Impact of air pollution on hospital admissions in Southwestern Ontario, Canada: Generating hypotheses in sentinel high-exposure places
Bibliographic record
Abstract
BACKGROUND: Southwestern Ontario (SWO) in Canada has been known as a 'hot spot' in terms of environmental exposure and potential effects. We chose to study 3 major cities in SWO in this paper. We compared age-standardized hospital admission ratios of Sarnia and Windsor to London, and to generate hypotheses about potential pollutant-induced health effects in the 'Chemical Valley', Sarnia. METHODS: The number of daily hospital admissions was obtained from all hospitals in London, Windsor and Sarnia from January 1, 1996 to December 31, 2000. We used indirect age adjustment method to obtain standardized admissions ratios for males and females and we chose London as the reference population. This process of adjustment was to apply the age-specific admission rates of London to the population of Sarnia and Windsor in order to yield expected admissions. The observed number of admissions was then compared to the expected admissions in terms of a ratio. These standardized admissions ratios and their corresponding confidence intervals were calculated for Sarnia and Windsor. RESULTS: Our findings showed that Sarnia and Windsor had significantly higher age-adjusted hospital admissions rates compared to London. This finding was true for all admissions, and especially pronounced for cardiovascular and respiratory admissions. For example, in 1996, the observed number of admissions in Sarnia was 3.11 (CI: 2.80, 3.44) times for females and 2.83 (CI: 2.54, 3.14) times for males as would be expected by using London's admission rates. CONCLUSION: Since hospital admissions rates were significantly higher in 'Chemical Valley' as compared to both London and Windsor, we hypothesize that these higher rates are pollution related. A critical look at the way ambient air quality and other pollutants are monitored in this area is warranted. Further epidemiological research is needed to verify our preliminary indications of harmful effects in people living in 'Chemical Valley'.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".