A Natural Experiment: Closure of an Oil Refinery & Influence on Local Hospitalizations
Bibliographic record
Abstract
INTRODUCTION: Under the Clean Air Regulatory Agenda, Health Canada is mandated to study the effect of air pollution from various industrial sources on Canadian population health. An oil refinery in Oakville, Ontario was closed in 2004-05, providing an opportunity for a natural experiment to study the effects of changes in oil refinery-related air pollution on human health. METHODS: To evaluate the impact of the oil refinery closure, we compared air pollution levels and cause-specific hospitalizations and mortality counts before/after the refinery closure (based on a balanced two-period design). The refinery was located between two cities, Oakville and Burlington, which were considered the primary areas. The surrounding cities (the greater Toronto area and Hamilton) were used as a reference population. We examined wind speed and direction to be able to demonstrate reduction in air pollution. RESULTS: About 6,000 tonnes of sulphur dioxide (SO2) were emitted annually by the refinery. Following the refinery closure, ambient SO2 concentrations dropped on average 13% in Oakville and 21% in Burlington. Strong associations (p < 0.01) were observed between daily concentrations of SO2 and hospitalizations, and Oakville cold-season age-standardized respiratory hospitalization rates showed a significant step-function reduction of 2.3 counts-per-thousand-persons-per-year at the time of the closure (p < 0.001). Reductions in hospitalizations in Burlington and other surrounding municipalities were not tied to the closure of the refinery. CONCLUSION: The refinery closure was associated with an immediate and measurable reduction in ambient SO2 concentration and cold-season respiratory hospitalizations for Oakville, a result which was not duplicated in any of the socioeconomically similar reference populations. This natural experiment provides evidence on the association between refinery emissions and adverse health effects and demonstrates the health benefits of reduced exposure.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".