Neighbourhood socioeconomic status, maternal education and adverse birth outcomes among mothers living near highways
Bibliographic record
Abstract
BACKGROUND: Residential proximity to highways is a potential proxy for exposure to traffic-related pollution that has been linked to adverse birth outcomes. We evaluated whether proximity to highway interacts with individual and neighbourhood socioeconomic status (SES) to influence birth outcomes. METHODS: The study population consisted of all live singleton births in Montréal, Canada, from 1997 to 2001 (n = 99,819). Proximity was defined as residing within 200 m of a highway. Neighbourhood SES was measured for census tracts as the proportion of families below the low-income threshold. Individual SES was represented by maternal education. Using multilevel logistic regression, the odds of preterm birth (PTB), low birthweight (LBW) and small-for-gestational-age (SGA) birth were calculated for mothers residing in proximity to highways, accounting for individual and neighbourhood SES. Effect modification between SES and proximity to highway was tested for each outcome. RESULTS: In wealthy neighbourhoods, proximity to highway was associated with an elevated odds of PTB (OR 1.58, 95% CI 1.23 to 2.04), LBW (OR 1.81, 95% CI 1.36 to 2.41) and SGA birth (OR 1.32, 95% CI 1.05 to 1.66). For highly educated mothers, proximity to highway was associated with PTB (OR 1.25, 95% CI 1.07 to 1.46) and LBW (OR 1.24, 95% CI 1.03 to 1.49), but the association was borderline for SGA birth (OR 1.15, 95% CI 1.00 to 1.32). Proximity to highway was not associated with birth outcomes in other maternal and neighbourhood SES categories. CONCLUSION: Counterintuitively, high SES mothers may be more likely than low SES mothers to experience adverse births associated with residential proximity to highway.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".