outcomes, but the evidence is limited, particularly in low- and middle-income countries (LMICs). We assessed the associations between maternal PM2.5 exposure and low birth weight (LBW) and preterm birth (PTB) in Africa. We used standard Demographic and Health Surveys (DHS) data (2005–2015) from 15 countries in Africa to conduct a cross-sectional study. The study population was composed of 131,594 births with detailed information on maternal and household variables. LBW was defined as a birth weight of < 2500 g after 37 weeks, and PTB was defined as live birth occurring before 37 weeks of gestation. Average exposure to PM2.5 during pregnancy was estimated using satellite-based models. Multivariable logistic regression models were constructed, and analyses of data by region (Western, Eastern, Central, and Southern Africa) and data stratified by potential effect modifiers were conducted. A total of 13,214 (10%) LBW and 4,377 (3.3%) PTB cases were identified. An interquartile range (IQR) (33.9 μg/m3) increase in PM2.5 during pregnancy was associated with increased odds of LBW and PTB, with odds ratios (ORs) of 1.28 (95% CI: 1.23, 1.34) and 1.08 (95% CI: 1.01, 1.16), respectively. Region-specific analyses revealed significant associations between PM2.5 and LBW in all regions, and significant associations between PM2.5 and PTB in Western and Southern Africa. Subgroup analyses revealed that the association between PM2.5 and LBW was present in all subgroups, and stronger associations were observed in female infants, while the association between PM2.5 and PTB was larger in subgroups of older individuals living in urban areas. This multicountry study in Africa demonstrated significant associations between maternal exposure to PM2.5 and higher odds of LBW and PTB. Our findings may facilitate air quality control strategies that address adverse birth outcomes in LMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.013 | 0.001 |
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 teacher head, 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".