Impact of air pollution on stunting among children in Africa
Bibliographic record
Abstract
Abstract Background Undernutrition is a global public health crisis, causing nearly half of deaths for children under age 5 years. Little is known regarding the impact of air pollution in-utero and early childhood on health outcomes related to undernutrition. The aim of our study is to evaluate the association of prenatal and early-life exposure to PM 2.5 and child malnutrition as captured by the height-for-age z-score (HAZ), and stunting in 32 countries in Africa. We also evaluated critical windows of susceptibility during pregnancy to each environmental risk. Methods We linked nationally representative anthropometric data from 58 Demographic and Health Surveys (DHS) ( n = 264,207 children < 5 years of age) with the average in-utero PM 2.5 concentrations derived from satellite imagery. We then estimated associations between PM 2.5 and stunting and HAZ after controlling for child, mother and household factors, and trends in time and seasonality. Results We observed lower HAZ and increased stunting with higher in-utero PM 2.5 exposure, with statistically significant associations observed for stunting (OR: 1.016 (95% CI: 1.002, 1.030), for a 10 μg/m 3 increase). The associations observed were robust to various model specifications. Wald tests revealed that sex, wealth quintile and urban/rural were not significant effect modifiers of these associations. When evaluating associations between trimester-specific PM 2.5 levels, we observed that associations between PM 2.5 and stunting was the largest. Conclusions This is one of the first studies for the African continent to investigate in-utero and early-life exposure to PM 2.5 is an important marker of childhood undernutrition. Our results highlight that PM 2.5 concentrations need to be urgently mitigated to help address undernutrition in children on the continent.
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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 | 0.001 | 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) | 0.005 | 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 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".