Estimating Toronto’s health services use for the 2015 Pan American and Parapan American Games
Bibliographic record
Abstract
AIMS: Ambient air temperature may exacerbate the burden of chronic diseases on Ontario's healthcare system during mass gathering events. This study aimed to estimate the impact of increasing temperature in July and August on health services use for chronic conditions in Ontario's Golden Horseshoe region during the 2015 Pan American and Parapan American Games, using environmental and health administrative data from previous years. METHOD: Negative binomial regression was used to calculate incidence risk ratios for same-day health services use (hospitalisations, emergency department visits, physician office visits) for all causes, asthma, asthma-related conditions, diabetes and hypertension associated with unit increases in daily maximum temperature from 1 May to 31 August in 2008-2010. Sensitivity analysis was performed to estimate the added burden of an increased population size, in order to model an influx of visitors during the Games. RESULTS: In July and August, on days with daily maximum temperatures of 35 °C compared to 25 °C, we estimated seeing 7,827 more physician office visits for all causes in Ontario's Golden Horseshoe region. The estimated relative increase in physician office visits for diabetes due to temperature alone was 8.4%. With an estimated 10% increase in population, the increase in physician office visits for all causes tripled to an estimated 23,590. CONCLUSION: Temperature was identified as a potential contributor to greater health services use during the Games, particularly for those living with diabetes. These results highlight the importance of strategic delivery of health services during mass gathering events, and suggest a role for educating at-risk individuals on prevention behaviours, particularly on very hot days.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".