ASSOCIATIONS BETWEEN EXPOSURE TO AIR POLLUTION AND GESTATIONAL HYPERTENSION IN URBAN HALIFAX
Bibliographic record
Abstract
Introduction Exposure to air pollution has been linked with an increased risk for cardiovascular morbidity and mortality. Few studies have examined possible associations between air pollution and gestational hypertension (GH). The prevalence of GH in Canada is approximately 6%, translating into roughly 300 women developing GH in Halifax annually. GH disorders account for 2–8% of pregnancy complications worldwide. Objective The current study examined the relationship between exposure to air pollution during pregnancy and GH. Methods A retrospective cohort study including women residing in urban Halifax, Nova Scotia who delivered a singleton infant between 2008 and 2012. The Nova Scotia Atlee Perinatal Database was used to determine socio-demographic characteristics and pregnancy conditions. Spatial estimates of air pollution were determined from land-use regression (LUR) models and were linked to women in the Perinatal Database based on their six-digit postal code. Residential concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM1, PM2.5, PM10), toluene, and benzene were averaged over three seasons and categorized into quartiles. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using logistic regression, adjusting for potential confounders. Results Of the 11,724 singleton births analyzed, 7.7% of the cohort developed GH. When adjusted for smoking, pre-pregnancy weight, maternal age and parity a significant inverse relationship was observed for exposure to all pollutants (top quartile of exposure relative to lowest quartile); SO2 (OR: 0.75; 95% CI: 0.62, 0.92), NO2 (0.66; 0.54, 0.81), PM1 (0.71; 0.57, 0.87), PM2.5 (0.68, 0.56, 0.83), PM10 (0.71; 0.58, 0.87) toluene (0.68; 0.56, 0.83) and benzene (0.62; 0.51, 0.75). Conclusion The inverse relationships observed were contrary to what has been observed in past research. These results stress the importance of conducting more research in this field to better understand the characteristics that influence the relationship between air pollution and GH.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".