Extreme heat impacts on acute care: Examining emergency department visits and hospital admissions during the 2021 British Columbia heatwave
Bibliographic record
Abstract
Introduction: Emergency department (E.D.) visits and hospitalization patterns shifted dramatically during the 2021 extreme heat event (EHE) across the Greater Vancouver Area of southern British Columbia, Canada. Methods: =18,624) between June 4th, 2021, and July 29th, 2021, using data from two administrative databases: (1) the Canadian National Ambulatory Care Reporting System; and (2) and the Canadian Discharge Abstract Database data. Using Mann-U Whitney tests, we compared how E.D. visits, hospitalizations, and patient diagnoses changed during a seven-day EHE and two subsequent lag periods compared to the surrounding baseline period. We also use a distributed lag non-linear model to analyze the relationship between daily maximum temperatures and daily E.D. visits during the study period. Results: We observed a statistically significant increase in overall E.D. visits during the EHE and during the week following the EHE, and a positive relationship between daily maximum temperature and relative risk of an E.D. visit. Further, there were significant increases in critically ill patients presenting to the E.D. during the EHE, based on Canadian Triage Acuity Scale (CTAS) and increases in key diagnoses, including acute kidney failure, heatstroke, and dehydration. Conclusions: Heatwaves have significant impacts on public health and acute care systems beyond heat-related deaths. Complications associated with heat exposure and surges in patient volume have implications for internal medicine, emergency medicine, and psychiatry departments. Better understanding the disease patterns associated with extreme heat events is essential to health system planning and response.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".