The human health effects of unconventional oil and gas development (UOGD): A scoping review of epidemiologic studies
Bibliographic record
Abstract
OBJECTIVE: Unconventional oil and gas development (UOGD, sometimes termed "fracking" or "hydraulic fracturing") is an industrial process to extract methane gas and/or oil deposits. Many chemicals used in UOGD have known adverse human health effects. Canada is a major producer of UOGD-derived gas with wells frequently located in and around rural and Indigenous communities. Our objective was to conduct a scoping review to identify the extent of research evidence assessing UOGD exposure-related health impacts, with an additional focus on Canadian studies. METHODS: We included English- or French-language peer-reviewed epidemiologic studies (January 2000-December 2022) which measured exposure to UOGD chemicals directly or by proxy, and where health outcomes were plausibly caused by UOGD-related chemical exposure. Results synthesis was descriptive with results ordered by outcome and hierarchy of methodological approach. SYNTHESIS: We identified 52 studies from nine jurisdictions. Only two were set in Canada. A majority (n = 27) used retrospective cohort and case-control designs. Almost half (n = 24) focused on birth outcomes, with a majority (n = 22) reporting one or more significant adverse associations of UOGD exposure with: low birthweight; small for gestational age; preterm birth; and one or more birth defects. Other studies identified adverse impacts including asthma (n = 7), respiratory (n = 13), cardiovascular (n = 6), childhood acute lymphocytic leukemia (n = 2), and all-cause mortality (n = 4). CONCLUSION: There is a growing body of research, across different jurisdictions, reporting associations of UOGD with adverse health outcomes. Despite the rapid growth of UOGD, which is often located in remote, rural, and Indigenous communities, Canadian research on its effects on human health is remarkably sparse. There is a pressing need for additional evidence.
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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 | 0.024 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".