AIR POLLUTION AND ADVERSE BIRTH OUTCOMES: AN INTERNATIONAL ANALYSIS OF WORLD HEALTH ORGANIZATION GLOBAL SURVEY ON MATERNAL AND PERINATAL HEALTH
Bibliographic record
Abstract
Background and Aims: Inhaling fine particles (PM2.5), one component of air pollution, into the deep regions of the lung can induce oxidative stress and inflammation, and may contribute to onset of preterm labor. The aim of this research was to examine the relationship between PM2.5 and adverse birth outcomes among 22 countries in the World Health Organization Global Survey on Maternal and Perinatal Health from 2004-2008. Methods: Global PM2.5 estimates from remote sensing data were developed to produce long-term average values (2001-2006). Clinics were geocoded, and PM2.5 levels were generated in 50 kilometer radius circular buffers around each clinic. We used generalized estimating equations to determine the relationship between clinic-level PM2.5 levels and preterm birth and low birthweight at the individual level, adjusting for seasonality and potential confounders at the individual, clinic and country levels. Region-specific and country-specific associations were also investigated. Results: When looking across all countries and adjusting for seasonality, PM2.5 was not associated with preterm birth or low birthweight. Higher PM2.5 was associated with higher odds of low birthweight in African countries. In China, the country with the largest range of particulate levels, higher PM2.5 was associated with higher odds of preterm birth and low birthweight, with some evidence of a threshold effect when comparing the fourth quartile to the first quartile of PM2.5 (Odds Ratio [OR] = 1.79; 95% Confidence Interval [CI]: 0.97-3.31 and OR = 1.60; CI: 1.02-2.51 for preterm birth and low birthweight, respectively). Conclusions: Looking at the relationship between fine particles and adverse birth outcomes across countries and within countries with a large range of particulate levels gives additional insight into the potential causal mechanisms between air pollution and adverse birth outcomes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".