Impact of Noise and Air Pollution on Pregnancy Outcomes
Bibliographic record
Abstract
BACKGROUND: Motorized traffic is an important source of both air pollution and community noise. While there is growing evidence for an adverse effect of ambient air pollution on reproductive health, little is known about the association between traffic noise and pregnancy outcomes. METHODS: We evaluated the impact of residential noise exposure on small size for gestational age, preterm birth, term birth weight, and low birth weight at term in a population-based cohort study, for which we previously reported associations between air pollution and pregnancy outcomes. We also evaluated potential confounding of air pollution effects by noise and vice versa. Linked administrative health data sets were used to identify 68,238 singleton births (1999-2002) in Vancouver, British Columbia, Canada, with complete covariate data (sex, ethnicity, parity, birth month and year, income, and education) and maternal residential history. We estimated exposure to noise with a deterministic model (CadnaA) and exposure to air pollution using temporally adjusted land-use regression models and inverse distance weighting of stationary monitors for the entire pregnancy. RESULTS: Noise exposure was negatively associated with term birth weight (mean difference = -19 [95% confidence interval = -23 to -15] g per 6 dB(A)). In joint air pollution-noise models, associations between noise and term birth weight remained largely unchanged, whereas associations decreased for all air pollutants. CONCLUSION: Traffic may affect birth weight through exposure to both air pollution and noise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".