Ambient Air Pollution and Birth Defects in Brisbane, Australia
Bibliographic record
Abstract
BACKGROUND: Birth defects are a major public health concern as they are the leading cause of neonatal and infant mortality. Observational studies have linked environmental pollution to adverse birth outcomes, including congenital anomalies. This study examined potential associations between ambient air pollution and congenital heart defects and cleft lip or palate among births in Brisbane, Australia (1998-2004). METHODS: Ambient air pollution levels were averaged over weeks 3-8 of pregnancy among 150,308 births. Using a case-control design, we used conditional logistic regression and matched cases to 5 controls. Analyses were conducted using all births, and then births where the mother resided within 6 and 12 kilometers of an ambient air quality monitor. FINDINGS: When analyzing all births there was no indication that ambient air pollution in Brisbane was associated with a higher risk of cardiac defects. Among births where the mother resided within 6 kilometers of an ambient air quality monitor, a 5 ppb increase in O(3) was associated with an increased risk of pulmonary artery and valve defects (OR 2.96, 95% CI: 1.34, 7.52) while a 0.6 ppb increase in SO(2) was associated with an increased risk of aortic artery and valve defects (OR 10.76, 95% CI: 1.50, 179.8). For oral cleft defects among all births, the only adverse association was between SO(2) and cleft lip with or without cleft palate (OR 1.27, 95% CI: 1.01, 1.62). However, various significant inverse associations were also found between air pollutants and birth defects. CONCLUSIONS: This study found mixed results and it is difficult to conclude whether ambient air pollution in Brisbane has an adverse association with the birth defects examined. Studies using more detailed estimates of air pollution exposure are needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".