Air pollution and pregnancy outcomes based on exposure evaluation using a land use regression model: A systematic review
Bibliographic record
Abstract
This review systematically assessed those studies investigating the association between air pollution and birth outcomes using land use regression (LUR) models for exposure assessment. Fifty-four studies were identified which were published between 2007 and 2019. Most of these were conducted in America, Spain and Canada, while only five were conducted in China. One hundred and ninety-seven LUR models were developed for different pollutants. The main pollutants that these studies assessed were NO2 and PM2.5, and the main pregnancy outcomes investigated were preterm birth (PTB), small for gestational age (SGA) and birth weight. Studies consistently found that NO2 exposure during pregnancy was associated with reduced fetal growth and development. The effect of NO2 on other adverse pregnancy outcomes is unclear. In addition, it was found that increased PM2.5 (aerodynamic equivalent diameter ≤ 2.5 um) exposure during pregnancy reduced birth weight. The effect of PM2.5 on other adverse pregnancy outcomes is also unclear. The relationship between other pollutants and adverse pregnancy outcomes is uncertain based on the existing research. Exposure assessment with LUR modeling has been widely used in Europe and North America, but used less in China. Future studies are recommended to use LUR modeling for individual exposure evaluation in China to better characterize the relationship between air pollution and adverse pregnancy outcomes. In addition, further research is required given that a lot of the associations looked at in the review were inconclusive.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".