Environmental Noise Pollution and Risk of Hypertension in Pregnancy
Bibliographic record
Abstract
Background: A growing number of studies suggest that environmental noise pollution may impact the risk of hypertension, but the relationship during pregnancy is poorly understood. We investigated the association between environmental noise levels and preeclampsia, a hypertensive disorder of pregnancy.Methods: We undertook a retrospective cohort study of 269,263 pregnancies in Montreal, 2000-2013. Using postal codes, we assigned environmental noise pollution levels (dBA) from land use regression models to each pregnancy. We calculated odds ratios (OR) and 95% confidence intervals (CI) for the association of environmental noise with preeclampsia, adjusted for air pollutants, neighbourhood walkability, maternal age, parity, multiple pregnancy, comorbidity, socioeconomic deprivation, and year of delivery. We assessed if associations varied according to preeclampsia severity (mild and severe) and onset time (<34 and ≥34 weeks of gestation).Results: Women exposed to elevated environmental noise levels (≥65 vs. <50 dBA) had a higher prevalence of preeclampsia (37.9 vs. 27.9 per 1,000). Compared with 50 dBA, exposure to a noise level of 65 dBA was associated with 1.09 times the odds of preeclampsia (95% CI 0.99-1.20). Associations were stronger for severe preeclampsia (OR 1.29, 95% CI 1.09-1.54) and preeclampsia before 34 gestational weeks (OR 1.71, 95% CI 1.20-2.43). There was no association with mild preeclampsia and preeclampsia at ≥34 weeks.Conclusion: Environmental noise pollution may be a risk factor for preeclampsia, particularly severe or early onset preeclampsia. In light of rising levels of urban noise, these results suggest that vulnerable populations, including pregnant women, could benefit from residential noise reduction policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".