Proximity and density of unconventional natural gas wells and mental illness and substance use among pregnant individuals: An exploratory study in Canada
Bibliographic record
Abstract
BACKGROUND: Hydraulic fracturing (fracking) is a method used to extract unconventional natural gas (UNG). Living near UNG operations has been associated with various health outcomes, but few have explored the association between UNG and mental health and substance use. Our objective was to evaluate the association between metrics of residential UNG well density/proximity and mental illness and substance use among pregnant individuals in Northeastern British Columbia, Canada. METHODS: Individuals who gave birth at the Fort St John hospital between December 30, 2006 and December 29, 2016 (n = 6278) were included in the study. Exposure was determined using inverse distance weighting (IDW) to calculate the density and proximity of UNG wells to the postal code centroid ofindividual's residential address at delivery. Four exposure metrics, categorized by quartiles, were calculated based on 50, 10, 5 and 2.5 km buffer zones around each postal code centroid. Logistic regression was used to separately evaluate associations between IDW quartiles of each metric and diagnosis of depression and anxiety prior to or during pregnancy, and self-reported substance use during pregnancy, controlling for relevant and available confounders. RESULTS: The second and third quartile (Q) of the 10 km IDW were associated with greater odds of depression (Q2: adjusted (aOR) 1.30, 95% (confidence interval) CI 1.03-1.64; Q3: aOR 1.35, 95% CI 1.07-1.70) compared to the first quartile, but not the fourth. Using the 5 km IDW, we observed a suggestive positive association with depression in the second and third quartile (aOR Q2: 1.21, 95% CI 0.96-1.53; aOR Q3: 1.24, 95% CI 0.98-1.57) compared to the first quartile. No statistically significant association was observed using the 2.5 km IDW exposure metric. CONCLUSION: We observed some evidence of greater odds of mental illness prior to or during pregnancy, and substance use during pregnancy in pregnant individuals living in postal codes with increased UNG well density/proximity, although associations were not observed in smaller buffer zones. This study adds to the growing literature on the adverse health outcomes surrounding living in proximity to UNG operations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".