Maternal exposure to black carbon and nitrogen dioxide during pregnancy and birth weight: Using machine-learning methods to achieve balance in inverse-probability weights
Bibliographic record
Abstract
Low birth weight is associated with increased risks of health problems in infancy and later life. Among the epidemiological analyses suggesting an association between air pollution and birth weight, few have estimated the effects of black carbon (BC) or together with nitrogen dioxide (NO2), and even fewer studies have used causal modelling. We examined 1,119,011 birth records between 2001/01/01 and 2015/12/31 from the Massachusetts Birth Registry to investigate causal associations between prenatal exposure to BC and NO2 and birth weight. We calculated mean residential BC and NO2 exposures 0–30, and 31–280 days prior to birth from validated spatial-temporal models. We fit generalized propensity score models with gradient boosting tuned by a new algorithm to achieve covariate balance, then fit marginal structural models with stabilized inverse-probability weights. Throughout pregnancy, the average birth weight would drop by 17.0 g (95% CI: 15.4, 18.6) for an IQR increase of 0.14 μg/m3 in BC and would independently drop by 19.9 g (95% CI: 18.6, 21.3) for an IQR increase of 9.8 ppb in NO2. Most of the negative effects of BC on birth weight are from 0 to 30 days before the delivery date. The estimated odds ratio of low birth weight for every IQR increase during the entire pregnancy was 1.131 (95% CI: 1.106, 1.156) for BC and 1.082 (95% CI: 1.062, 1.103) for NO2. We found that prenatal exposures to both BC and NO2 were associated with lower birth weight.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".