Traffic Volumes and Respiratory Health Care Utilization Among Residents in Close Proximity to the Peace Bridge Before and After September 11, 2001
Bibliographic record
Abstract
A recent study based on data over a 10-year period (1991-2000) showed a positive association between health care utilization and prevalence of asthma, and commercial traffic at a U.S.-Canada border crossing. We wanted to determine whether decreases in total traffic would also be associated with decreases in health care utilization for respiratory illnesses. Following September 11, 2001, there was a 50% drop in total traffic at the Peace Bridge border crossing point between Buffalo, New York and Fort Erie, Ontario, Canada. To investigate the impact of such a traffic decline on health care utilization for respiratory illnesses, weekly respiratory admissions to Kaleida Health System, Western New York's largest health care provider were analyzed according to ICD9CM classification and compared with total weekly traffic volumes for 3-month periods in 2000 and 2001 (August, September, and October). The total number of patients admitted to hospital or seen in emergency departments for respiratory illnesses during the 3-month periods of both years was 5288. A 50% drop in total traffic following Labor Day and September 11, 2001, from week 4 to week 7 was found to be statistically significant (p = 0.031) when a one-way ANOVA was performed. Likewise, the drop in total respiratory cases approached statistical significance (p = 0.052) when a one-way ANOVA was conducted. The results suggest an association between decrease in traffic volumes with decrease in health care utilization for respiratory diseases. These results suggest that current levels of traffic may be impacting on the respiratory health of residents in the nearby community.
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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) | 0.000 | 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".