PRELIMINARY INVESTIGATION OF EXCESS RESPIRATORY DISEASE HOSPITALISATIONS DOWNWIND OF AN URBAN PETROCHEMICAL COMPLEX
Bibliographic record
Abstract
In the course of environmental impact hearings to review the siting of a new plastics plant, protesting citizens living adjacent to the existing Montreal (Canada) petroleum refinery/chemical complex where the plant was to be situated, invoked data showing that rates of hospitalisation for respiratory disease were already higher in their neighbourhood than elsewhere. The Montreal Public Health Department initiated a joint medical/environmental assessment to verify the citizens' complaints and to better characterise respiratory disease rates as related geographically to the industrial zone. Methods We extracted all hospitalisations by discharge diagnosis, by age, and street address for Montreal residents(population 2,000,000) during 1995–2000, identified neighbourhood-level rates of adult smoking and residential wood stove use, and classified neighbourhoods by their distance from and downwind position relative to the refinery/chemical complex. Results The two neighbourhoods (each of around 50,000 inhabitants) most subject to winds blowing off the complex are suburban in character, with adult residents whose educational status is slightly lower and mean family income somewhat higher than that of Montreal as a whole. Among children of 6 months-4years old, rates of hospitalisation for disease not associated with the respiratory system were slightly greater (RR = 1.33, 1.11) in the downwind neighbourhoods than for all of Montreal; for respiratory diseases overall, relative rates for the two neighbourhoods were 1.71 and 1.45. Excesses were also seen for acute hospitalisations related to pneumonia and to chronic pulmonary disease among neighbourhood residents aged over 60 years. While the proportion of adults smoking was somewhat higher in the neighbourhoods of concern than elsewhere, risk assessment assuming an RR of 2.0 for a parent who smokes, explained only 9% of the excess in children's respiratory hospitalisations. And while levels of SO2 were higher downwind of the refinery/chemical complex, the gradient was small and concentrations well below those associated with health effects in other situations. Conclusion The geographically-based excess of respiratory hospitalisations is not easily explained by community level exposure surrogates. We have initiated a migrant study and additional monitoring coupled with time-series analysis of hospitalisations to further assess the linkage of respiratory effects to emissions from local industry.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".